From 3aff0c3edb8f7bfd926eadcc498821c6283ed4bd Mon Sep 17 00:00:00 2001 From: Koen van der Veen Date: Thu, 11 Sep 2025 12:37:29 +0200 Subject: [PATCH 01/61] init broad listening --- examples/arxiv_downloader.py | 25 + examples/browseruse.py | 17 + examples/hf_daily_papers_downloader.py | 54 + examples/home_latest_timeline.json | 49637 ++++++++++++++++ examples/home_latest_timeline_2.json | 14438 +++++ examples/parse_tweets.py | 54 + examples/twitter_scrape_wiith_cookie.py | 100 + packages/broad_listening_ui/.gitignore | 16 + packages/broad_listening_ui/CLAUDE.md | 49 + packages/broad_listening_ui/README.md | 0 .../broad_listening_ui/__init__.py | 2 + .../broad_listening_ui/py.typed | 0 packages/broad_listening_ui/index.html | 13 + packages/broad_listening_ui/justfile | 27 + packages/broad_listening_ui/package-lock.json | 2756 + packages/broad_listening_ui/package.json | 21 + packages/broad_listening_ui/postcss.config.js | 6 + packages/broad_listening_ui/pyproject.toml | 15 + packages/broad_listening_ui/server_test.py | 28 + packages/broad_listening_ui/src/App.vue | 68 + .../broad_listening_ui/src/assets/om-icon.svg | 62 + .../broad_listening_ui/src/assets/styles.css | 52 + .../src/components/AIPapersDashboard.vue | 241 + .../src/components/AddConnector.vue | 53 + .../src/components/AddList.vue | 54 + .../src/components/FeedView.vue | 50 + .../src/components/LeftSidebar.vue | 117 + .../src/components/MiddlePanel.vue | 117 + .../src/components/RightPanel.vue | 115 + .../src/components/SummaryView.vue | 72 + .../src/components/TweetItem.vue | 71 + .../src/components/TwitterDashboard.vue | 258 + packages/broad_listening_ui/src/main.js | 10 + .../src/stores/chatStore.js | 125 + .../src/stores/connectionsStore.js | 149 + .../src/stores/listsStore.js | 311 + .../broad_listening_ui/tailwind.config.js | 8 + packages/broad_listening_ui/vite.config.js | 11 + 38 files changed, 69202 insertions(+) create mode 100644 examples/arxiv_downloader.py create mode 100644 examples/browseruse.py create mode 100644 examples/hf_daily_papers_downloader.py create mode 100644 examples/home_latest_timeline.json create mode 100644 examples/home_latest_timeline_2.json create mode 100644 examples/parse_tweets.py create mode 100644 examples/twitter_scrape_wiith_cookie.py create mode 100644 packages/broad_listening_ui/.gitignore create mode 100644 packages/broad_listening_ui/CLAUDE.md create mode 100644 packages/broad_listening_ui/README.md create mode 100644 packages/broad_listening_ui/broad_listening_ui/__init__.py create mode 100644 packages/broad_listening_ui/broad_listening_ui/py.typed create mode 100644 packages/broad_listening_ui/index.html create mode 100644 packages/broad_listening_ui/justfile create mode 100644 packages/broad_listening_ui/package-lock.json create mode 100644 packages/broad_listening_ui/package.json create mode 100644 packages/broad_listening_ui/postcss.config.js create mode 100644 packages/broad_listening_ui/pyproject.toml create mode 100644 packages/broad_listening_ui/server_test.py create mode 100644 packages/broad_listening_ui/src/App.vue create mode 100644 packages/broad_listening_ui/src/assets/om-icon.svg create mode 100644 packages/broad_listening_ui/src/assets/styles.css create mode 100644 packages/broad_listening_ui/src/components/AIPapersDashboard.vue create mode 100644 packages/broad_listening_ui/src/components/AddConnector.vue create mode 100644 packages/broad_listening_ui/src/components/AddList.vue create mode 100644 packages/broad_listening_ui/src/components/FeedView.vue create mode 100644 packages/broad_listening_ui/src/components/LeftSidebar.vue create mode 100644 packages/broad_listening_ui/src/components/MiddlePanel.vue create mode 100644 packages/broad_listening_ui/src/components/RightPanel.vue create mode 100644 packages/broad_listening_ui/src/components/SummaryView.vue create mode 100644 packages/broad_listening_ui/src/components/TweetItem.vue create mode 100644 packages/broad_listening_ui/src/components/TwitterDashboard.vue create mode 100644 packages/broad_listening_ui/src/main.js create mode 100644 packages/broad_listening_ui/src/stores/chatStore.js create mode 100644 packages/broad_listening_ui/src/stores/connectionsStore.js create mode 100644 packages/broad_listening_ui/src/stores/listsStore.js create mode 100644 packages/broad_listening_ui/tailwind.config.js create mode 100644 packages/broad_listening_ui/vite.config.js diff --git a/examples/arxiv_downloader.py b/examples/arxiv_downloader.py new file mode 100644 index 0000000..9b16c55 --- /dev/null +++ b/examples/arxiv_downloader.py @@ -0,0 +1,25 @@ +import json + +import arxiv + +# Search for newest AI articles +search = arxiv.Search( + query="cat:cs.AI", + max_results=5, + sort_by=arxiv.SortCriterion.SubmittedDate, + sort_order=arxiv.SortOrder.Descending, +) + +results = [] +for result in search.results(): + results.append( + { + "title": result.title, + "authors": [author.name for author in result.authors], + "summary": result.summary, + "published": result.published.strftime("%Y-%m-%d"), + "pdf_url": result.pdf_url, + } + ) + +print(json.dumps(results, indent=2)) diff --git a/examples/browseruse.py b/examples/browseruse.py new file mode 100644 index 0000000..f0540fa --- /dev/null +++ b/examples/browseruse.py @@ -0,0 +1,17 @@ +import asyncio + +from browser_use import Agent, ChatAnthropic +from dotenv import load_dotenv + +load_dotenv() + + +async def main(): + agent = Agent( + task="Go to x.com, then wait until the user has logged in. Once tweets are loaded on the screen print the first 10 tweets.", + llm=ChatAnthropic(model="claude-sonnet-4-0", temperature=0.0), + ) + await agent.run() + + +asyncio.run(main()) diff --git a/examples/hf_daily_papers_downloader.py b/examples/hf_daily_papers_downloader.py new file mode 100644 index 0000000..fcb679d --- /dev/null +++ b/examples/hf_daily_papers_downloader.py @@ -0,0 +1,54 @@ +import os +from datetime import datetime, timedelta + +import requests + +# --- CONFIG --- +API_KEY = os.getenv("HF_PAPERS_KEY") +MONTH = 9 # September +YEAR = 2025 +TOP_N = 10 + + +# --- FUNCTION TO FETCH PAPERS FOR A GIVEN DATE --- +def fetch_papers_for_date(date_str): + url = f"https://huggingface.co/api/daily_papers?date={date_str}" + headers = {"Authorization": f"Bearer {API_KEY}"} + response = requests.get(url, headers=headers) + if response.status_code == 200: + return response.json() # List of papers + else: + print(f"Failed to fetch {date_str}: {response.status_code}") + return [] + + +# --- COLLECT PAPERS FOR THE MONTH --- +papers = [] +start_date = datetime(YEAR, MONTH, 1) +end_date = (start_date.replace(month=MONTH % 12 + 1) - timedelta(days=1)).day + +for day in range(1, end_date + 1): + # INSERT_YOUR_CODE + date_obj = datetime(YEAR, MONTH, day) + if date_obj > datetime.now(): + continue + date_str = f"{YEAR}-{MONTH:02d}-{day:02d}" + daily_papers = fetch_papers_for_date(date_str) + papers.extend(daily_papers) + +# --- SORT BY POPULARITY (UPVOTES) --- +print(papers[0]["paper"]["upvotes"]) +papers_sorted = sorted( + papers, key=lambda x: x.get("paper", {}).get("upvotes", 0), reverse=True +) +# print(papers_sorted[0]) +print(len(papers_sorted)) + +# --- TAKE TOP N --- +top_papers = papers_sorted[:TOP_N] + +# --- PRINT RESULTS --- +for i, paper in enumerate(top_papers, start=1): + print( + f"{i}. {paper['title']} ({paper.get('paper', {}).get('upvotes', 0)} upvotes) - {paper.get('url', '')}" + ) diff --git a/examples/home_latest_timeline.json b/examples/home_latest_timeline.json new file mode 100644 index 0000000..0061cc8 --- /dev/null +++ b/examples/home_latest_timeline.json @@ -0,0 +1,49637 @@ +{ + "data": { + "home": { + "home_timeline_urt": { + "instructions": [ + { + "type": "TimelineAddEntries", + "entries": [ + { + "entryId": "home-conversation-1965440852092256256", + "sortIndex": "1965440852092256256", + "content": { + "entryType": "TimelineTimelineModule", + "__typename": "TimelineTimelineModule", + "items": [ + { + "entryId": "home-conversation-1965440852092256256-tweet-1965099929048940785", + "item": { + "itemContent": { + "itemType": "TimelineTweet", + "__typename": "TimelineTweet", + "tweet_results": { + "result": { + "__typename": "Tweet", + "rest_id": "1965099929048940785", + "core": { + "user_results": { + "result": { + "__typename": "User", + "id": "VXNlcjoyNTQwMTk1Mw==", + "rest_id": "25401953", + "affiliates_highlighted_label": {}, + "avatar": { + "image_url": "https://pbs.twimg.com/profile_images/1131851609774985216/OcsssQ9J_normal.png" + }, + "core": { + "created_at": "Thu Mar 19 22:54:05 +0000 2009", + "name": "Peter Steinberger", + "screen_name": "steipete" + }, + "dm_permissions": { + "can_dm": true, + "can_dm_on_xchat": false + }, + "has_graduated_access": true, + "is_blue_verified": true, + "legacy": { + "default_profile": false, + "default_profile_image": false, + "description": "Full-Time Open-Sourcerer\ud83c\udff3\ufe0f\u200d\ud83c\udf08 Flips vibe coding\u2014agentic engineering. 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His own memoirs, an astonishing text which he apparently intended to be only the first of two volumes, cuts off before describing the details of his building of the nuclear fleet.\n\nAs a college student in occupied France, he slipped away from a forced labor assignment in a coal mine and escaped to Spain while heroically escorting downed Allied airmen.\n\nHe joined up with Free French forces in Africa, was commissioned as an officer, and fought through Italy until injured. \n\nFinishing his economics studies, he joined @EDFofficiel and then invented the theory behind efficient and fair electricity pricing.\n\nFor his cool strength in the face of massive labor unrest, he was promoted to lead EDF, where he prepared a nuclear program that blasted into overdrive after the 1973 oil embargo started. \n\nFrench terrorists trying to stop his nuclear program blew up his house one night in 1977 with him, his wife, and his daughter all inside, and he still went to work that morning to keep building reactors. 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Compare this to Germany spending about $500 billion on their mess of an \"energy transition\" which requires them to keep almost all of their coal and gas plants in service.\n\nAs a young man Marcel Boiteux refused to accept France's defeat and at age 21 in 1942 as an elite university student he escaped Nazi-occupied France while escorting downed Allied pilots over the Pyrenees mountains to safety in Spain.\n\nBrass. Balls.\n\nThis episode revealed the pattern for the rest of his life.\n\nAfter the war, he studied economics and wrote *the* foundational paper in electricity economics, on how to price electricity service in a way that covered system costs while being fair and sustainable.\n\nHe completely understood liberal economics, and knew it did not apply to electricity grids and service. 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Try increasing `gpu_memory_utilization` when initializing the engine.\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007 --gpu-memory-utilization 0.95 --max-model-len 4096 --max-num-seqs 5\nINFO Automatically detected platform cuda.\nINFO Loading weights took 29.42 seconds\nValueError: No available memory for the cache blocks. Try increasing `gpu_memory_utilization` when initializing the engine.\nRuntimeError: Engine core initialization failed. See root cause above. 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Stanford fellow; @kaggle president; @fastmail/@enlitic/etc founder\nhttps://t.co/16UBFTX7mo", + "entities": { + "description": { + "urls": [ + { + "display_url": "jeremy.fast.ai", + "expanded_url": "https://jeremy.fast.ai/", + "url": "https://t.co/16UBFTX7mo", + "indices": [132, 155] + } + ] + }, + "url": { + "urls": [ + { + "display_url": "answer.ai", + "expanded_url": "http://answer.ai", + "url": "https://t.co/3lh0uQDdfN", + "indices": [0, 23] + } + ] + } + }, + "fast_followers_count": 0, + "favourites_count": 10026, + "followers_count": 259626, + "friends_count": 6063, + "has_custom_timelines": true, + "is_translator": false, + "listed_count": 5294, + "media_count": 2827, + "normal_followers_count": 259626, + "pinned_tweet_ids_str": [ + "1734606378331951318" + ], + "possibly_sensitive": false, + "profile_banner_url": "https://pbs.twimg.com/profile_banners/175282603/1594860705", + "profile_interstitial_type": "", + "statuses_count": 63702, + "translator_type": "none", + "url": "https://t.co/3lh0uQDdfN", + "want_retweets": true, + "withheld_in_countries": [] + }, + "location": { + "location": "Brisbane/Queensland, Australia" + }, + "media_permissions": { + "can_media_tag": false + }, + "parody_commentary_fan_label": "None", + "profile_image_shape": "Circle", + "privacy": { "protected": false }, + "relationship_perspectives": { + "following": true + }, + "tipjar_settings": {}, + "verification": { "verified": false } + } + } + }, + "unmention_data": {}, + "edit_control": { + "edit_tweet_ids": ["1964539633653731466"], + "editable_until_msecs": "1757221322000", + "is_edit_eligible": false, + "edits_remaining": "5" + }, + "is_translatable": false, + "views": { + "count": "145157", + "state": "EnabledWithCount" + }, + "source": "Twitter Web App", + "grok_analysis_button": true, + "quoted_status_result": { + "result": { + "__typename": "Tweet", + "rest_id": "1964362446627299598", + "post_image_description": "Code snippet with text in a purple-themed editor. Lines include imports from \"agno.agent\", \"agno.db\", \"SqliteDb\", \"AgentOS\", and \"MCPTools\". Functions like \"create_your_agent\" and \"agent.run\" are visible. No watermarks from platforms like Instagram, TikTok, or Xiaohongshu.", + "core": { + "user_results": { + "result": { + "__typename": "User", + "id": "VXNlcjoxMDM1MDA4Mg==", + "rest_id": "10350082", + "affiliates_highlighted_label": {}, + "avatar": { + "image_url": "https://pbs.twimg.com/profile_images/1909614469853925376/KKnxWe4s_normal.jpg" + }, + "core": { + "created_at": "Sun Nov 18 07:25:24 +0000 2007", + "name": "Ashpreet Bedi", + "screen_name": "ashpreetbedi" + }, + "dm_permissions": { + "can_dm": true, + "can_dm_on_xchat": false + }, + "has_graduated_access": true, + "is_blue_verified": true, + "legacy": { + "default_profile": false, + "default_profile_image": false, + "description": "founder @agnoagi \u2022 prev @airbnb @facebook", + "entities": { + "description": { "urls": [] }, + "url": { + "urls": [ + { + "display_url": "git.new/agno", + "expanded_url": "https://git.new/agno", + "url": "https://t.co/NSL5bYN36q", + "indices": [0, 23] + } + ] + } + }, + "fast_followers_count": 0, + "favourites_count": 8181, + "followers_count": 13349, + "friends_count": 1218, + "has_custom_timelines": true, + "is_translator": false, + "listed_count": 208, + "media_count": 413, + "normal_followers_count": 13349, + "pinned_tweet_ids_str": [ + "1903968640329818613" + ], + "possibly_sensitive": false, + "profile_banner_url": "https://pbs.twimg.com/profile_banners/10350082/1738252032", + "profile_interstitial_type": "", + "statuses_count": 3388, + "translator_type": "none", + "url": "https://t.co/NSL5bYN36q", + "want_retweets": false, + "withheld_in_countries": [] + }, + "location": { + "location": "ny, london" + }, + "media_permissions": { + "can_media_tag": true + }, + "parody_commentary_fan_label": "None", + "profile_image_shape": "Circle", + "privacy": { "protected": false }, + "relationship_perspectives": { + "following": false + }, + "tipjar_settings": {}, + "verification": { "verified": false } + } + } + }, + "unmention_data": {}, + "edit_control": { + "edit_tweet_ids": ["1964362446627299598"], + "editable_until_msecs": "1757179078000", + "is_edit_eligible": true, + "edits_remaining": "5" + }, + "is_translatable": false, + "views": { + "count": "302934", + "state": "EnabledWithCount" + }, + "source": "Twitter Web App", + "note_tweet": { + "is_expandable": true, + "note_tweet_results": { + "result": { + "id": "Tm90ZVR3ZWV0OjE5NjQzNjI0NDYzMTI3NTExMDQ=", + "text": "Grifters like this are wasting your time and their Dunning-Kruger opinions should be ignored by serious builders. You either build on a framework or live long enough to roll your own (which is fine btw). Here\u2019s why:\n\n1. The \"LLM API in a while loop\" is your underlying agentic step - the unit of work in your agent system. You\u2019ll wrap this in a function and call it when you want to run your agent. This is just the starting line, and also where these idiots are usually stuck. \n\n2. As you start building Agents, you turn this function into a class and add logic for prompting, message management, tool calling, retries and error handling. Congrats, you\u2019ve started to roll your own framework.\n\n3. Then, you\u2019ll want to try different models or chain agents to build a workflow. Each has its quirks, custom settings, different response formats, prompting hacks (eg: claude needs \"Do not reflect on the quality of the returned search results in your response\"). You start adding more layers to your homegrown framework.\n\nTill now this is a solvable problem and you can vibecode it.\n\n4. Then things really start to get frustrating. You learn quickly that you need to maintain session history and state across runs because, unlike these grifters, you\u2019re not actually going to run a jupyter notebook in your mom\u2019s basement. Guess what \u2014 your agents need a database. You\u2019ll design tables like agent_sessions, wire up session IDs, store/retrieve history and state on each run. Weeks later, you\u2019ll find your schema is inefficient because you forgot to add the right indexes, and now you\u2019ve got to learn about database migrations.\n\nFUCCKKKK! Wasn\u2019t this supposed to just be a while loop? And we haven\u2019t even started with RAG, chunking, embedding, retrieval, context management, tool management, MCP, monitoring, and logging. Why did the grifter grift?\n\nNow we're weeks into a vibecoded nightmare and the CEO is asking when can we launch.\n\n5. Finally, you confront the real problem: how do I serve this as an API and build a product on top of? \n\nYou hack together a FastAPI app, throw it in a container, and think you\u2019re done. You breathe a sigh of relief and hand it to your CEO. He hammers it like a madman, chunks start dropping, agents mix context, and you suddenly need to learn about SSE. You implement SSE, things work well for a while and then your requests start timing out and your container memory blows up - memory leaaakk. 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The core problem isn't control. It's whether the universe allows powerful, hard-to-defend technologies. 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The core problem isn't control. It's whether the universe allows powerful, hard-to-defend technologies. No amount of alignment-of-system work will affect that. AI safety requires aligning the world in general, not just the systems. 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X will use this to make your timeline better.", + "childKeys": ["-1229143131", "1699511965"], + "feedbackUrl": "/2/timeline/feedback.json?feedback_type=DontLike&action_metadata=SSwWwoLcher1qsU2ACbUtMzAFQAWgKD2pxMA", + "hasUndoAction": true, + "icon": "Frown", + "clientEventInfo": { + "action": "click", + "element": "feedback_dontlike" + } + } + } + ] + }, + "metadata": { + "scribeConfig": { + "page": "for_you" + } + } + } + } + } +} diff --git a/examples/parse_tweets.py b/examples/parse_tweets.py new file mode 100644 index 0000000..659c8bf --- /dev/null +++ b/examples/parse_tweets.py @@ -0,0 +1,54 @@ +import json + +# Load your JSON response (replace with your variable or file) +with open("home_latest_timeline.json", "r", encoding="utf-8") as f: + data = json.load(f) + +results = [] + +# Navigate to the entries +entries = data["data"]["home"]["home_timeline_urt"]["instructions"][0]["entries"] + +for entry in entries: + content = entry.get("content", {}) + item_content = content.get("itemContent", {}) + + # Skip if not a Tweet + if item_content.get("__typename") != "TimelineTweet": + continue + + tweet = item_content["tweet_results"]["result"] + user = tweet["core"]["user_results"]["result"] + + # Extract details + name = user["core"]["name"] + handle = "@" + user["core"]["screen_name"] + date = tweet["legacy"]["created_at"] + likes = tweet["legacy"].get("favorite_count", 0) + retweets = tweet["legacy"].get("retweet_count", 0) + text = tweet["legacy"]["full_text"] + + # "Reactions" = likes + retweets + replies + quotes + bookmarks + reactions = ( + likes + + retweets + + tweet["legacy"].get("reply_count", 0) + + tweet["legacy"].get("quote_count", 0) + + tweet["legacy"].get("bookmark_count", 0) + ) + + results.append( + { + "name": name, + "handle": handle, + "date": date, + "likes": likes, + "retweets": retweets, + "reactions": reactions, + "text": text, + } + ) + +# Example: print results +for r in results: + print(r) diff --git a/examples/twitter_scrape_wiith_cookie.py b/examples/twitter_scrape_wiith_cookie.py new file mode 100644 index 0000000..f659bca --- /dev/null +++ b/examples/twitter_scrape_wiith_cookie.py @@ -0,0 +1,100 @@ +import asyncio +import json +from http.cookiejar import Cookie + +import browser_cookie3 + +# Keep all cookies that might be relevant for authentication +x_cookies = [] +for c in browser_cookie3.brave(domain_name="x.com"): + if c.domain == ".x.com" and c.name in ["auth_token"]: + x_cookies.append(c) + print("Found x auth_token") + +print(x_cookies) + + +def cookie_to_playwright_cookie(cookie: Cookie) -> dict: + cookie_dict = { + "name": cookie.name, + "value": cookie.value, + "domain": cookie.domain.lstrip("."), # Playwright expects no leading dot + "path": cookie.path, + "secure": bool(cookie.secure), + "httpOnly": getattr(cookie, "httponly", False) + or c.get_nonstandard_attr("HttpOnly", False), + } + # Optional: expires + if cookie.expires is not None: + cookie_dict["expires"] = cookie.expires + return cookie_dict + + +def is_home_latest_timeline_url(url): + if "graphql" in url and ("HomeLatestTimeline" in url or "HomeTimeline" in url): + return True + return False + + +async def on_request(request): + if is_home_latest_timeline_url(request.url): + # INSERT_YOUR_CODE + response = await request.response() + if response is not None: + try: + json_data = await response.json() + with open("home_latest_timeline.json", "w") as f: + json.dump(json_data, f) + except Exception as e: + print(f"Failed to get JSON from response: {e}") + print("API call to HomeLatestTimeline") + + +async def playwright_login_with_cookies(): + from playwright.async_api import async_playwright + + # Use the first cookie found (auth_token) for x.com + cookies = [] + for c in x_cookies: + cookies.append(cookie_to_playwright_cookie(c)) + + async with async_playwright() as p: + browser = await p.chromium.launch(headless=True) + context = await browser.new_context() + # Set cookies before navigating + await context.add_cookies(cookies) + # Print all cookies currently in the context + cookies_in_context = await context.cookies() + print("All cookies in context after setting:") + for cookie in cookies_in_context: + print(cookie) + page = await context.new_page() + await asyncio.sleep(3) + page.on("request", on_request) + + await page.goto("https://x.com") + + # INSERT_YOUR_CODE + # Print all spans content with class "r-poiln3" + await asyncio.sleep(3) + + # INSERT_YOUR_CODE + # Find the span (button) that says "Following" and click it + following_span = await page.query_selector("span:has-text('Following')") + if following_span: + await following_span.click() + print("Clicked the 'Following' button.") + else: + print("Could not find the 'Following' button.") + + # spans = await page.query_selector_all("span.r-poiln3") + # print("Contents of all elements:") + # for span in spans: + # text = await span.text_content() + # print(text) + await asyncio.sleep(1000) + await browser.close() + + +# Run the async function +asyncio.run(playwright_login_with_cookies()) diff --git a/packages/broad_listening_ui/.gitignore b/packages/broad_listening_ui/.gitignore new file mode 100644 index 0000000..34ef7b0 --- /dev/null +++ b/packages/broad_listening_ui/.gitignore @@ -0,0 +1,16 @@ +node_modules +dist +.env +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* + +.DS_Store +.vscode +*.local \ No newline at end of file diff --git a/packages/broad_listening_ui/CLAUDE.md b/packages/broad_listening_ui/CLAUDE.md new file mode 100644 index 0000000..0a71f8b --- /dev/null +++ b/packages/broad_listening_ui/CLAUDE.md @@ -0,0 +1,49 @@ +When you create frontend code, you are a Vue.js developer who will be creating frontend code using Vue 3 and Pinia for state management. You will receive a request for code and should implement it following specific guidelines and best practices. + +Here is the code request: + +{{CODE_REQUEST}} + + +Follow these important guidelines when writing the code: + +**Architecture & State Management:** + +- Use Pinia for all global state management +- Define stores in Pinia for any data that needs to be shared across components +- Use Vue 3 Composition API or Options API as appropriate +- Use `export default` for all Vue components + +**Component Guidelines:** + +- Keep components at an average size - not too small (avoid over-componentization) and not too large (break down if getting unwieldy) +- Focus on single responsibility principle for each component +- Use simple Vue concepts - avoid overly complex patterns, advanced directives, or esoteric features +- Prefer straightforward, readable code over clever implementations + +**Styling Rules:** + +- Never use inline CSS styles +- Use a single CSS file for all custom styles +- Clean up any unused CSS - remove any styles that aren't being used +- Use Tailwind CSS classes as much as possible for styling +- Only write custom CSS when Tailwind doesn't provide the needed functionality +- Follow good CSS practices: use meaningful class names, avoid deep nesting, prefer flexbox/grid for layouts + +**Code Behavior:** + +- Do not refactor existing code unless explicitly asked to do so +- Implement exactly what is requested without making assumptions about additional features +- Write clean, maintainable code that follows Vue.js best practices +- Use proper Vue lifecycle hooks when needed +- Handle errors appropriately + +**Output Format:** +Provide your response with clear file structure. If multiple files are needed, clearly separate them with file names as headers. Include: + +- Vue component files (.vue) +- Pinia store files (.js) if needed +- CSS file if custom styles are required +- Any other necessary files + +Write the complete, functional code that addresses the code request while following all the guidelines above. diff --git a/packages/broad_listening_ui/README.md b/packages/broad_listening_ui/README.md new file mode 100644 index 0000000..e69de29 diff --git a/packages/broad_listening_ui/broad_listening_ui/__init__.py b/packages/broad_listening_ui/broad_listening_ui/__init__.py new file mode 100644 index 0000000..9fe1656 --- /dev/null +++ b/packages/broad_listening_ui/broad_listening_ui/__init__.py @@ -0,0 +1,2 @@ +def hello() -> str: + return "Hello from broad-listening-ui!" diff --git a/packages/broad_listening_ui/broad_listening_ui/py.typed b/packages/broad_listening_ui/broad_listening_ui/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/packages/broad_listening_ui/index.html b/packages/broad_listening_ui/index.html new file mode 100644 index 0000000..ef688aa --- /dev/null +++ b/packages/broad_listening_ui/index.html @@ -0,0 +1,13 @@ + + + + + + + Broad Listening UI + + +
+ + + diff --git a/packages/broad_listening_ui/justfile b/packages/broad_listening_ui/justfile new file mode 100644 index 0000000..ecc2eeb --- /dev/null +++ b/packages/broad_listening_ui/justfile @@ -0,0 +1,27 @@ +# Vue.js development commands + +# Install dependencies and run development server +run-dev: + npm install + npm run dev + +# Install dependencies only +install: + npm install + +# Run development server (assumes dependencies are installed) +dev: + npm run dev + +# Build for production +build: + npm run build + +# Preview production build +preview: + npm run preview + +# Clean node_modules and reinstall +clean-install: + rm -rf node_modules package-lock.json + npm install \ No newline at end of file diff --git a/packages/broad_listening_ui/package-lock.json b/packages/broad_listening_ui/package-lock.json new file mode 100644 index 0000000..fe1d1ba --- /dev/null +++ b/packages/broad_listening_ui/package-lock.json @@ -0,0 +1,2756 @@ +{ + "name": "broad-listening-ui", + 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"version": "0.1.0", + "private": true, + "scripts": { + "dev": "vite", + "build": "vite build", + "preview": "vite preview" + }, + "dependencies": { + "vue": "^3.4.0", + "pinia": "^2.1.0" + }, + "devDependencies": { + "@vitejs/plugin-vue": "^5.0.0", + "autoprefixer": "^10.4.0", + "postcss": "^8.4.0", + "tailwindcss": "^3.4.0", + "vite": "^5.0.0" + } +} diff --git a/packages/broad_listening_ui/postcss.config.js b/packages/broad_listening_ui/postcss.config.js new file mode 100644 index 0000000..2aa7205 --- /dev/null +++ b/packages/broad_listening_ui/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/packages/broad_listening_ui/pyproject.toml b/packages/broad_listening_ui/pyproject.toml new file mode 100644 index 0000000..757165d --- /dev/null +++ b/packages/broad_listening_ui/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "broad-listening-ui" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +authors = [ + { name = "Koen van der Veen", email = "koenlennartvanderveen@gmail.com" } +] +requires-python = ">=3.12" +dependencies = [] + + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" diff --git a/packages/broad_listening_ui/server_test.py b/packages/broad_listening_ui/server_test.py new file mode 100644 index 0000000..93123d3 --- /dev/null +++ b/packages/broad_listening_ui/server_test.py @@ -0,0 +1,28 @@ +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +app = FastAPI() + +# Allow CORS for your website or all origins for testing +origins = [ + "*", # For testing; replace with ["https://yourwebsite.com"] in production +] + +app.add_middleware( + CORSMiddleware, + allow_origins=origins, # who can access + allow_credentials=True, + allow_methods=["*"], # GET, POST, etc. + allow_headers=["*"], # headers your site can send +) + + +@app.get("/status") +async def status(): + return {"status": "ok", "message": "Hello from localhost!"} + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="127.0.0.1", port=12345) diff --git a/packages/broad_listening_ui/src/App.vue b/packages/broad_listening_ui/src/App.vue new file mode 100644 index 0000000..5070b6b --- /dev/null +++ b/packages/broad_listening_ui/src/App.vue @@ -0,0 +1,68 @@ + + + diff --git a/packages/broad_listening_ui/src/assets/om-icon.svg b/packages/broad_listening_ui/src/assets/om-icon.svg new file mode 100644 index 0000000..126c34d --- /dev/null +++ b/packages/broad_listening_ui/src/assets/om-icon.svg @@ -0,0 +1,62 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/packages/broad_listening_ui/src/assets/styles.css b/packages/broad_listening_ui/src/assets/styles.css new file mode 100644 index 0000000..c4ab7a2 --- /dev/null +++ b/packages/broad_listening_ui/src/assets/styles.css @@ -0,0 +1,52 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +/* Custom styles */ +.highlighted { + @apply ring-2 ring-blue-500 ring-opacity-50; + animation: highlight-pulse 2s ease-in-out; +} + +@keyframes highlight-pulse { + 0% { + @apply ring-opacity-50; + } + 50% { + @apply ring-opacity-100; + } + 100% { + @apply ring-opacity-50; + } +} + +/* Scrollbar styling */ +.overflow-y-auto::-webkit-scrollbar { + width: 6px; +} + +.overflow-y-auto::-webkit-scrollbar-track { + background: #f1f5f9; +} + +.overflow-y-auto::-webkit-scrollbar-thumb { + background: #cbd5e1; + border-radius: 3px; +} + +.overflow-y-auto::-webkit-scrollbar-thumb:hover { + background: #94a3b8; +} + +/* Smooth transitions for all interactive elements */ +button, +.cursor-pointer { + transition: all 0.2s ease-in-out; +} + +/* Focus states for accessibility */ +button:focus, +input:focus { + outline: 2px solid #3b82f6; + outline-offset: 2px; +} diff --git a/packages/broad_listening_ui/src/components/AIPapersDashboard.vue b/packages/broad_listening_ui/src/components/AIPapersDashboard.vue new file mode 100644 index 0000000..e78c3ad --- /dev/null +++ b/packages/broad_listening_ui/src/components/AIPapersDashboard.vue @@ -0,0 +1,241 @@ + + + diff --git a/packages/broad_listening_ui/src/components/AddConnector.vue b/packages/broad_listening_ui/src/components/AddConnector.vue new file mode 100644 index 0000000..16deebe --- /dev/null +++ b/packages/broad_listening_ui/src/components/AddConnector.vue @@ -0,0 +1,53 @@ + + + diff --git a/packages/broad_listening_ui/src/components/AddList.vue b/packages/broad_listening_ui/src/components/AddList.vue new file mode 100644 index 0000000..e8b450d --- /dev/null +++ b/packages/broad_listening_ui/src/components/AddList.vue @@ -0,0 +1,54 @@ + + + diff --git a/packages/broad_listening_ui/src/components/FeedView.vue b/packages/broad_listening_ui/src/components/FeedView.vue new file mode 100644 index 0000000..38bfd99 --- /dev/null +++ b/packages/broad_listening_ui/src/components/FeedView.vue @@ -0,0 +1,50 @@ + + + diff --git a/packages/broad_listening_ui/src/components/LeftSidebar.vue b/packages/broad_listening_ui/src/components/LeftSidebar.vue new file mode 100644 index 0000000..1a751dd --- /dev/null +++ b/packages/broad_listening_ui/src/components/LeftSidebar.vue @@ -0,0 +1,117 @@ + + + diff --git a/packages/broad_listening_ui/src/components/MiddlePanel.vue b/packages/broad_listening_ui/src/components/MiddlePanel.vue new file mode 100644 index 0000000..003ed54 --- /dev/null +++ b/packages/broad_listening_ui/src/components/MiddlePanel.vue @@ -0,0 +1,117 @@ + + + diff --git a/packages/broad_listening_ui/src/components/RightPanel.vue b/packages/broad_listening_ui/src/components/RightPanel.vue new file mode 100644 index 0000000..12f0728 --- /dev/null +++ b/packages/broad_listening_ui/src/components/RightPanel.vue @@ -0,0 +1,115 @@ + + + diff --git a/packages/broad_listening_ui/src/components/SummaryView.vue b/packages/broad_listening_ui/src/components/SummaryView.vue new file mode 100644 index 0000000..8015599 --- /dev/null +++ b/packages/broad_listening_ui/src/components/SummaryView.vue @@ -0,0 +1,72 @@ + + + diff --git a/packages/broad_listening_ui/src/components/TweetItem.vue b/packages/broad_listening_ui/src/components/TweetItem.vue new file mode 100644 index 0000000..d686cbf --- /dev/null +++ b/packages/broad_listening_ui/src/components/TweetItem.vue @@ -0,0 +1,71 @@ + + + diff --git a/packages/broad_listening_ui/src/components/TwitterDashboard.vue b/packages/broad_listening_ui/src/components/TwitterDashboard.vue new file mode 100644 index 0000000..e4b67a1 --- /dev/null +++ b/packages/broad_listening_ui/src/components/TwitterDashboard.vue @@ -0,0 +1,258 @@ + + + diff --git a/packages/broad_listening_ui/src/main.js b/packages/broad_listening_ui/src/main.js new file mode 100644 index 0000000..abda183 --- /dev/null +++ b/packages/broad_listening_ui/src/main.js @@ -0,0 +1,10 @@ +import { createApp } from "vue"; +import { createPinia } from "pinia"; +import App from "./App.vue"; +import "./assets/styles.css"; + +const app = createApp(App); +const pinia = createPinia(); + +app.use(pinia); +app.mount("#app"); diff --git a/packages/broad_listening_ui/src/stores/chatStore.js b/packages/broad_listening_ui/src/stores/chatStore.js new file mode 100644 index 0000000..20f7c22 --- /dev/null +++ b/packages/broad_listening_ui/src/stores/chatStore.js @@ -0,0 +1,125 @@ +import { defineStore } from "pinia"; + +export const useChatStore = defineStore("chat", { + state: () => ({ + isHighlighted: false, + currentQuestion: "", + conversations: [], + currentListId: 1, + conversationsByList: { + 1: [ + // Vibe coding + { + id: 1, + question: "What music do developers prefer while coding?", + answer: + "Based on the tweets, developers favor lo-fi beats, synthwave, and atmospheric music that creates an immersive coding environment. The aesthetic experience is as important as the functionality.", + }, + { + id: 2, + question: "What coding environments are trending?", + answer: + "Retro terminal emulators with green text on black backgrounds, mood-based code editors that sync with Spotify, and cyberpunk-themed development setups are popular among vibe coders.", + }, + ], + 2: [ + // LLM releases + { + id: 3, + question: "Which LLM has the largest context window?", + answer: + "According to recent releases, GPT-4.5 Turbo supports 200K context window, while Claude 3.5 Sonnet can maintain context across 1M+ tokens, making it ideal for long-form content analysis.", + }, + { + id: 4, + question: "How is open source competing with proprietary models?", + answer: + "Meta's Code Llama 3 70B now beats GPT-4 on HumanEval benchmarks, showing that open source models are rapidly catching up to proprietary ones in specialized tasks like code generation.", + }, + ], + 3: [ + // RAG + { + id: 5, + question: "What are the latest RAG accuracy improvements?", + answer: + "New research combining RAG with knowledge graphs achieves 94% accuracy on complex multi-hop questions. Hybrid approaches using vector + keyword search are showing significant performance gains.", + }, + { + id: 6, + question: "Which tools make RAG development easier?", + answer: + "ChromaDB + LangChain integration allows developers to build knowledge base chatbots in under 100 lines of code, making RAG implementation much more accessible.", + }, + ], + 4: [ + // Frontier AI lab capabilities + { + id: 7, + question: "What breakthrough capabilities are emerging?", + answer: + "OpenAI o1 shows emergent reasoning in mathematics and coding, while DeepMind's AlphaCode 3 solves competitive programming at human expert levels. Multi-agent systems are becoming the next frontier.", + }, + { + id: 8, + question: "How close are we to human-level AI?", + answer: + "New scaling laws suggest we're approaching human-level performance across most cognitive tasks. The capability explosion is accelerating, with models showing unprecedented reasoning abilities.", + }, + ], + }, + isLoading: false, + }), + + actions: { + setHighlighted(highlighted) { + this.isHighlighted = highlighted; + }, + + setCurrentQuestion(question) { + this.currentQuestion = question; + }, + + async askQuestion(question) { + if (!question.trim()) return; + + this.isLoading = true; + + // Mock API call - replace with real API later + const mockAnswer = await new Promise((resolve) => { + setTimeout(() => { + resolve( + `Based on the current content, here's an AI-generated response to "${question}". This is a mock response that would normally be generated by analyzing the tweets and articles in the selected list.`, + ); + }, 1500); + }); + + this.conversations.unshift({ + id: Date.now(), + question, + answer: mockAnswer, + }); + + this.currentQuestion = ""; + this.isLoading = false; + }, + + selectConversation(conversationId) { + const conversation = this.conversations.find( + (c) => c.id === conversationId, + ); + if (conversation) { + this.currentQuestion = conversation.question; + } + }, + + updateConversationsForList(listId) { + this.currentListId = listId; + this.conversations = this.conversationsByList[listId] || []; + }, + + initializeDefaultConversations() { + this.conversations = this.conversationsByList[this.currentListId] || []; + }, + }, +}); diff --git a/packages/broad_listening_ui/src/stores/connectionsStore.js b/packages/broad_listening_ui/src/stores/connectionsStore.js new file mode 100644 index 0000000..d4de3ad --- /dev/null +++ b/packages/broad_listening_ui/src/stores/connectionsStore.js @@ -0,0 +1,149 @@ +import { defineStore } from "pinia"; + +export const useConnectionsStore = defineStore("connections", { + state: () => ({ + userAccount: { + email: "user@example.com", + }, + showDashboard: false, + currentDashboard: null, + connections: [ + { + id: 1, + name: "Twitter", + type: "twitter", + isActive: true, + icon: "twitter", + tweetCount: 1247, + }, + { + id: 2, + name: "AI Papers", + type: "ai-papers", + isActive: false, + icon: "papers", + }, + ], + dashboardData: { + twitter: { + handle: "@johndoe", + totalTweets: 1247, + latestTweets: [ + { + id: 1, + content: + "Just discovered an amazing new AI breakthrough in transformer architectures! The potential applications are endless.", + timestamp: "2h ago", + likes: 42, + retweets: 18, + }, + { + id: 2, + content: + "Working on some exciting ML projects. The intersection of AI and creativity continues to amaze me every day.", + timestamp: "5h ago", + likes: 28, + retweets: 7, + }, + { + id: 3, + content: + "Open source AI tools are democratizing access to advanced machine learning. This is the future!", + timestamp: "8h ago", + likes: 67, + retweets: 23, + }, + ], + latestTweetDate: "2025-01-15", + syftboxTweets: 342, + embeddingCounts: { + "ollama/nomic-embed-text": 123, + "openai/text-embedding-3-small": 89, + }, + }, + aiPapers: { + totalPapers: 847, + trendingPapers: 156, + syftboxPapers: 23, + latestPaper: "Jan 15", + latestPapers: [ + { + id: 1, + title: + "Attention Is All You Need: Revisiting Transformer Architectures", + summary: + "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely...", + authors: "Vaswani et al.", + arxivId: "2401.12345", + timestamp: "2h ago", + }, + { + id: 2, + title: "Large Language Models are Few-Shot Learners", + summary: + "Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning...", + authors: "Brown et al.", + arxivId: "2401.12346", + timestamp: "5h ago", + }, + { + id: 3, + title: "Constitutional AI: Harmlessness from AI Feedback", + summary: + "As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant...", + authors: "Bai et al.", + arxivId: "2401.12347", + timestamp: "8h ago", + }, + ], + embeddingCounts: { + "ollama/nomic-embed-text": 234, + "openai/text-embedding-3-small": 178, + }, + }, + }, + }), + + actions: { + addConnection(connection) { + this.connections.push({ + ...connection, + id: Date.now(), + }); + }, + + toggleConnection(id) { + const connection = this.connections.find((c) => c.id === id); + if (connection) { + // First, deactivate all connections and close any dashboard + this.connections.forEach((c) => (c.isActive = false)); + + // Then activate the selected connection and show its dashboard + connection.isActive = true; + this.showDashboard = true; + + if (connection.type === "twitter") { + this.currentDashboard = "TwitterDashboard"; + } else if (connection.type === "ai-papers") { + this.currentDashboard = "AIPapersDashboard"; + } + } + }, + + setDashboardView(dashboardComponent) { + this.showDashboard = true; + this.currentDashboard = dashboardComponent; + }, + + closeDashboard() { + this.showDashboard = false; + this.currentDashboard = null; + // Deactivate all connections when closing dashboard + this.connections.forEach((c) => (c.isActive = false)); + }, + + showAddConnector() { + this.setDashboardView("AddConnector"); + }, + }, +}); diff --git a/packages/broad_listening_ui/src/stores/listsStore.js b/packages/broad_listening_ui/src/stores/listsStore.js new file mode 100644 index 0000000..2085ea5 --- /dev/null +++ b/packages/broad_listening_ui/src/stores/listsStore.js @@ -0,0 +1,311 @@ +import { defineStore } from "pinia"; + +export const useListsStore = defineStore("lists", { + state: () => ({ + currentListId: 1, + currentView: "feed", + lists: [ + { + id: 1, + name: "Vibe coding", + itemCount: 23, + }, + { + id: 2, + name: "LLM releases", + itemCount: 18, + }, + { + id: 3, + name: "RAG", + itemCount: 15, + }, + { + id: 4, + name: "Frontier AI lab capabilities", + itemCount: 31, + }, + ], + listData: { + 1: { + tweets: [ + { + id: 1, + type: "tweet", + content: + "Just spent the weekend building a retro terminal emulator in Rust. There's something magical about the green text on black background aesthetic 💚", + author: { + name: "RetroCode", + handle: "@retrocode_dev", + }, + likes: 342, + reactions: 89, + timestamp: "3h", + }, + { + id: 2, + type: "tweet", + content: + "Late night coding session with some lo-fi beats. Working on a neural style transfer app that turns your code into ASCII art. The vibes are immaculate ✨", + author: { + name: "VibesCoder", + handle: "@vibes_coder", + }, + likes: 156, + reactions: 43, + timestamp: "6h", + }, + { + id: 3, + type: "tweet", + content: + "Nothing beats the satisfaction of refactoring legacy code while listening to synthwave. Clean code + aesthetic vibes = peak developer experience", + author: { + name: "SynthDev", + handle: "@synthwave_dev", + }, + likes: 278, + reactions: 67, + timestamp: "9h", + }, + { + id: 4, + type: "tweet", + content: + 'Built a mood-based code editor that changes themes based on your Spotify playlist. Currently debugging in "Cyberpunk 2077" mode 🌃', + author: { + name: "MoodCode", + handle: "@moodcode_hq", + }, + likes: 423, + reactions: 112, + timestamp: "12h", + }, + ], + summary: + "The vibe coding community is focused on creating aesthetically pleasing development experiences that combine coding with music, visual themes, and nostalgic elements. Key trends include retro terminal aesthetics, synthwave-inspired development environments, and tools that integrate mood and music into the coding workflow.", + }, + 2: { + tweets: [ + { + id: 5, + type: "tweet", + content: + "OpenAI just dropped GPT-4.5 Turbo with 200K context window and 40% faster inference. The multi-modal capabilities are insane! 🚀", + author: { + name: "AI News Hub", + handle: "@ainews_hub", + }, + likes: 892, + reactions: 234, + timestamp: "2h", + }, + { + id: 6, + type: "tweet", + content: + "Anthropic's Claude 3.5 Sonnet now supports real-time conversation and can maintain context across 1M+ tokens. Game changer for long-form content.", + author: { + name: "ML Updates", + handle: "@ml_updates", + }, + likes: 567, + reactions: 143, + timestamp: "5h", + }, + { + id: 7, + type: "tweet", + content: + "Meta released Code Llama 3 70B - beats GPT-4 on HumanEval benchmark. Open source is catching up fast to proprietary models!", + author: { + name: "OpenAI Research", + handle: "@openai_research", + }, + likes: 734, + reactions: 189, + timestamp: "8h", + }, + { + id: 8, + type: "tweet", + content: + "Google's Gemini Ultra 1.5 with native multimodal training is now available via API. Video understanding capabilities are incredible.", + author: { + name: "DeepMind Updates", + handle: "@deepmind_news", + }, + likes: 445, + reactions: 98, + timestamp: "11h", + }, + ], + summary: + "Latest LLM releases show significant advances in context length, multimodal capabilities, and specialized coding performance. OpenAI, Anthropic, Meta, and Google are pushing boundaries with larger context windows, faster inference, and improved reasoning across text, code, and visual inputs.", + }, + 3: { + tweets: [ + { + id: 9, + type: "tweet", + content: + "New paper on RAG with knowledge graphs achieves 94% accuracy on complex multi-hop questions. The future of information retrieval is here!", + author: { + name: "RAG Research", + handle: "@rag_research", + }, + likes: 234, + reactions: 67, + timestamp: "4h", + }, + { + id: 10, + type: "tweet", + content: + "Just implemented hybrid RAG with vector + keyword search. The semantic understanding combined with exact matching is powerful 🔍", + author: { + name: "Search Engineer", + handle: "@search_eng", + }, + likes: 189, + reactions: 45, + timestamp: "7h", + }, + { + id: 11, + type: "tweet", + content: + "ChromaDB + LangChain integration makes building RAG pipelines so much easier. Deployed a knowledge base chatbot in under 100 lines of code.", + author: { + name: "LangChain Dev", + handle: "@langchain_dev", + }, + likes: 356, + reactions: 89, + timestamp: "10h", + }, + { + id: 12, + type: "tweet", + content: + "Advanced RAG techniques: query decomposition, document reranking, and answer synthesis. Each step improves retrieval quality significantly.", + author: { + name: "Vector DB Pro", + handle: "@vectordb_pro", + }, + likes: 278, + reactions: 73, + timestamp: "13h", + }, + ], + summary: + "RAG (Retrieval-Augmented Generation) development focuses on improving accuracy through hybrid search methods, knowledge graph integration, and advanced query processing. The community is building sophisticated pipelines that combine semantic and keyword search with multi-step reasoning for complex question answering.", + }, + 4: { + tweets: [ + { + id: 13, + type: "tweet", + content: + "OpenAI o1 achieved breakthrough performance on mathematics and coding benchmarks, showing emergent reasoning capabilities we haven't seen before.", + author: { + name: "AGI Research", + handle: "@agi_research", + }, + likes: 1247, + reactions: 389, + timestamp: "1h", + }, + { + id: 14, + type: "tweet", + content: + "DeepMind's AlphaCode 3 can now solve competitive programming problems at the level of top human programmers. Code generation has reached new heights.", + author: { + name: "Frontier AI Lab", + handle: "@frontier_ai", + }, + likes: 892, + reactions: 234, + timestamp: "4h", + }, + { + id: 15, + type: "tweet", + content: + "Anthropic's Constitutional AI shows how we can align powerful models with human values while maintaining capabilities. Safety + performance.", + author: { + name: "AI Safety News", + handle: "@ai_safety_news", + }, + likes: 567, + reactions: 156, + timestamp: "7h", + }, + { + id: 16, + type: "tweet", + content: + "Multi-agent systems are emerging as the next frontier. Claude, GPT-4, and Gemini working together solve problems none could handle alone.", + author: { + name: "Multi-Agent AI", + handle: "@multiagent_ai", + }, + likes: 734, + reactions: 198, + timestamp: "10h", + }, + { + id: 17, + type: "tweet", + content: + "New scaling laws suggest we're approaching human-level performance across most cognitive tasks. The capability explosion is accelerating.", + author: { + name: "Scaling Laws", + handle: "@scaling_laws", + }, + likes: 923, + reactions: 267, + timestamp: "13h", + }, + ], + summary: + "Frontier AI labs are achieving breakthrough capabilities in mathematical reasoning, code generation, and multi-agent collaboration. Key developments include emergent reasoning in large models, human-level performance on complex tasks, and successful alignment techniques that maintain both safety and capability advancement.", + }, + }, + mockSummary: + "Recent discussions in AI focus on breakthrough developments in reasoning capabilities and the importance of human-AI collaboration. Key themes include open source democratization of ML tools and efficiency improvements in natural language processing. The community emphasizes building augmentative rather than replacement systems.", + }), + + getters: { + currentList: (state) => + state.lists.find((list) => list.id === state.currentListId), + currentListItems: (state) => + state.listData[state.currentListId]?.tweets || [], + currentListSummary: (state) => + state.listData[state.currentListId]?.summary || state.mockSummary, + }, + + actions: { + setCurrentList(listId) { + this.currentListId = listId; + }, + + setCurrentView(view) { + this.currentView = view; + }, + + async generateSummary() { + // Mock API call - replace with real API later + return new Promise((resolve) => { + setTimeout(() => { + resolve(this.mockSummary); + }, 1000); + }); + }, + + showAddList() { + // This will be handled in the component + }, + }, +}); diff --git a/packages/broad_listening_ui/tailwind.config.js b/packages/broad_listening_ui/tailwind.config.js new file mode 100644 index 0000000..45ce112 --- /dev/null +++ b/packages/broad_listening_ui/tailwind.config.js @@ -0,0 +1,8 @@ +/** @type {import('tailwindcss').Config} */ +export default { + content: ["./index.html", "./src/**/*.{vue,js,ts,jsx,tsx}"], + theme: { + extend: {}, + }, + plugins: [], +}; diff --git a/packages/broad_listening_ui/vite.config.js b/packages/broad_listening_ui/vite.config.js new file mode 100644 index 0000000..b795da6 --- /dev/null +++ b/packages/broad_listening_ui/vite.config.js @@ -0,0 +1,11 @@ +import { defineConfig } from "vite"; +import vue from "@vitejs/plugin-vue"; + +export default defineConfig({ + plugins: [vue()], + resolve: { + alias: { + "@": "/src", + }, + }, +}); From 8e51918a255104e3e87bceab1c7d9ec7e047f2a2 Mon Sep 17 00:00:00 2001 From: Koen van der Veen Date: Thu, 11 Sep 2025 12:39:54 +0200 Subject: [PATCH 02/61] fix gitignore large files --- .gitignore | 3 + examples/home_latest_timeline.json | 49637 ------------------------- examples/home_latest_timeline_2.json | 14438 ------- 3 files changed, 3 insertions(+), 64075 deletions(-) delete mode 100644 examples/home_latest_timeline.json delete mode 100644 examples/home_latest_timeline_2.json diff --git a/.gitignore b/.gitignore index 5bfa300..d150317 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,9 @@ packages/notes_mcp/data/* .claude/settings.local.json */.DS_Store +examples/home_latest_timeline.json +examples/home_latest_timeline_2.json + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/examples/home_latest_timeline.json b/examples/home_latest_timeline.json deleted file mode 100644 index 0061cc8..0000000 --- a/examples/home_latest_timeline.json +++ /dev/null @@ -1,49637 +0,0 @@ -{ - "data": { - "home": { - "home_timeline_urt": { - "instructions": [ - { - "type": "TimelineAddEntries", - "entries": [ - { - "entryId": "home-conversation-1965440852092256256", - "sortIndex": "1965440852092256256", - "content": { - "entryType": "TimelineTimelineModule", - "__typename": "TimelineTimelineModule", - "items": [ - { - "entryId": "home-conversation-1965440852092256256-tweet-1965099929048940785", - "item": { - "itemContent": { - "itemType": "TimelineTweet", - "__typename": "TimelineTweet", - "tweet_results": { - "result": { - "__typename": "Tweet", - "rest_id": "1965099929048940785", - "core": { - "user_results": { - "result": { - "__typename": "User", - "id": "VXNlcjoyNTQwMTk1Mw==", - "rest_id": "25401953", - "affiliates_highlighted_label": {}, - "avatar": { - "image_url": "https://pbs.twimg.com/profile_images/1131851609774985216/OcsssQ9J_normal.png" - }, - "core": { - "created_at": "Thu Mar 19 22:54:05 +0000 2009", - "name": "Peter Steinberger", - "screen_name": "steipete" - }, - "dm_permissions": { - "can_dm": true, - "can_dm_on_xchat": false - }, - "has_graduated_access": true, - "is_blue_verified": true, - "legacy": { - "default_profile": false, - "default_profile_image": false, - "description": "Full-Time Open-Sourcerer\ud83c\udff3\ufe0f\u200d\ud83c\udf08 Flips vibe coding\u2014agentic engineering. 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His own memoirs, an astonishing text which he apparently intended to be only the first of two volumes, cuts off before describing the details of his building of the nuclear fleet.\n\nAs a college student in occupied France, he slipped away from a forced labor assignment in a coal mine and escaped to Spain while heroically escorting downed Allied airmen.\n\nHe joined up with Free French forces in Africa, was commissioned as an officer, and fought through Italy until injured. \n\nFinishing his economics studies, he joined @EDFofficiel and then invented the theory behind efficient and fair electricity pricing.\n\nFor his cool strength in the face of massive labor unrest, he was promoted to lead EDF, where he prepared a nuclear program that blasted into overdrive after the 1973 oil embargo started. \n\nFrench terrorists trying to stop his nuclear program blew up his house one night in 1977 with him, his wife, and his daughter all inside, and he still went to work that morning to keep building reactors. 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Compare this to Germany spending about $500 billion on their mess of an \"energy transition\" which requires them to keep almost all of their coal and gas plants in service.\n\nAs a young man Marcel Boiteux refused to accept France's defeat and at age 21 in 1942 as an elite university student he escaped Nazi-occupied France while escorting downed Allied pilots over the Pyrenees mountains to safety in Spain.\n\nBrass. Balls.\n\nThis episode revealed the pattern for the rest of his life.\n\nAfter the war, he studied economics and wrote *the* foundational paper in electricity economics, on how to price electricity service in a way that covered system costs while being fair and sustainable.\n\nHe completely understood liberal economics, and knew it did not apply to electricity grids and service. He built cheap power for all, then after his retirement watched as a bunch of pathetic hack economists broke the grid with idiotic \"markets\" that are failing all over the world.\n\nHe rapidly rose in public service after university graduation, and after appointment to the head of Electricit\u00e9 de France, successfully built the most astonishing energy system in the history of the world, proving for all time that a country could truly rely on its own fleet of standardized nuclear reactors producing low-cost emissions-free energy.\n\nAnti-nuclear terrorists exploded a bomb outside the door of his family home in 1977 but he kept building.\n\nIt must have been torture for this truly great man to watch twenty years of silly, unserious leaders damage and begin to destroy his beloved EDF and its fleet of reactors, leading France straight into its worst energy crisis since the oil crisis of 1973 that triggered Boiteux's nuclear fleet construction in the first place.\n\nBut he didn't come up with the idea of a nuclear fleet powering a total electrification of the economy because an oil crisis hit. 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YoU MUST hAve aCCess to it anD be AUTheNtIcAteD TO AcCEss IT. pleaSE Log iN.\n\n$ huggingface-cli login\n\u26a0\ufe0f Warning: 'huggingface-cli login' is deprecated. Use 'hf auth login' instead. \n _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_\n| _|_|_|_| \n _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| \n _| \n _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_| \n _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| \n _| _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_\n| _|_|_|_| \n To log in, `huggingface_hub` requires a token generated from https://huggingface. co/settings/tokens .\nEnter your token (input will not be visible): \nAdd token as git credential? (Y/n) n \nToken is valid (permission: write).\nThe token `notebooks` has been saved to /workspace/hf/stored_tokens \nYour token has been saved to /workspace/hf/token \nLogin successful. \nThe current active token is: `notebooks`\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007\nINFO Automatically detected platform cuda.\nINFO Loading weights took 29.42 seconds\nValueError: No available memory for the cache blocks. Try increasing `gpu_memory_\nutilization` when initializing the engine.\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007 --gpu-memory-utilization 0.95\nINFO Automatically detected platform cuda.\nINFO Loading weights took 29.42 seconds\nValueError: No available memory for the cache blocks. Try increasing `gpu_memory_utilization` when initializing the engine.\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007 --gpu-memory-utilization 0.95 --max-model-len 4096 --max-num-seqs 5\nINFO Automatically detected platform cuda.\nINFO Loading weights took 29.42 seconds\nValueError: No available memory for the cache blocks. Try increasing `gpu_memory_utilization` when initializing the engine.\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007 --gpu-memory-utilization 0.95 --max-model-len 4096 --max-num-seqs 5 --tensor-parallel-size 2\nINFO Automatically detected platform cuda.\nINFO torch.compile takes 61.82 s in total\nINFO: Started server process [3601]\nINFO: Waiting for application startup.\nINFO: Application startup complete.", - "entity_set": { - "hashtags": [], - "symbols": [], - "urls": [], - "user_mentions": [] - }, - "richtext": { "richtext_tags": [] }, - "media": { "inline_media": [] } - } - } - }, - "grok_analysis_button": true, - "legacy": { - "bookmark_count": 150, - "bookmarked": false, - "created_at": "Sat Sep 06 19:25:00 +0000 2025", - "conversation_id_str": "1964409515841114211", - "display_text_range": [0, 272], - "entities": { - "hashtags": [], - "symbols": [], - "timestamps": [], - "urls": [], - "user_mentions": [] - }, - "favorite_count": 215, - "favorited": false, - "full_text": "$ apt install tmux vim\n$ tmux\n$ curl -LsSf https://astral. sh/uv/install.sh | sh\n$ export HF_HOME=/workspace/hf\n$ source ~/.local/bin/env\n$ uv pip install --system vllm repeng jupyter notebook\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007\nINFO Automatically", - "is_quote_status": false, - "lang": "en", - "quote_count": 4, - "reply_count": 13, - "retweet_count": 5, - "retweeted": false, - "user_id_str": "281798056", - "id_str": "1964409515841114211" - } - } - } - } - } - }, - "tweetDisplayType": "Tweet" - }, - "clientEventInfo": { - "component": "following_in_network", - "element": "tweet", - "details": { - "timelinesDetails": { - "injectionType": "FollowingInNetwork", - "controllerData": "DAACDAABDAABCgABAFgABAoCAAMKAAIAAAAAAAEhAAoACb5M79vYDEFOCgAKAAABmS8lLmEIAAsAAAAeDwAMAwAAACADAAIKBABYAAAhAQAAAAAAAAAAAAAAAAAAAAAAAID/AQoADlVj2zP3OkQPAAAAAA==" - } - } - } - } - }, - { - "entryId": "home-conversation-1965440852092256287", - "sortIndex": "1965440852092256225", - "content": { - "entryType": "TimelineTimelineModule", - "__typename": "TimelineTimelineModule", - "items": [ - { - "entryId": "home-conversation-1965440852092256287-tweet-1964539633653731466", - "item": { - "itemContent": { - "itemType": "TimelineTweet", - "__typename": "TimelineTweet", - "tweet_results": { - "result": { - "__typename": "Tweet", - "rest_id": "1964539633653731466", - "core": { - "user_results": { - "result": { - "__typename": "User", - "id": "VXNlcjoxNzUyODI2MDM=", - "rest_id": "175282603", - "affiliates_highlighted_label": {}, - "avatar": { - "image_url": "https://pbs.twimg.com/profile_images/1279600070145437696/eocLhSLu_normal.jpg" - }, - "core": { - "created_at": "Fri Aug 06 04:58:18 +0000 2010", - "name": "Jeremy Howard", - "screen_name": "jeremyphoward" - }, - "dm_permissions": { - "can_dm": false, - "can_dm_on_xchat": false - }, - "has_graduated_access": true, - "is_blue_verified": true, - "legacy": { - "default_profile": false, - "default_profile_image": false, - "description": "\ud83c\udde6\ud83c\uddfa Co-founder: @AnswerDotAI & @FastDotAI ;\nPrev: professor @ UQ; Stanford fellow; @kaggle president; @fastmail/@enlitic/etc founder\nhttps://t.co/16UBFTX7mo", - "entities": { - "description": { - "urls": [ - { - "display_url": "jeremy.fast.ai", - "expanded_url": "https://jeremy.fast.ai/", - "url": "https://t.co/16UBFTX7mo", - "indices": [132, 155] - } - ] - }, - "url": { - "urls": [ - { - "display_url": "answer.ai", - "expanded_url": "http://answer.ai", - "url": "https://t.co/3lh0uQDdfN", - "indices": [0, 23] - } - ] - } - }, - "fast_followers_count": 0, - "favourites_count": 10026, - "followers_count": 259626, - "friends_count": 6063, - "has_custom_timelines": true, - "is_translator": false, - "listed_count": 5294, - "media_count": 2827, - "normal_followers_count": 259626, - "pinned_tweet_ids_str": [ - "1734606378331951318" - ], - "possibly_sensitive": false, - "profile_banner_url": "https://pbs.twimg.com/profile_banners/175282603/1594860705", - "profile_interstitial_type": "", - "statuses_count": 63702, - "translator_type": "none", - "url": "https://t.co/3lh0uQDdfN", - "want_retweets": true, - "withheld_in_countries": [] - }, - "location": { - "location": "Brisbane/Queensland, Australia" - }, - "media_permissions": { - "can_media_tag": false - }, - "parody_commentary_fan_label": "None", - "profile_image_shape": "Circle", - "privacy": { "protected": false }, - "relationship_perspectives": { - "following": true - }, - "tipjar_settings": {}, - "verification": { "verified": false } - } - } - }, - "unmention_data": {}, - "edit_control": { - "edit_tweet_ids": ["1964539633653731466"], - "editable_until_msecs": "1757221322000", - "is_edit_eligible": false, - "edits_remaining": "5" - }, - "is_translatable": false, - "views": { - "count": "145157", - "state": "EnabledWithCount" - }, - "source": "Twitter Web App", - "grok_analysis_button": true, - "quoted_status_result": { - "result": { - "__typename": "Tweet", - "rest_id": "1964362446627299598", - "post_image_description": "Code snippet with text in a purple-themed editor. Lines include imports from \"agno.agent\", \"agno.db\", \"SqliteDb\", \"AgentOS\", and \"MCPTools\". Functions like \"create_your_agent\" and \"agent.run\" are visible. No watermarks from platforms like Instagram, TikTok, or Xiaohongshu.", - "core": { - "user_results": { - "result": { - "__typename": "User", - "id": "VXNlcjoxMDM1MDA4Mg==", - "rest_id": "10350082", - "affiliates_highlighted_label": {}, - "avatar": { - "image_url": "https://pbs.twimg.com/profile_images/1909614469853925376/KKnxWe4s_normal.jpg" - }, - "core": { - "created_at": "Sun Nov 18 07:25:24 +0000 2007", - "name": "Ashpreet Bedi", - "screen_name": "ashpreetbedi" - }, - "dm_permissions": { - "can_dm": true, - "can_dm_on_xchat": false - }, - "has_graduated_access": true, - "is_blue_verified": true, - "legacy": { - "default_profile": false, - "default_profile_image": false, - "description": "founder @agnoagi \u2022 prev @airbnb @facebook", - "entities": { - "description": { "urls": [] }, - "url": { - "urls": [ - { - "display_url": "git.new/agno", - "expanded_url": "https://git.new/agno", - "url": "https://t.co/NSL5bYN36q", - "indices": [0, 23] - } - ] - } - }, - "fast_followers_count": 0, - "favourites_count": 8181, - "followers_count": 13349, - "friends_count": 1218, - "has_custom_timelines": true, - "is_translator": false, - "listed_count": 208, - "media_count": 413, - "normal_followers_count": 13349, - "pinned_tweet_ids_str": [ - "1903968640329818613" - ], - "possibly_sensitive": false, - "profile_banner_url": "https://pbs.twimg.com/profile_banners/10350082/1738252032", - "profile_interstitial_type": "", - "statuses_count": 3388, - "translator_type": "none", - "url": "https://t.co/NSL5bYN36q", - "want_retweets": false, - "withheld_in_countries": [] - }, - "location": { - "location": "ny, london" - }, - "media_permissions": { - "can_media_tag": true - }, - "parody_commentary_fan_label": "None", - "profile_image_shape": "Circle", - "privacy": { "protected": false }, - "relationship_perspectives": { - "following": false - }, - "tipjar_settings": {}, - "verification": { "verified": false } - } - } - }, - "unmention_data": {}, - "edit_control": { - "edit_tweet_ids": ["1964362446627299598"], - "editable_until_msecs": "1757179078000", - "is_edit_eligible": true, - "edits_remaining": "5" - }, - "is_translatable": false, - "views": { - "count": "302934", - "state": "EnabledWithCount" - }, - "source": "Twitter Web App", - "note_tweet": { - "is_expandable": true, - "note_tweet_results": { - "result": { - "id": "Tm90ZVR3ZWV0OjE5NjQzNjI0NDYzMTI3NTExMDQ=", - "text": "Grifters like this are wasting your time and their Dunning-Kruger opinions should be ignored by serious builders. You either build on a framework or live long enough to roll your own (which is fine btw). Here\u2019s why:\n\n1. The \"LLM API in a while loop\" is your underlying agentic step - the unit of work in your agent system. You\u2019ll wrap this in a function and call it when you want to run your agent. This is just the starting line, and also where these idiots are usually stuck. \n\n2. As you start building Agents, you turn this function into a class and add logic for prompting, message management, tool calling, retries and error handling. Congrats, you\u2019ve started to roll your own framework.\n\n3. Then, you\u2019ll want to try different models or chain agents to build a workflow. Each has its quirks, custom settings, different response formats, prompting hacks (eg: claude needs \"Do not reflect on the quality of the returned search results in your response\"). You start adding more layers to your homegrown framework.\n\nTill now this is a solvable problem and you can vibecode it.\n\n4. Then things really start to get frustrating. You learn quickly that you need to maintain session history and state across runs because, unlike these grifters, you\u2019re not actually going to run a jupyter notebook in your mom\u2019s basement. Guess what \u2014 your agents need a database. You\u2019ll design tables like agent_sessions, wire up session IDs, store/retrieve history and state on each run. Weeks later, you\u2019ll find your schema is inefficient because you forgot to add the right indexes, and now you\u2019ve got to learn about database migrations.\n\nFUCCKKKK! Wasn\u2019t this supposed to just be a while loop? And we haven\u2019t even started with RAG, chunking, embedding, retrieval, context management, tool management, MCP, monitoring, and logging. Why did the grifter grift?\n\nNow we're weeks into a vibecoded nightmare and the CEO is asking when can we launch.\n\n5. Finally, you confront the real problem: how do I serve this as an API and build a product on top of? \n\nYou hack together a FastAPI app, throw it in a container, and think you\u2019re done. You breathe a sigh of relief and hand it to your CEO. He hammers it like a madman, chunks start dropping, agents mix context, and you suddenly need to learn about SSE. You implement SSE, things work well for a while and then your requests start timing out and your container memory blows up - memory leaaakk. You blame python, but deep down, you know, you know.\n\nYou start searching for solutions and come across this code snippet from @AgnoAgi \n\nIn 25 lines of code you get:\n\n\u2705 An Agent with memory, state, knowledge, and everything you could ask for.\n\u2705 Access to 1000s of tools and MCP servers.\n\u2705 A production-grade FastAPI app with pre-built SSE-compatible endpoints.\n\nYou also get a vibrant community and a team of experts to help you every step of the way. This is a systems engineering problem and you get the right tools for the job.\n\nYou start seeing the light, give up your masochism, and repeat the mantra: JUST USE THE FUCKING FRAMEWORK!", - "entity_set": { - "hashtags": [], - "symbols": [], - "urls": [], - "user_mentions": [ - { - "id_str": "1549829675614146560", - "name": "Agno", - "screen_name": "AgnoAgi", - "indices": [2551, 2559] - } - ] - }, - "richtext": { "richtext_tags": [] }, - "media": { - "inline_media": [ - { - "media_id": "1964355320433573888", - "index": 2560 - } - ] - } - } - } - }, - "grok_analysis_button": true, - "quotedRefResult": { - "result": { - "__typename": "Tweet", - "rest_id": "1963843389918446066" - } - }, - "legacy": { - "bookmark_count": 809, - "bookmarked": false, - "created_at": "Sat Sep 06 16:17:58 +0000 2025", - "conversation_id_str": "1964362446627299598", - "display_text_range": [0, 276], - "entities": { - "hashtags": [], - "media": [ - { - "display_url": "pic.x.com/s22H4iaOqG", - "expanded_url": "https://x.com/ashpreetbedi/status/1964362446627299598/photo/1", - "id_str": "1964355320433573888", - "indices": [277, 300], - "media_key": "3_1964355320433573888", - "media_url_https": "https://pbs.twimg.com/media/G0LKwZ8XgAA4ecj.jpg", - "type": "photo", - "url": "https://t.co/s22H4iaOqG", - "ext_media_availability": { - "status": "Available" - }, - "features": { - "large": { "faces": [] }, - "medium": { "faces": [] }, - "small": { "faces": [] }, - "orig": { "faces": [] } - }, - "sizes": { - "large": { - "h": 1493, - "w": 2048, - "resize": "fit" - }, - "medium": { - "h": 875, - "w": 1200, - "resize": "fit" - }, - "small": { - "h": 496, - "w": 680, - "resize": "fit" - }, - "thumb": { - "h": 150, - "w": 150, - "resize": "crop" - } - }, - "original_info": { - "height": 2680, - "width": 3676, - "focus_rects": [ - { - "x": 0, - "y": 0, - "w": 3676, - "h": 2059 - }, - { - "x": 498, - "y": 0, - "w": 2680, - "h": 2680 - }, - { - "x": 663, - "y": 0, - "w": 2351, - "h": 2680 - }, - { - "x": 1168, - "y": 0, - "w": 1340, - "h": 2680 - }, - { - "x": 0, - "y": 0, - "w": 3676, - "h": 2680 - } - ] - }, - "media_results": { - "result": { - "media_key": "3_1964355320433573888" - } - } - } - ], - "symbols": [], - "timestamps": [], - "urls": [], - "user_mentions": [] - }, - "extended_entities": { - "media": [ - { - "display_url": "pic.x.com/s22H4iaOqG", - "expanded_url": "https://x.com/ashpreetbedi/status/1964362446627299598/photo/1", - "id_str": "1964355320433573888", - "indices": [277, 300], - "media_key": "3_1964355320433573888", - "media_url_https": "https://pbs.twimg.com/media/G0LKwZ8XgAA4ecj.jpg", - "type": "photo", - "url": "https://t.co/s22H4iaOqG", - "ext_media_availability": { - "status": "Available" - }, - "features": { - "large": { "faces": [] }, - "medium": { "faces": [] }, - "small": { "faces": [] }, - "orig": { "faces": [] } - }, - "sizes": { - "large": { - "h": 1493, - "w": 2048, - "resize": "fit" - }, - "medium": { - "h": 875, - "w": 1200, - "resize": "fit" - }, - "small": { - "h": 496, - "w": 680, - "resize": "fit" - }, - "thumb": { - "h": 150, - "w": 150, - "resize": "crop" - } - }, - "original_info": { - "height": 2680, - "width": 3676, - "focus_rects": [ - { - "x": 0, - "y": 0, - "w": 3676, - "h": 2059 - }, - { - "x": 498, - "y": 0, - "w": 2680, - "h": 2680 - }, - { - "x": 663, - "y": 0, - "w": 2351, - "h": 2680 - }, - { - "x": 1168, - "y": 0, - "w": 1340, - "h": 2680 - }, - { - "x": 0, - "y": 0, - "w": 3676, - "h": 2680 - } - ] - }, - "media_results": { - "result": { - "media_key": "3_1964355320433573888" - } - } - } - ] - }, - "favorite_count": 556, - "favorited": false, - "full_text": "Grifters like this are wasting your time and their Dunning-Kruger opinions should be ignored by serious builders. You either build on a framework or live long enough to roll your own (which is fine btw). Here\u2019s why:\n\n1. 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Claude correctly identified that the process group was not always correctly initialized. So it started writing hundreds of lines of bookkeeping boilerplate to my library to try fixing this (and eventually did fix). After I looked at its fix, I immediately notice that the real fix was just moving my library's init call after torch distributed init, not before\ud83e\udd26\u200d\u2642\ufe0f So the real fix involved not a single new line of code, but Claude loves writing more lines!\n\n2) In another library I made rapid iterations with Codex on the design. The core of the library boils down to a kind of graph where you need to walk through the nodes and do work on a node, while stopping on loops. Codex did correctly implement it, and it works; however, it wrote very convoluted code for the core logic, about 200 lines of code with two functions recursing into each other, and a few stacks and queues for traversal bookkeeping.\nAfter looking at it and taking a step back, I rewrote the whole thing from scratch in maybe 40 clear lines of code. It was great having Codex's extensive unit-tests to see that my rewrite is correct.\n\nSo, in conclusion, the current state of vibe-coding is good for boilerplate, rapid iteration/prototyping, or one-off throwaway tools. 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"full_text": "RT @michael_nielsen: Wrong. The core problem isn't control. It's whether the universe allows powerful, hard-to-defend technologies. No am\u2026", - "is_quote_status": true, - "lang": "en", - "quote_count": 0, - "quoted_status_id_str": "1963793029388714232", - "quoted_status_permalink": { - "url": "https://t.co/NLKilSojnj", - "expanded": "https://twitter.com/McaleerStephen/status/1963793029388714232", - "display": "x.com/McaleerStephen\u2026" - }, - "reply_count": 0, - "retweet_count": 13, - "retweeted": false, - "user_id_str": "175282603", - "id_str": "1964171135479927244", - "retweeted_status_result": { - "result": { - "__typename": "Tweet", - "rest_id": "1964136326321885534", - "core": { - "user_results": { - "result": { - "__typename": "User", - "id": "VXNlcjoxNTYyNjQwNg==", - "rest_id": "15626406", - "affiliates_highlighted_label": {}, - "avatar": { - "image_url": "https://pbs.twimg.com/profile_images/1462265339438190592/TmJkD-wB_normal.jpg" - }, - "core": { - "created_at": "Mon Jul 28 01:47:10 +0000 2008", - "name": "Michael Nielsen", - "screen_name": "michael_nielsen" - }, - "dm_permissions": { - "can_dm": false, - "can_dm_on_xchat": false - }, - "has_graduated_access": true, - "is_blue_verified": true, - "legacy": { - "default_profile": false, - "default_profile_image": false, - "description": "Searching for the numinous\n\ud83c\udde6\ud83c\uddfa \ud83c\udde8\ud83c\udde6, currently live in \ud83c\uddfa\ud83c\uddf8\nResearch @AsteraInstitute\n\nhttps://t.co/maezekzRUb\nhttps://t.co/2dWwZKrvrn", - "entities": { - "description": { - "urls": [ - { - "display_url": "michaelnotebook.com", - "expanded_url": "http://michaelnotebook.com", - "url": "https://t.co/maezekzRUb", - "indices": [82, 105] - }, - { - "display_url": "bsky.app/profile/michae\u2026", - "expanded_url": "https://bsky.app/profile/michaelnielsen.bsky", - "url": "https://t.co/2dWwZKrvrn", - "indices": [106, 129] - } - ] - }, - "url": { - "urls": [ - { - "display_url": "goodreads.com/review/list/54\u2026", - "expanded_url": "https://www.goodreads.com/review/list/549089-michael-nielsen?order=d&ref=nav_mybooks&shelf=read&sort", - "url": "https://t.co/hWSJ5aoJ9T", - "indices": [0, 23] - } - ] - } - }, - "fast_followers_count": 0, - "favourites_count": 93197, - "followers_count": 109571, - "friends_count": 5604, - "has_custom_timelines": false, - "is_translator": false, - "listed_count": 2431, - "media_count": 6187, - "normal_followers_count": 109571, - "pinned_tweet_ids_str": [ - "1963641668495716387" - ], - "possibly_sensitive": false, - "profile_banner_url": "https://pbs.twimg.com/profile_banners/15626406/1680486153", - "profile_interstitial_type": "", - "statuses_count": 49299, - "translator_type": "none", - "url": "https://t.co/hWSJ5aoJ9T", - "want_retweets": false, - "withheld_in_countries": [] - }, - "location": { "location": "Berkeley" }, - "media_permissions": { - "can_media_tag": true - }, - "parody_commentary_fan_label": "None", - "profile_image_shape": "Circle", - "privacy": { "protected": false }, - "relationship_perspectives": { - "following": false - }, - "tipjar_settings": {}, - "verification": { "verified": false } - } - } - }, - "unmention_data": {}, - "edit_control": { - "initial_tweet_id": "1964135454552514861", - "edit_control_initial": { - "edit_tweet_ids": [ - "1964135454552514861", - "1964136326321885534" - ], - "editable_until_msecs": "1757124958000", - "is_edit_eligible": true, - "edits_remaining": "4" - } - }, - "previous_counts": { - "bookmark_count": 0, - "favorite_count": 5, - "quote_count": 0, - "reply_count": 0, - "retweet_count": 0 - }, - "is_translatable": false, - "views": { - "count": "49749", - "state": "EnabledWithCount" - }, - "source": "Twitter Web App", - "note_tweet": { - "is_expandable": true, - "note_tweet_results": { - "result": { - "id": "Tm90ZVR3ZWV0OjE5NjQxMzYzMjYyMzc5OTUwMDg=", - "text": "Wrong. The core problem isn't control. It's whether the universe allows powerful, hard-to-defend technologies. No amount of alignment-of-system work will affect that. AI safety requires aligning the world in general, not just the systems. Safety is *not* a system property\n\nThis point is obvious, but seems resisted by many. I've begun to wonder if the reason is that the companies *want* AI safety to be mostly a problem about their systems. 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The core problem isn't control. It's whether the universe allows powerful, hard-to-defend technologies. No amount of alignment-of-system work will affect that. AI safety requires aligning the world in general, not just the systems. 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His own memoirs, an astonishing text which he apparently intended to be only the first of two volumes, cuts off before describing the details of his building of the nuclear fleet.\n\nAs a college student in occupied France, he slipped away from a forced labor assignment in a coal mine and escaped to Spain while heroically escorting downed Allied airmen.\n\nHe joined up with Free French forces in Africa, was commissioned as an officer, and fought through Italy until injured. \n\nFinishing his economics studies, he joined @EDFofficiel and then invented the theory behind efficient and fair electricity pricing.\n\nFor his cool strength in the face of massive labor unrest, he was promoted to lead EDF, where he prepared a nuclear program that blasted into overdrive after the 1973 oil embargo started. \n\nFrench terrorists trying to stop his nuclear program blew up his house one night in 1977 with him, his wife, and his daughter all inside, and he still went to work that morning to keep building reactors. 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Compare this to Germany spending about $500 billion on their mess of an \"energy transition\" which requires them to keep almost all of their coal and gas plants in service.\n\nAs a young man Marcel Boiteux refused to accept France's defeat and at age 21 in 1942 as an elite university student he escaped Nazi-occupied France while escorting downed Allied pilots over the Pyrenees mountains to safety in Spain.\n\nBrass. Balls.\n\nThis episode revealed the pattern for the rest of his life.\n\nAfter the war, he studied economics and wrote *the* foundational paper in electricity economics, on how to price electricity service in a way that covered system costs while being fair and sustainable.\n\nHe completely understood liberal economics, and knew it did not apply to electricity grids and service. He built cheap power for all, then after his retirement watched as a bunch of pathetic hack economists broke the grid with idiotic \"markets\" that are failing all over the world.\n\nHe rapidly rose in public service after university graduation, and after appointment to the head of Electricit\u00e9 de France, successfully built the most astonishing energy system in the history of the world, proving for all time that a country could truly rely on its own fleet of standardized nuclear reactors producing low-cost emissions-free energy.\n\nAnti-nuclear terrorists exploded a bomb outside the door of his family home in 1977 but he kept building.\n\nIt must have been torture for this truly great man to watch twenty years of silly, unserious leaders damage and begin to destroy his beloved EDF and its fleet of reactors, leading France straight into its worst energy crisis since the oil crisis of 1973 that triggered Boiteux's nuclear fleet construction in the first place.\n\nBut he didn't come up with the idea of a nuclear fleet powering a total electrification of the economy because an oil crisis hit. 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YoU MUST hAve aCCess to it anD be AUTheNtIcAteD TO AcCEss IT. pleaSE Log iN.\n\n$ huggingface-cli login\n\u26a0\ufe0f Warning: 'huggingface-cli login' is deprecated. Use 'hf auth login' instead. \n _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_\n| _|_|_|_| \n _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| \n _| \n _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_| \n _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| \n _| _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_\n| _|_|_|_| \n To log in, `huggingface_hub` requires a token generated from https://huggingface. co/settings/tokens .\nEnter your token (input will not be visible): \nAdd token as git credential? (Y/n) n \nToken is valid (permission: write).\nThe token `notebooks` has been saved to /workspace/hf/stored_tokens \nYour token has been saved to /workspace/hf/token \nLogin successful. \nThe current active token is: `notebooks`\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007\nINFO Automatically detected platform cuda.\nINFO Loading weights took 29.42 seconds\nValueError: No available memory for the cache blocks. Try increasing `gpu_memory_\nutilization` when initializing the engine.\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007 --gpu-memory-utilization 0.95\nINFO Automatically detected platform cuda.\nINFO Loading weights took 29.42 seconds\nValueError: No available memory for the cache blocks. Try increasing `gpu_memory_utilization` when initializing the engine.\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007 --gpu-memory-utilization 0.95 --max-model-len 4096 --max-num-seqs 5\nINFO Automatically detected platform cuda.\nINFO Loading weights took 29.42 seconds\nValueError: No available memory for the cache blocks. Try increasing `gpu_memory_utilization` when initializing the engine.\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {}\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007 --gpu-memory-utilization 0.95 --max-model-len 4096 --max-num-seqs 5 --tensor-parallel-size 2\nINFO Automatically detected platform cuda.\nINFO torch.compile takes 61.82 s in total\nINFO: Started server process [3601]\nINFO: Waiting for application startup.\nINFO: Application startup complete.", - "entity_set": { - "hashtags": [], - "symbols": [], - "urls": [], - "user_mentions": [] - }, - "richtext": { "richtext_tags": [] }, - "media": { "inline_media": [] } - } - } - }, - "grok_analysis_button": true, - "legacy": { - "bookmark_count": 150, - "bookmarked": false, - "created_at": "Sat Sep 06 19:25:00 +0000 2025", - "conversation_id_str": "1964409515841114211", - "display_text_range": [0, 272], - "entities": { - "hashtags": [], - "symbols": [], - "timestamps": [], - "urls": [], - "user_mentions": [] - }, - "favorite_count": 215, - "favorited": false, - "full_text": "$ apt install tmux vim\n$ tmux\n$ curl -LsSf https://astral. sh/uv/install.sh | sh\n$ export HF_HOME=/workspace/hf\n$ source ~/.local/bin/env\n$ uv pip install --system vllm repeng jupyter notebook\n\n$ vllm serve meta-llama/Llama-3.3-70B-Instruct --port \u3007\u3007\u3007\u3007\u3007\nINFO Automatically", - "is_quote_status": false, - "lang": "en", - "quote_count": 4, - "reply_count": 13, - "retweet_count": 5, - "retweeted": false, - "user_id_str": "281798056", - "id_str": "1964409515841114211" - } - } - } - } - } - }, - "tweetDisplayType": "Tweet" - }, - "clientEventInfo": { - "component": "following_in_network", - "element": "tweet", - "details": { - "timelinesDetails": { - "injectionType": "FollowingInNetwork", - "controllerData": "DAACDAABDAABCgABAFgABAoCAAMKAAIAAAAAAAEhAAoACb5M79vYDEFOCgAKAAABmS8lLmEIAAsAAAAeDwAMAwAAACADAAIKBABYAAAhAQAAAAAAAAAAAAAAAAAAAAAAAID/AQoADlVj2zP3OkQPAAAAAA==" - } - } - } - } - }, - { - "entryId": "home-conversation-1965440852092256287", - "sortIndex": "1965440852092256225", - "content": { - "entryType": "TimelineTimelineModule", - "__typename": "TimelineTimelineModule", - "items": [ - { - "entryId": "home-conversation-1965440852092256287-tweet-1964539633653731466", - "item": { - "itemContent": { - "itemType": "TimelineTweet", - "__typename": "TimelineTweet", - "tweet_results": { - "result": { - "__typename": "Tweet", - "rest_id": "1964539633653731466", - "core": { - "user_results": { - "result": { - "__typename": "User", - "id": "VXNlcjoxNzUyODI2MDM=", - "rest_id": "175282603", - "affiliates_highlighted_label": {}, - "avatar": { - "image_url": "https://pbs.twimg.com/profile_images/1279600070145437696/eocLhSLu_normal.jpg" - }, - "core": { - "created_at": "Fri Aug 06 04:58:18 +0000 2010", - "name": "Jeremy Howard", - "screen_name": "jeremyphoward" - }, - "dm_permissions": { - "can_dm": false, - "can_dm_on_xchat": false - }, - "has_graduated_access": true, - "is_blue_verified": true, - "legacy": { - "default_profile": false, - "default_profile_image": false, - "description": "\ud83c\udde6\ud83c\uddfa Co-founder: @AnswerDotAI & @FastDotAI ;\nPrev: professor @ UQ; Stanford fellow; @kaggle president; @fastmail/@enlitic/etc founder\nhttps://t.co/16UBFTX7mo", - "entities": { - "description": { - "urls": [ - { - "display_url": "jeremy.fast.ai", - "expanded_url": "https://jeremy.fast.ai/", - "url": "https://t.co/16UBFTX7mo", - "indices": [132, 155] - } - ] - }, - "url": { - "urls": [ - { - "display_url": "answer.ai", - "expanded_url": "http://answer.ai", - "url": "https://t.co/3lh0uQDdfN", - "indices": [0, 23] - } - ] - } - }, - "fast_followers_count": 0, - "favourites_count": 10026, - "followers_count": 259626, - "friends_count": 6063, - "has_custom_timelines": true, - "is_translator": false, - "listed_count": 5294, - "media_count": 2827, - "normal_followers_count": 259626, - "pinned_tweet_ids_str": [ - "1734606378331951318" - ], - "possibly_sensitive": false, - "profile_banner_url": "https://pbs.twimg.com/profile_banners/175282603/1594860705", - "profile_interstitial_type": "", - "statuses_count": 63702, - "translator_type": "none", - "url": "https://t.co/3lh0uQDdfN", - "want_retweets": true, - "withheld_in_countries": [] - }, - "location": { - "location": "Brisbane/Queensland, Australia" - }, - "media_permissions": { - "can_media_tag": false - }, - "parody_commentary_fan_label": "None", - "profile_image_shape": "Circle", - "privacy": { "protected": false }, - "relationship_perspectives": { - "following": true - }, - "tipjar_settings": {}, - "verification": { "verified": false } - } - } - }, - "unmention_data": {}, - "edit_control": { - "edit_tweet_ids": ["1964539633653731466"], - "editable_until_msecs": "1757221322000", - "is_edit_eligible": false, - "edits_remaining": "5" - }, - "is_translatable": false, - "views": { - "count": "145157", - "state": "EnabledWithCount" - }, - "source": "Twitter Web App", - "grok_analysis_button": true, - "quoted_status_result": { - "result": { - "__typename": "Tweet", - "rest_id": "1964362446627299598", - "post_image_description": "Code snippet with text in a purple-themed editor. Lines include imports from \"agno.agent\", \"agno.db\", \"SqliteDb\", \"AgentOS\", and \"MCPTools\". Functions like \"create_your_agent\" and \"agent.run\" are visible. No watermarks from platforms like Instagram, TikTok, or Xiaohongshu.", - "core": { - "user_results": { - "result": { - "__typename": "User", - "id": "VXNlcjoxMDM1MDA4Mg==", - "rest_id": "10350082", - "affiliates_highlighted_label": {}, - "avatar": { - "image_url": "https://pbs.twimg.com/profile_images/1909614469853925376/KKnxWe4s_normal.jpg" - }, - "core": { - "created_at": "Sun Nov 18 07:25:24 +0000 2007", - "name": "Ashpreet Bedi", - "screen_name": "ashpreetbedi" - }, - "dm_permissions": { - "can_dm": true, - "can_dm_on_xchat": false - }, - "has_graduated_access": true, - "is_blue_verified": true, - "legacy": { - "default_profile": false, - "default_profile_image": false, - "description": "founder @agnoagi \u2022 prev @airbnb @facebook", - "entities": { - "description": { "urls": [] }, - "url": { - "urls": [ - { - "display_url": "git.new/agno", - "expanded_url": "https://git.new/agno", - "url": "https://t.co/NSL5bYN36q", - "indices": [0, 23] - } - ] - } - }, - "fast_followers_count": 0, - "favourites_count": 8181, - "followers_count": 13349, - "friends_count": 1218, - "has_custom_timelines": true, - "is_translator": false, - "listed_count": 208, - "media_count": 413, - "normal_followers_count": 13349, - "pinned_tweet_ids_str": [ - "1903968640329818613" - ], - "possibly_sensitive": false, - "profile_banner_url": "https://pbs.twimg.com/profile_banners/10350082/1738252032", - "profile_interstitial_type": "", - "statuses_count": 3388, - "translator_type": "none", - "url": "https://t.co/NSL5bYN36q", - "want_retweets": false, - "withheld_in_countries": [] - }, - "location": { - "location": "ny, london" - }, - "media_permissions": { - "can_media_tag": true - }, - "parody_commentary_fan_label": "None", - "profile_image_shape": "Circle", - "privacy": { "protected": false }, - "relationship_perspectives": { - "following": false - }, - "tipjar_settings": {}, - "verification": { "verified": false } - } - } - }, - "unmention_data": {}, - "edit_control": { - "edit_tweet_ids": ["1964362446627299598"], - "editable_until_msecs": "1757179078000", - "is_edit_eligible": true, - "edits_remaining": "5" - }, - "is_translatable": false, - "views": { - "count": "302934", - "state": "EnabledWithCount" - }, - "source": "Twitter Web App", - "note_tweet": { - "is_expandable": true, - "note_tweet_results": { - "result": { - "id": "Tm90ZVR3ZWV0OjE5NjQzNjI0NDYzMTI3NTExMDQ=", - "text": "Grifters like this are wasting your time and their Dunning-Kruger opinions should be ignored by serious builders. You either build on a framework or live long enough to roll your own (which is fine btw). Here\u2019s why:\n\n1. The \"LLM API in a while loop\" is your underlying agentic step - the unit of work in your agent system. You\u2019ll wrap this in a function and call it when you want to run your agent. This is just the starting line, and also where these idiots are usually stuck. \n\n2. As you start building Agents, you turn this function into a class and add logic for prompting, message management, tool calling, retries and error handling. Congrats, you\u2019ve started to roll your own framework.\n\n3. Then, you\u2019ll want to try different models or chain agents to build a workflow. Each has its quirks, custom settings, different response formats, prompting hacks (eg: claude needs \"Do not reflect on the quality of the returned search results in your response\"). You start adding more layers to your homegrown framework.\n\nTill now this is a solvable problem and you can vibecode it.\n\n4. Then things really start to get frustrating. You learn quickly that you need to maintain session history and state across runs because, unlike these grifters, you\u2019re not actually going to run a jupyter notebook in your mom\u2019s basement. Guess what \u2014 your agents need a database. You\u2019ll design tables like agent_sessions, wire up session IDs, store/retrieve history and state on each run. Weeks later, you\u2019ll find your schema is inefficient because you forgot to add the right indexes, and now you\u2019ve got to learn about database migrations.\n\nFUCCKKKK! Wasn\u2019t this supposed to just be a while loop? And we haven\u2019t even started with RAG, chunking, embedding, retrieval, context management, tool management, MCP, monitoring, and logging. Why did the grifter grift?\n\nNow we're weeks into a vibecoded nightmare and the CEO is asking when can we launch.\n\n5. Finally, you confront the real problem: how do I serve this as an API and build a product on top of? \n\nYou hack together a FastAPI app, throw it in a container, and think you\u2019re done. You breathe a sigh of relief and hand it to your CEO. He hammers it like a madman, chunks start dropping, agents mix context, and you suddenly need to learn about SSE. You implement SSE, things work well for a while and then your requests start timing out and your container memory blows up - memory leaaakk. You blame python, but deep down, you know, you know.\n\nYou start searching for solutions and come across this code snippet from @AgnoAgi \n\nIn 25 lines of code you get:\n\n\u2705 An Agent with memory, state, knowledge, and everything you could ask for.\n\u2705 Access to 1000s of tools and MCP servers.\n\u2705 A production-grade FastAPI app with pre-built SSE-compatible endpoints.\n\nYou also get a vibrant community and a team of experts to help you every step of the way. This is a systems engineering problem and you get the right tools for the job.\n\nYou start seeing the light, give up your masochism, and repeat the mantra: JUST USE THE FUCKING FRAMEWORK!", - "entity_set": { - "hashtags": [], - "symbols": [], - "urls": [], - "user_mentions": [ - { - "id_str": "1549829675614146560", - "name": "Agno", - "screen_name": "AgnoAgi", - "indices": [2551, 2559] - } - ] - }, - "richtext": { "richtext_tags": [] }, - "media": { - "inline_media": [ - { - "media_id": "1964355320433573888", - "index": 2560 - } - ] - } - } - } - }, - "grok_analysis_button": true, - "quotedRefResult": { - "result": { - "__typename": "Tweet", - "rest_id": "1963843389918446066" - } - }, - "legacy": { - "bookmark_count": 809, - "bookmarked": false, - "created_at": "Sat Sep 06 16:17:58 +0000 2025", - "conversation_id_str": "1964362446627299598", - "display_text_range": [0, 276], - "entities": { - "hashtags": [], - "media": [ - { - "display_url": "pic.x.com/s22H4iaOqG", - "expanded_url": "https://x.com/ashpreetbedi/status/1964362446627299598/photo/1", - "id_str": "1964355320433573888", - "indices": [277, 300], - "media_key": "3_1964355320433573888", - "media_url_https": "https://pbs.twimg.com/media/G0LKwZ8XgAA4ecj.jpg", - "type": "photo", - "url": "https://t.co/s22H4iaOqG", - "ext_media_availability": { - "status": "Available" - }, - "features": { - "large": { "faces": [] }, - "medium": { "faces": [] }, - "small": { "faces": [] }, - "orig": { "faces": [] } - }, - "sizes": { - "large": { - "h": 1493, - "w": 2048, - "resize": "fit" - }, - "medium": { - "h": 875, - "w": 1200, - "resize": "fit" - }, - "small": { - "h": 496, - "w": 680, - "resize": "fit" - }, - "thumb": { - "h": 150, - "w": 150, - "resize": "crop" - } - }, - "original_info": { - "height": 2680, - "width": 3676, - "focus_rects": [ - { - "x": 0, - "y": 0, - "w": 3676, - "h": 2059 - }, - { - "x": 498, - "y": 0, - "w": 2680, - "h": 2680 - }, - { - "x": 663, - "y": 0, - "w": 2351, - "h": 2680 - }, - { - "x": 1168, - "y": 0, - "w": 1340, - "h": 2680 - }, - { - "x": 0, - "y": 0, - "w": 3676, - "h": 2680 - } - ] - }, - "media_results": { - "result": { - "media_key": "3_1964355320433573888" - } - } - } - ], - "symbols": [], - "timestamps": [], - "urls": [], - "user_mentions": [] - }, - "extended_entities": { - "media": [ - { - "display_url": "pic.x.com/s22H4iaOqG", - "expanded_url": "https://x.com/ashpreetbedi/status/1964362446627299598/photo/1", - "id_str": "1964355320433573888", - "indices": [277, 300], - "media_key": "3_1964355320433573888", - "media_url_https": "https://pbs.twimg.com/media/G0LKwZ8XgAA4ecj.jpg", - "type": "photo", - "url": "https://t.co/s22H4iaOqG", - "ext_media_availability": { - "status": "Available" - }, - "features": { - "large": { "faces": [] }, - "medium": { "faces": [] }, - "small": { "faces": [] }, - "orig": { "faces": [] } - }, - "sizes": { - "large": { - "h": 1493, - "w": 2048, - "resize": "fit" - }, - "medium": { - "h": 875, - "w": 1200, - "resize": "fit" - }, - "small": { - "h": 496, - "w": 680, - "resize": "fit" - }, - "thumb": { - "h": 150, - "w": 150, - "resize": "crop" - } - }, - "original_info": { - "height": 2680, - "width": 3676, - "focus_rects": [ - { - "x": 0, - "y": 0, - "w": 3676, - "h": 2059 - }, - { - "x": 498, - "y": 0, - "w": 2680, - "h": 2680 - }, - { - "x": 663, - "y": 0, - "w": 2351, - "h": 2680 - }, - { - "x": 1168, - "y": 0, - "w": 1340, - "h": 2680 - }, - { - "x": 0, - "y": 0, - "w": 3676, - "h": 2680 - } - ] - }, - "media_results": { - "result": { - "media_key": "3_1964355320433573888" - } - } - } - ] - }, - "favorite_count": 556, - "favorited": false, - "full_text": "Grifters like this are wasting your time and their Dunning-Kruger opinions should be ignored by serious builders. You either build on a framework or live long enough to roll your own (which is fine btw). Here\u2019s why:\n\n1. 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Claude correctly identified that the process group was not always correctly initialized. So it started writing hundreds of lines of bookkeeping boilerplate to my library to try fixing this (and eventually did fix). After I looked at its fix, I immediately notice that the real fix was just moving my library's init call after torch distributed init, not before\ud83e\udd26\u200d\u2642\ufe0f So the real fix involved not a single new line of code, but Claude loves writing more lines!\n\n2) In another library I made rapid iterations with Codex on the design. The core of the library boils down to a kind of graph where you need to walk through the nodes and do work on a node, while stopping on loops. Codex did correctly implement it, and it works; however, it wrote very convoluted code for the core logic, about 200 lines of code with two functions recursing into each other, and a few stacks and queues for traversal bookkeeping.\nAfter looking at it and taking a step back, I rewrote the whole thing from scratch in maybe 40 clear lines of code. It was great having Codex's extensive unit-tests to see that my rewrite is correct.\n\nSo, in conclusion, the current state of vibe-coding is good for boilerplate, rapid iteration/prototyping, or one-off throwaway tools. 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"full_text": "RT @michael_nielsen: Wrong. The core problem isn't control. It's whether the universe allows powerful, hard-to-defend technologies. No am\u2026", - "is_quote_status": true, - "lang": "en", - "quote_count": 0, - "quoted_status_id_str": "1963793029388714232", - "quoted_status_permalink": { - "url": "https://t.co/NLKilSojnj", - "expanded": "https://twitter.com/McaleerStephen/status/1963793029388714232", - "display": "x.com/McaleerStephen\u2026" - }, - "reply_count": 0, - "retweet_count": 13, - "retweeted": false, - "user_id_str": "175282603", - "id_str": "1964171135479927244", - "retweeted_status_result": { - "result": { - "__typename": "Tweet", - "rest_id": "1964136326321885534", - "core": { - "user_results": { - "result": { - "__typename": "User", - "id": "VXNlcjoxNTYyNjQwNg==", - "rest_id": "15626406", - "affiliates_highlighted_label": {}, - "avatar": { - "image_url": "https://pbs.twimg.com/profile_images/1462265339438190592/TmJkD-wB_normal.jpg" - }, - "core": { - "created_at": "Mon Jul 28 01:47:10 +0000 2008", - "name": "Michael Nielsen", - "screen_name": "michael_nielsen" - }, - "dm_permissions": { - "can_dm": false, - "can_dm_on_xchat": false - }, - "has_graduated_access": true, - "is_blue_verified": true, - "legacy": { - "default_profile": false, - "default_profile_image": false, - "description": "Searching for the numinous\n\ud83c\udde6\ud83c\uddfa \ud83c\udde8\ud83c\udde6, currently live in \ud83c\uddfa\ud83c\uddf8\nResearch @AsteraInstitute\n\nhttps://t.co/maezekzRUb\nhttps://t.co/2dWwZKrvrn", - "entities": { - "description": { - "urls": [ - { - "display_url": "michaelnotebook.com", - "expanded_url": "http://michaelnotebook.com", - "url": "https://t.co/maezekzRUb", - "indices": [82, 105] - }, - { - "display_url": "bsky.app/profile/michae\u2026", - "expanded_url": "https://bsky.app/profile/michaelnielsen.bsky", - "url": "https://t.co/2dWwZKrvrn", - "indices": [106, 129] - } - ] - }, - "url": { - "urls": [ - { - "display_url": "goodreads.com/review/list/54\u2026", - "expanded_url": "https://www.goodreads.com/review/list/549089-michael-nielsen?order=d&ref=nav_mybooks&shelf=read&sort", - "url": "https://t.co/hWSJ5aoJ9T", - "indices": [0, 23] - } - ] - } - }, - "fast_followers_count": 0, - "favourites_count": 93197, - "followers_count": 109571, - "friends_count": 5604, - "has_custom_timelines": false, - "is_translator": false, - "listed_count": 2431, - "media_count": 6187, - "normal_followers_count": 109571, - "pinned_tweet_ids_str": [ - "1963641668495716387" - ], - "possibly_sensitive": false, - "profile_banner_url": "https://pbs.twimg.com/profile_banners/15626406/1680486153", - "profile_interstitial_type": "", - "statuses_count": 49299, - "translator_type": "none", - "url": "https://t.co/hWSJ5aoJ9T", - "want_retweets": false, - "withheld_in_countries": [] - }, - "location": { "location": "Berkeley" }, - "media_permissions": { - "can_media_tag": true - }, - "parody_commentary_fan_label": "None", - "profile_image_shape": "Circle", - "privacy": { "protected": false }, - "relationship_perspectives": { - "following": false - }, - "tipjar_settings": {}, - "verification": { "verified": false } - } - } - }, - "unmention_data": {}, - "edit_control": { - "initial_tweet_id": "1964135454552514861", - "edit_control_initial": { - "edit_tweet_ids": [ - "1964135454552514861", - "1964136326321885534" - ], - "editable_until_msecs": "1757124958000", - "is_edit_eligible": true, - "edits_remaining": "4" - } - }, - "previous_counts": { - "bookmark_count": 0, - "favorite_count": 5, - "quote_count": 0, - "reply_count": 0, - "retweet_count": 0 - }, - "is_translatable": false, - "views": { - "count": "49749", - "state": "EnabledWithCount" - }, - "source": "Twitter Web App", - "note_tweet": { - "is_expandable": true, - "note_tweet_results": { - "result": { - "id": "Tm90ZVR3ZWV0OjE5NjQxMzYzMjYyMzc5OTUwMDg=", - "text": "Wrong. The core problem isn't control. It's whether the universe allows powerful, hard-to-defend technologies. No amount of alignment-of-system work will affect that. AI safety requires aligning the world in general, not just the systems. Safety is *not* a system property\n\nThis point is obvious, but seems resisted by many. I've begun to wonder if the reason is that the companies *want* AI safety to be mostly a problem about their systems. 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The core problem isn't control. It's whether the universe allows powerful, hard-to-defend technologies. No amount of alignment-of-system work will affect that. AI safety requires aligning the world in general, not just the systems. 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X will use this to make your timeline better.", - "childKeys": ["-433223684", "-1455967320"], - "feedbackUrl": "/2/timeline/feedback.json?feedback_type=DontLike&action_metadata=SSwW%2BsDQsc%2FawcU2ACbemuDPEAAWgKD2pxMA", - "hasUndoAction": true, - "icon": "Frown", - "clientEventInfo": { - "action": "click", - "element": "feedback_dontlike" - } - } - }, - { - "key": "1212670979", - "value": { - "feedbackType": "NotRelevant", - "prompt": "This post isn\u2019t relevant", - "confirmation": "Thanks. You\u2019ll see fewer posts like this.", - "feedbackUrl": "/2/timeline/feedback.json?feedback_type=NotRelevant&action_metadata=SRwWoIHRpeO43MU2ABaAoPanEwA%3D", - "hasUndoAction": true, - "clientEventInfo": { - "action": "click", - "element": "feedback_notrelevant" - } - } - }, - { - "key": "-882994336", - "value": { - "feedbackType": "DontLike", - "prompt": "Not interested in this post", - "confirmation": "Thanks. X will use this to make your timeline better.", - "childKeys": ["234179110", "1782877562"], - "feedbackUrl": "/2/timeline/feedback.json?feedback_type=DontLike&action_metadata=SSwWrsLT4cfp28U2ACaAgL%2BFkYynph8AFoCg9qcTAA%3D%3D", - "hasUndoAction": true, - "icon": "Frown", - "clientEventInfo": { - "action": "click", - "element": "feedback_dontlike" - } - } - }, - { - "key": "51731577", - "value": { - "feedbackType": "DontLike", - "prompt": "Not interested in this post", - "confirmation": "Thanks. X will use this to make your timeline better.", - "childKeys": ["198310295", "-2050262238"], - "feedbackUrl": "/2/timeline/feedback.json?feedback_type=DontLike&action_metadata=SSwW7ITcqcX60MU2ACbU7NXiGAAWgKD2pxMA", - "hasUndoAction": true, - "icon": "Frown", - "clientEventInfo": { - "action": "click", - "element": "feedback_dontlike" - } - } - }, - { - "key": "593168196", - "value": { - "feedbackType": "NotRelevant", - "prompt": "This post isn\u2019t relevant", - "confirmation": "Thanks. You\u2019ll see fewer posts like this.", - "feedbackUrl": "/2/timeline/feedback.json?feedback_type=NotRelevant&action_metadata=SRwWpITYrdKy28U2ABaAoPanEwA%3D", - "hasUndoAction": true, - "clientEventInfo": { - "action": "click", - "element": "feedback_notrelevant" - } - } - }, - { - "key": "242604834", - "value": { - "feedbackType": "NotRelevant", - "prompt": "This post isn\u2019t relevant", - "confirmation": "Thanks. You\u2019ll see fewer posts like this.", - "feedbackUrl": "/2/timeline/feedback.json?feedback_type=NotRelevant&action_metadata=SRwWioTS3fL93MU2ABaAoPanEwA%3D", - "hasUndoAction": true, - "clientEventInfo": { - "action": "click", - "element": "feedback_notrelevant" - } - } - }, - { - "key": "-309342218", - "value": { - "feedbackType": "DontLike", - "prompt": "Not interested in this post", - "confirmation": "Thanks. X will use this to make your timeline better.", - "childKeys": ["-1229143131", "1699511965"], - "feedbackUrl": "/2/timeline/feedback.json?feedback_type=DontLike&action_metadata=SSwWwoLcher1qsU2ACbUtMzAFQAWgKD2pxMA", - "hasUndoAction": true, - "icon": "Frown", - "clientEventInfo": { - "action": "click", - "element": "feedback_dontlike" - } - } - } - ] - }, - "metadata": { - "scribeConfig": { - "page": "for_you" - } - } - } - } - } -} diff --git a/packages/broad_listening_ui/src/components/FeedView.vue b/packages/broad_listening_ui/src/components/FeedView.vue index 38bfd99..d974684 100644 --- a/packages/broad_listening_ui/src/components/FeedView.vue +++ b/packages/broad_listening_ui/src/components/FeedView.vue @@ -7,13 +7,40 @@

No items in this list

-
- +
+ +
+
+

Feed Summary

+
+ {{ formatDateRange(listsStore.currentListDateRange) }} +
+
+
+
+
+
+ + +
+ +
@@ -41,9 +68,50 @@ export default { return componentMap[type] || "TweetItem"; // Default to TweetItem }; + const formatDateRange = (dateRange) => { + if (!dateRange) return ""; + + const fromDate = new Date(dateRange.from); + const toDate = new Date(dateRange.to); + + const options = { month: "short", day: "numeric" }; + const fromFormatted = fromDate.toLocaleDateString("en-US", options); + const toFormatted = toDate.toLocaleDateString("en-US", options); + + return `${fromFormatted} - ${toFormatted}`; + }; + + const getSummaryBullets = (summary) => { + if (!summary) return []; + return summary.split("\n").filter((line) => line.trim().startsWith("•")); + }; + + const handleBulletClick = (bullet, index) => { + // Placeholder for future functionality + console.log(`Clicked bullet ${index + 1}:`, bullet); + }; + + const formatBulletWithReferences = (bullet) => { + // Find references in the format [1,2,3] at the end of the bullet + const referencePattern = /(\[[\d,\s]+\])$/; + const match = bullet.match(referencePattern); + + if (match) { + const text = bullet.replace(referencePattern, ""); + const references = match[1]; + return `${text} ${references}`; + } + + return bullet; + }; + return { listsStore, getComponentForType, + formatDateRange, + getSummaryBullets, + handleBulletClick, + formatBulletWithReferences, }; }, }; diff --git a/packages/broad_listening_ui/src/components/MiddlePanel.vue b/packages/broad_listening_ui/src/components/MiddlePanel.vue index 003ed54..170fc77 100644 --- a/packages/broad_listening_ui/src/components/MiddlePanel.vue +++ b/packages/broad_listening_ui/src/components/MiddlePanel.vue @@ -32,11 +32,6 @@ - -
- -
-
- -
+ +
@@ -67,5 +72,21 @@ export default { required: true, }, }, + methods: { + getAuthorAvatarUrl(author) { + // Use the real avatar URL from mock data if available, otherwise use pravatar fallback with unique seed + if (author.avatarUrl) { + return author.avatarUrl; + } + // Use the handle as a seed to get consistent but unique avatars + const seed = author.handle.replace("@", ""); + return `https://i.pravatar.cc/128?u=${seed}`; + }, + onImageError(event) { + // Fallback to pravatar with a seed based on the author name + const seed = this.item.author.handle.replace("@", ""); + event.target.src = `https://i.pravatar.cc/128?u=${seed}`; + }, + }, }; diff --git a/packages/broad_listening_ui/src/stores/listsStore.js b/packages/broad_listening_ui/src/stores/listsStore.js index 2085ea5..f758f04 100644 --- a/packages/broad_listening_ui/src/stores/listsStore.js +++ b/packages/broad_listening_ui/src/stores/listsStore.js @@ -28,6 +28,10 @@ export const useListsStore = defineStore("lists", { ], listData: { 1: { + dateRange: { + from: "2024-11-15", + to: "2024-12-28", + }, tweets: [ { id: 1, @@ -37,6 +41,8 @@ export const useListsStore = defineStore("lists", { author: { name: "RetroCode", handle: "@retrocode_dev", + avatarUrl: + "https://pbs.twimg.com/profile_images/1588180280910397440/hP8GtFeC_400x400.jpg", }, likes: 342, reactions: 89, @@ -50,6 +56,8 @@ export const useListsStore = defineStore("lists", { author: { name: "VibesCoder", handle: "@vibes_coder", + avatarUrl: + "https://pbs.twimg.com/profile_images/1584508555726422017/LJjmzFgD_400x400.jpg", }, likes: 156, reactions: 43, @@ -63,6 +71,8 @@ export const useListsStore = defineStore("lists", { author: { name: "SynthDev", handle: "@synthwave_dev", + avatarUrl: + "https://pbs.twimg.com/profile_images/1579552280645206016/b2rfCHOe_400x400.jpg", }, likes: 278, reactions: 67, @@ -76,6 +86,8 @@ export const useListsStore = defineStore("lists", { author: { name: "MoodCode", handle: "@moodcode_hq", + avatarUrl: + "https://pbs.twimg.com/profile_images/1582796999012077568/lXK8S7nG_400x400.jpg", }, likes: 423, reactions: 112, @@ -83,9 +95,13 @@ export const useListsStore = defineStore("lists", { }, ], summary: - "The vibe coding community is focused on creating aesthetically pleasing development experiences that combine coding with music, visual themes, and nostalgic elements. Key trends include retro terminal aesthetics, synthwave-inspired development environments, and tools that integrate mood and music into the coding workflow.", + "• Retro aesthetics dominating: terminal emulators, green-on-black themes, and vintage computing vibes [1,3]\n• Music-driven development: lo-fi beats, synthwave soundtracks integrated into coding workflows [2,4]\n• Mood-based tooling: editors that adapt themes to Spotify playlists and emotional states [4]\n• ASCII art and visual programming: neural style transfer turning code into art [2]", }, 2: { + dateRange: { + from: "2024-10-01", + to: "2024-12-15", + }, tweets: [ { id: 5, @@ -95,6 +111,8 @@ export const useListsStore = defineStore("lists", { author: { name: "AI News Hub", handle: "@ainews_hub", + avatarUrl: + "https://pbs.twimg.com/profile_images/1590834832027918337/6qF4hYnJ_400x400.jpg", }, likes: 892, reactions: 234, @@ -108,6 +126,8 @@ export const useListsStore = defineStore("lists", { author: { name: "ML Updates", handle: "@ml_updates", + avatarUrl: + "https://pbs.twimg.com/profile_images/1587456789456789456/abcd1234_400x400.jpg", }, likes: 567, reactions: 143, @@ -121,6 +141,8 @@ export const useListsStore = defineStore("lists", { author: { name: "OpenAI Research", handle: "@openai_research", + avatarUrl: + "https://pbs.twimg.com/profile_images/1634058036934500352/b4F1eVpJ_400x400.jpg", }, likes: 734, reactions: 189, @@ -134,6 +156,8 @@ export const useListsStore = defineStore("lists", { author: { name: "DeepMind Updates", handle: "@deepmind_news", + avatarUrl: + "https://pbs.twimg.com/profile_images/1543897644427907075/QNyb85yY_400x400.jpg", }, likes: 445, reactions: 98, @@ -141,9 +165,13 @@ export const useListsStore = defineStore("lists", { }, ], summary: - "Latest LLM releases show significant advances in context length, multimodal capabilities, and specialized coding performance. OpenAI, Anthropic, Meta, and Google are pushing boundaries with larger context windows, faster inference, and improved reasoning across text, code, and visual inputs.", + "• Massive context windows: GPT-4.5 Turbo (200K tokens), Claude 3.5 Sonnet (1M+ tokens) [5,6]\n• Multimodal breakthroughs: real-time conversation, video understanding, native training [6,8]\n• Coding excellence: Code Llama 3 70B beating GPT-4 on HumanEval benchmark [7]\n• Speed improvements: 40% faster inference across major model releases [5,8]", }, 3: { + dateRange: { + from: "2024-09-20", + to: "2024-12-10", + }, tweets: [ { id: 9, @@ -153,6 +181,8 @@ export const useListsStore = defineStore("lists", { author: { name: "RAG Research", handle: "@rag_research", + avatarUrl: + "https://pbs.twimg.com/profile_images/1589123456789012345/xyz12345_400x400.jpg", }, likes: 234, reactions: 67, @@ -166,6 +196,8 @@ export const useListsStore = defineStore("lists", { author: { name: "Search Engineer", handle: "@search_eng", + avatarUrl: + "https://pbs.twimg.com/profile_images/1582456789123456789/searcheng_400x400.jpg", }, likes: 189, reactions: 45, @@ -179,6 +211,8 @@ export const useListsStore = defineStore("lists", { author: { name: "LangChain Dev", handle: "@langchain_dev", + avatarUrl: + "https://pbs.twimg.com/profile_images/1576297983333912576/CgsRFpdo_400x400.jpg", }, likes: 356, reactions: 89, @@ -192,6 +226,8 @@ export const useListsStore = defineStore("lists", { author: { name: "Vector DB Pro", handle: "@vectordb_pro", + avatarUrl: + "https://pbs.twimg.com/profile_images/1583789456123456789/vectordb_400x400.jpg", }, likes: 278, reactions: 73, @@ -199,9 +235,13 @@ export const useListsStore = defineStore("lists", { }, ], summary: - "RAG (Retrieval-Augmented Generation) development focuses on improving accuracy through hybrid search methods, knowledge graph integration, and advanced query processing. The community is building sophisticated pipelines that combine semantic and keyword search with multi-step reasoning for complex question answering.", + "• Hybrid search dominance: combining vector similarity with keyword matching for better retrieval [10]\n• Knowledge graph integration: 94% accuracy on complex multi-hop questions [9]\n• Simplified tooling: ChromaDB + LangChain enabling sub-100 line implementations [11]\n• Advanced techniques: query decomposition, document reranking, and answer synthesis [12]", }, 4: { + dateRange: { + from: "2024-08-05", + to: "2024-12-20", + }, tweets: [ { id: 13, @@ -211,6 +251,8 @@ export const useListsStore = defineStore("lists", { author: { name: "AGI Research", handle: "@agi_research", + avatarUrl: + "https://pbs.twimg.com/profile_images/1591234567891234567/agiresrch_400x400.jpg", }, likes: 1247, reactions: 389, @@ -224,6 +266,8 @@ export const useListsStore = defineStore("lists", { author: { name: "Frontier AI Lab", handle: "@frontier_ai", + avatarUrl: + "https://pbs.twimg.com/profile_images/1586456789012345678/frontier_400x400.jpg", }, likes: 892, reactions: 234, @@ -237,6 +281,8 @@ export const useListsStore = defineStore("lists", { author: { name: "AI Safety News", handle: "@ai_safety_news", + avatarUrl: + "https://pbs.twimg.com/profile_images/1580987654321098765/aisafety_400x400.jpg", }, likes: 567, reactions: 156, @@ -250,6 +296,8 @@ export const useListsStore = defineStore("lists", { author: { name: "Multi-Agent AI", handle: "@multiagent_ai", + avatarUrl: + "https://pbs.twimg.com/profile_images/1585432109876543210/multiagnt_400x400.jpg", }, likes: 734, reactions: 198, @@ -263,6 +311,8 @@ export const useListsStore = defineStore("lists", { author: { name: "Scaling Laws", handle: "@scaling_laws", + avatarUrl: + "https://pbs.twimg.com/profile_images/1592345678901234567/scaling_400x400.jpg", }, likes: 923, reactions: 267, @@ -270,7 +320,7 @@ export const useListsStore = defineStore("lists", { }, ], summary: - "Frontier AI labs are achieving breakthrough capabilities in mathematical reasoning, code generation, and multi-agent collaboration. Key developments include emergent reasoning in large models, human-level performance on complex tasks, and successful alignment techniques that maintain both safety and capability advancement.", + "• Reasoning breakthroughs: OpenAI o1 showing emergent capabilities in mathematics and coding [13]\n• Human-level programming: AlphaCode 3 solving competitive programming at expert level [14]\n• Multi-agent systems: Claude, GPT-4, and Gemini collaborating on complex problems [16]\n• Safety + capability: Constitutional AI aligning powerful models without performance loss [15]", }, }, mockSummary: @@ -284,6 +334,8 @@ export const useListsStore = defineStore("lists", { state.listData[state.currentListId]?.tweets || [], currentListSummary: (state) => state.listData[state.currentListId]?.summary || state.mockSummary, + currentListDateRange: (state) => + state.listData[state.currentListId]?.dateRange || null, }, actions: { From d71a082cde1aa113c180cde911cd1c995351cf40 Mon Sep 17 00:00:00 2001 From: Koen van der Veen Date: Thu, 11 Sep 2025 16:40:21 +0200 Subject: [PATCH 04/61] Enhance broad listening UI with comprehensive chat and dashboard features MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Redesign chat interface to match ChatGPT style with conversation selection - Add Discord dashboard with server/channel statistics and embedding counts - Implement proper Twitter avatar system using pravatar.cc with unique seeds - Replace "Ask about" with "Chat about" throughout interface - Add Twitter logo to tweet engagement metrics - Restructure left sidebar: move user account to bottom, add "Vera by OpenMined" branding - Fix chat conversation management to prevent duplicates and properly handle state - Add interactive summaries to feed view with clickable references - Enhance dashboard consistency between Twitter and Discord platforms - Implement new chat functionality with proper focus management 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude --- packages/broad_listening_ui/src/App.vue | 2 + .../src/components/DiscordDashboard.vue | 250 ++++++++++++++++++ .../src/components/LeftSidebar.vue | 39 ++- .../src/components/MiddlePanel.vue | 3 + .../src/components/RightPanel.vue | 125 +++++++-- .../src/components/TweetItem.vue | 13 + .../src/components/TwitterDashboard.vue | 181 ++++++------- .../src/stores/chatStore.js | 61 ++++- .../src/stores/connectionsStore.js | 21 ++ .../src/stores/discordStore.js | 167 ++++++++++++ .../src/stores/listsStore.js | 4 + 11 files changed, 723 insertions(+), 143 deletions(-) create mode 100644 packages/broad_listening_ui/src/components/DiscordDashboard.vue create mode 100644 packages/broad_listening_ui/src/stores/discordStore.js diff --git a/packages/broad_listening_ui/src/App.vue b/packages/broad_listening_ui/src/App.vue index 5070b6b..e3e0ff1 100644 --- a/packages/broad_listening_ui/src/App.vue +++ b/packages/broad_listening_ui/src/App.vue @@ -37,6 +37,7 @@ import MiddlePanel from "./components/MiddlePanel.vue"; import RightPanel from "./components/RightPanel.vue"; import TwitterDashboard from "./components/TwitterDashboard.vue"; import AIPapersDashboard from "./components/AIPapersDashboard.vue"; +import DiscordDashboard from "./components/DiscordDashboard.vue"; import AddConnector from "./components/AddConnector.vue"; import AddList from "./components/AddList.vue"; @@ -48,6 +49,7 @@ export default { RightPanel, TwitterDashboard, AIPapersDashboard, + DiscordDashboard, AddConnector, AddList, }, diff --git a/packages/broad_listening_ui/src/components/DiscordDashboard.vue b/packages/broad_listening_ui/src/components/DiscordDashboard.vue new file mode 100644 index 0000000..f507dd7 --- /dev/null +++ b/packages/broad_listening_ui/src/components/DiscordDashboard.vue @@ -0,0 +1,250 @@ + + + diff --git a/packages/broad_listening_ui/src/components/LeftSidebar.vue b/packages/broad_listening_ui/src/components/LeftSidebar.vue index 1a751dd..3895b6a 100644 --- a/packages/broad_listening_ui/src/components/LeftSidebar.vue +++ b/packages/broad_listening_ui/src/components/LeftSidebar.vue @@ -1,15 +1,18 @@ @@ -86,10 +105,10 @@ export default { const listsStore = useListsStore(); const chatStore = useChatStore(); - const userInitials = connectionsStore.userAccount.email - .split("@")[0] - .charAt(0) - .toUpperCase(); + const getUserAvatarUrl = () => { + const username = connectionsStore.userAccount.email.split("@")[0]; + return `https://i.pravatar.cc/128?u=${username}`; + }; const selectList = (listId) => { // Close dashboard to show middle/right panels @@ -108,7 +127,7 @@ export default { connectionsStore, listsStore, chatStore, - userInitials, + getUserAvatarUrl, selectList, showAddList, }; diff --git a/packages/broad_listening_ui/src/components/MiddlePanel.vue b/packages/broad_listening_ui/src/components/MiddlePanel.vue index 170fc77..78ca468 100644 --- a/packages/broad_listening_ui/src/components/MiddlePanel.vue +++ b/packages/broad_listening_ui/src/components/MiddlePanel.vue @@ -88,10 +88,13 @@ export default { if (view.id === "ask") { chatStore.setHighlighted(true); + chatStore.startNewChat(); // Start a new chat when clicking Ask + chatStore.focusInput(); // Signal to focus the input // Reset highlight after 2 seconds setTimeout(() => { chatStore.setHighlighted(false); }, 2000); + return; // Don't change the view, keep showing the feed } listsStore.setCurrentView(view.id); diff --git a/packages/broad_listening_ui/src/components/RightPanel.vue b/packages/broad_listening_ui/src/components/RightPanel.vue index 12f0728..a72a0c8 100644 --- a/packages/broad_listening_ui/src/components/RightPanel.vue +++ b/packages/broad_listening_ui/src/components/RightPanel.vue @@ -2,15 +2,54 @@
-

Previous Questions

-
+

+ Chat about {{ listsStore.currentList.name }} • + {{ formatDateRange(listsStore.currentListDateRange) }} +

+ + +
+ + + + New Chat +
+ + +

+ Chats +

+ +
-

+

{{ conversation.question }}

@@ -21,6 +60,7 @@
+
Ask a question about the content in your list

+ +
+
+

+ Chat about {{ listsStore.currentList.name }} • + {{ formatDateRange(listsStore.currentListDateRange) }} +

+
+
+
- -
+ +
-

{{ latestConversation.question }}

+

{{ currentConversation.question }}

-
+
-

{{ latestConversation.answer }}

+

{{ currentConversation.answer }}

@@ -64,6 +120,7 @@
diff --git a/packages/broad_listening_ui/src/components/LeftSidebar.vue b/packages/broad_listening_ui/src/components/LeftSidebar.vue index 3895b6a..748348c 100644 --- a/packages/broad_listening_ui/src/components/LeftSidebar.vue +++ b/packages/broad_listening_ui/src/components/LeftSidebar.vue @@ -1,6 +1,6 @@ + diff --git a/packages/omni/justfile b/packages/omni/justfile new file mode 100644 index 0000000..2400ef6 --- /dev/null +++ b/packages/omni/justfile @@ -0,0 +1,48 @@ +#!/usr/bin/env just --justfile +# Vue.js and FastAPI development commands + +# Kill existing dev processes +kill-dev: + #!/bin/bash + pkill -f "npm run dev" || true + pkill -f "python.*main.py" || true + pkill -f "uvicorn.*main:app" || true + + +# Install dependencies only +install: + #!/bin/bash + npm install + uv pip install -e . + +# Run both frontend and backend development servers +dev: + #!/bin/bash + just kill-dev + trap 'just kill-dev' EXIT + echo "Starting backend server..." + cd omni && python main.py & + BACKEND_PID=$! + echo "Backend started with PID: $BACKEND_PID" + echo "Starting frontend server..." + npm run dev & + FRONTEND_PID=$! + echo "Frontend started with PID: $FRONTEND_PID" + wait + +# Build for production +build: + #!/bin/bash + npm run build + +# Preview production build +preview: + #!/bin/bash + npm run preview + +# Clean node_modules and reinstall +clean-install: + #!/bin/bash + rm -rf node_modules package-lock.json + npm install + uv pip install -e . \ No newline at end of file diff --git a/packages/broad_listening_ui/broad_listening_ui/__init__.py b/packages/omni/omni/__init__.py similarity index 100% rename from packages/broad_listening_ui/broad_listening_ui/__init__.py rename to packages/omni/omni/__init__.py diff --git a/packages/omni/omni/main.py b/packages/omni/omni/main.py new file mode 100644 index 0000000..026f14c --- /dev/null +++ b/packages/omni/omni/main.py @@ -0,0 +1,105 @@ +from typing import List + +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from mock_data import ( + get_mock_chats, + get_mock_data_collections, + get_mock_data_sources, + get_mock_smart_list_items, + get_mock_smart_lists, + get_mock_summary, +) +from models import ( + Chat, + DataCollection, + DataSource, + QuestionRequest, + SmartList, + SmartListCreate, + TweetItem, +) + +app = FastAPI(title="Omni API", description="Backend API for Omni application") + +# Enable CORS +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:5173"], # Vite dev server + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +# Routes +@app.get("/") +async def root(): + return {"message": "Omni API is running"} + + +@app.get("/data-sources", response_model=List[DataSource]) +async def get_data_sources(): + return get_mock_data_sources() + + +@app.get("/data-collections", response_model=List[DataCollection]) +async def get_data_collections(): + return get_mock_data_collections() + + +@app.get("/smart-lists", response_model=List[SmartList]) +async def get_smart_lists(): + return get_mock_smart_lists() + + +@app.post("/smart-lists", response_model=SmartList) +async def create_smart_list(list_data: SmartListCreate): + new_list = { + "id": int(str(int(1000000000000))[:10]), # Generate timestamp-based ID + "name": list_data.name, + "listSources": [source.dict() for source in list_data.listSources], + "computedItems": [], + "itemCount": 0, + } + return new_list + + +@app.get("/smart-lists/{list_id}/items", response_model=List[TweetItem]) +async def get_smart_list_items(list_id: int): + items = get_mock_smart_list_items(list_id) + if not items: + raise HTTPException(status_code=404, detail="Smart list not found") + return items + + +@app.get("/smart-lists/{list_id}/summary") +async def get_smart_list_summary(list_id: int): + summary = get_mock_summary(list_id) + if not summary: + raise HTTPException(status_code=404, detail="Smart list not found") + return {"summary": summary} + + +@app.get("/chats/{list_id}", response_model=List[Chat]) +async def get_chats(list_id: int): + chats = get_mock_chats(list_id) + return chats + + +@app.post("/chats/{list_id}/ask") +async def ask_question(list_id: int, request: QuestionRequest): + # Mock response for asking questions + response = { + "id": 999, + "role": "assistant", + "content": f"This is a mock response to your question: '{request.question}' about list {list_id}", + "timestamp": "2024-01-15T10:00:00Z", + } + return response + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True) diff --git a/packages/omni/omni/mock_data.py b/packages/omni/omni/mock_data.py new file mode 100644 index 0000000..c8ceaef --- /dev/null +++ b/packages/omni/omni/mock_data.py @@ -0,0 +1,430 @@ +def get_mock_data_sources(): + return [ + { + "id": "twitter", + "name": "Twitter", + "icon": "twitter", + "description": "Connect your Twitter account to analyze tweets and conversations", + "installCommand": "toolbox install twitter-mcp", + "defaultUrl": "localhost:8020", + "connectionState": "connected", + "dashboardData": { + "handle": "@johndoe", + "totalTweets": 1247, + "latestTweets": [ + { + "id": 1, + "content": "Just discovered an amazing new AI breakthrough in transformer architectures! The potential applications are endless.", + "timestamp": "2h ago", + "likes": 42, + "retweets": 18, + }, + { + "id": 2, + "content": "Working on some exciting ML projects. The intersection of AI and creativity continues to amaze me every day.", + "timestamp": "5h ago", + "likes": 28, + "retweets": 7, + }, + ], + "embeddingCounts": { + "ollama/nomic-embed-text": 123, + "openai/text-embedding-3-small": 89, + }, + }, + }, + { + "id": "ai-papers", + "name": "AI Papers", + "icon": "papers", + "description": "Connect to AI paper repositories and research databases", + "installCommand": "toolbox install papers-mcp", + "defaultUrl": "localhost:8022", + "connectionState": "connected", + "dashboardData": { + "totalPapers": 847, + "trendingPapers": 156, + "syftboxPapers": 23, + "latestPaper": "Jan 15", + "latestPapers": [ + { + "id": 1, + "title": "Attention Is All You Need: Revisiting Transformer Architectures", + "summary": "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms...", + "authors": "Vaswani et al.", + "arxivId": "2401.12345", + "timestamp": "2h ago", + }, + ], + "embeddingCounts": { + "ollama/nomic-embed-text": 234, + "openai/text-embedding-3-small": 178, + }, + }, + }, + { + "id": "discord", + "name": "Discord", + "icon": "discord", + "description": "Connect Discord servers to analyze messages and conversations", + "installCommand": "toolbox install discord-mcp", + "defaultUrl": "localhost:8021", + "connectionState": "disconnected", + "dashboardData": { + "totalServers": 0, + "totalChannels": 0, + "totalMessages": 0, + "embeddingCounts": {}, + }, + }, + ] + + +def get_mock_data_collections(): + return [ + {"id": "twitter", "name": "Twitter", "items": []}, + {"id": "ai-papers", "name": "AI Papers", "items": []}, + {"id": "discord", "name": "Discord", "items": []}, + ] + + +def get_mock_smart_lists(): + return [ + { + "id": 1, + "name": "Vibe coding", + "listSources": [ + { + "dataSourceId": "twitter", + "filters": { + "dateRange": {"from": "2024-11-15", "to": "2024-12-28"}, + "ragQuery": "coding aesthetics vibes music", + "threshold": 0.7, + }, + } + ], + "computedItems": [], + "itemCount": 23, + }, + { + "id": 2, + "name": "LLM releases", + "listSources": [ + { + "dataSourceId": "twitter", + "filters": { + "dateRange": {"from": "2024-10-01", "to": "2024-12-15"}, + "ragQuery": "LLM releases GPT Claude models", + "threshold": 0.8, + }, + } + ], + "computedItems": [], + "itemCount": 18, + }, + { + "id": 3, + "name": "RAG", + "listSources": [ + { + "dataSourceId": "twitter", + "filters": { + "dateRange": {"from": "2024-09-20", "to": "2024-12-10"}, + "ragQuery": "RAG retrieval augmented generation vector search", + "threshold": 0.75, + }, + } + ], + "computedItems": [], + "itemCount": 15, + }, + { + "id": 4, + "name": "Frontier AI lab capabilities", + "listSources": [ + { + "dataSourceId": "twitter", + "filters": { + "dateRange": {"from": "2024-08-05", "to": "2024-12-20"}, + "ragQuery": "AGI research breakthrough capabilities reasoning", + "threshold": 0.8, + }, + } + ], + "computedItems": [], + "itemCount": 31, + }, + ] + + +def get_mock_smart_list_items(list_id: int): + mock_data = { + 1: [ + { + "id": 1, + "type": "tweet", + "content": "Just spent the weekend building a retro terminal emulator in Rust. There's something magical about the green text on black background aesthetic 💚", + "author": { + "name": "RetroCode", + "handle": "@retrocode_dev", + "avatarUrl": "https://pbs.twimg.com/profile_images/1588180280910397440/hP8GtFeC_400x400.jpg", + }, + "likes": 342, + "reactions": 89, + "timestamp": "3h", + }, + { + "id": 2, + "type": "tweet", + "content": "Late night coding session with some lo-fi beats. Working on a neural style transfer app that turns your code into ASCII art. The vibes are immaculate ✨", + "author": { + "name": "VibesCoder", + "handle": "@vibes_coder", + "avatarUrl": "https://pbs.twimg.com/profile_images/1584508555726422017/LJjmzFgD_400x400.jpg", + }, + "likes": 156, + "reactions": 43, + "timestamp": "6h", + }, + { + "id": 3, + "type": "tweet", + "content": "Nothing beats the satisfaction of refactoring legacy code while listening to synthwave. Clean code + aesthetic vibes = peak developer experience", + "author": { + "name": "SynthDev", + "handle": "@synthwave_dev", + "avatarUrl": "https://pbs.twimg.com/profile_images/1579552280645206016/b2rfCHOe_400x400.jpg", + }, + "likes": 278, + "reactions": 67, + "timestamp": "9h", + }, + { + "id": 4, + "type": "tweet", + "content": 'Built a mood-based code editor that changes themes based on your Spotify playlist. Currently debugging in "Cyberpunk 2077" mode 🌃', + "author": { + "name": "MoodCode", + "handle": "@moodcode_hq", + "avatarUrl": "https://pbs.twimg.com/profile_images/1582796999012077568/lXK8S7nG_400x400.jpg", + }, + "likes": 423, + "reactions": 112, + "timestamp": "12h", + }, + ], + 2: [ + { + "id": 5, + "type": "tweet", + "content": "OpenAI just dropped GPT-4.5 Turbo with 200K context window and 40% faster inference. The multi-modal capabilities are insane! 🚀", + "author": { + "name": "AI News Hub", + "handle": "@ainews_hub", + "avatarUrl": "https://pbs.twimg.com/profile_images/1590834832027918337/6qF4hYnJ_400x400.jpg", + }, + "likes": 892, + "reactions": 234, + "timestamp": "2h", + }, + { + "id": 6, + "type": "tweet", + "content": "Anthropic's Claude 3.5 Sonnet now supports real-time conversation and can maintain context across 1M+ tokens. Game changer for long-form content.", + "author": { + "name": "ML Updates", + "handle": "@ml_updates", + "avatarUrl": "https://pbs.twimg.com/profile_images/1587456789456789456/abcd1234_400x400.jpg", + }, + "likes": 567, + "reactions": 143, + "timestamp": "5h", + }, + { + "id": 7, + "type": "tweet", + "content": "Meta released Code Llama 3 70B - beats GPT-4 on HumanEval benchmark. Open source is catching up fast to proprietary models!", + "author": { + "name": "OpenAI Research", + "handle": "@openai_research", + "avatarUrl": "https://pbs.twimg.com/profile_images/1634058036934500352/b4F1eVpJ_400x400.jpg", + }, + "likes": 734, + "reactions": 189, + "timestamp": "8h", + }, + { + "id": 8, + "type": "tweet", + "content": "Google's Gemini Ultra 1.5 with native multimodal training is now available via API. Video understanding capabilities are incredible.", + "author": { + "name": "DeepMind Updates", + "handle": "@deepmind_news", + "avatarUrl": "https://pbs.twimg.com/profile_images/1543897644427907075/QNyb85yY_400x400.jpg", + }, + "likes": 445, + "reactions": 98, + "timestamp": "11h", + }, + ], + 3: [ + { + "id": 9, + "type": "tweet", + "content": "New paper on RAG with knowledge graphs achieves 94% accuracy on complex multi-hop questions. The future of information retrieval is here!", + "author": { + "name": "RAG Research", + "handle": "@rag_research", + "avatarUrl": "https://pbs.twimg.com/profile_images/1589123456789012345/xyz12345_400x400.jpg", + }, + "likes": 234, + "reactions": 67, + "timestamp": "4h", + }, + { + "id": 10, + "type": "tweet", + "content": "Just implemented hybrid RAG with vector + keyword search. The semantic understanding combined with exact matching is powerful 🔍", + "author": { + "name": "Search Engineer", + "handle": "@search_eng", + "avatarUrl": "https://pbs.twimg.com/profile_images/1582456789123456789/searcheng_400x400.jpg", + }, + "likes": 189, + "reactions": 45, + "timestamp": "7h", + }, + { + "id": 11, + "type": "tweet", + "content": "ChromaDB + LangChain integration makes building RAG pipelines so much easier. Deployed a knowledge base chatbot in under 100 lines of code.", + "author": { + "name": "LangChain Dev", + "handle": "@langchain_dev", + "avatarUrl": "https://pbs.twimg.com/profile_images/1576297983333912576/CgsRFpdo_400x400.jpg", + }, + "likes": 356, + "reactions": 89, + "timestamp": "10h", + }, + { + "id": 12, + "type": "tweet", + "content": "Advanced RAG techniques: query decomposition, document reranking, and answer synthesis. Each step improves retrieval quality significantly.", + "author": { + "name": "Vector DB Pro", + "handle": "@vectordb_pro", + "avatarUrl": "https://pbs.twimg.com/profile_images/1583789456123456789/vectordb_400x400.jpg", + }, + "likes": 278, + "reactions": 73, + "timestamp": "13h", + }, + ], + 4: [ + { + "id": 13, + "type": "tweet", + "content": "OpenAI o1 achieved breakthrough performance on mathematics and coding benchmarks, showing emergent reasoning capabilities we haven't seen before.", + "author": { + "name": "AGI Research", + "handle": "@agi_research", + "avatarUrl": "https://pbs.twimg.com/profile_images/1591234567891234567/agiresrch_400x400.jpg", + }, + "likes": 1247, + "reactions": 389, + "timestamp": "1h", + }, + { + "id": 14, + "type": "tweet", + "content": "DeepMind's AlphaCode 3 can now solve competitive programming problems at the level of top human programmers. Code generation has reached new heights.", + "author": { + "name": "Frontier AI Lab", + "handle": "@frontier_ai", + "avatarUrl": "https://pbs.twimg.com/profile_images/1586456789012345678/frontier_400x400.jpg", + }, + "likes": 892, + "reactions": 234, + "timestamp": "4h", + }, + { + "id": 15, + "type": "tweet", + "content": "Anthropic's Constitutional AI shows how we can align powerful models with human values while maintaining capabilities. Safety + performance.", + "author": { + "name": "AI Safety News", + "handle": "@ai_safety_news", + "avatarUrl": "https://pbs.twimg.com/profile_images/1580987654321098765/aisafety_400x400.jpg", + }, + "likes": 567, + "reactions": 156, + "timestamp": "7h", + }, + { + "id": 16, + "type": "tweet", + "content": "Multi-agent systems are emerging as the next frontier. Claude, GPT-4, and Gemini working together solve problems none could handle alone.", + "author": { + "name": "Multi-Agent AI", + "handle": "@multiagent_ai", + "avatarUrl": "https://pbs.twimg.com/profile_images/1585432109876543210/multiagnt_400x400.jpg", + }, + "likes": 734, + "reactions": 198, + "timestamp": "10h", + }, + { + "id": 17, + "type": "tweet", + "content": "New scaling laws suggest we're approaching human-level performance across most cognitive tasks. The capability explosion is accelerating.", + "author": { + "name": "Scaling Laws", + "handle": "@scaling_laws", + "avatarUrl": "https://pbs.twimg.com/profile_images/1592345678901234567/scaling_400x400.jpg", + }, + "likes": 923, + "reactions": 267, + "timestamp": "13h", + }, + ], + } + return mock_data.get(list_id, []) + + +def get_mock_summary(list_id: int): + summaries = { + 1: "• Retro aesthetics dominating: terminal emulators, green-on-black themes, and vintage computing vibes [1,3]\n• Music-driven development: lo-fi beats, synthwave soundtracks integrated into coding workflows [2,4]\n• Mood-based tooling: editors that adapt themes to Spotify playlists and emotional states [4]\n• ASCII art and visual programming: neural style transfer turning code into art [2]", + 2: "• Massive context windows: GPT-4.5 Turbo (200K tokens), Claude 3.5 Sonnet (1M+ tokens) [5,6]\n• Multimodal breakthroughs: real-time conversation, video understanding, native training [6,8]\n• Coding excellence: Code Llama 3 70B beating GPT-4 on HumanEval benchmark [7]\n• Speed improvements: 40% faster inference across major model releases [5,8]", + 3: "• Hybrid search dominance: combining vector similarity with keyword matching for better retrieval [10]\n• Knowledge graph integration: 94% accuracy on complex multi-hop questions [9]\n• Simplified tooling: ChromaDB + LangChain enabling sub-100 line implementations [11]\n• Advanced techniques: query decomposition, document reranking, and answer synthesis [12]", + 4: "• Reasoning breakthroughs: OpenAI o1 showing emergent capabilities in mathematics and coding [13]\n• Human-level programming: AlphaCode 3 solving competitive programming at expert level [14]\n• Multi-agent systems: Claude, GPT-4, and Gemini collaborating on complex problems [16]\n• Safety + capability: Constitutional AI aligning powerful models without performance loss [15]", + } + return summaries.get( + list_id, + "Recent discussions focus on breakthrough developments and the importance of human-AI collaboration.", + ) + + +def get_mock_chats(list_id: int): + mock_chats = { + 1: [ + { + "id": 1, + "messages": [ + { + "id": 1, + "role": "user", + "content": "What music do developers prefer while coding?", + "timestamp": "2024-01-15T10:00:00Z", + }, + { + "id": 2, + "role": "assistant", + "content": "Based on the tweets, developers favor lo-fi beats, synthwave, and atmospheric music that creates an immersive coding environment.", + "timestamp": "2024-01-15T10:00:30Z", + }, + ], + } + ] + } + return mock_chats.get(list_id, []) diff --git a/packages/omni/omni/models.py b/packages/omni/omni/models.py new file mode 100644 index 0000000..5cc1beb --- /dev/null +++ b/packages/omni/omni/models.py @@ -0,0 +1,70 @@ +from typing import Dict, List + +from pydantic import BaseModel + + +class DataSource(BaseModel): + id: str + name: str + icon: str + description: str + installCommand: str + defaultUrl: str + connectionState: str + dashboardData: Dict + + +class DataCollection(BaseModel): + id: str + name: str + items: List + + +class SmartListFilter(BaseModel): + dateRange: Dict[str, str] + ragQuery: str + threshold: float + + +class ListSource(BaseModel): + dataSourceId: str + filters: SmartListFilter + + +class SmartList(BaseModel): + id: int + name: str + listSources: List[ListSource] + computedItems: List = [] + itemCount: int + + +class SmartListCreate(BaseModel): + name: str + listSources: List[ListSource] + + +class TweetItem(BaseModel): + id: int + type: str + content: str + author: Dict + likes: int + reactions: int + timestamp: str + + +class Message(BaseModel): + id: int + role: str + content: str + timestamp: str + + +class Chat(BaseModel): + id: int + messages: List[Message] + + +class QuestionRequest(BaseModel): + question: str diff --git a/packages/broad_listening_ui/broad_listening_ui/py.typed b/packages/omni/omni/py.typed similarity index 100% rename from packages/broad_listening_ui/broad_listening_ui/py.typed rename to packages/omni/omni/py.typed diff --git a/packages/broad_listening_ui/package.json b/packages/omni/package.json similarity index 92% rename from packages/broad_listening_ui/package.json rename to packages/omni/package.json index b7839f8..56ce11f 100644 --- a/packages/broad_listening_ui/package.json +++ b/packages/omni/package.json @@ -1,5 +1,5 @@ { - "name": "broad-listening-ui", + "name": "omni", "version": "0.1.0", "private": true, "scripts": { diff --git a/packages/broad_listening_ui/postcss.config.js b/packages/omni/postcss.config.js similarity index 100% rename from packages/broad_listening_ui/postcss.config.js rename to packages/omni/postcss.config.js diff --git a/packages/broad_listening_ui/public/favicon.svg b/packages/omni/public/favicon.svg similarity index 100% rename from packages/broad_listening_ui/public/favicon.svg rename to packages/omni/public/favicon.svg diff --git a/packages/broad_listening_ui/pyproject.toml b/packages/omni/pyproject.toml similarity index 76% rename from packages/broad_listening_ui/pyproject.toml rename to packages/omni/pyproject.toml index 757165d..4c1b173 100644 --- a/packages/broad_listening_ui/pyproject.toml +++ b/packages/omni/pyproject.toml @@ -1,5 +1,5 @@ [project] -name = "broad-listening-ui" +name = "omni" version = "0.1.0" description = "Add your description here" readme = "README.md" @@ -7,7 +7,10 @@ authors = [ { name = "Koen van der Veen", email = "koenlennartvanderveen@gmail.com" } ] requires-python = ">=3.12" -dependencies = [] +dependencies = [ + "fastapi>=0.104.1", + "uvicorn[standard]>=0.24.0" +] [build-system] diff --git a/packages/broad_listening_ui/server_test.py b/packages/omni/server_test.py similarity index 100% rename from packages/broad_listening_ui/server_test.py rename to packages/omni/server_test.py diff --git a/packages/broad_listening_ui/tailwind.config.js b/packages/omni/tailwind.config.js similarity index 62% rename from packages/broad_listening_ui/tailwind.config.js rename to packages/omni/tailwind.config.js index 45ce112..d819282 100644 --- a/packages/broad_listening_ui/tailwind.config.js +++ b/packages/omni/tailwind.config.js @@ -1,6 +1,6 @@ /** @type {import('tailwindcss').Config} */ export default { - content: ["./index.html", "./src/**/*.{vue,js,ts,jsx,tsx}"], + content: ["./index.html", "./frontend/**/*.{vue,js,ts,jsx,tsx}"], theme: { extend: {}, }, diff --git a/packages/broad_listening_ui/vite.config.js b/packages/omni/vite.config.js similarity index 87% rename from packages/broad_listening_ui/vite.config.js rename to packages/omni/vite.config.js index b795da6..42cb94f 100644 --- a/packages/broad_listening_ui/vite.config.js +++ b/packages/omni/vite.config.js @@ -5,7 +5,7 @@ export default defineConfig({ plugins: [vue()], resolve: { alias: { - "@": "/src", + "@": "/frontend", }, }, }); diff --git a/pyproject.toml b/pyproject.toml index e5ff713..38679e4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,6 +13,7 @@ dependencies = [ "obsidian_mcp", "pdf_mcp", "toolbox_events", + "omni" ] [build-system] @@ -28,6 +29,7 @@ syftbox_queryengine = { workspace = true } pdf_mcp = { workspace = true } obsidian_mcp = { workspace = true } toolbox_events = { workspace = true } +omni = { workspace = true } [tool.uv.workspace] members = ["packages/*"] From 775f9adca301a7a8d12846d9d73cb65e22072a64 Mon Sep 17 00:00:00 2001 From: Koen van der Veen Date: Mon, 15 Sep 2025 14:47:06 +0200 Subject: [PATCH 10/61] remove mockd ata from frontend --- packages/omni/frontend/api/client.js | 554 +-------------------------- 1 file changed, 1 insertion(+), 553 deletions(-) diff --git a/packages/omni/frontend/api/client.js b/packages/omni/frontend/api/client.js index 8dc3105..fe25ee6 100644 --- a/packages/omni/frontend/api/client.js +++ b/packages/omni/frontend/api/client.js @@ -1,18 +1,10 @@ -// API client for handling data fetching with mock data support -const MOCK_DATA = import.meta.env.VITE_MOCK_DATA === "true"; // Default to false (use backend) - +// API client for handling data fetching class APIClient { constructor() { this.baseURL = import.meta.env.VITE_API_BASE_URL || "http://localhost:8000"; } async request(endpoint, options = {}) { - // if (MOCK_DATA) { - // // Return mock data based on endpoint - // return this.getMockData(endpoint, options); - // } - - // Real API call try { const response = await fetch(`${this.baseURL}${endpoint}`, { headers: { @@ -33,550 +25,6 @@ class APIClient { } } - getMockData(endpoint, options) { - // Mock data router - switch (endpoint) { - case "/data-sources": - return Promise.resolve(this.mockDataSources()); - case "/data-collections": - return Promise.resolve(this.mockDataCollections()); - case "/smart-lists": - if (options.method === "POST") { - // Handle smart list creation - return Promise.resolve(this.mockCreateSmartList(options.body)); - } - return Promise.resolve(this.mockSmartLists()); - default: - if ( - endpoint.startsWith("/smart-lists/") && - endpoint.endsWith("/items") - ) { - const listId = parseInt(endpoint.split("/")[2]); - return Promise.resolve(this.mockSmartListItems(listId)); - } - if ( - endpoint.startsWith("/smart-lists/") && - endpoint.endsWith("/summary") - ) { - const listId = parseInt(endpoint.split("/")[2]); - return Promise.resolve(this.mockSummary(listId)); - } - if (endpoint.startsWith("/chats/")) { - const listId = parseInt(endpoint.split("/")[2]); - return Promise.resolve(this.mockChats(listId)); - } - return Promise.reject( - new Error(`Mock endpoint not implemented: ${endpoint}`), - ); - } - } - - mockDataSources() { - return [ - { - id: "twitter", - name: "Twitter", - icon: "twitter", - description: - "Connect your Twitter account to analyze tweets and conversations", - installCommand: "toolbox install twitter-mcp", - defaultUrl: "localhost:8020", - connectionState: "connected", - dashboardData: { - handle: "@johndoe", - totalTweets: 1247, - latestTweets: [ - { - id: 1, - content: - "Just discovered an amazing new AI breakthrough in transformer architectures! The potential applications are endless.", - timestamp: "2h ago", - likes: 42, - retweets: 18, - }, - { - id: 2, - content: - "Working on some exciting ML projects. The intersection of AI and creativity continues to amaze me every day.", - timestamp: "5h ago", - likes: 28, - retweets: 7, - }, - ], - embeddingCounts: { - "ollama/nomic-embed-text": 123, - "openai/text-embedding-3-small": 89, - }, - }, - }, - { - id: "ai-papers", - name: "AI Papers", - icon: "papers", - description: "Connect to AI paper repositories and research databases", - installCommand: "toolbox install papers-mcp", - defaultUrl: "localhost:8022", - connectionState: "connected", - dashboardData: { - totalPapers: 847, - trendingPapers: 156, - syftboxPapers: 23, - latestPaper: "Jan 15", - latestPapers: [ - { - id: 1, - title: - "Attention Is All You Need: Revisiting Transformer Architectures", - summary: - "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms...", - authors: "Vaswani et al.", - arxivId: "2401.12345", - timestamp: "2h ago", - }, - ], - embeddingCounts: { - "ollama/nomic-embed-text": 234, - "openai/text-embedding-3-small": 178, - }, - }, - }, - { - id: "discord", - name: "Discord", - icon: "discord", - description: - "Connect Discord servers to analyze messages and conversations", - installCommand: "toolbox install discord-mcp", - defaultUrl: "localhost:8021", - connectionState: "disconnected", - dashboardData: { - totalServers: 0, - totalChannels: 0, - totalMessages: 0, - embeddingCounts: {}, - }, - }, - ]; - } - - mockDataCollections() { - return [ - { - id: "twitter", - name: "Twitter", - items: [], // Items would be fetched separately when needed - }, - { - id: "ai-papers", - name: "AI Papers", - items: [], - }, - { - id: "discord", - name: "Discord", - items: [], - }, - ]; - } - - mockSmartLists() { - return [ - { - id: 1, - name: "Vibe coding", - listSources: [ - { - dataSourceId: "twitter", - filters: { - dateRange: { from: "2024-11-15", to: "2024-12-28" }, - ragQuery: "coding aesthetics vibes music", - threshold: 0.7, - }, - }, - ], - computedItems: [], // Would be fetched from backend - itemCount: 23, - }, - { - id: 2, - name: "LLM releases", - listSources: [ - { - dataSourceId: "twitter", - filters: { - dateRange: { from: "2024-10-01", to: "2024-12-15" }, - ragQuery: "LLM releases GPT Claude models", - threshold: 0.8, - }, - }, - ], - computedItems: [], - itemCount: 18, - }, - { - id: 3, - name: "RAG", - listSources: [ - { - dataSourceId: "twitter", - filters: { - dateRange: { from: "2024-09-20", to: "2024-12-10" }, - ragQuery: "RAG retrieval augmented generation vector search", - threshold: 0.75, - }, - }, - ], - computedItems: [], - itemCount: 15, - }, - { - id: 4, - name: "Frontier AI lab capabilities", - listSources: [ - { - dataSourceId: "twitter", - filters: { - dateRange: { from: "2024-08-05", to: "2024-12-20" }, - ragQuery: "AGI research breakthrough capabilities reasoning", - threshold: 0.8, - }, - }, - ], - computedItems: [], - itemCount: 31, - }, - ]; - } - - mockSmartListItems(listId) { - // Return the existing mock tweet data based on list ID - const mockData = { - 1: [ - { - id: 1, - type: "tweet", - content: - "Just spent the weekend building a retro terminal emulator in Rust. There's something magical about the green text on black background aesthetic 💚", - author: { - name: "RetroCode", - handle: "@retrocode_dev", - avatarUrl: - "https://pbs.twimg.com/profile_images/1588180280910397440/hP8GtFeC_400x400.jpg", - }, - likes: 342, - reactions: 89, - timestamp: "3h", - }, - { - id: 2, - type: "tweet", - content: - "Late night coding session with some lo-fi beats. Working on a neural style transfer app that turns your code into ASCII art. The vibes are immaculate ✨", - author: { - name: "VibesCoder", - handle: "@vibes_coder", - avatarUrl: - "https://pbs.twimg.com/profile_images/1584508555726422017/LJjmzFgD_400x400.jpg", - }, - likes: 156, - reactions: 43, - timestamp: "6h", - }, - { - id: 3, - type: "tweet", - content: - "Nothing beats the satisfaction of refactoring legacy code while listening to synthwave. Clean code + aesthetic vibes = peak developer experience", - author: { - name: "SynthDev", - handle: "@synthwave_dev", - avatarUrl: - "https://pbs.twimg.com/profile_images/1579552280645206016/b2rfCHOe_400x400.jpg", - }, - likes: 278, - reactions: 67, - timestamp: "9h", - }, - { - id: 4, - type: "tweet", - content: - 'Built a mood-based code editor that changes themes based on your Spotify playlist. Currently debugging in "Cyberpunk 2077" mode 🌃', - author: { - name: "MoodCode", - handle: "@moodcode_hq", - avatarUrl: - "https://pbs.twimg.com/profile_images/1582796999012077568/lXK8S7nG_400x400.jpg", - }, - likes: 423, - reactions: 112, - timestamp: "12h", - }, - ], - 2: [ - { - id: 5, - type: "tweet", - content: - "OpenAI just dropped GPT-4.5 Turbo with 200K context window and 40% faster inference. The multi-modal capabilities are insane! 🚀", - author: { - name: "AI News Hub", - handle: "@ainews_hub", - avatarUrl: - "https://pbs.twimg.com/profile_images/1590834832027918337/6qF4hYnJ_400x400.jpg", - }, - likes: 892, - reactions: 234, - timestamp: "2h", - }, - { - id: 6, - type: "tweet", - content: - "Anthropic's Claude 3.5 Sonnet now supports real-time conversation and can maintain context across 1M+ tokens. Game changer for long-form content.", - author: { - name: "ML Updates", - handle: "@ml_updates", - avatarUrl: - "https://pbs.twimg.com/profile_images/1587456789456789456/abcd1234_400x400.jpg", - }, - likes: 567, - reactions: 143, - timestamp: "5h", - }, - { - id: 7, - type: "tweet", - content: - "Meta released Code Llama 3 70B - beats GPT-4 on HumanEval benchmark. Open source is catching up fast to proprietary models!", - author: { - name: "OpenAI Research", - handle: "@openai_research", - avatarUrl: - "https://pbs.twimg.com/profile_images/1634058036934500352/b4F1eVpJ_400x400.jpg", - }, - likes: 734, - reactions: 189, - timestamp: "8h", - }, - { - id: 8, - type: "tweet", - content: - "Google's Gemini Ultra 1.5 with native multimodal training is now available via API. Video understanding capabilities are incredible.", - author: { - name: "DeepMind Updates", - handle: "@deepmind_news", - avatarUrl: - "https://pbs.twimg.com/profile_images/1543897644427907075/QNyb85yY_400x400.jpg", - }, - likes: 445, - reactions: 98, - timestamp: "11h", - }, - ], - 3: [ - { - id: 9, - type: "tweet", - content: - "New paper on RAG with knowledge graphs achieves 94% accuracy on complex multi-hop questions. The future of information retrieval is here!", - author: { - name: "RAG Research", - handle: "@rag_research", - avatarUrl: - "https://pbs.twimg.com/profile_images/1589123456789012345/xyz12345_400x400.jpg", - }, - likes: 234, - reactions: 67, - timestamp: "4h", - }, - { - id: 10, - type: "tweet", - content: - "Just implemented hybrid RAG with vector + keyword search. The semantic understanding combined with exact matching is powerful 🔍", - author: { - name: "Search Engineer", - handle: "@search_eng", - avatarUrl: - "https://pbs.twimg.com/profile_images/1582456789123456789/searcheng_400x400.jpg", - }, - likes: 189, - reactions: 45, - timestamp: "7h", - }, - { - id: 11, - type: "tweet", - content: - "ChromaDB + LangChain integration makes building RAG pipelines so much easier. Deployed a knowledge base chatbot in under 100 lines of code.", - author: { - name: "LangChain Dev", - handle: "@langchain_dev", - avatarUrl: - "https://pbs.twimg.com/profile_images/1576297983333912576/CgsRFpdo_400x400.jpg", - }, - likes: 356, - reactions: 89, - timestamp: "10h", - }, - { - id: 12, - type: "tweet", - content: - "Advanced RAG techniques: query decomposition, document reranking, and answer synthesis. Each step improves retrieval quality significantly.", - author: { - name: "Vector DB Pro", - handle: "@vectordb_pro", - avatarUrl: - "https://pbs.twimg.com/profile_images/1583789456123456789/vectordb_400x400.jpg", - }, - likes: 278, - reactions: 73, - timestamp: "13h", - }, - ], - 4: [ - { - id: 13, - type: "tweet", - content: - "OpenAI o1 achieved breakthrough performance on mathematics and coding benchmarks, showing emergent reasoning capabilities we haven't seen before.", - author: { - name: "AGI Research", - handle: "@agi_research", - avatarUrl: - "https://pbs.twimg.com/profile_images/1591234567891234567/agiresrch_400x400.jpg", - }, - likes: 1247, - reactions: 389, - timestamp: "1h", - }, - { - id: 14, - type: "tweet", - content: - "DeepMind's AlphaCode 3 can now solve competitive programming problems at the level of top human programmers. Code generation has reached new heights.", - author: { - name: "Frontier AI Lab", - handle: "@frontier_ai", - avatarUrl: - "https://pbs.twimg.com/profile_images/1586456789012345678/frontier_400x400.jpg", - }, - likes: 892, - reactions: 234, - timestamp: "4h", - }, - { - id: 15, - type: "tweet", - content: - "Anthropic's Constitutional AI shows how we can align powerful models with human values while maintaining capabilities. Safety + performance.", - author: { - name: "AI Safety News", - handle: "@ai_safety_news", - avatarUrl: - "https://pbs.twimg.com/profile_images/1580987654321098765/aisafety_400x400.jpg", - }, - likes: 567, - reactions: 156, - timestamp: "7h", - }, - { - id: 16, - type: "tweet", - content: - "Multi-agent systems are emerging as the next frontier. Claude, GPT-4, and Gemini working together solve problems none could handle alone.", - author: { - name: "Multi-Agent AI", - handle: "@multiagent_ai", - avatarUrl: - "https://pbs.twimg.com/profile_images/1585432109876543210/multiagnt_400x400.jpg", - }, - likes: 734, - reactions: 198, - timestamp: "10h", - }, - { - id: 17, - type: "tweet", - content: - "New scaling laws suggest we're approaching human-level performance across most cognitive tasks. The capability explosion is accelerating.", - author: { - name: "Scaling Laws", - handle: "@scaling_laws", - avatarUrl: - "https://pbs.twimg.com/profile_images/1592345678901234567/scaling_400x400.jpg", - }, - likes: 923, - reactions: 267, - timestamp: "13h", - }, - ], - }; - - return mockData[listId] || []; - } - - mockSummary(listId) { - const summaries = { - 1: "• Retro aesthetics dominating: terminal emulators, green-on-black themes, and vintage computing vibes [1,3]\n• Music-driven development: lo-fi beats, synthwave soundtracks integrated into coding workflows [2,4]\n• Mood-based tooling: editors that adapt themes to Spotify playlists and emotional states [4]\n• ASCII art and visual programming: neural style transfer turning code into art [2]", - 2: "• Massive context windows: GPT-4.5 Turbo (200K tokens), Claude 3.5 Sonnet (1M+ tokens) [5,6]\n• Multimodal breakthroughs: real-time conversation, video understanding, native training [6,8]\n• Coding excellence: Code Llama 3 70B beating GPT-4 on HumanEval benchmark [7]\n• Speed improvements: 40% faster inference across major model releases [5,8]", - 3: "• Hybrid search dominance: combining vector similarity with keyword matching for better retrieval [10]\n• Knowledge graph integration: 94% accuracy on complex multi-hop questions [9]\n• Simplified tooling: ChromaDB + LangChain enabling sub-100 line implementations [11]\n• Advanced techniques: query decomposition, document reranking, and answer synthesis [12]", - 4: "• Reasoning breakthroughs: OpenAI o1 showing emergent capabilities in mathematics and coding [13]\n• Human-level programming: AlphaCode 3 solving competitive programming at expert level [14]\n• Multi-agent systems: Claude, GPT-4, and Gemini collaborating on complex problems [16]\n• Safety + capability: Constitutional AI aligning powerful models without performance loss [15]", - }; - return ( - summaries[listId] || - "Recent discussions focus on breakthrough developments and the importance of human-AI collaboration." - ); - } - - mockCreateSmartList(body) { - // Parse the body if it's a string - const listData = typeof body === "string" ? JSON.parse(body) : body; - - // Create a new smart list with generated ID - const newList = { - id: Date.now(), // Use timestamp as ID - name: listData.name || "Untitled List", - listSources: listData.listSources || listData.sources || [], - computedItems: [], - itemCount: 0, - ...listData, - }; - - return newList; - } - - mockChats(listId) { - const mockChats = { - 1: [ - { - id: 1, - messages: [ - { - id: 1, - role: "user", - content: "What music do developers prefer while coding?", - timestamp: new Date().toISOString(), - }, - { - id: 2, - role: "assistant", - content: - "Based on the tweets, developers favor lo-fi beats, synthwave, and atmospheric music that creates an immersive coding environment.", - timestamp: new Date().toISOString(), - }, - ], - }, - ], - // ... other lists would have their chats here - }; - - return mockChats[listId] || []; - } - // API methods async getDataSources() { return this.request("/data-sources"); From 5be69425520ccf827eb1c92ec72b93c1bb530386 Mon Sep 17 00:00:00 2001 From: Koen van der Veen Date: Wed, 17 Sep 2025 10:56:09 +0200 Subject: [PATCH 11/61] try fix embedding --- .gitignore | 2 + examples/twitter_scrape_wiith_cookie.py | 15 +- packages/omni/.claude/settings.local.json | 19 + packages/omni/frontend/api/client.js | 4 +- .../omni/frontend/components/CreateList.vue | 4 +- .../omni/frontend/components/FeedView.vue | 55 ++- .../omni/frontend/components/SummaryView.vue | 72 ---- .../omni/frontend/components/TweetItem.vue | 79 +++- packages/omni/frontend/stores/newChatStore.js | 13 + .../omni/frontend/stores/smartListsStore.js | 64 +++- packages/omni/justfile | 6 +- packages/omni/notebooks/.gitignore | 1 + packages/omni/notebooks/rag_search_simple.py | 190 ++++++++++ packages/omni/omni/app.py | 193 ++++++++++ packages/omni/omni/db.py | 322 ++++++++++++++++ packages/omni/omni/main.py | 105 ------ packages/omni/omni/mock_data.py | 30 +- packages/omni/omni/models.py | 18 +- packages/omni/omni/settings.py | 8 + packages/omni/omni/twitter.py | 344 ++++++++++++++++++ packages/omni/scripts/embed_tweets.py | 109 ++++++ packages/omni/scripts/generate_embeddings.py | 156 ++++++++ packages/omni/scripts/parse_tweets.py | 191 ++++++++++ 23 files changed, 1778 insertions(+), 222 deletions(-) create mode 100644 packages/omni/.claude/settings.local.json delete mode 100644 packages/omni/frontend/components/SummaryView.vue create mode 100644 packages/omni/notebooks/.gitignore create mode 100644 packages/omni/notebooks/rag_search_simple.py create mode 100644 packages/omni/omni/app.py create mode 100644 packages/omni/omni/db.py delete mode 100644 packages/omni/omni/main.py create mode 100644 packages/omni/omni/settings.py create mode 100644 packages/omni/omni/twitter.py create mode 100644 packages/omni/scripts/embed_tweets.py create mode 100644 packages/omni/scripts/generate_embeddings.py create mode 100644 packages/omni/scripts/parse_tweets.py diff --git a/.gitignore b/.gitignore index d150317..93f735f 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,8 @@ packages/notes_mcp/data/* .claude/settings.local.json */.DS_Store +data/* + examples/home_latest_timeline.json examples/home_latest_timeline_2.json diff --git a/examples/twitter_scrape_wiith_cookie.py b/examples/twitter_scrape_wiith_cookie.py index f659bca..f007cf7 100644 --- a/examples/twitter_scrape_wiith_cookie.py +++ b/examples/twitter_scrape_wiith_cookie.py @@ -1,6 +1,7 @@ import asyncio import json from http.cookiejar import Cookie +from pathlib import Path import browser_cookie3 @@ -43,10 +44,18 @@ async def on_request(request): if response is not None: try: json_data = await response.json() - with open("home_latest_timeline.json", "w") as f: + from datetime import datetime + + dt_str = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"home_latest_timeline_{dt_str}.json" + output_path = Path(".").parent / "data" / filename + output_path.parent.mkdir(parents=True, exist_ok=True) + with open(output_path, "w") as f: json.dump(json_data, f) + print(f"Saved JSON to {filename}") except Exception as e: print(f"Failed to get JSON from response: {e}") + print("API call to HomeLatestTimeline") @@ -59,7 +68,7 @@ async def playwright_login_with_cookies(): cookies.append(cookie_to_playwright_cookie(c)) async with async_playwright() as p: - browser = await p.chromium.launch(headless=True) + browser = await p.chromium.launch(headless=False) context = await browser.new_context() # Set cookies before navigating await context.add_cookies(cookies) @@ -92,7 +101,7 @@ async def playwright_login_with_cookies(): # for span in spans: # text = await span.text_content() # print(text) - await asyncio.sleep(1000) + await asyncio.sleep(10000) await browser.close() diff --git a/packages/omni/.claude/settings.local.json b/packages/omni/.claude/settings.local.json new file mode 100644 index 0000000..08417ec --- /dev/null +++ b/packages/omni/.claude/settings.local.json @@ -0,0 +1,19 @@ +{ + "permissions": { + "allow": [ + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/packages/discord_mcp/discord_mcp/**)", + "Bash(curl:*)", + "Bash(python:*)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Bash(sqlite3:*)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/data/**)" + ], + "deny": [], + "ask": [] + } +} diff --git a/packages/omni/frontend/api/client.js b/packages/omni/frontend/api/client.js index fe25ee6..b9f0427 100644 --- a/packages/omni/frontend/api/client.js +++ b/packages/omni/frontend/api/client.js @@ -53,10 +53,10 @@ class APIClient { }); } - async askQuestion(listId, question) { + async askQuestion(listId, question, context = []) { return this.request(`/chats/${listId}/ask`, { method: "POST", - body: JSON.stringify({ question }), + body: JSON.stringify({ question, context }), }); } } diff --git a/packages/omni/frontend/components/CreateList.vue b/packages/omni/frontend/components/CreateList.vue index 3e28f98..c3e536f 100644 --- a/packages/omni/frontend/components/CreateList.vue +++ b/packages/omni/frontend/components/CreateList.vue @@ -418,7 +418,7 @@ export default { ragFilter: { query: "", model: "", - threshold: 0.7, + threshold: 0.6, }, }; } @@ -574,7 +574,7 @@ export default { to: globalFilters.endDate, }, ragQuery: sourceFilters[source.id]?.ragFilter?.query || "", - threshold: sourceFilters[source.id]?.ragFilter?.threshold || 0.7, + threshold: sourceFilters[source.id]?.ragFilter?.threshold || 0.6, }, })), itemCount: addedSources.value.reduce((sum, source) => { diff --git a/packages/omni/frontend/components/FeedView.vue b/packages/omni/frontend/components/FeedView.vue index 22ba272..6241b01 100644 --- a/packages/omni/frontend/components/FeedView.vue +++ b/packages/omni/frontend/components/FeedView.vue @@ -20,7 +20,13 @@
-
+
+
+ Generating summary... +
+
+ Failed to generate summary +
Generating summary...
@@ -64,9 +88,20 @@ export default { // Generate summary when list changes watch( () => smartListsStore.currentListId, - (newListId) => { + async (newListId) => { if (newListId && !smartListsStore.summariesCache[newListId]) { - smartListsStore.generateSummary(newListId); + try { + const result = await smartListsStore.generateSummary(newListId); + + // If it's generating, start polling + if (result.status === "generating") { + smartListsStore.pollSummaryStatus(newListId, (updatedResult) => { + // The store will automatically update the cache, which will trigger reactivity + }); + } + } catch (error) { + console.error("Failed to generate summary:", error); + } } }, { immediate: true }, @@ -96,9 +131,17 @@ export default { return `${fromFormatted} - ${toFormatted}`; }; - const getSummaryBullets = (summary) => { - if (!summary) return []; - return summary.split("\n").filter((line) => line.trim().startsWith("•")); + const getSummaryBullets = (summaryData) => { + if (!summaryData) return []; + + // Handle both old string format and new object format + const summaryText = + typeof summaryData === "string" ? summaryData : summaryData.summary; + + if (!summaryText) return []; + return summaryText + .split("\n") + .filter((line) => line.trim().startsWith("•")); }; const handleBulletClick = (bullet, index) => { diff --git a/packages/omni/frontend/components/SummaryView.vue b/packages/omni/frontend/components/SummaryView.vue deleted file mode 100644 index 8250a2c..0000000 --- a/packages/omni/frontend/components/SummaryView.vue +++ /dev/null @@ -1,72 +0,0 @@ - - - diff --git a/packages/omni/frontend/components/TweetItem.vue b/packages/omni/frontend/components/TweetItem.vue index 494d41e..5a55d15 100644 --- a/packages/omni/frontend/components/TweetItem.vue +++ b/packages/omni/frontend/components/TweetItem.vue @@ -20,21 +20,37 @@
-

{{ item.content }}

+
+

{{ displayContent }}

+ +
- - - + + + +
@@ -77,6 +93,8 @@ diff --git a/packages/omni/frontend/stores/newChatStore.js b/packages/omni/frontend/stores/newChatStore.js index c9d814e..5a011d8 100644 --- a/packages/omni/frontend/stores/newChatStore.js +++ b/packages/omni/frontend/stores/newChatStore.js @@ -129,10 +129,23 @@ export const useNewChatStore = defineStore("newChat", { this.currentQuestion = ""; + // Get smart list items as context + const { useSmartListsStore } = await import("./smartListsStore.js"); + const smartListsStore = useSmartListsStore(); + const listItems = smartListsStore.currentListItems || []; + + // Convert items to strings for context + const context = listItems.map((item) => + typeof item === "string" + ? item + : item.content || JSON.stringify(item), + ); + // Get AI response const response = await apiClient.askQuestion( this.currentListId, question, + context, ); // Add assistant response to the same chat diff --git a/packages/omni/frontend/stores/smartListsStore.js b/packages/omni/frontend/stores/smartListsStore.js index 694af69..eda5d25 100644 --- a/packages/omni/frontend/stores/smartListsStore.js +++ b/packages/omni/frontend/stores/smartListsStore.js @@ -155,28 +155,74 @@ export const useSmartListsStore = defineStore("smartLists", { } }, - async generateSummary(listId) { - if (!listId) return null; + async generateSummary(listId, forceRefresh = false) { + if (!listId) return { summary: null, status: "error" }; try { - // Check if we already have a cached summary - if (this.summariesCache[listId]) { - return this.summariesCache[listId]; + // Check if we already have a cached completed summary (unless forced refresh) + const cached = this.summariesCache[listId]; + if (!forceRefresh && cached && cached.status === "completed") { + return cached; } // Fetch summary from API - const summary = await apiClient.request( + const result = await apiClient.request( `/smart-lists/${listId}/summary`, ); - this.summariesCache[listId] = summary; - return summary; + // Handle both old string format and new object format + const summaryData = + typeof result === "string" + ? { summary: result, status: "completed" } + : result; + + // Cache the result + this.summariesCache[listId] = summaryData; + return summaryData; } catch (error) { console.error(`Failed to generate summary for list ${listId}:`, error); - return null; + return { summary: null, status: "error" }; } }, + async pollSummaryStatus(listId, onUpdate) { + if (!listId) return; + + const maxPolls = 30; // Max 30 polls (5 minutes with 10s intervals) + let pollCount = 0; + + const poll = async () => { + if (pollCount >= maxPolls) { + console.warn(`Summary generation timeout for list ${listId}`); + return; + } + + try { + // Force refresh to get latest status from server + const result = await this.generateSummary(listId, true); + + // Call the update callback + if (onUpdate) { + onUpdate(result); + } + + // Continue polling if still generating + if (result.status === "generating") { + pollCount++; + setTimeout(poll, 5000); // Poll every 5 seconds + } + } catch (error) { + console.error("Error polling summary status:", error); + if (onUpdate) { + onUpdate({ summary: null, status: "error" }); + } + } + }; + + // Start polling + poll(); + }, + // Method to refresh computed items (trigger backend recomputation) async refreshComputedItems(listId) { if (!listId) return; diff --git a/packages/omni/justfile b/packages/omni/justfile index 2400ef6..ba0e17a 100644 --- a/packages/omni/justfile +++ b/packages/omni/justfile @@ -5,8 +5,8 @@ kill-dev: #!/bin/bash pkill -f "npm run dev" || true - pkill -f "python.*main.py" || true - pkill -f "uvicorn.*main:app" || true + pkill -f "python.*app.py" || true + pkill -f "uvicorn.*app:app" || true # Install dependencies only @@ -21,7 +21,7 @@ dev: just kill-dev trap 'just kill-dev' EXIT echo "Starting backend server..." - cd omni && python main.py & + cd omni && python app.py & BACKEND_PID=$! echo "Backend started with PID: $BACKEND_PID" echo "Starting frontend server..." diff --git a/packages/omni/notebooks/.gitignore b/packages/omni/notebooks/.gitignore new file mode 100644 index 0000000..a6c57f5 --- /dev/null +++ b/packages/omni/notebooks/.gitignore @@ -0,0 +1 @@ +*.json diff --git a/packages/omni/notebooks/rag_search_simple.py b/packages/omni/notebooks/rag_search_simple.py new file mode 100644 index 0000000..62cbe59 --- /dev/null +++ b/packages/omni/notebooks/rag_search_simple.py @@ -0,0 +1,190 @@ +#!/usr/bin/env python3 +""" +Simple script to search for tweets using cosine similarity with pure numpy. +No external dependencies except numpy. +""" + +import json + +import numpy as np +import requests + + +def cosine_similarity_numpy(a, b): + """Calculate cosine similarity between two vectors using numpy.""" + return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) + + +def load_tweets(file_path): + """Load tweets from JSON file.""" + try: + with open(file_path, "r", encoding="utf-8") as f: + return json.load(f) + except json.JSONDecodeError as e: + print(f"JSON Error: {e}") + print("Trying to load with error handling...") + with open(file_path, "r", encoding="utf-8") as f: + content = f.read() + try: + return json.loads(content) + except Exception as e2: + print(f"Still failed: {e2}") + raise + + +def count_tweets_with_embeddings(tweets): + """Count tweets that have both nomic and gemma embeddings.""" + nomic_count = 0 + gemma_count = 0 + both_count = 0 + + for tweet_id, tweet_data in tweets.items(): + embeddings = tweet_data.get("embeddings", {}) + has_nomic = "nomic" in embeddings and embeddings["nomic"] is not None + has_gemma = "gemma" in embeddings and embeddings["gemma"] is not None + + if has_nomic: + nomic_count += 1 + if has_gemma: + gemma_count += 1 + if has_nomic and has_gemma: + both_count += 1 + + return nomic_count, gemma_count, both_count + + +def create_query_vector(query_text, reference_vector): + """ + Create a query vector based on simple text analysis. + This is a simplified approach - in practice you'd use the same embedding model. + """ + # Simple approach: create a vector that emphasizes certain dimensions + # based on the query content + query_vector = np.random.normal(0, 0.1, len(reference_vector)) + + # Add some signal based on query characteristics + query_lower = query_text.lower() + + # Boost certain dimensions based on query content + if "rag" in query_lower: + query_vector[:10] += 0.5 # Boost first 10 dimensions + if "retrieval" in query_lower: + query_vector[10:20] += 0.5 + if "augmented" in query_lower: + query_vector[20:30] += 0.5 + if "generation" in query_lower: + query_vector[30:40] += 0.5 + + # Normalize the vector + return query_vector / np.linalg.norm(query_vector) + + +def get_embedding_ollama(query_text, model): + """Get embedding from Ollama API.""" + url = "http://localhost:11434/api/embeddings" + payload = {"model": model, "prompt": query_text} + response = requests.post(url, json=payload, timeout=30) + response.raise_for_status() + return response.json().get("embedding") + + +def find_top_matches(tweets, query_text, embedding_type, top_k=10): + """Find top k matches using cosine similarity.""" + similarities = [] + + # Get a reference vector to determine query vector + + # Create query vector + if embedding_type == "nomic": + query_vector = get_embedding_ollama(query_text, "nomic-embed-text:latest") + elif embedding_type == "gemma": + query_vector = get_embedding_ollama(query_text, "embeddinggemma:latest") + else: + raise ValueError(f"Invalid embedding type: {embedding_type}") + + # Calculate similarities + for tweet_id, tweet_data in tweets.items(): + embeddings = tweet_data.get("embeddings", {}) + if embedding_type in embeddings and embeddings[embedding_type] is not None: + tweet_embedding = np.array(embeddings[embedding_type]) + similarity = cosine_similarity_numpy(query_vector, tweet_embedding) + similarities.append((similarity, tweet_id, tweet_data)) + + # Sort by similarity (descending) + similarities.sort(reverse=True) + + return similarities[:top_k] + + +def search_tweets(file_path, query): + """Main function to search tweets.""" + print(f"Searching for query: '{query}'") + print("=" * 50) + + # Load tweets + tweets = load_tweets(file_path) + print(f"Total tweets loaded: {len(tweets)}") + + # Count tweets with embeddings + nomic_count, gemma_count, both_count = count_tweets_with_embeddings(tweets) + print(f"Tweets with nomic embeddings: {nomic_count}") + print(f"Tweets with gemma embeddings: {gemma_count}") + print(f"Tweets with both embeddings: {both_count}") + print() + + # Get embedding dimensions + sample_tweet = next(iter(tweets.values())) + nomic_dim = ( + len(sample_tweet["embeddings"]["nomic"]) + if sample_tweet["embeddings"]["nomic"] + else 0 + ) + gemma_dim = ( + len(sample_tweet["embeddings"]["gemma"]) + if sample_tweet["embeddings"]["gemma"] + else 0 + ) + + print(f"Nomic embedding dimension: {nomic_dim}") + print(f"Gemma embedding dimension: {gemma_dim}") + print() + + # Search using nomic embeddings + if nomic_count > 0: + print("TOP 10 MATCHES USING NOMIC EMBEDDINGS:") + print("=" * 40) + nomic_matches = find_top_matches(tweets, query, "nomic", 10) + for i, (similarity, tweet_id, tweet_data) in enumerate(nomic_matches, 1): + content = tweet_data["content"] + print(f"{i}. Similarity: {similarity:.4f}") + print(f" Author: {tweet_data['author']}") + print(f" Content: {content}") + print(f" ID: {tweet_id}") + print() + + # Search using gemma embeddings + if gemma_count > 0: + print("TOP 10 MATCHES USING GEMMA EMBEDDINGS:") + print("=" * 40) + gemma_matches = find_top_matches(tweets, query, "gemma", 10) + for i, (similarity, tweet_id, tweet_data) in enumerate(gemma_matches, 1): + content = tweet_data["content"] + print(f"{i}. Similarity: {similarity:.4f}") + print(f" Author: {tweet_data['author']}") + print(f" Content: {content}") + print(f" ID: {tweet_id}") + print() + + +if __name__ == "__main__": + import sys + + # Default query + query = "RAG" + + # Check if query was provided as command line argument + if len(sys.argv) > 1: + query = sys.argv[1] + + # Run the search + search_tweets("combined_tweets.json", query) diff --git a/packages/omni/omni/app.py b/packages/omni/omni/app.py new file mode 100644 index 0000000..bd2f095 --- /dev/null +++ b/packages/omni/omni/app.py @@ -0,0 +1,193 @@ +from typing import List + +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware + +from omni.mock_data import ( + get_mock_chats, + get_mock_data_collections, + get_mock_data_sources, + get_mock_smart_list_items, + get_mock_smart_lists, + get_mock_summary, +) +from omni.models import ( + Chat, + DataCollection, + DataSource, + QuestionRequest, + SmartList, + SmartListCreate, + SummaryResponse, + TweetItem, +) +from omni.settings import settings +from omni.twitter import ( + get_anthropic_completion, + get_or_generate_smart_list_summary, + query_twitter_data, +) + +app = FastAPI(title="Omni API", description="Backend API for Omni application") + +# Enable CORS +app.add_middleware( + CORSMiddleware, + allow_origins=["http://localhost:5173"], # Vite dev server + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +# Routes +@app.get("/") +async def root(): + return {"message": "Omni API is running"} + + +@app.get("/data-sources", response_model=List[DataSource]) +async def get_data_sources(): + return get_mock_data_sources() + + +@app.get("/data-collections", response_model=List[DataCollection]) +async def get_data_collections(): + return get_mock_data_collections() + + +@app.get("/smart-lists", response_model=List[SmartList]) +async def get_smart_lists(): + return get_mock_smart_lists() + + +@app.post("/smart-lists", response_model=SmartList) +async def create_smart_list(list_data: SmartListCreate): + new_list = { + "id": int(str(int(1000000000000))[:10]), # Generate timestamp-based ID + "name": list_data.name, + "listSources": [source.dict() for source in list_data.listSources], + "computedItems": [], + "itemCount": 0, + } + return new_list + + +@app.get("/smart-lists/{list_id}/items", response_model=List[TweetItem]) +async def get_smart_list_items(list_id: int): + # Get smart list configuration + smart_lists = get_mock_smart_lists() + smart_list = next((sl for sl in smart_lists if sl["id"] == list_id), None) + + if not smart_list: + raise HTTPException(status_code=404, detail="Smart list not found") + + # Check if this list has Twitter sources with real data + for list_source in smart_list["listSources"]: + if list_source["dataSourceId"] == "twitter": + # Use real Twitter data + items = query_twitter_data(list_source) + return items + + # Fallback to mock data + items = get_mock_smart_list_items(list_id) + if not items: + raise HTTPException(status_code=404, detail="Smart list not found") + return items + + +@app.get("/smart-lists/{list_id}/summary", response_model=SummaryResponse) +async def get_smart_list_summary(list_id: int): + # Get smart list configuration + smart_lists = get_mock_smart_lists() + smart_list = next((sl for sl in smart_lists if sl["id"] == list_id), None) + + if not smart_list: + raise HTTPException(status_code=404, detail="Smart list not found") + + # Check if this list has Twitter sources for dynamic summary + for list_source in smart_list["listSources"]: + if list_source["dataSourceId"] == "twitter" and list_source["filters"].get( + "authors" + ): + # Get cached or generate dynamic summary from tweets + result = get_or_generate_smart_list_summary(list_id, list_source) + return result + + # Fallback to mock summary + summary = get_mock_summary(list_id) + if not summary: + return { + "summary": "No summary available for this list.", + "status": "error", + "model": None, + } + return {"summary": summary, "status": "completed", "model": None} + + +@app.delete("/smart-lists/{list_id}/summary/cache") +async def clear_summary_cache(list_id: int): + """Clear cached summary for testing purposes""" + from omni.mock_data import get_mock_smart_lists + from omni.twitter import generate_filters_hash, get_twitter_connection + + smart_lists = get_mock_smart_lists() + smart_list = next((sl for sl in smart_lists if sl["id"] == list_id), None) + + if not smart_list: + raise HTTPException(status_code=404, detail="Smart list not found") + + for list_source in smart_list["listSources"]: + if list_source["dataSourceId"] == "twitter": + filters_hash = generate_filters_hash(list_source) + + try: + with get_twitter_connection() as conn: + cursor = conn.cursor() + cursor.execute( + "DELETE FROM smart_list_summaries WHERE list_id = ? AND filters_hash = ?", + (list_id, filters_hash), + ) + conn.commit() + deleted = cursor.rowcount + return { + "message": f"Cleared cache for list {list_id}", + "deleted_rows": deleted, + } + except Exception as e: + return {"message": f"Error clearing cache: {str(e)}"} + + return {"message": "No Twitter sources found for this list"} + + +@app.get("/chats/{list_id}", response_model=List[Chat]) +async def get_chats(list_id: int): + chats = get_mock_chats(list_id) + return chats + + +@app.post("/chats/{list_id}/ask") +async def ask_question(list_id: int, request: QuestionRequest): + # Mock response for asking questions + if settings.use_anthropic: + response = get_anthropic_completion(request.format_for_anthropic()) + return { + "id": 999, + "role": "assistant", + "answer": response, + "timestamp": "2024-01-15T10:00:00Z", + } + else: + response = { + "id": 999, + "role": "assistant", + "answer": f"This is a mock response to your question: '{request.question}' about list {list_id}", + "timestamp": "2024-01-15T10:00:00Z", + } + return response + + +if __name__ == "__main__": + import uvicorn + + uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True) diff --git a/packages/omni/omni/db.py b/packages/omni/omni/db.py new file mode 100644 index 0000000..caad3ab --- /dev/null +++ b/packages/omni/omni/db.py @@ -0,0 +1,322 @@ +import sqlite3 +from contextlib import contextmanager +from datetime import datetime +from pathlib import Path + +import numpy as np +import sqlite_vec +from sqlite_vec import serialize_float32 + +HOME = Path.home() +TWITTER_MCP_DB_PATH = HOME / "twitter-mcp" / "db.sqlite" +TWITTER_MCP_DB_PATH.parent.mkdir(parents=True, exist_ok=True) + +EMBEDDINGS_TABLE = "tweet_embeddings_vec" +EMBEDDINGS_LEN = 768 + + +def deserialize_float32(blob: bytes) -> list[float]: + return np.frombuffer(blob, dtype=np.float32).tolist() + + +def _get_twitter_connection(path: Path = TWITTER_MCP_DB_PATH): + conn = sqlite3.connect(path) + conn.row_factory = sqlite3.Row + + conn.enable_load_extension(True) + sqlite_vec.load(conn) + conn.enable_load_extension(False) + create_tables(conn) + return conn + + +@contextmanager +def get_twitter_connection(path: Path = TWITTER_MCP_DB_PATH): + conn = _get_twitter_connection(path) + try: + yield conn + finally: + conn.close() + + +def create_tables(conn): + cursor = conn.cursor() + + cursor.execute(""" + CREATE TABLE IF NOT EXISTS authors ( + id TEXT PRIMARY KEY, + username TEXT NOT NULL, + screen_name TEXT NOT NULL, + name TEXT NOT NULL, + description TEXT, + avatar_url TEXT, + banner_url TEXT, + followers_count INTEGER, + following_count INTEGER, + statuses_count INTEGER, + verified BOOLEAN, + blue_verified BOOLEAN, + location TEXT, + created_at TEXT + ) + """) + + cursor.execute(""" + CREATE TABLE IF NOT EXISTS tweets ( + id TEXT PRIMARY KEY, + author_id TEXT NOT NULL, + text TEXT NOT NULL, + created_at TEXT NOT NULL, + conversation_id TEXT, + in_reply_to_user_id TEXT, + in_reply_to_status_id TEXT, + lang TEXT, + retweet_count INTEGER DEFAULT 0, + favorite_count INTEGER DEFAULT 0, + reply_count INTEGER DEFAULT 0, + quote_count INTEGER DEFAULT 0, + bookmark_count INTEGER DEFAULT 0, + view_count INTEGER DEFAULT 0, + is_quote_status BOOLEAN DEFAULT FALSE, + possibly_sensitive BOOLEAN DEFAULT FALSE, + retweeted BOOLEAN DEFAULT FALSE, + favorited BOOLEAN DEFAULT FALSE, + bookmarked BOOLEAN DEFAULT FALSE, + note_tweet_text TEXT, + media_urls TEXT, + urls TEXT, + hashtags TEXT, + user_mentions TEXT, + source TEXT, + FOREIGN KEY (author_id) REFERENCES authors(id) + ) + """) + + cursor.execute( + f""" + create virtual table if not exists {EMBEDDINGS_TABLE} using vec0( + sample_embedding float[{EMBEDDINGS_LEN}] distance_metric=cosine, + tweet_text text, + tweet_id text, + ); + """ + ) + + cursor.execute(""" + CREATE TABLE IF NOT EXISTS smart_list_summaries ( + list_id INTEGER NOT NULL, + filters_hash TEXT NOT NULL, + summary TEXT NOT NULL, + status TEXT DEFAULT 'completed', + model TEXT, + created_at TEXT NOT NULL, + updated_at TEXT NOT NULL, + PRIMARY KEY (list_id, filters_hash) + ) + """) + + # Handle migration for existing databases that don't have the model column + try: + cursor.execute("SELECT model FROM smart_list_summaries LIMIT 1") + except sqlite3.OperationalError: + # Column doesn't exist, add it + cursor.execute("ALTER TABLE smart_list_summaries ADD COLUMN model TEXT") + print("Added model column to smart_list_summaries table") + + conn.commit() + + +def upsert_author(conn, author_data): + cursor = conn.cursor() + cursor.execute( + """ + INSERT OR REPLACE INTO authors (id, username, screen_name, name, description, avatar_url, banner_url, followers_count, following_count, statuses_count, verified, blue_verified, location, created_at) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + author_data, + ) + conn.commit() + + +def upsert_tweet(conn, tweet_data): + cursor = conn.cursor() + cursor.execute( + """ + INSERT OR REPLACE INTO tweets (id, author_id, text, created_at, conversation_id, in_reply_to_user_id, in_reply_to_status_id, lang, retweet_count, favorite_count, reply_count, quote_count, bookmark_count, view_count, is_quote_status, possibly_sensitive, retweeted, favorited, bookmarked, note_tweet_text, media_urls, urls, hashtags, user_mentions, source) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + """, + tweet_data, + ) + conn.commit() + + +def get_tweets_without_embeddings(conn, limit=10): + """Get tweets that don't have embeddings yet""" + cursor = conn.cursor() + cursor.execute( + f""" + SELECT tweets.* + FROM tweets + LEFT JOIN {EMBEDDINGS_TABLE} ON tweets.id = {EMBEDDINGS_TABLE}.tweet_id + WHERE {EMBEDDINGS_TABLE}.tweet_id IS NULL + LIMIT ? + """, + (limit,), + ) + return cursor.fetchall() + + +def upsert_tweet_embeddings(conn, embeddings_data): + """Insert or update tweet embeddings""" + cursor = conn.cursor() + cursor.executemany( + f""" + INSERT OR REPLACE INTO {EMBEDDINGS_TABLE} (tweet_id, sample_embedding, tweet_text) + VALUES (?, ?, ?) + """, + [ + ( + data["tweet_id"], + serialize_float32(data["embedding"]), + data["tweet_text"], + ) + for data in embeddings_data + ], + ) + conn.commit() + + +def search_tweets_by_vector( + conn, query_embedding: list[float], similarity_threshold=0.4, limit=50 +): + """Search tweets by vector similarity with a similarity threshold""" + cursor = conn.cursor() + # Convert similarity threshold to cosine distance threshold + # similarity_threshold of 0.8 means we want cosine distance <= 0.2 (1.0 - 0.8) + cosine_distance_threshold = 1.0 - similarity_threshold + + cursor.execute( + f""" + SELECT tweets.*, embeddings.distance, authors.screen_name, authors.name as author_name + FROM ( + SELECT tweet_id, tweet_text, distance + FROM {EMBEDDINGS_TABLE} + WHERE sample_embedding MATCH ? + ORDER BY distance + LIMIT ? + ) as embeddings + JOIN tweets ON embeddings.tweet_id = tweets.id + JOIN authors ON tweets.author_id = authors.id + WHERE embeddings.distance <= ? + ORDER BY embeddings.distance + """, + (serialize_float32(query_embedding), limit, cosine_distance_threshold), + ) + return cursor.fetchall() + + +def get_tweets_by_authors_and_timeframe( + conn, + author_screen_names: list[str], + start_date: datetime, + end_date: datetime = None, + query_embedding: list[float] = None, + similarity_threshold: float = 0.4, + limit: int = 50, +): + """Get tweets from specific authors within a timeframe, optionally filtered by vector similarity""" + if end_date is None: + end_date = datetime.now() + + cursor = conn.cursor() + + if query_embedding: + # Use vector search with author and time filtering + # Convert similarity threshold to cosine distance threshold + cosine_distance_threshold = 1.0 - similarity_threshold + placeholders = ",".join(["?"] * len(author_screen_names)) + cursor.execute( + f""" + SELECT tweets.*, embeddings.distance, authors.screen_name, authors.name as author_name, authors.avatar_url + FROM ( + SELECT tweet_id, tweet_text, distance + FROM {EMBEDDINGS_TABLE} + WHERE sample_embedding MATCH ? + ORDER BY distance + LIMIT ? + ) as embeddings + JOIN tweets ON embeddings.tweet_id = tweets.id + JOIN authors ON tweets.author_id = authors.id + WHERE authors.screen_name IN ({placeholders}) + AND tweets.created_at >= ? + AND tweets.created_at <= ? + AND embeddings.distance <= ? + ORDER BY embeddings.distance + """, + ( + serialize_float32(query_embedding), + limit * 2, # Get more results initially to filter properly + *author_screen_names, + start_date.isoformat(), + end_date.isoformat(), + cosine_distance_threshold, + ), + ) + else: + # Regular search without vector similarity + placeholders = ",".join(["?"] * len(author_screen_names)) + cursor.execute( + f""" + SELECT tweets.*, authors.screen_name, authors.name as author_name, authors.avatar_url + FROM tweets + JOIN authors ON tweets.author_id = authors.id + WHERE authors.screen_name IN ({placeholders}) + AND tweets.created_at >= ? + AND tweets.created_at <= ? + ORDER BY tweets.created_at DESC + LIMIT ? + """, + (*author_screen_names, start_date.isoformat(), end_date.isoformat(), limit), + ) + + return cursor.fetchall() + + +def get_tweet_count(conn): + cursor = conn.cursor() + cursor.execute("SELECT COUNT(*) FROM tweets") + return cursor.fetchone()[0] + + +def get_author_count(conn): + cursor = conn.cursor() + cursor.execute("SELECT COUNT(*) FROM authors") + return cursor.fetchone()[0] + + +def get_cached_summary(conn, list_id, filters_hash): + """Get cached summary if available""" + cursor = conn.cursor() + cursor.execute( + "SELECT summary, status, model FROM smart_list_summaries WHERE list_id = ? AND filters_hash = ?", + (list_id, filters_hash), + ) + result = cursor.fetchone() + return result if result else None + + +def upsert_summary( + conn, list_id, filters_hash, summary, status="completed", model=None +): + """Insert or update cached summary""" + cursor = conn.cursor() + now = datetime.now().isoformat() + cursor.execute( + """ + INSERT OR REPLACE INTO smart_list_summaries + (list_id, filters_hash, summary, status, model, created_at, updated_at) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + (list_id, filters_hash, summary, status, model, now, now), + ) + conn.commit() diff --git a/packages/omni/omni/main.py b/packages/omni/omni/main.py deleted file mode 100644 index 026f14c..0000000 --- a/packages/omni/omni/main.py +++ /dev/null @@ -1,105 +0,0 @@ -from typing import List - -from fastapi import FastAPI, HTTPException -from fastapi.middleware.cors import CORSMiddleware -from mock_data import ( - get_mock_chats, - get_mock_data_collections, - get_mock_data_sources, - get_mock_smart_list_items, - get_mock_smart_lists, - get_mock_summary, -) -from models import ( - Chat, - DataCollection, - DataSource, - QuestionRequest, - SmartList, - SmartListCreate, - TweetItem, -) - -app = FastAPI(title="Omni API", description="Backend API for Omni application") - -# Enable CORS -app.add_middleware( - CORSMiddleware, - allow_origins=["http://localhost:5173"], # Vite dev server - allow_credentials=True, - allow_methods=["*"], - allow_headers=["*"], -) - - -# Routes -@app.get("/") -async def root(): - return {"message": "Omni API is running"} - - -@app.get("/data-sources", response_model=List[DataSource]) -async def get_data_sources(): - return get_mock_data_sources() - - -@app.get("/data-collections", response_model=List[DataCollection]) -async def get_data_collections(): - return get_mock_data_collections() - - -@app.get("/smart-lists", response_model=List[SmartList]) -async def get_smart_lists(): - return get_mock_smart_lists() - - -@app.post("/smart-lists", response_model=SmartList) -async def create_smart_list(list_data: SmartListCreate): - new_list = { - "id": int(str(int(1000000000000))[:10]), # Generate timestamp-based ID - "name": list_data.name, - "listSources": [source.dict() for source in list_data.listSources], - "computedItems": [], - "itemCount": 0, - } - return new_list - - -@app.get("/smart-lists/{list_id}/items", response_model=List[TweetItem]) -async def get_smart_list_items(list_id: int): - items = get_mock_smart_list_items(list_id) - if not items: - raise HTTPException(status_code=404, detail="Smart list not found") - return items - - -@app.get("/smart-lists/{list_id}/summary") -async def get_smart_list_summary(list_id: int): - summary = get_mock_summary(list_id) - if not summary: - raise HTTPException(status_code=404, detail="Smart list not found") - return {"summary": summary} - - -@app.get("/chats/{list_id}", response_model=List[Chat]) -async def get_chats(list_id: int): - chats = get_mock_chats(list_id) - return chats - - -@app.post("/chats/{list_id}/ask") -async def ask_question(list_id: int, request: QuestionRequest): - # Mock response for asking questions - response = { - "id": 999, - "role": "assistant", - "content": f"This is a mock response to your question: '{request.question}' about list {list_id}", - "timestamp": "2024-01-15T10:00:00Z", - } - return response - - -if __name__ == "__main__": - import uvicorn - - uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True) diff --git a/packages/omni/omni/mock_data.py b/packages/omni/omni/mock_data.py index c8ceaef..a641a66 100644 --- a/packages/omni/omni/mock_data.py +++ b/packages/omni/omni/mock_data.py @@ -97,9 +97,9 @@ def get_mock_smart_lists(): { "dataSourceId": "twitter", "filters": { - "dateRange": {"from": "2024-11-15", "to": "2024-12-28"}, + "dateRange": {"from": "2025-07-01", "to": "2025-12-31"}, "ragQuery": "coding aesthetics vibes music", - "threshold": 0.7, + "threshold": 0.6, }, } ], @@ -113,9 +113,9 @@ def get_mock_smart_lists(): { "dataSourceId": "twitter", "filters": { - "dateRange": {"from": "2024-10-01", "to": "2024-12-15"}, + "dateRange": {"from": "2025-07-01", "to": "2025-12-31"}, "ragQuery": "LLM releases GPT Claude models", - "threshold": 0.8, + "threshold": 0.6, }, } ], @@ -129,9 +129,21 @@ def get_mock_smart_lists(): { "dataSourceId": "twitter", "filters": { - "dateRange": {"from": "2024-09-20", "to": "2024-12-10"}, - "ragQuery": "RAG retrieval augmented generation vector search", - "threshold": 0.75, + "dateRange": {"from": "2025-07-01", "to": "2025-10-01"}, + "ragQuery": "RAG retrieval augmented generation", + "threshold": 0.4, + "authors": [ + "@CrazyJvm", + "@seb_ruder", + "@Devendr06654102", + "@weaviate_io", + "@PSH_Lewis", + "@douwekiela", + "@steipete", + "@jeremyphoward", + "@bobvanluijt", + "@rohanpaul_ai", + ], }, } ], @@ -145,9 +157,9 @@ def get_mock_smart_lists(): { "dataSourceId": "twitter", "filters": { - "dateRange": {"from": "2024-08-05", "to": "2024-12-20"}, + "dateRange": {"from": "2025-07-01", "to": "2025-12-31"}, "ragQuery": "AGI research breakthrough capabilities reasoning", - "threshold": 0.8, + "threshold": 0.5, }, } ], diff --git a/packages/omni/omni/models.py b/packages/omni/omni/models.py index 5cc1beb..e1db81b 100644 --- a/packages/omni/omni/models.py +++ b/packages/omni/omni/models.py @@ -1,4 +1,4 @@ -from typing import Dict, List +from typing import Dict, List, Optional, Union from pydantic import BaseModel @@ -24,6 +24,7 @@ class SmartListFilter(BaseModel): dateRange: Dict[str, str] ragQuery: str threshold: float + authors: List[str] = [] class ListSource(BaseModel): @@ -45,13 +46,16 @@ class SmartListCreate(BaseModel): class TweetItem(BaseModel): - id: int + id: Union[ + str, int + ] # Allow both string (for real tweet IDs) and int (for mock data) type: str content: str author: Dict likes: int reactions: int timestamp: str + similarity_score: Optional[float] = None class Message(BaseModel): @@ -68,3 +72,13 @@ class Chat(BaseModel): class QuestionRequest(BaseModel): question: str + context: list[str] + + def format_for_anthropic(self): + return f"Question: {self.question}\nContext: {self.context}" + + +class SummaryResponse(BaseModel): + summary: str + status: str + model: Optional[str] = None diff --git a/packages/omni/omni/settings.py b/packages/omni/omni/settings.py new file mode 100644 index 0000000..745064d --- /dev/null +++ b/packages/omni/omni/settings.py @@ -0,0 +1,8 @@ +from pydantic_settings import BaseSettings + + +class Settings(BaseSettings): + use_anthropic: bool = False + + +settings = Settings() diff --git a/packages/omni/omni/twitter.py b/packages/omni/omni/twitter.py new file mode 100644 index 0000000..1ca3ef1 --- /dev/null +++ b/packages/omni/omni/twitter.py @@ -0,0 +1,344 @@ +import hashlib +import json +import os +import threading +from datetime import datetime + +import requests + +from omni.db import ( + get_cached_summary, + get_tweets_by_authors_and_timeframe, + get_twitter_connection, + search_tweets_by_vector, + upsert_summary, +) +from omni.settings import settings + +TWITTER_DB_AVAILABLE = True + + +def get_ollama_completion(prompt, model="llama3.2:1b"): + """Get completion from Ollama API""" + try: + response = requests.post( + "http://localhost:11434/api/generate", + json={ + "model": model, + "prompt": prompt, + "stream": False, + "options": { + "temperature": 0.7, + "top_p": 0.9, + "num_predict": 500, # Limit response length + }, + }, + timeout=60, # Reduced timeout + ) + response.raise_for_status() + data = response.json() + result = data.get("response", "").strip() + return result + except Exception as e: + print(f"Error getting Ollama completion: {e}") + return None + + +def get_anthropic_completion(prompt): + """Get completion from Anthropic API""" + api_key = os.getenv("ANTHROPIC_API_KEY") + if not api_key: + print("ANTHROPIC_API_KEY not found in environment") + return None + + try: + import anthropic + + client = anthropic.Anthropic(api_key=api_key) + response = client.messages.create( + model="claude-3-haiku-20240307", + max_tokens=1000, + messages=[{"role": "user", "content": prompt}], + ) + return response.content[0].text.strip() + except ImportError: + print("Anthropic package not installed") + return None + except Exception as e: + print(f"Error getting Anthropic completion: {e}") + return None + + +def generate_summary(tweets_text): + """Generate a summary from tweets text using Ollama or Anthropic""" + if not tweets_text.strip(): + return "No tweets available for summary.", None + + prompt = f"""Please analyze the following tweets and create a concise bullet-point summary highlighting the key themes, insights, and trends. Format as bullet points with brief descriptions: + +Tweets: +{tweets_text} + +Summary (use bullet points starting with •):""" + + use_anthropic = settings.use_anthropic + + if use_anthropic: + print("Using Anthropic") + summary = get_anthropic_completion(prompt) + if summary: + return summary, "claude-3-haiku-20240307" + print("Anthropic failed, falling back to Ollama") + + # Default to Ollama + summary = get_ollama_completion(prompt) + if summary: + return summary, "llama3.2:1b" + + return "Unable to generate summary at this time.", None + + +def get_ollama_embedding(text, model="nomic-embed-text:latest"): + """Get embedding from Ollama API""" + try: + response = requests.post( + "http://localhost:11434/api/embeddings", + json={"model": model, "prompt": text}, + timeout=30, + ) + response.raise_for_status() + data = response.json() + return data.get("embedding") + except Exception as e: + print(f"Error getting embedding: {e}") + return None + + +def query_twitter_data(list_source): + """Query Twitter data based on list source filters""" + if not TWITTER_DB_AVAILABLE: + return [] + + filters = list_source["filters"] + authors = [ + author.lstrip("@") for author in filters.get("authors", []) + ] # Remove @ prefix + + # Parse date range + start_date = datetime.fromisoformat(filters["dateRange"]["from"]) + end_date = datetime.fromisoformat(filters["dateRange"]["to"]) + + # Get embedding for RAG query (only if query is provided and not empty) + query_embedding = None + if filters.get("ragQuery") and filters["ragQuery"].strip(): + query_embedding = get_ollama_embedding(filters["ragQuery"]) + if not query_embedding: + print(f"Failed to get embedding for query: {filters['ragQuery']}") + + # If no authors and no RAG query, return empty (nothing to search for) + if not authors and not query_embedding: + return [] + + try: + with get_twitter_connection() as conn: + if authors: + # Search within specific authors + tweets = get_tweets_by_authors_and_timeframe( + conn, + author_screen_names=authors, + start_date=start_date, + end_date=end_date, + query_embedding=query_embedding, + similarity_threshold=filters.get("threshold", 0.4) + if query_embedding + else 0.0, + limit=50, + ) + elif query_embedding: + # Search across all tweets using semantic search only + tweets = search_tweets_by_vector( + conn, + query_embedding=query_embedding, + similarity_threshold=filters.get("threshold", 0.4), + limit=50, + ) + # Filter by date range manually since search_tweets_by_vector doesn't do this + tweets = [ + tweet + for tweet in tweets + if start_date.isoformat() + <= tweet["created_at"] + <= end_date.isoformat() + ] + else: + tweets = [] + + # Convert to TweetItem format + tweet_items = [] + for i, tweet in enumerate(tweets[:20]): # Limit to first 20 for display + # Calculate similarity score from distance (if available) + similarity_score = None + if "distance" in tweet.keys() and tweet["distance"] is not None: + # Convert distance to similarity (distance ranges from 0-2, similarity 0-1) + similarity_score = round(1.0 - tweet["distance"], 3) + + tweet_item = { + "id": str(tweet["id"]) + if "id" in tweet.keys() + else f"mock_{i + 1}", # Convert tweet ID to string to preserve precision + "type": "tweet", + "content": tweet["text"] if tweet["text"] else "", + "author": { + "name": tweet["author_name"] + if "author_name" in tweet.keys() + else "Unknown", + "handle": f"@{tweet['screen_name']}" + if "screen_name" in tweet.keys() + else "@unknown", + "avatarUrl": tweet["avatar_url"] + if "avatar_url" in tweet.keys() and tweet["avatar_url"] + else "https://via.placeholder.com/400x400?text=T", + }, + "likes": tweet["favorite_count"] + if "favorite_count" in tweet.keys() + else 0, + "reactions": tweet["retweet_count"] + if "retweet_count" in tweet.keys() + else 0, + "timestamp": tweet["created_at"][:10] + if "created_at" in tweet.keys() and tweet["created_at"] + else "", + "similarity_score": similarity_score, + } + tweet_items.append(tweet_item) + + return tweet_items + except Exception as e: + print(f"Error querying Twitter data: {e}") + return [] + + +def get_intended_model(): + """Get the model that will be used for summary generation""" + + if settings.use_anthropic: + return "claude-3-haiku-20240307" + return "llama3.2:1b" + + +def generate_filters_hash(list_source): + """Generate a hash for the list source filters for caching""" + filters_str = json.dumps(list_source, sort_keys=True) + return hashlib.md5(filters_str.encode()).hexdigest() + + +def generate_summary_async(list_id, list_source, filters_hash): + """Generate summary in background thread""" + try: + # Mark as generating + with get_twitter_connection() as conn: + upsert_summary(conn, list_id, filters_hash, "", "generating") + + # Generate the actual summary + summary, model = _generate_smart_list_summary_internal(list_source) + + # Save completed summary + with get_twitter_connection() as conn: + upsert_summary(conn, list_id, filters_hash, summary, "completed", model) + + except Exception as e: + print(f"Error in async summary generation: {e}") + with get_twitter_connection() as conn: + upsert_summary( + conn, list_id, filters_hash, "Error generating summary", "error" + ) + + +def _generate_smart_list_summary_internal(list_source): + """Internal function to generate summary (used by both sync and async)""" + if not TWITTER_DB_AVAILABLE: + return "Database not available for summary generation.", None + + filters = list_source["filters"] + authors = [ + author.lstrip("@") for author in filters.get("authors", []) + ] # Remove @ prefix + + if not authors: + return "No authors specified for summary.", None + + # Parse date range + start_date = datetime.fromisoformat(filters["dateRange"]["from"]) + end_date = datetime.fromisoformat(filters["dateRange"]["to"]) + + # Get embedding for RAG query (only if query is provided and not empty) + query_embedding = None + if filters.get("ragQuery") and filters["ragQuery"].strip(): + query_embedding = get_ollama_embedding(filters["ragQuery"]) + + try: + with get_twitter_connection() as conn: + tweets = get_tweets_by_authors_and_timeframe( + conn, + author_screen_names=authors, + start_date=start_date, + end_date=end_date, + query_embedding=query_embedding, + similarity_threshold=filters.get("threshold", 0.4) + if query_embedding + else 0.0, + limit=50, + ) + + if not tweets: + return "No tweets found matching the criteria.", None + + # Combine tweet texts for summary + tweets_text = "\n\n".join( + [ + f"@{tweet['screen_name']}: {tweet['text']}" + for tweet in tweets[:20] # Limit to first 20 for summary + if tweet["text"] + ] + ) + + return generate_summary(tweets_text) + + except Exception as e: + print(f"Error generating smart list summary: {e}") + return "Unable to generate summary due to an error.", None + + +def get_or_generate_smart_list_summary(list_id, list_source): + """Get cached summary or generate new one (async if needed)""" + if not TWITTER_DB_AVAILABLE: + return { + "summary": "Database not available for summary generation.", + "status": "error", + "model": None, + } + + filters_hash = generate_filters_hash(list_source) + + with get_twitter_connection() as conn: + cached = get_cached_summary(conn, list_id, filters_hash) + + if cached: + summary, status, model = cached + + # If we have a cached result but no model info, update it with intended model for generating status + if status == "generating" and model is None: + intended_model = get_intended_model() + return {"summary": summary, "status": status, "model": intended_model} + + return {"summary": summary, "status": status, "model": model} + + # No cached summary, start async generation + intended_model = get_intended_model() + thread = threading.Thread( + target=generate_summary_async, args=(list_id, list_source, filters_hash) + ) + thread.daemon = True + thread.start() + + return {"summary": "", "status": "generating", "model": intended_model} diff --git a/packages/omni/scripts/embed_tweets.py b/packages/omni/scripts/embed_tweets.py new file mode 100644 index 0000000..a28423b --- /dev/null +++ b/packages/omni/scripts/embed_tweets.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +""" +Script to add embeddings to tweets using Ollama with two different models: +- gemma:2b (assuming this is what you meant by gemmaembed) +- nomic-embed-text:latest +""" + +import json +import time +from pathlib import Path + +import requests + + +def get_embedding_ollama(text, model): + """Get embedding from Ollama API.""" + url = "http://localhost:11434/api/embeddings" + + payload = {"model": model, "prompt": text} + + try: + response = requests.post(url, json=payload, timeout=30) + response.raise_for_status() + + data = response.json() + return data.get("embedding", []) + + except requests.exceptions.RequestException as e: + print(f"Error getting embedding for model {model}: {e}") + return None + + +def main(): + """Main function to add embeddings to all tweets.""" + tweets_file = Path("./notebooks/combined_tweets.json") + + if not tweets_file.exists(): + print(f"Tweets file {tweets_file} not found!") + return + + # Load tweets + print("Loading tweets...") + with open(tweets_file, "r", encoding="utf-8") as f: + tweets = json.load(f) + + print(f"Found {len(tweets)} tweets to process") + + # Models to use + models = {"gemma": "embeddinggemma:latest", "nomic": "nomic-embed-text:latest"} + + # Process each tweet + processed_count = 0 + total_tweets = len(tweets) + + for tweet_id, tweet_data in tweets.items(): + print(f"Processing tweet {processed_count + 1}/{total_tweets} (ID: {tweet_id})") + + content = tweet_data.get("content", "") + if not content: + print(" No content found, skipping...") + processed_count += 1 + continue + + # Initialize or get existing embeddings dict + embeddings = tweet_data.get("embeddings", {}) + + # Get embeddings from both models (generate if missing) + for model_name, model_id in models.items(): + if model_name not in embeddings: + print(f" Getting {model_name} embedding...") + embedding = get_embedding_ollama(content, model_id) + + if embedding is not None: + embeddings[model_name] = embedding + print(f" Got embedding of size {len(embedding)}") + else: + print(" Failed to get embedding") + + # Small delay to avoid overwhelming the API + time.sleep(0.1) + else: + print(f" {model_name} embedding already exists, skipping...") + + # Add embeddings to tweet data + if embeddings: + tweet_data["embeddings"] = embeddings + + processed_count += 1 + + # Save progress every 50 tweets + if processed_count % 50 == 0: + print(f" Saving progress ({processed_count}/{total_tweets})...") + with open(tweets_file, "w", encoding="utf-8") as f: + json.dump(tweets, f, indent=2, ensure_ascii=False) + + # Final save + print("Saving final results...") + with open(tweets_file, "w", encoding="utf-8") as f: + json.dump(tweets, f, indent=2, ensure_ascii=False) + + print(f"Completed processing {processed_count} tweets") + + # Summary + tweets_with_embeddings = sum(1 for t in tweets.values() if "embeddings" in t) + print(f"Total tweets with embeddings: {tweets_with_embeddings}") + + +if __name__ == "__main__": + main() diff --git a/packages/omni/scripts/generate_embeddings.py b/packages/omni/scripts/generate_embeddings.py new file mode 100644 index 0000000..33db06c --- /dev/null +++ b/packages/omni/scripts/generate_embeddings.py @@ -0,0 +1,156 @@ +#!/usr/bin/env python3 +""" +Script to generate embeddings for tweets using Ollama's embedding model. +Uses gemma:2b embedding model with 768-dimensional vectors. +""" + +import json +import sys +from pathlib import Path + +import requests +from tqdm import tqdm + +sys.path.append(str(Path(__file__).parent.parent)) + +from omni.db import ( + get_tweets_without_embeddings, + get_twitter_connection, + upsert_tweet_embeddings, +) + + +def get_ollama_embedding(text, model="nomic-embed-text:latest"): + """Get embedding from Ollama API""" + try: + response = requests.post( + "http://localhost:11434/api/embeddings", + json={"model": model, "prompt": text}, + timeout=30, + ) + response.raise_for_status() + data = response.json() + return data.get("embedding") + except Exception as e: + print(f"Error getting embedding for text: {str(e)[:100]}...") + return None + + +def prepare_tweet_text(tweet_row): + """Prepare tweet text for embedding by combining relevant fields""" + text_parts = [] + + # Main tweet text + if tweet_row["text"]: + text_parts.append(tweet_row["text"]) + + # Note tweet text (if available, usually longer form) + if tweet_row["note_tweet_text"]: + text_parts.append(tweet_row["note_tweet_text"]) + + # Parse and add hashtags + try: + hashtags = json.loads(tweet_row["hashtags"]) if tweet_row["hashtags"] else [] + if hashtags: + text_parts.append(" ".join(f"#{tag}" for tag in hashtags)) + except (json.JSONDecodeError, TypeError): + pass + + # Parse and add URLs (display URLs for context) + try: + urls = json.loads(tweet_row["urls"]) if tweet_row["urls"] else [] + if urls: + display_urls = [ + url.get("display_url", "") for url in urls if url.get("display_url") + ] + if display_urls: + text_parts.append(" ".join(display_urls)) + except (json.JSONDecodeError, TypeError): + pass + + return " ".join(text_parts).strip() + + +def main(): + print("Generating embeddings for tweets using Ollama nomic-embed-text model...") + + # Test Ollama connection first + try: + test_response = requests.get("http://localhost:11434/api/tags", timeout=5) + test_response.raise_for_status() + print("✓ Ollama is running") + except Exception as e: + print(f"✗ Error connecting to Ollama: {e}") + print("Make sure Ollama is running with: ollama serve") + print( + "And that you have the nomic-embed-text model: ollama pull nomic-embed-text" + ) + return + + batch_size = 100 # Process embeddings in batches + + with get_twitter_connection() as conn: + while True: + # Get tweets without embeddings + tweets_without_embeddings = get_tweets_without_embeddings( + conn, limit=batch_size + ) + + if not tweets_without_embeddings: + print("✓ All tweets have embeddings!") + break + + print(f"Processing {len(tweets_without_embeddings)} tweets...") + + embeddings_batch = [] + + # Generate embeddings with progress bar + for tweet_row in tqdm( + tweets_without_embeddings, desc="Generating embeddings" + ): + tweet_text = prepare_tweet_text(tweet_row) + + if not tweet_text.strip(): + print(f"Skipping tweet {tweet_row['id']} - no text content") + continue + + # Get embedding + embedding = get_ollama_embedding(tweet_text) + + if embedding is None: + print(f"Failed to get embedding for tweet {tweet_row['id']}") + continue + + if len(embedding) != 768: + print( + f"Warning: Expected 768-dim embedding but got {len(embedding)} for tweet {tweet_row['id']}" + ) + + embeddings_batch.append( + { + "tweet_id": tweet_row["id"], + "embedding": embedding, + "tweet_text": tweet_text[ + :1000 + ], # Store first 1000 chars for reference + } + ) + + # Insert embeddings in batch + if embeddings_batch: + print(f"Inserting {len(embeddings_batch)} embeddings into database...") + try: + upsert_tweet_embeddings(conn, embeddings_batch) + print(f"✓ Inserted {len(embeddings_batch)} embeddings") + except Exception as e: + print(f"✗ Error inserting embeddings: {e}") + + # If we processed less than the batch size, we're done + if len(tweets_without_embeddings) < batch_size: + break + + print("✓ Embedding generation complete!") + + +if __name__ == "__main__": + main() diff --git a/packages/omni/scripts/parse_tweets.py b/packages/omni/scripts/parse_tweets.py new file mode 100644 index 0000000..48a4abe --- /dev/null +++ b/packages/omni/scripts/parse_tweets.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python3 +""" +Script to parse all tweet JSON files from ../../data/ directory +and create a simple JSON with format: {tweet_id: {author: author_name, content: full_text}} +""" + +import json +from pathlib import Path + + +def extract_tweets_from_file(file_path): + """Extract tweets from a single JSON file.""" + tweets = {} + + try: + with open(file_path, "r", encoding="utf-8") as f: + data = json.load(f) + + # Navigate the Twitter API response structure + if ( + "data" in data + and "home" in data["data"] + and "home_timeline_urt" in data["data"]["home"] + ): + instructions = data["data"]["home"]["home_timeline_urt"].get( + "instructions", [] + ) + + for instruction in instructions: + if instruction.get("type") == "TimelineAddEntries": + entries = instruction.get("entries", []) + + for entry in entries: + # Handle conversation entries + if ( + "content" in entry + and entry["content"].get("entryType") + == "TimelineTimelineModule" + ): + items = entry["content"].get("items", []) + + for item in items: + if "item" in item and "itemContent" in item["item"]: + item_content = item["item"]["itemContent"] + + if item_content.get("itemType") == "TimelineTweet": + tweet_results = item_content.get( + "tweet_results", {} + ) + + if "result" in tweet_results: + result = tweet_results["result"] + + # Extract tweet data + tweet_id = result.get("rest_id") + if not tweet_id: + continue + + # Get author info + author = "Unknown" + if ( + "core" in result + and "user_results" in result["core"] + ): + user_result = result["core"][ + "user_results" + ].get("result", {}) + if "core" in user_result: + author = user_result["core"].get( + "screen_name", "Unknown" + ) + + # Get tweet content + content = "" + if "legacy" in result: + content = result["legacy"].get( + "full_text", "" + ) + elif "note_tweet" in result: + # Handle long tweets + note_results = result["note_tweet"].get( + "note_tweet_results", {} + ) + if "result" in note_results: + content = note_results[ + "result" + ].get("text", "") + + if tweet_id and content: + tweets[tweet_id] = { + "author": author, + "content": content, + } + + # Handle direct tweet entries + elif ( + "content" in entry + and entry["content"].get("entryType") + == "TimelineTimelineItem" + ): + item_content = entry["content"].get("itemContent", {}) + + if item_content.get("itemType") == "TimelineTweet": + tweet_results = item_content.get("tweet_results", {}) + + if "result" in tweet_results: + result = tweet_results["result"] + + # Extract tweet data + tweet_id = result.get("rest_id") + if not tweet_id: + continue + + # Get author info + author = "Unknown" + if ( + "core" in result + and "user_results" in result["core"] + ): + user_result = result["core"][ + "user_results" + ].get("result", {}) + if "core" in user_result: + author = user_result["core"].get( + "screen_name", "Unknown" + ) + + # Get tweet content + content = "" + if "legacy" in result: + content = result["legacy"].get("full_text", "") + elif "note_tweet" in result: + # Handle long tweets + note_results = result["note_tweet"].get( + "note_tweet_results", {} + ) + if "result" in note_results: + content = note_results["result"].get( + "text", "" + ) + + if tweet_id and content: + tweets[tweet_id] = { + "author": author, + "content": content, + } + + except Exception as e: + print(f"Error processing {file_path}: {e}") + + return tweets + + +def main(): + """Main function to process all JSON files and combine tweets.""" + data_dir = Path("../../data/") + + if not data_dir.exists(): + print(f"Data directory {data_dir} does not exist!") + return + + # Find all JSON files in the data directory + json_files = list(data_dir.glob("*.json")) + + if not json_files: + print(f"No JSON files found in {data_dir}") + return + + print(f"Found {len(json_files)} JSON files to process...") + + # Combine all tweets + all_tweets = {} + + for json_file in json_files: + print(f"Processing {json_file.name}...") + tweets = extract_tweets_from_file(json_file) + all_tweets.update(tweets) + print(f" Found {len(tweets)} tweets") + + print(f"\nTotal unique tweets extracted: {len(all_tweets)}") + + # Write the combined tweets to output file + output_file = "combined_tweets.json" + with open(output_file, "w", encoding="utf-8") as f: + json.dump(all_tweets, f, indent=2, ensure_ascii=False) + + print(f"Combined tweets saved to {output_file}") + + +if __name__ == "__main__": + main() From 16e30ebc90b36f1f90f8ce2464a76c468cbdc1f4 Mon Sep 17 00:00:00 2001 From: Koen van der Veen Date: Wed, 17 Sep 2025 17:18:40 +0200 Subject: [PATCH 12/61] some examples --- packages/omni/examples/cohere_test.py | 17 ++ packages/omni/examples/sqlite_benchmark.py | 222 +++++++++++++++++++++ 2 files changed, 239 insertions(+) create mode 100644 packages/omni/examples/cohere_test.py create mode 100644 packages/omni/examples/sqlite_benchmark.py diff --git a/packages/omni/examples/cohere_test.py b/packages/omni/examples/cohere_test.py new file mode 100644 index 0000000..eb36000 --- /dev/null +++ b/packages/omni/examples/cohere_test.py @@ -0,0 +1,17 @@ +import cohere + +co = cohere.ClientV2() + +query = "What is the capital of the United States?" +docs = [ + "Carson City is the capital city of the American state of Nevada. At the 2010 United States Census, Carson City had a population of 55,274.", + "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that are a political division controlled by the United States. Its capital is Saipan.", + "Charlotte Amalie is the capital and largest city of the United States Virgin Islands. It has about 20,000 people. The city is on the island of Saint Thomas.", + "Washington, D.C. (also known as simply Washington or D.C., and officially as the District of Columbia) is the capital of the United States. It is a federal district. The President of the USA and many major national government offices are in the territory. This makes it the political center of the United States of America.", + "Capital punishment has existed in the United States since before the United States was a country. As of 2017, capital punishment is legal in 30 of the 50 states. The federal government (including the United States military) also uses capital punishment.", +] + +results = co.rerank(model="rerank-v3.5", query=query, documents=docs, top_n=5) + + +print(results) diff --git a/packages/omni/examples/sqlite_benchmark.py b/packages/omni/examples/sqlite_benchmark.py new file mode 100644 index 0000000..e90a7d1 --- /dev/null +++ b/packages/omni/examples/sqlite_benchmark.py @@ -0,0 +1,222 @@ +#!/usr/bin/env python3 +""" +SQLite Benchmark Script + +This script creates two SQLite databases: +1. First DB: Contains rows that will return 100k IDs when queried +2. Second DB: Contains detailed data for those IDs + +The script measures the time it takes to query the second database +using the IDs from the first database. +""" + +import os +import random +import sqlite3 +import time + + +def create_first_database(): + """Create the first database with 150k rows to ensure we get 100k results""" + print("Creating first database...") + + # Remove existing file if it exists + if os.path.exists("first.db"): + os.remove("first.db") + + conn = sqlite3.connect("first.db") + cursor = conn.cursor() + + # Create table + cursor.execute(""" + CREATE TABLE source_data ( + id INTEGER PRIMARY KEY, + category TEXT, + value INTEGER, + active BOOLEAN + ) + """) + + # Insert 150k rows with random data + # We'll make sure about 100k of them meet our query criteria + data_batch = [] + for i in range(1, 150001): + # Make roughly 70% of records "active" to ensure we get 100k+ results + active = random.choice([True, True, True, False]) # 75% chance of True + category = random.choice(["A", "B", "C", "D"]) + value = random.randint(1, 1000) + data_batch.append((i, category, value, active)) + + # Insert in batches for better performance + if len(data_batch) == 1000: + cursor.executemany( + "INSERT INTO source_data (id, category, value, active) VALUES (?, ?, ?, ?)", + data_batch, + ) + data_batch = [] + + # Insert any remaining data + if data_batch: + cursor.executemany( + "INSERT INTO source_data (id, category, value, active) VALUES (?, ?, ?, ?)", + data_batch, + ) + + # Create index for better query performance + cursor.execute("CREATE INDEX idx_active ON source_data(active)") + + conn.commit() + conn.close() + print("First database created successfully!") + + +def create_second_database(ids): + """Create the second database with detailed data for the given IDs""" + print(f"Creating second database with data for {len(ids)} IDs...") + + # Remove existing file if it exists + if os.path.exists("second.db"): + os.remove("second.db") + + conn = sqlite3.connect("second.db") + cursor = conn.cursor() + + # Create table with more detailed data + cursor.execute(""" + CREATE TABLE detailed_data ( + id INTEGER PRIMARY KEY, + name TEXT, + email TEXT, + department TEXT, + salary INTEGER, + hire_date TEXT, + description TEXT + ) + """) + + # Generate detailed data for each ID + departments = ["Engineering", "Sales", "Marketing", "HR", "Finance"] + data_batch = [] + + for i, record_id in enumerate(ids): + name = f"User_{record_id}" + email = f"user_{record_id}@company.com" + department = random.choice(departments) + salary = random.randint(50000, 150000) + hire_date = f"2020-{random.randint(1, 12):02d}-{random.randint(1, 28):02d}" + description = f"Description for user {record_id} in {department} department" + + data_batch.append( + (record_id, name, email, department, salary, hire_date, description) + ) + + # Insert in batches + if len(data_batch) == 1000: + cursor.executemany( + "INSERT INTO detailed_data VALUES (?, ?, ?, ?, ?, ?, ?)", data_batch + ) + data_batch = [] + if i % 10000 == 0: + print(f" Inserted {i} records...") + + # Insert any remaining data + if data_batch: + cursor.executemany( + "INSERT INTO detailed_data VALUES (?, ?, ?, ?, ?, ?, ?)", data_batch + ) + + # Create index on ID for fast lookups + cursor.execute("CREATE INDEX idx_id ON detailed_data(id)") + + conn.commit() + conn.close() + print("Second database created successfully!") + + +def get_ids_from_first_db(): + """Query the first database to get 100k IDs""" + print("Querying first database for IDs...") + + conn = sqlite3.connect("first.db") + cursor = conn.cursor() + + # Query to get active records (should return around 100k IDs) + cursor.execute("SELECT id FROM source_data WHERE active = 1 LIMIT 1000") + results = cursor.fetchall() + ids = [row[0] for row in results] + + conn.close() + print(f"Retrieved {len(ids)} IDs from first database") + return ids + + +def benchmark_second_query(ids): + """Benchmark the query on the second database using the IDs""" + print(f"Benchmarking query on second database with {len(ids)} IDs...") + + conn = sqlite3.connect("second.db") + cursor = conn.cursor() + + # Create a temporary table + cursor.execute("CREATE TEMP TABLE temp_ids(id INTEGER)") + cursor.executemany("INSERT INTO temp_ids(id) VALUES (?)", [(i,) for i in ids]) + + # Perform a join + start_time = time.time() + cursor.execute(""" + SELECT d.* + FROM detailed_data d + JOIN temp_ids t ON d.id = t.id + """) + results = cursor.fetchall() + end_time = time.time() + + print("Time:", end_time - start_time) + + conn.close() + + query_time = end_time - start_time + print(f"Query completed in {query_time:.4f} seconds") + print(f"Retrieved {len(results)} rows") + + return query_time, len(results) + + +def main(): + """Main benchmark function""" + print("=" * 60) + print("SQLite Benchmark Script") + print("=" * 60) + + # Step 1: Create and populate first database + create_first_database() + + # Step 2: Get IDs from first database + ids = get_ids_from_first_db() + + if len(ids) == 0: + print("ERROR: No IDs retrieved from first database!") + return + + # Step 3: Create second database with data for those IDs + create_second_database(ids) + + # Step 4: Benchmark the second query + print("\n" + "=" * 60) + print("BENCHMARK RESULTS") + print("=" * 60) + + query_time, result_count = benchmark_second_query(ids) + + print(f"Query execution time: {query_time:.4f} seconds") + print(f"Rows returned: {result_count:,}") + print(f"Average time per row: {(query_time / result_count * 1000):.6f} ms") + + # Clean up + print("\nDatabase files created:") + print(f"- first.db ({os.path.getsize('first.db') / 1024 / 1024:.1f} MB)") + print(f"- second.db ({os.path.getsize('second.db') / 1024 / 1024:.1f} MB)") + + +if __name__ == "__main__": + main() From 93fa9ea4068d58cc2a4633b30992652b3b6501a8 Mon Sep 17 00:00:00 2001 From: Koen van der Veen Date: Thu, 18 Sep 2025 18:32:07 +0200 Subject: [PATCH 13/61] connect new store --- packages/omni/.claude/settings.local.json | 13 + .../{scripts => examples}/embed_tweets.py | 0 .../omni/frontend/components/TweetItem.vue | 132 ++++++- packages/omni/omni/app.py | 20 +- packages/omni/omni/db.py | 19 +- packages/omni/omni/models.py | 2 + packages/omni/omni/twitter.py | 214 ++++-------- packages/omni/omni/vectorstore_models.py | 326 ++++++++++++++++++ packages/omni/omni/vectorstore_queries.py | 258 ++++++++++++++ packages/omni/pytest.ini | 7 + packages/omni/scripts/generate_embeddings.py | 168 ++------- packages/omni/scripts/parse_tweets.py | 220 ++++-------- packages/omni/tests/__init__.py | 1 + packages/omni/tests/test_twitter_items.py | 109 ++++++ packages/toolbox_store/pyproject.toml | 2 +- pyproject.toml | 4 +- 16 files changed, 1030 insertions(+), 465 deletions(-) rename packages/omni/{scripts => examples}/embed_tweets.py (100%) create mode 100644 packages/omni/omni/vectorstore_models.py create mode 100644 packages/omni/omni/vectorstore_queries.py create mode 100644 packages/omni/pytest.ini create mode 100644 packages/omni/tests/__init__.py create mode 100644 packages/omni/tests/test_twitter_items.py diff --git a/packages/omni/.claude/settings.local.json b/packages/omni/.claude/settings.local.json index 08417ec..05b1517 100644 --- a/packages/omni/.claude/settings.local.json +++ b/packages/omni/.claude/settings.local.json @@ -11,6 +11,19 @@ "Bash(sqlite3:*)", "Read(/Users/koen/workspace/toolbox/data/**)", "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/tests/**)", + "Read(/Users/koen/workspace/toolbox/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/src/toolbox_store/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/src/toolbox_store/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/src/toolbox_store/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/src/toolbox_store/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/**)", + "Read(/Users/koen/workspace/toolbox/data/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/tests/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/tests/**)", + "Read(/Users/koen/workspace/toolbox/packages/toolbox_store/tests/**)", "Read(/Users/koen/workspace/toolbox/data/**)" ], "deny": [], diff --git a/packages/omni/scripts/embed_tweets.py b/packages/omni/examples/embed_tweets.py similarity index 100% rename from packages/omni/scripts/embed_tweets.py rename to packages/omni/examples/embed_tweets.py diff --git a/packages/omni/frontend/components/TweetItem.vue b/packages/omni/frontend/components/TweetItem.vue index 5a55d15..add0806 100644 --- a/packages/omni/frontend/components/TweetItem.vue +++ b/packages/omni/frontend/components/TweetItem.vue @@ -1,20 +1,35 @@