V·G·E·R

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    KNOWLEDGE  ASSIMILATION  UNIT  001

"It has gathered so much knowledge... it wants to share it." — Star Trek: The Motion Picture

V'Ger is a command-line AI agent that ingests online conference videos — KubeCon, CloudNativeCon, and beyond — and produces structured intelligence reports: summaries, technology radars, and learning recommendations.

Named after the legendary probe, V'Ger travels the information stream, assimilates cloud-native knowledge, and returns it to you in a form you can act on.

MISSION PARAMETERS

Fetch video metadata from the YouTube Data API
Browse channel video catalogues and playlists, search across complete upload history
Submit individual videos or entire playlists to Gemini 2.5 Flash for multimodal analysis
Extract a structured summary, speaker/presenter list, and a technology radar with CNCF project context
Cache all analysis locally so follow-up questions are instant and free
Answer follow-up questions from cached context — or re-submit the video for deep analysis
Generate playlist digests: technology radar across all talks, AI-synthesised learning paths
Track technology signals as you discover them — capture, AI-enrich, and synthesise your backlog into actionable digests; optionally backed by a git repo for version-controlled signal history
Persistent user context — tell V'Ger about your stack once; all Gemini answers are automatically tailored to your environment

INSTALLATION

Homebrew (macOS and Linux) — Recommended

brew install costap/tap/vger

GitHub Releases — Pre-built binaries

Download the latest binary for your platform from the Releases page:

Platform	File
macOS (Apple Silicon)	`vger_darwin_arm64.tar.gz`
macOS (Intel)	`vger_darwin_amd64.tar.gz`
Linux (x86-64)	`vger_linux_amd64.tar.gz`
Linux (ARM64)	`vger_linux_arm64.tar.gz`
Windows (x86-64)	`vger_windows_amd64.zip`

# Example: Linux amd64
curl -L https://github.com/costap/vger/releases/latest/download/vger_linux_amd64.tar.gz \
  | tar xz
sudo mv vger /usr/local/bin/

Go (requires Go toolchain)

go install github.com/costap/vger/cmd/vger@latest

Build from source

git clone https://github.com/costap/vger.git
cd vger
go build -o vger ./cmd/vger

TRANSMISSION CODES (API KEYS)

V'Ger requires two API keys to operate. Set them as environment variables, pass them as flags, or place them in a .env file in your working directory.

YouTube Data API v3

Go to Google Cloud Console
Create or select a project
Enable YouTube Data API v3 under APIs & Services
Create an API Key credential

Gemini API

Go to Google AI Studio
Click Get API key → Create API key

Configure

# Option A — environment variables
export YOUTUBE_API_KEY=your_youtube_key
export GEMINI_API_KEY=your_gemini_key

# Option B — .env file (auto-loaded from working directory)
cat > .env <<EOF
YOUTUBE_API_KEY=your_youtube_key
GEMINI_API_KEY=your_gemini_key
EOF

# Option C — flags (per command)
vger --youtube-key YOUR_KEY --gemini-key YOUR_KEY scan <url>

COMMAND REFERENCE

`vger scan` — Analyse a video or playlist

Submits a conference video to Gemini for full multimodal analysis. Results are cached to ~/.vger/cache/ for instant follow-up queries.

# Single video
vger scan <youtube-url>

# Entire playlist (parallel, resumable)
vger scan --playlist <playlist-id-or-url>

Examples:

# Scan a single talk
vger scan https://www.youtube.com/watch?v=H06qrNmGqyE

# Scan all videos in a playlist (3 parallel by default)
vger scan --playlist "https://www.youtube.com/playlist?list=PLj6h78yzYM2P..."

# Faster on a paid Gemini tier
vger scan --playlist PLj6h78yzYM2P... --concurrency 5

# Re-scan everything, ignoring the cache
vger scan --playlist PLj6h78yzYM2P... --refresh

Output includes:

Video title, channel, duration, and stardate
Speakers / presenters detected from the video (name and affiliation)
3–5 sentence technical summary
Technology radar: each identified project with description, CNCF stage, why it matters, and where to learn more

Playlist scans are resumable — already-cached videos are skipped automatically. Re-run after an interruption and V'Ger picks up where it left off.

Flags:

Flag	Default	Description
`--playlist`	—	Playlist ID or URL — scan all videos in the playlist
`--concurrency`	`1`	Number of parallel analyses in playlist mode
`--refresh`	`false`	Force re-analysis even if a cached result exists
`--model`	`gemini-2.5-flash`	Override the Gemini model

Note on concurrency: the default of 1 avoids Gemini token quota errors on Tier 1 (1M tokens/min). Raise it on a paid tier with higher quota.

`vger list` — Browse a channel's videos or playlists

Lists videos or playlists from a YouTube channel ordered by publish date, newest first. Results are retrieved by walking the channel's complete upload history — not limited by YouTube's search index, so all videos are found regardless of age.

Cached videos are marked with ★ and show the technology tags extracted by Gemini. Use --cached to search everything you have ever scanned without any YouTube API call.

# List videos from a channel
vger list --channel <channel-id-or-handle>

# List a channel's playlists
vger list --channel <channel-id-or-handle> --playlists

# List videos inside a specific playlist
vger list --playlist <playlist-id-or-url>

# Browse all locally cached videos (no API call)
vger list --cached

Examples:

# Browse the CNCF channel
vger list --channel @cncf

# Find all ArgoCD talks (scans full upload history)
vger list --channel @cncf --search argocon

# List all playlists on the CNCF channel
vger list --channel @cncf --playlists

# Filter playlists by name
vger list --channel @cncf --playlists --search kubecon

# List all videos in a specific playlist
vger list --playlist "https://www.youtube.com/playlist?list=PLj6h78yzYM2P..."

# Filter playlist videos by keyword
vger list --playlist PLj6h78yzYM2P... --search "service mesh"

# Filter by Gemini-extracted technology tag (only cached videos match)
vger list --channel @cncf --tags ebpf

# Combine tag and keyword filters
vger list --channel @cncf --tags kubernetes --search 2024

# Search all cached videos for a technology — no channel needed
vger list --cached --tags ebpf

# Search all cached KubeCon talks (matches playlist title)
vger list --cached --tags kubecon

# eBPF talks from KubeCon specifically
vger list --cached --tags kubecon --search ebpf

Flags:

Flag	Default	Description
`--channel`	—	Channel ID (`UCxx...`) or handle (`@cncf`)
`--playlist`	—	Playlist ID or URL — list videos from a specific playlist
`--playlists`	`false`	List playlists instead of videos
`--search`	—	Filter by title/description keyword
`--tags`	—	Filter by Gemini technology tag or playlist name (substring match)
`--cached`	`false`	Browse all locally cached videos without any YouTube API call
`--max`	`50`	Maximum number of results

Tag filtering matches against technology names extracted by Gemini (e.g. --tags cilium), playlist titles set when scanning (e.g. --tags kubecon, --tags "eu 2025"), and speaker names detected from the video (e.g. --tags "jessie frazelle"). Only cached videos can match tag filters — uncached videos show a dim · indicator.

`vger ask` — Follow-up questions

Ask a question about a previously scanned video without re-uploading it.

vger ask <youtube-url> "<question>"

V'Ger stores detailed notes during every scan — covering everything mentioned in the video, not just the top technologies. Most questions can be answered from this cache instantly and at no extra cost.

For questions that require direct access to the video (exact quotes, timestamps, demo details not captured in the notes), use --deep:

vger ask --deep <youtube-url> "<question>"

Analytical lenses

Use --lens to apply a built-in analytical preset instead of typing the same verbose prompt every time. The question argument becomes optional when a lens is set — the lens provides a default question. You can still add a custom question to focus the lens on something specific.

# Use a lens (no question needed)
vger ask --lens architect https://www.youtube.com/watch?v=H06qrNmGqyE

# Lens + custom focus
vger ask --lens architect https://www.youtube.com/watch?v=H06qrNmGqyE \
  "focus specifically on the database connection pooling approach"

# Lens + deep video re-read
vger ask --deep --lens radar https://www.youtube.com/watch?v=H06qrNmGqyE

Available lenses:

Lens	What it produces
`architect`	Solutions architect analysis: approach, decisions, trade-offs, novelty
`engineer`	Hands-on deep-dive: implementation patterns, config details, getting started, gotchas
`radar`	Tech radar recommendations: Adopt / Trial / Assess / Hold per technology
`brief`	3–5 bullet team briefing: problem, approach, takeaways, action

Examples:

# Fast — answered from cached notes
vger ask https://www.youtube.com/watch?v=H06qrNmGqyE \
  "Who were the speakers and what companies do they work for?"

vger ask https://www.youtube.com/watch?v=H06qrNmGqyE \
  "Which of the technologies mentioned are production-ready today?"

# Analytical lenses — no question needed
vger ask --lens architect https://www.youtube.com/watch?v=H06qrNmGqyE
vger ask --lens radar     https://www.youtube.com/watch?v=H06qrNmGqyE
vger ask --lens brief     https://www.youtube.com/watch?v=H06qrNmGqyE

# Deep — Gemini re-reads the full video
vger ask --deep https://www.youtube.com/watch?v=H06qrNmGqyE \
  "What exact kubectl command did they run in the live demo?"

Flags:

Flag	Description
`--deep`	Re-submit the video to Gemini for video-grounded answers
`--lens <name>`	Apply a built-in analytical preset (`architect`, `engineer`, `radar`, `brief`)

Note: vger scan must be run at least once before vger ask can be used. Use vger scan --refresh to refresh the cache and pick up the deep notes format if you scanned before the notes feature was added.

`vger research` — Topic research brief

Search all available sources about a technology topic and receive a structured synthesis with a CNCF landscape map, evidence from cached videos, tracked signals, investigation paths, and a bottom-line verdict.

vger research <topic> [flags]

Flags:

Flag	Default	Description
`--discover`	`false`	Search YouTube for unscanned relevant talks
`--channel`	`@cncf`	YouTube channel to search when `--discover` is used
`--lens`	—	Apply analytical lens (`architect`, `engineer`, `radar`, `brief`)
`--max-videos`	`10`	Max cached videos to include in context
`--output`	—	Write full report to a Markdown file

Sources searched (always):

Local analysis cache — scored full-text search across summaries, notes, technology names
CNCF landscape — related projects by name and category
Track signals — matching signals from your backlog

Source searched with --discover:

YouTube channel — unscanned talks on the topic (deduplicated against cache)

Output sections:

Brief — 2–3 sentence what-and-why
CNCF Landscape — related projects with stage and relevance
Evidence from Cache — cached talks that mention the topic
Tracked Signals — signals from your backlog matching the topic
Investigation Paths — 2–4 distinct routes to explore further
Competing Approaches — alternative technologies
Verdict — bottom-line recommendation
Undiscovered Talks — unscanned talks found via --discover

Examples:

# Basic research from local knowledge base
vger research "eBPF"

# Include YouTube discovery for unscanned talks
vger research "multi-cluster networking" --discover

# Apply an architectural lens to the synthesis
vger research "WASM in Kubernetes" --lens architect

# Save full report as Markdown
vger research "service mesh" --output service-mesh-brief.md

# Discover from a specific channel
vger research "eBPF" --discover --channel @isovalent

Tip: Run vger scan --playlist <id> first on relevant playlists to build up your local knowledge base. The more you've scanned, the richer the evidence section.

`vger digest` — Playlist overview and learning path

After scanning a playlist, produce a cross-talk overview without re-running any analysis.

Layer 1 (always, zero API cost): reads all cached analyses and renders:

Compact talk table with duration and top technologies per talk
Technology radar — bar chart of which technologies appeared across how many talks

Layer 2 (--ai, one Gemini text call): AI synthesis across all talks:

Overarching theme of the playlist
Recommended learning path (technologies in study order)
3–5 priority talks to watch first with reasons
Key insights narrative

# Local overview — instant, no API call
vger digest --playlist <playlist-id-or-url>

# + AI synthesis
vger digest --playlist <playlist-id-or-url> --ai

# Export a shareable Markdown report
vger digest --playlist <playlist-id-or-url> --ai --output kubecon2024.md

Flags:

Flag	Description
`--playlist`	Playlist ID or URL (required)
`--ai`	Use Gemini to synthesise a cross-playlist learning path
`--output`	Write a Markdown report to a file

vger scan --playlist must be run first to populate the cache.

`vger track` — Technology signal tracking

Capture, manage, and review technologies and ideas you encounter day-to-day. Signals accumulate in a personal backlog; Gemini enriches them on demand and synthesises your backlog into an actionable digest.

Storage adapts automatically:

Condition	Store
`TECHDR_DIR` env set, or `~/code/github.com/costap/tech-signals` exists	Markdown files + git auto-commit (compatible with tech-signals)
Default	JSON files at `~/.vger/signals/`

`track add` — Capture a new signal

# Interactive — prompts for title, URL, source, category, note
vger track add

# AI-assisted — describe it in natural language; Gemini extracts the fields
vger track add --ai "saw a KubeCon talk on HolmesGPT for AI-driven k8s remediation https://..."

# AI-assisted + enrich in one step — no second command needed
vger track add --ai "..." --enrich

# AI-assisted + open $EDITOR for review (default when TECHDR_DIR is set)
vger track add --ai "..." --edit

Flag	Description
`--ai <text>`	Free-text description; Gemini extracts all signal fields
`--enrich`	AI-enrich the signal immediately after capture
`--edit`	Open `$VISUAL`/`$EDITOR` after capture for manual review

`track list` — Browse your backlog

vger track list
vger track list --status spotted
vger track list --status evaluating --category security

Flag	Description
`--status`	Filter by status: `spotted` \| `evaluating` \| `adopted` \| `rejected` \| `parked`
`--category`	Filter by category: `platform` \| `networking` \| `security` \| `observability` \| `developer-experience` \| `ai-ml` \| `data` \| `other`

`track show` — View a signal in detail

vger track show 0001

Displays all fields including any AI enrichment (what it is, maturity, alternatives, stack fit, next steps).

`track enrich` — AI-enrich a signal

vger track enrich 0001

Calls Gemini to fill in: What it is, Maturity & Risk, Alternatives, How it could fit your stack, Suggested next steps. Enrichment is stored alongside the signal and shown by track show.

`track status` — Update investigation status

vger track status 0001 evaluating
vger track status 0001 adopted

Valid progression: spotted → evaluating → adopted | rejected | parked

When using the Markdown store, produces a status: 0001 old → new git commit.

`track link` — Link a signal to a conference talk scan

vger track link 0001 --video https://www.youtube.com/watch?v=abc123

Records the video ID on the signal so you can cross-reference vger scan results.

Flag	Description
`--video <url>`	YouTube URL or video ID to associate with this signal

`track digest` — AI synthesis of your backlog

Synthesises your signal backlog into a structured report: focus areas, technology clusters, and a pulse reading on your radar.

# Full backlog digest
vger track digest

# Digest only spotted signals, enrich any that haven't been enriched yet
vger track digest --status spotted --enrich

# Export a Markdown report
vger track digest --output ~/tech-review-2026-04.md

# Filter by category
vger track digest --category security

Flag	Description
`--status`	Only digest signals with this status
`--category`	Only digest signals in this category
`--enrich`	AI-enrich unenriched signals before synthesising
`--output`	Write Markdown report to this file path

Requires GEMINI_API_KEY. Uses Genkit for typed structured output.

`vger config` — Persistent user context

Tell V'Ger about your environment once. The context is stored in ~/.vger/config.yaml and automatically injected into every Gemini prompt — ask, research, digest, track enrich, and track digest. This makes all recommendations and answers tailored to your actual stack without repeating yourself.

# Set your context (update anytime)
vger config set user_context "We run AWS EKS 1.29 with Cilium CNI, Istio service mesh,
Go microservices, GitOps with Flux, and Prometheus/Grafana for observability."

# View current config
vger config show

# Clear the context
vger config clear user_context

Supported fields:

Field	Description
`user_context`	Free-text description of your team's stack, cloud provider, scale, goals, and constraints

Tip: The more specific you are, the better. Include your cloud provider, Kubernetes version, networking stack, observability tools, languages, and any hard constraints (e.g. "no vendor lock-in", "FIPS compliance required"). V'Ger uses this to focus stack_fit assessments and next_steps on what's actually relevant to you.

TYPICAL WORKFLOW

Single video

# 0. Tell V'Ger about your environment (once — persists across sessions)
vger config set user_context "We run AWS EKS 1.29, Cilium CNI, Go microservices."

# 1. Find talks you want to analyse
vger list --channel @cncf --search "kubecon 2024" --max 20

# 2. Scan a talk — Gemini analyses the full video
vger scan https://www.youtube.com/watch?v=TALK_ID

# 3. Ask follow-up questions from the cache (instant, context-aware)
vger ask https://www.youtube.com/watch?v=TALK_ID \
  "Which technologies are worth prioritising for my platform team?"

# 4. Use an analytical lens — no question needed
vger ask --lens architect https://www.youtube.com/watch?v=TALK_ID
vger ask --lens radar     https://www.youtube.com/watch?v=TALK_ID

# 5. Dig deeper into something specific (re-reads video)
vger ask --deep https://www.youtube.com/watch?v=TALK_ID \
  "What did they say about multi-cluster networking?"

Full playlist

# 1. Find the playlist you want
vger list --channel @cncf --playlists --search kubecon

# 2. Scan all videos in the playlist (resumable, cached per video)
vger scan --playlist PLj6h78yzYM2P... --concurrency 3

# 3. Get an instant overview — technology radar across all talks
vger digest --playlist PLj6h78yzYM2P...

# 4. Add AI synthesis — learning path and priority talks
vger digest --playlist PLj6h78yzYM2P... --ai

# 5. Export a shareable Markdown report
vger digest --playlist PLj6h78yzYM2P... --ai --output kubecon2024.md

# 6. Drill into a specific talk
vger ask https://www.youtube.com/watch?v=TALK_ID \
  "What was shown in the live demo?"

Cross-playlist / cross-event search

Scan multiple editions of a conference and search across all of them at once. V'Ger records the playlist title as an event tag on every video it scans.

# Scan multiple KubeCon editions
vger scan --playlist PLj6h78yzYM2N...  # KubeCon EU 2025
vger scan --playlist PLj6h78yzYM2P...  # KubeCon NA 2024

# Browse everything you have scanned (no YouTube API call)
vger list --cached

# All KubeCon talks (matches playlist title)
vger list --cached --tags kubecon

# eBPF talks from any scanned playlist
vger list --cached --tags ebpf

# eBPF talks specifically from KubeCon
vger list --cached --tags kubecon --search ebpf

# Find talks on Cilium across any event
vger list --cached --tags cilium

Tech signal tracking + research

Connect your scanned talks with your signal backlog and research topics across everything V'Ger knows.

# After scanning a playlist, research a specific topic
vger research "eBPF"

# Discover unscanned talks too
vger research "eBPF" --discover

# Apply a lens for a focused perspective
vger research "service mesh" --lens architect

# Save as Markdown for sharing or archiving
vger research "WASM in Kubernetes" --output wasm-brief.md

Tech signal tracking

Capture technologies as you encounter them — blog posts, talks, colleague recommendations. Enrich them later and synthesise your backlog when it's time to prioritise.

# 1. Quick capture from a tweet or article (AI extracts the fields + enrich in one step)
vger track add --ai "read a blog post about HolmesGPT for AI-driven k8s remediation https://..." --enrich

# 2. Browse your backlog
vger track list --status spotted

# 3. Review a signal in detail
vger track show 0001

# 4. AI-enrich it (if not done at capture time)
vger track enrich 0001

# 5. Link it to a conference talk you scanned
vger track link 0001 --video https://www.youtube.com/watch?v=abc123

# 6. Update investigation status as you make progress
vger track status 0001 evaluating

# 7. Synthesise your backlog into an actionable digest
vger track digest --status spotted --enrich --output ~/tech-review.md

With tech-signals git repo (set TECHDR_DIR or place repo at ~/code/github.com/costap/tech-signals): every add, enrich, status change, and link is automatically git-committed with a structured message — giving you a full audit trail of your technology evaluation history.

export TECHDR_DIR=~/code/github.com/costap/tech-signals
vger track add --ai "eBPF replacing sidecars in Cilium 1.15..."
# → creates signals/2026/0002-2026-04-04-ebpf-sidecarless.md
# → git commit: "signal: 0002 eBPF replacing sidecars in Cilium 1.15"

CACHE

Analysis results are stored in ~/.vger/cache/<video-id>.json.

Each cache entry includes:

Video metadata (title, channel, duration, publish date)
Structured report (summary, technologies)
Detailed notes (exhaustive narrative for follow-up Q&A)

Cache entries do not expire — conference talks don't change. Use vger scan --refresh <url> to force a re-analysis.

GLOBAL FLAGS

These flags apply to all commands:

Flag	Env var	Default	Description
`--gemini-key`	`GEMINI_API_KEY`	—	Gemini API key
`--youtube-key`	`YOUTUBE_API_KEY`	—	YouTube Data API key
`--model`	—	`gemini-2.5-flash`	Gemini model to use

SHELL COMPLETION

V'GER supports tab-completion for all commands and flags in Fish, Bash, Zsh, and PowerShell.

Fish (recommended)

vger completion fish > ~/.config/fish/completions/vger.fish

Fish loads completions from that directory automatically — no config.fish edit needed. Completions are active immediately (or after source ~/.config/fish/completions/vger.fish).

Bash

vger completion bash > /etc/bash_completion.d/vger
# or for the current session only:
source <(vger completion bash)

Zsh

vger completion zsh > "${fpath[1]}/_vger"

PowerShell

vger completion powershell | Out-String | Invoke-Expression

What gets completed:

All subcommands (scan, ask, digest, completion, …)
All flag names for each command
--lens flag values: architect, engineer, radar, brief (with descriptions)

ARCHITECTURE

V'Ger uses an onion (ports-and-adapters) architecture:

cmd/vger/          — binary entry point
internal/
  domain/          — model structs and port interfaces (zero external deps)
  agent/           — analysis pipeline orchestration
  adapters/
    youtube/       — YouTube Data API v3 client
    gemini/        — Gemini multimodal analyser, Q&A, and signal AI (enrich/add)
    genkit/        — Genkit flows for typed structured output (track digest)
    cache/         — local JSON file cache (~/.vger/cache/)
    config/        — user config (~/.vger/config.yaml): persistent user context
    signals/       — signal store adapters: JSONStore + MarkdownStore (tech-signals)
  cli/             — Cobra commands with LCARS terminal styling
docs/              — architecture options, dataflow diagrams, technical spec

Video analysis is performed via Gemini's native YouTube URL passthrough — no video is downloaded. Gemini processes audio, slides, on-screen code, and speaker names directly from the stream.

RELEASING A NEW VERSION

Releases are fully automated via GoReleaser and GitHub Actions.

git tag v0.1.0
git push --tags

This triggers .github/workflows/release.yml, which:

Cross-compiles binaries for all 6 platforms
Creates a GitHub Release with all assets and checksums
Pushes an updated Homebrew formula to costap/homebrew-tap

One-time setup for maintainers

Create the Homebrew tap repo:

gh repo create costap/homebrew-tap --public --description "Homebrew tap for costap tools"

Add the required GitHub secret:

Go to github.com/costap/vger → Settings → Secrets → Actions and add:

Secret	Value
`HOMEBREW_TAP_GITHUB_TOKEN`	A GitHub PAT with `repo` scope on `costap/homebrew-tap`

The built-in GITHUB_TOKEN is used automatically for creating the release itself — no extra setup needed.

STARDATE LOG

Stardate	Event
1262.4	V'Ger bootstrap — project initialised
1262.5	YouTube Data API and Gemini multimodal integration
1262.6	Channel listing, analysis caching, follow-up Q&A
1262.7	`vger track` — technology signal tracking with AI enrichment, Genkit digest, and Markdown/git store integration
1262.8	Speaker/presenter detection, `vger config` persistent user context, `track add --enrich` one-step capture

"V'Ger has been damaged... but it has not been destroyed."

Name		Name	Last commit message	Last commit date
Latest commit History 59 Commits
.github/workflows		.github/workflows
cmd/vger		cmd/vger
digests		digests
docs		docs
internal		internal
.gitignore		.gitignore
.goreleaser.yaml		.goreleaser.yaml
LICENSE		LICENSE
README.md		README.md
go.mod		go.mod
go.sum		go.sum

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V·G·E·R

MISSION PARAMETERS

INSTALLATION

Homebrew (macOS and Linux) — Recommended

GitHub Releases — Pre-built binaries

Go (requires Go toolchain)

Build from source

TRANSMISSION CODES (API KEYS)

YouTube Data API v3

Gemini API

Configure

COMMAND REFERENCE

vger scan — Analyse a video or playlist

vger list — Browse a channel's videos or playlists

vger ask — Follow-up questions

Analytical lenses

vger research — Topic research brief

vger digest — Playlist overview and learning path

vger track — Technology signal tracking

track add — Capture a new signal

track list — Browse your backlog

track show — View a signal in detail

track enrich — AI-enrich a signal

track status — Update investigation status

track link — Link a signal to a conference talk scan

track digest — AI synthesis of your backlog

vger config — Persistent user context

TYPICAL WORKFLOW

Single video

Full playlist

Cross-playlist / cross-event search

Tech signal tracking + research

Tech signal tracking

CACHE

GLOBAL FLAGS

SHELL COMPLETION

ARCHITECTURE

RELEASING A NEW VERSION

One-time setup for maintainers

STARDATE LOG

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Contributors

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Languages

`vger scan` — Analyse a video or playlist

`vger list` — Browse a channel's videos or playlists

`vger ask` — Follow-up questions

`vger research` — Topic research brief

`vger digest` — Playlist overview and learning path

`vger track` — Technology signal tracking

`track add` — Capture a new signal

`track list` — Browse your backlog

`track show` — View a signal in detail

`track enrich` — AI-enrich a signal

`track status` — Update investigation status

`track link` — Link a signal to a conference talk scan

`track digest` — AI synthesis of your backlog

`vger config` — Persistent user context

Packages