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remove program specific branding for the new evergreen JavaScript AI Buildathon
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.github/ISSUE_TEMPLATE/project-submission.md

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---
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name: Quest or content feedback
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about: Report a problem or suggest an improvement to a Quest or the learning site
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title: "[Quest feedback] "
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labels: documentation
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assignees: ''
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---
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## Quest or page
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Which Quest, page, or section is this feedback about?
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## What happened?
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Describe the problem, unclear instruction, or suggested improvement.
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## What did you expect?
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Explain what you expected to find or what would make the content more useful.
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## Additional context
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Include relevant links, screenshots, environment details, or error messages.
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Do not include credentials, access tokens, or other secrets.

.github/workflows/deploy.yml

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- uses: actions/setup-python@v6
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with:
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python-version: "3.x"
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python-version: "3.12"
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cache: pip
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- run: pip install -r requirements.txt

01-Local-AI-Development/README.md

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![Foundry Local](./assets/foundry-local-logo.png)
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**Click the image below to watch the recording.**
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**Optional companion recording:** click the image below for a guided walkthrough.
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[![Reactor Livestream](./assets/poster-quest1.png)](https://www.youtube.com/live/GHH50rDlLn0?si=-i3hPYq1o6H271_z)
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[![Watch the Quest 1 companion recording](./assets/quest-1-companion-recording.png)](https://www.youtube.com/live/GHH50rDlLn0?si=-i3hPYq1o6H271_z)
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## Overview
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- **Cost Efficiency**: No cloud compute costs incurred.
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- **Offline Access**: AI capabilities even without internet connectivity.
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> [!NOTE]
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>
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> **Hackathon Award Category: Offline-Ready AI Award**
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>
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> This quest mapped to our Offline-Ready AI Award category, which recognized the best performing AI solutions with standout offline capabilities (local inference).
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>
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> Winning solutions featured apps that:
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>
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> - Processed sensitive data entirely on-device.
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> - Used Foundry Local for reasoning and cloud storage services for optional sync or analytics.
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>
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> Winners demonstrated:
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>
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> - **Privacy** (no sensitive data leaving the device).
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> - **Latency** optimization using local inference.
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>
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> Checkout some Foundry Local + JavaScript projects: -
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>
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> 1. [Interview Doctor](https://github.com/leestott/interview-doctor-js) - AI-powered interview preparation assistant that runs **100% offline** on your local machine
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> 1. [Gas Field Support Agent](https://github.com/leestott/local-rag) - A fully offline, on-device Retrieval-Augmented Generation (RAG) support agent for gas field inspection and maintenance engineers.
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Here are two Foundry Local and JavaScript projects you can explore:
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1. [Interview Doctor](https://github.com/leestott/interview-doctor-js) - An AI-powered interview preparation assistant that runs entirely on your local machine.
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2. [Gas Field Support Agent](https://github.com/leestott/local-rag) - An offline, on-device RAG support agent for gas-field inspection and maintenance engineers.
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First step, **installation instructions** for your OS:
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| **Qwen models** | Multilingual code generation |
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| **GPT models** | Advanced capabilities and broad compatibility |
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> [!NOTE]
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> For the Offline-Ready AI Award, using GitHub Copilot with Foundry Local demonstrates a powerful offline development workflow. Highlight this capability in your submission!
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### 3. Software Development Kits (SDKs)
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Foundry Local provides SDKs to programmatically send requests to the local management service. Since the endpoint is dynamically allocated each time the service starts, the SDK handles endpoint discovery and management for you (control plane).
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#### Step 1: Initialize New Project
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Create a parent folder for your Build-a-thon projects and navigate into it:
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Create a parent folder for your JavaScript AI projects and navigate into it:
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```bash
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mkdir buildathon
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cd buildathon
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mkdir javascript-ai-quests
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cd javascript-ai-quests
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```
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Create a new folder for this quest, navigate into it and initialize a Node.js project:
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## Stay connected
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Have a question, project, or insight to share? Join the [Build-a-thon Discord channel](https://aka.ms/JSAIonDiscord)
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Have a question, project, or insight to share? Join the [JavaScript AI community on Discord](https://aka.ms/JSAIonDiscord).
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## AI Note
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02-E2E-Model-Development-on-Foundry/README.md

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![Foundry Local](./assets/foundry-logo.png)
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**Click the image below to watch the recording.**
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**Optional companion recording:** click the image below for a guided walkthrough.
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[![Reactor Livestream](./assets/poster-quest2.png)](https://www.youtube.com/live/8ded7UAdPTA?si=UwncP4b4tIs1dwLS)
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[![Watch the Quest 2 companion recording](./assets/quest-2-companion-recording.png)](https://www.youtube.com/live/8ded7UAdPTA?si=UwncP4b4tIs1dwLS)
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# Building AI Agents E2E On Microsoft Foundry
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> The **Foundry SDK is under active development**.
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> If you encounter any blockers, unclear steps, or have suggestions to improve the developer experience, please open an issue using the [provided issue template](https://github.com/microsoft/microsoft-foundry-e2e-js/issues/new/choose) - *Bug report/ Build-a-thon Feedback*. Make sure to **apply the label** that matches the step you were on when the issue occurred.
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>
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> ![Buildathon issue with labels](./assets/buildathon-feedback.png)
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> If you encounter a blocker or unclear instruction in the linked sample, open an issue in the [Microsoft Foundry end-to-end JavaScript repository](https://github.com/microsoft/microsoft-foundry-e2e-js/issues/new/choose). Include the task you were completing and enough environment detail to reproduce the problem.
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The repoistory is setup with a `devcontainer.json` that installs all the required depdencencies and gives you access to a free tier of GitHub Copilot.
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**Explore the rest at your own pace later**. Create a fork of the repo, then launch a new Codespaces instance on that fork and work through the steps in order. Try customizing the data to suit a different scenario. Or change the code to try a different evaluator or attack strategy for red-teaming. Use your fork as a sandbox for building a deeper understanding of these capabilities with hands-on experiments.
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### Return to the Build-a-thon
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### Continue learning
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Once you have completed this quest and get an intuitive sense for end-to-end development with Microsoft Foundry, return to the main Build-a-thon repository to continue with the next quests.
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When you have an intuitive sense of end-to-end development with Microsoft Foundry, return to the [Quest directory](https://github.com/Azure-Samples/JavaScript-AI-Buildathon#the-learning-path) to choose what to explore next.
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## AI Note
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