[tab] examples/agi_cast.html (html)
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  <d-front-matter>
    <script id='distill-front-matter' type="text/json">{
    "title": "agi-cast: Making Agents Work Like Humans",
    "description": "We introduce agi-cast, a continually growing dataset of unlabeled screen recordings of AGI research.",
    "published": "November 02, 2025",
    "url": "https://pdoom.org/agi_cast.html",
    "authors": [
      {
        "author":"Franz Srambical",
        "authorURL":"https://srambical.fr/",
        "affiliations": [{"name": "p(doom)", "url": "https://pdoom.org/"},
                         {"name": "TUM"}]
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  <d-title>
    <p>
        We introduce <a href="https://github.com/p-doom/crowd-code">crowd-code</a>, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.
        <a href="https://github.com/p-doom/crowd-code/releases">Install once</a>, and forget about it.
    </p>
  </d-title>
  <d-byline></d-byline>
  <d-article>
    <a class="marker" href="#section-1" id="section-1"><span>1</span></a>
    <h2>Data Is The New Oil</h2>
    <p>
        Neural networks are simulators. Anything you want your model to do, you have to teach it. Base models represent the mean of the data distribution of the internet.
        Post-training permits shifting that distribution towards desired behaviours. The higher the required skill, the scarcer the corresponding data on the internet.
    </p>
    <p>
        The most straightforward way to teach a model to do something is to give it a dataset to behaviour-clone off of. No software engineer in the world writes code linearly,
        in a single branch, in a single commit, in a single PR, without jumping around the codebase, without making mistakes, without debugging. crowd-code is our first attempt
        at capturing a broad spectrum of human software engineering, including character-level text insertions, deletions, undo, redo, cursor movement, file switches, jumping
        to function definitions, git checkouts, terminal command execution, autocompletes, LLM changes, and more.
    </p>
    <a class="marker" href="#section-2" id="section-2"><span>2</span></a>
    <h2>Going Beyond Open-Source Towards Open-Engineering</h2>
    <p>
        Every day, millions of developers are writing open-source code. We propose going beyond open-source and towards open-engineering, a paradigm where the value of the
        developer's time is not just captured by the code they produce, but also by the mere act of engineering.
    </p>
    <p>
        We introduce crowd-code, a VS Code/Cursor extension that allows anyone to participate in crowd-sourcing a software engineering dataset to eventually finetune models on.
        We want to make it as easy as possible for developers to participate in crowd-sourcing. All you need to do is <a href="https://github.com/p-doom/crowd-code/releases">
        install the crowd-code extension</a>, and forget about it.
    </p>
    <figure style="grid-column: page; margin: 1rem 0; display: flex; justify-content: center"><img src="crowd_code_preview.gif"
      style="width:100%; border-radius: 8px;" /></figure>
    <figcaption style="grid-column: page; text-align: center; margin-bottom: 2rem; font-size: 0.8em; color: rgba(0, 0, 0, 0.5);">Figure 1: A preview of the crowd-code extension in action. Figure from Mattia Consiglio.</figcaption>
    <p>
        The extension periodically uploads the user's captured IDE actions to a server, where they are cleaned, filtered, thoroughly anonymized, and periodically released to the
        public under the most permissive Creative Commons license (CC0).
        An ongoing recording is transparently indicated in the IDE's status bar, and can be stopped at any time. If the user has inserted sensitive data, they can simply
        press the 'panic button' in the status bar to remove the last actions from the recording before they even leave the user's machine. Additionally, the user is asked for
        consent to participate in crowd-sourcing upon extension installation, and can opt-out at any time. We take user privacy very seriously and welcome any feedback on how to
        make data collection and release more transparent.
    </p>
    <p>
        Beyond behaviour-cloning on a dataset crowd-sourced using crowd-code, we eventually want to use crowd-code to annotate the entirety of IDE screencasts on the internet 
        using an inverse dynamics model trained on screen recordings paired with crowd-code's IDE action annotations. This would unlock an entirely new trove of training data
        for software engineering agents.
    </p>
    <p>
        We are excited to see what the community builds with this dataset. We want to democratize AI research. We are greater than the sum of our parts. <u>Together</u>.
    </p>
    </d-article>

  <d-appendix>

    <h3>Contributions</h3>
    <p>FS worked on conceptualization of the project and implemented the IDE extension. MM implemented the server-side data collection pipeline. All authors contributed technically. FS wrote the manuscript. We thank Gemini Code Assist and Cursor for their help in writing the extension.</p>
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