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Inject VSCode code, errors, and definitions into your ChatGPT prompt

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Inject VSCode code, errors, and definitions into your ChatGPT prompt

I made this project for myself over the past two days. It's a web app that lets you build a ChatGPT prompt where you can add code, errors, and symbol definitions from VSCode in one click. 1. Run the web app from docker 2. Download the VSCode extension and run the connect command 3. Construct prompt 4. Paste into ChatGPT/some other tool Why it's useful: - LLM neutral: paste prompts into ChatGPT, Claude, Bing Chat, Bard, etc. - No need to fiddle with copy/pasting code snippets or error messages - Free Software under AGPLv3 - Fully local, no telemetry or tracking. The architecture is in three parts: 1. VSCode extension that sends symbols, diagnostics, and open files to the server over both REST and Websockets. 2. Haskell Server that talks with the VSCode extension and serves the static frontend. 3. Static frontend in SvelteKit. For production, the backend and frontend are packaged together with nix (see `flake.nix`) and turned into a Docker image, though they can also be run separately. The extension is packaged the normal way with `vsce`. Please let me know what you think!

Visit productView on Hacker NewsMar 23, 2023
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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, dock, chatgpt · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way, para · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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