Open-source LLM playground for VS Code

Hacker News

Hey HN! We made a VS Code extension that allows developers to play with their prompts right in their editor. It uses Oxc and RustPython to parse and detect the prompts in the source code. Devs can test their prompts against different models and/or variables (models*data matrix). With a CSV dataset, one can test their prompt changes against multiple rows and verify whether the change is good or not. Automated evals are coming soonish. The request/response JSONs are available to inspect, as well as usage stats with projected cost. The extension supports hundreds of models thanks to the Vercel Gateway. If you need more provider options (planned but not priority), please let me know. Users without keys will get their requests served by LM Studio running on my PC. Please don't crash my workstation! It only supports JS/TS and Python for now, but many more languages are coming. Compiling tree-sitter crates to Wasm is PITA, but I'm getting there. I also work on improving the heuristics so prompt strings can be detected from arrays or when passed as arguments to popular LLM APIs. Now you can use "prompt" in the variable name or put a comment with "@prompt" in front of it.

Visit product View on Hacker NewsDec 27, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users, way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon, users · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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