I built an open-source local vibe coding tool with Electron

Hacker News

Hi HN - Just wanted to share a local vibe coding tool that I created. You can download it here (free, no sign-up): https://dyad.sh/ It's open-source and on GitHub: https://github.com/dyad-sh/dyad If you've used one of the popular vibe coding tools like lovable, v0 and bolt, but want something more flexible and without the lock-in, I think you'll like dyad. Here's a few things that makes it different: - Runs locally, making it fast and frictionless. Because your code lives locally, you can easily switch back and forth between Dyad and your IDE like Cursor, etc. - Run local models using the Ollama and LM Studio integration ( https://www.dyad.sh/docs/guides/ai-models/local-models ), which lets you build with your favorite local LLMs. - Free and bring-your-own API key. This means you can use your free Gemini API key and get 500 free messages/day with Gemini Flash 2.5! Note: Dyad isn't an IDE (and not another VS code fork :) and instead is focused on people, esp. non-engineers, who want an all-in-one tool to build+preview web apps in a simple desktop UI. If you're interested in my experience building an Electron app (e.g. the pain of code signing, cross-platform issues, weird Node.js quirks), please let me know, I'm thinking about writing a technical blog post. I’d love any feedback about the project. Thanks!

Visit product View on Hacker NewsMay 30, 2025

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

98%98% 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: created, gemini · Missing: supports, reddit linkedin, podcasting

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

Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% 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.

Incorrect prediction on native model

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