15:I[981,["658","static/chunks/658-a698693f56d0116a.js","183","static/chunks/183-6beeeb801c274aaa.js","474","static/chunks/app/%5Btype%5D/layout-631c4730b83c8223.js"],"Posts"] 16:T428,Hey HN,

I’ve been building agents recently, and I hit a problem: I fell asleep while a script was running, and my agent got stuck in a loop. I woke up to a drained OpenAI credit balance.

I looked for a tool to prevent this, but most solutions were heavy enterprise proxies or cloud dashboards. I just wanted a simple "fuse" that runs on my laptop and stops the bleeding before it hits the API.

So I built AgentFuse.

It is a lightweight, local library that acts as a circuit breaker for LLM calls.

Drop-in Shim: It wraps the openai client (and supports LangChain) so you don't have to rewrite your agent logic.

Local State: It uses SQLite in WAL mode to track spend across multiple concurrent agents/terminal tabs.

Hard Limits: It enforces a daily budget (e.g., stops execution at $5.00).

It’s open source and available on PyPI (pip install agent-fuse).

I’d love feedback on the implementation, specifically the SQLite concurrency logic! I tried to make it as robust as possible without needing a separate server process.17:T467,Hey HN,

  I built Buildex (https://buildex.dev) to help engineers practice system design interviews interactively.

  The problem: System design prep usually means reading blogs or watching videos passively. There's no way to actually practice designing systems and get feedback.

  How it works:
  - Pick a challenge (URL shortener, rate limiter, chat system, etc.)
  - Drag-and-drop components onto a canvas to design your architecture
  - Connect them to show data flow
  - Submit and get scored on efficiency, cost, and reliability
  - AI evaluates your design and gives specific feedback

  Tech stack:
  - Frontend: React + Vite
  - Backend: Go
  - Database: PostgreSQL
  - AI: Claude API for design evaluation
  - Payments: Razorpay

  Free tier gives you 2

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