Automated bug fixing powered by LLMs
Hey everyone, My friend and I built a simple bug fixing app that listens for alerts/issues from Sentry, contextualizes it against your codebase, and any other data sources you wish to connect (right now we support Notion, Google Docs, and Slack), and deploys an ai agent to write a PR for review in Github or Gitlab to solve the bug. Our current demo shows the end-to-end process for a trivial bug fix, but we have been testing it with open source python repos like http-pie, comparing how our agent solves a bug compared to a human engineer and it gets fairly close. We are working on adding…
Automated bug fixing powered by LLMs launched on November 16, 2023 with 9 votes, #1,454 of 4,333 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
What it does
Resolvd deploys digital workers that take healthcare supply chain workflows from trigger to completion across ERPs, contracts, inboxes, vendors, and teams.
In the maker’s words, at launch
Hey everyone, My friend and I built a simple bug fixing app that listens for alerts/issues from Sentry, contextualizes it against your codebase, and any other data sources you wish to connect (right now we support Notion, Google Docs, and Slack), and deploys an ai agent to write a PR for review in Github or…
Does a similar job
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Why write code if the LLM can just do the thing? (web app experiment)Nov 2025 · github.com · ▲436I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
TrailsJan 2026 · trails-red.vercel.app · ▲81Hey all, Justin here. I previously built Phind, the AI search engine for developers. One of the biggest problems we had there was figuring out what went wrong with bad searches. We had tons of searches per day, but less than 1% of users gave any explicit feedback. So we were either manually digging through searches or making general system improvements and hoping they helped. This problem gets harder with agents. Traces are longer and more complex. It takes more effort to review them, so I'm building a tool that lets you analyze LLM outputs directly to help developers of LLM apps and agents…
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Give your coding agent production bug contextNov 2025 · sonarly.dev · ▲5Hi all, I've been working on this devtool for 1 month now for myself at first and I'll be curious to see if it's something that could work for you as well. So basically, it detects bugs in your website in production from real user sessions, an llm clusters them by severity and it provides the complete context of the issue that you can copy-paste into your coding agent to fix it in one go. Why did I create it? I've been shipping fast with tools like Cursor and Claude Code. The problem? When bugs happen in production, these tools have zero context about what actually went wrong. Sentry is…

I built a tool that helps fixing JavaScript production bugs much faster2023 · useflytrap.com · ▲5Logging and tools like Sentry are a thing of the past. A while back on a night out in northern Norway, I had to start debugging a critical production bug that broke the payment flow of my SaaS product. I had limited time to fix the bug or I would have lost about ~1K profit. Super stressful. I had logging and Sentry in place, but neither helped me reproduce or find the root cause of the bug. Ever since, I started thinking; why can’t we just have a tool that you setup once, and that allows us to reproduce every function call and function that the user ran before the bug? This is how the idea…
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