Watches user sessions, finds bugs that matter, and fixes them
Hey HN, I’m Abhishek. I'm building Opslane, an open-source agent that identifies user-facing issues and investigates them. It only creates a PR if it can verify the fix. Demo: https://youtu.be/ccuOTYQMeYg Docs: https://docs.opslane.com At my last job at Robinhood, we used to do a quarterly bug bash. We would go through our Sentry backlog and try to fix as many of them as possible. We only fixed bugs we knew were reported by customers. We had hundreds of bugs, and Sentry’s default priority levels made no sense. After the bug bash, we would declare bankruptcy - select…
In plain words
Opslane is an open-source agent that monitors user sessions to identify bugs affecting real users, then investigates and writes fixes for them. It only creates pull requests when it can verify a fix works. Designed for development teams drowning in error backlogs, Opslane focuses on user-facing issues rather than generating false positives, letting developers review and merge vetted solutions instead of manually triaging hundreds of low-priority alerts.
written from the facts on this page · September 2026
From the sources
Opslane finds the bugs your users run into by watching real user sessions, and writes the fix. You review and merge. - opslane/opslane
In the maker’s words, at launch
Hey HN, I’m Abhishek. I'm building Opslane, an open-source agent that identifies user-facing issues and investigates them. It only creates a PR if it can verify the fix. Demo: https://youtu.be/ccuOTYQMeYg Docs: https://docs.opslane.com At my last job at Robinhood, we used to do a quarterly bug bash. We would go through our Sentry backlog and try to fix as many of them as possible. We only fixed bugs we knew were reported by customers. We had hundreds of bugs, and Sentry’s default priority levels made no sense. After the bug bash, we would declare bankruptcy - select all remaining bugs and mark them as resolved. This problem has only gotten worse since coding agents have become more prevalent. So I started thinking: what would Sentry look like if it were built in 2026? To me, error trackers have two failure modes: 1. False positives: They show you thousands of errors, and you can’t tell the impact on the user 2. False negatives: Many user-facing issues don’t throw exceptions, so they go unnoticed. Opslane combines error tracking and session recording. And there is an agent that acts on both. To get started, you install the Opslane SDK. It captures everything the user did: errors, console logs, network requests, and session recordings. Opslane reduces false positives by ranking issues based on how many users are facing a particular issue. It also learns about your product by reading your code and watching your session recordings. False negatives are harder. Opslane reviews session recordings to spot frustration. They look for rage clicks, dead clicks, and abandoned forms. This recently caught a bug in an early customer’s onboarding flow: a dropdown that closed itself when clicked. No exception, no bug report. The recordings showed users clicking it, selecting nothing, and dropping out of onboarding. Opslane flagged it and the team fixed it. Three guiding principles when building Opslane: 1. Open Source: Self-host with one Docker Compose file. 2.Agent-first: I never want to open an error dashboard again. Opslane ships an MCP server, so you can ask "what broke for users this week" from Claude Code. You get back issues that need your attention and you drive the resolution. 3. It knows about your product: Opslane is continuously learning about your product. Every investigation begins with what it knows about your product. It’s early. Frontend apps work end to end today.I am currently focused on improving reliability and accuracy. Here is a link to our repo: https://github.com/opslane/opslane Would love to get feedback from folks on our approach to this problem!
Does the same job
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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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