SlopScore – Contributor Reputation for GitHub PRs
Open source maintainers are drowning in low effort PRs. Someone sees a help wanted issue, pastes it into an AI, submits without testing, loops through review comments without understanding the code. The PR looks plausible at first glance but falls apart under review. Maintainers waste 30 minutes before realizing it's garbage. This is happening at scale now. And it's worst in projects with bounty programs or GSoC where there's incentive to “contribute.” GitHub tells you if someone is a first-time contributor to your repo. It doesn't tell you anything about their history elsewhere. I built a…
What it does
In the maker’s words, at launch
Open source maintainers are drowning in low effort PRs. Someone sees a help wanted issue, pastes it into an AI, submits without testing, loops through review comments without understanding the code. The PR looks plausible at first glance but falls apart under review. Maintainers waste 30 minutes before realizing it's garbage. This is happening at scale now. And it's worst in projects with bounty programs or GSoC where there's incentive to “contribute.” GitHub tells you if someone is a first-time contributor to your repo. It doesn't tell you anything about their history elsewhere. I built a Chrome extension that shows contributor reputation right on the PR page: merge rates across repos, spray-and-pray patterns, red flags. Not sure I got the signals right. Would love feedback from maintainers.
Does the same job
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- GW"Git who" – A new CLI tool for industrial-scale Git blaming2025 · github.com · ▲252
I've always wanted a better way to explore the authorship data embedded in a Git commit log. I'm having fun building a CLI tool to do this. It's a bit like the "Contributors" tab on Github that shows you how many commits each contributor has made but much faster and with many more options. If you get a chance to try it out, please let me know. I'd love to hear feedback and suggestions. Thank you!
More ai this month
the category →
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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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, January 2026
the whole month →- IN
Hey HN! I wanted to share something I built over the last few weeks: isometric.nyc is a massive isometric pixel art map of NYC, built with nano banana and coding agents. I didn't write a single line of code. Of course no-code doesn't mean no-engineering. This project took a lot more manual labor than I'd hoped! I wrote a deep dive on the workflow and some thoughts about the future of AI coding and creativity: http://cannoneyed.com/projects/isometric-nyc
AI · Jan 2026 · cannoneyed.com
Automatic AI-powered code reviews the moment you open a PR
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