Scan any website for AI design patterns
Design slop is the feeling that tells you a website is purely AI-generated. This is an attempt to classify the design patterns behind it. Applying this scoring to recent ShowHN submissions shows that about a third of the submissions show medium to heavy design slop: https://slopcop.adriankrebs.ch/show How the scoring works: - A headless browser loads each site (Playwright) - A small in-page script analyzes the DOM and reads computed styles - Every pattern is a deterministic CSS or DOM check. There are false positives, but my manual QA run verified it’s maybe 5-10%. Is design…
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Design slop is the feeling that tells you a website is purely AI-generated. This is an attempt to classify the design patterns behind it. Applying this scoring to recent ShowHN submissions shows that about a third of the submissions show medium to heavy design slop: https://slopcop.adriankrebs.ch/show How the scoring works: - A headless browser loads each site (Playwright) - A small in-page script analyzes the DOM and reads computed styles - Every pattern is a deterministic CSS or DOM check. There are false positives, but my manual QA run verified it’s maybe 5-10%. Is design slop bad? Not really, just uninspired. After all, the web was never about the fanciest design, and before the AI era, everything looked like Bootstrap. However, there is a difference between trying to craft your own design and just shipping with whatever defaults the LLMs output. And the same has been the case pre-LLM when using CSS/HTML templates. I guess people will get back to crafting beautiful designs to stand out from the slop. On the other hand, I’m not sure how much design will still matter once AI agents are the primary users of the web.
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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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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…
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I might be the only SRE on Earth with his own bowling center. It's a more in-depth gig than you'd think. My family and I bought an abandoned 8-lane bowling center in the rural mid-west. In our small town there weren't many recreation options for families. You've heard of a food desert? This is an R&R desert. It had been abandoned for a good reason. The roof leaks, the electrical system was constantly surging, and my 70-year-old bowling equipment (still) doesn't work perfectly. The system that keeps your score is particularly interesting to me. It's the thing you watch during your game, but…
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