I scraped Reddit to find the most controversial chef knife
I wanted to quantify the endless "which knife should I buy" debates on r/chefknives, so I built a data analysis pipeline to get some real answers. The project is a 5-phase system built with Node.js. It first uses Fuse.js for fast, typo-tolerant fuzzy matching of ~450 known brands and ~8,700 models. The remaining text is then passed to an LLM (via OpenRouter) for discovering new, unknown entities and performing sentiment analysis on every mention. I ran it on over 1,000 threads, totaling more than 25,000 comments. A few interesting findings: The Underdog: Budget-friendly Tojiro has a…
What it does
In the maker’s words, at launch
I wanted to quantify the endless "which knife should I buy" debates on r/chefknives, so I built a data analysis pipeline to get some real answers. The project is a 5-phase system built with Node.js. It first uses Fuse.js for fast, typo-tolerant fuzzy matching of ~450 known brands and ~8,700 models. The remaining text is then passed to an LLM (via OpenRouter) for discovering new, unknown entities and performing sentiment analysis on every mention. I ran it on over 1,000 threads, totaling more than 25,000 comments. A few interesting findings: The Underdog: Budget-friendly Tojiro has a massive 27-to-1 positive-to-negative mention ratio. The Controversy King: Shun is by far the most polarizing brand, sparking strong love/hate discussions (59 positive vs. 24 negative mentions). The Unloved: Dalstrong was one of the few brands to receive more negative mentions than positive. The system isn't perfect—I'm open about a critical entity aggregation bug in the write-up. The full technical architecture, results, and raw data are available. I'm here to answer any questions! Blog Post (full story & visualizations): https://new.knife.day/blog/we-analyzed-25000-reddit-comments... GitHub (technical breakdown & raw data): https://github.com/pvijeh/reddit-named-entity-recognition/bl... Original Reddit Discussion: https://www.reddit.com/r/chefknives/comments/1o2p363/i_analy...
Does the same job
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Hey HN! I'm a bit of a knife steel geek and got tired of juggling tabs to compare stats. So, I built this tool: https://new.knife.day/blog/knife-steel-comparisons/all It lets you pick steels (like the ones in the screenshot) and see a radar chart comparing their edge retention, toughness, corrosion resistance, and ease of sharpening on a simple 1-10 scale. It's already been super handy for me, and I thought fellow knife/metallurgy enthusiasts here might find it useful too. Would love to hear your thoughts or any steel requests! Cheers!
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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…
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Launched alongside, October 2025
the whole month →
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Hey all, I built this. It’s not trying to capture every autistic experience (that’d be impossible). It’s based on my own lived experience as well as that of friends on the spectrum. I'm trying to give people a feel for what masking, decision fatigue, and burnout can look like day-to-day. That’s hard to explain in words, but easier to show through choices and stats. I'm not trying to "define autism". I’ve gotten good feedback here about resilience, meds, and difficulty tuning. I’ll keep tweaking it. If even a few people walk away thinking, "ah, maybe that’s why my coworker struggles in those…
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