PulseBot
AI product feedback intelligence for SaaS teams
What it does
PulseBot scans public reviews, communities, and competitor feedback, then turns repeated signals into evidence-backed product opportunity reports. It helps SaaS founders and PMs spot pain points, feature requests, and market shifts without reading every thread manually.
PulseBot turns public feedback from communities, review sites, and competitor channels into verified product opportunity signals.
PulseBot scans public reviews, forums, and product communities to surface emerging user pain points, feature requests, and delight moments. Public trend reports will appear here after the first daily run. New: payments are live. Early users can claim 1 free month of Pro after signup. Public pages show safe summaries only. Low-confidence or low-evidence candidates stay out of the homepage until verified. Aug 5, 2026 PulseBot payments are now live, SEO pages have been improved, and the product is ready for real SaaS teams to test. Sign up, email us, and we will manually activate 1 month of Pro for your account. Use PulseBot with a real product, review the reports, and tell us what feels…from pulse-bot.com
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Quickhunt: Customer Feedback ManagementSep 2025 · ▲59AI-Powered Feedback, Roadmap & Changelog, All in One


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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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Life & fun · 10d ago · louisabraham.github.io


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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…
Life & fun · Aug 2026 · toneyalexander.github.io


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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