knallhart.dev - Brutally honest feedback
Brutally honest AI website feedback for €10
What it does
Knallhart.dev gives your website a brutal honest review — no fluff, no diplomatic padding. Submit a URL, pay €10, and within minutes you get an email with 3 specific critiques plus actionable fixes. Our AI takes a full-page screenshot, analyzes it, and delivers feedback that actually stings. What you get: - 3 specific issues (UX, design, or structure) - Each with a concrete, actionable fix - Delivered via email in minutes - No subscription, no dashboard
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RoastMyWebsite Brutally Honest FeedbackMar 2026 · roastmywebsite.onlineBrutally honest AI website feedback. No sugar-coating.


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Hi HN, I built Reviewskits because of a recurring frustration I faced while working for a web agency in Switzerland. Our designers created beautiful, pixel-perfect layouts, but when it came to testimonials, we were stuck with rigid, pre-made widgets from tools like Senja or Trustpilot. Hacking them with CSS to match our UI was a nightmare. When we looked at their API pricing to build our own components for 100+ clients, the cost was astronomical ($90 to $300+/mo). Later, as an indie dev building my own SaaS, I simply couldn’t afford those prices. So I built Reviewskits: High…
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I’ve spent the last 2.5 months building a product that runs LLM-powered code reviews on my pull requests — and I just launched it. The tool is built specifically for solo developers. You install it on your repo, trigger a scan by creating a pull request, and it leaves structured review comments using OpenAI under the hood. Funnily enough, I used the dev version of this app to review its own pull requests while building it. It helped me spot bugs, simplify structure, and keep quality high — all with minimal need for another human in the loop. Things I want to try out in the next months : -…
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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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- H6Homebrew 6.0.0▲1,481
Today, I’m proud to announce Homebrew 6.0.0. The most significant changes since 5.1.0 are a new tap trust security mechanism, the new faster, smaller, default internal Homebrew JSON API, sandboxing on Linux, better defaults informed by our user survey, many brew bundle improvements, improved performance and initial support for macOS 27 (Golden Gate). Happy to discuss any questions here!
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