Prepl
Practice interviews with an AI that talks back
What it does
Prepl is an AI-powered interview coach that actually talks back. Instead of static question banks, you get a live voice and video interview with an AI that asks real follow-up questions, pushes on vague answers, and adapts based on what you say. After each session, you get a detailed breakdown covering communication, problem-solving, and behavioral depth, plus specific things to improve before your next real interview. Free to start.
Does the same job
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- IMI made a conversational AI for interview prep2025 · speakfast.ai · ▲8
SpeakFast helps you prep by actually talking—not just reading tips or recording yourself. The AI interviews you, challenges you in real time, and gives coaching based on how you're doing. It can even build a custom roadmap around your weak spots. There are 200K+ real roles from top tech companies to practice with. You can also paste a job description and get a tailored mock interview instantly. It’s composable, flexible, and built to feel real
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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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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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