Volja
AI nutrition tracking meets science-based workout scoring
What it does
Volja is a fitness app that does two things no other app does: logs meals instantly using AI (just describe what you ate) and scores your workouts using EMG-based muscle activation research. Instead of just counting sets and reps, Volja shows you exactly which muscle groups are undertrained and where you're at risk for imbalance injuries. Built by a solo developer over 6 months. Free 30-day trial.
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- MAMuze – AI fitness music for personal, responsive workout playlists2022 · muze.fit · ▲6
After years of upvoting others, I’m really excited to be able to share my first Show HN! Muze is our free app and service that learns from some of your favorite workout songs and artists, then uses our proprietary AI to generate new, on-demand workout playlists personalized for your individual tastes. Muze also includes a feature called BodySync which lets the playlists adapt to either your running pace, matching song tempos to your cadence, or to your heart rate, selecting tracks with intensities levels to match. Research has shown (1) that 3/4 of people who workout regularly rely on…
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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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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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