Show HN: Sweep, Open-weights 1.5B model for next-edit autocomplete 447 by williamzeng0 | 88 comments on Hacker News. Hey HN, we trained and open-sourced a 1.5B model that predicts your next edits, similar to Cursor. You can download the weights here ( https://ift.tt/glLXfPH ) or try it in our JetBrains plugin ( https://ift.tt/x2sDjyY... ). Next-edit autocomplete differs from standard autocomplete by using your recent edits as context when predicting completions. The model is small enough to run locally while outperforming models 4x its size on both speed and accuracy. We tested against Mercury (Inception), Zeta (Zed), and Instinct (Continue) across five benchmarks: next-edit above/below cursor, tab-to-jump for distant changes, standard FIM, and noisiness. We found exact-match accuracy correlates best with real usability because code is fairly precise and the solution space is small. Prompt format turned out to matter more than we expected. We ran a genetic algorithm over 30+ diff fo...
New best story on Hacker News: Show HN: ChartGPU – WebGPU-powered charting library (1M points at 60fps)
Show HN: ChartGPU – WebGPU-powered charting library (1M points at 60fps) 448 by huntergemmer | 139 comments on Hacker News. Creator here. I built ChartGPU because I kept hitting the same wall: charting libraries that claim to be "fast" but choke past 100K data points. The core insight: Canvas2D is fundamentally CPU-bound. Even WebGL chart libraries still do most computation on the CPU. So I moved everything to the GPU via WebGPU: - LTTB downsampling runs as a compute shader - Hit-testing for tooltips/hover is GPU-accelerated - Rendering uses instanced draws (one draw call per series) The result: 1M points at 60fps with smooth zoom/pan. Live demo: https://chartgpu.github.io/ChartGPU/examples/million-points/ Currently supports line, area, bar, scatter, pie, and candlestick charts. MIT licensed, available on npm: `npm install chartgpu` Happy to answer questions about WebGPU internals or architecture decisions.