Saturday, 31 January 2026

Friday, 30 January 2026

Monday, 26 January 2026

Sunday, 25 January 2026

Saturday, 24 January 2026

Friday, 23 January 2026

New top story on Hacker News: Show HN: Teemux – Zero-config log multiplexer with built-in MCP server
Show HN: Teemux – Zero-config log multiplexer with built-in MCP server
4 by gajus | 0 comments on Hacker News.
I started to use AI agents for coding and quickly ran into a frustrating limitation – there is no easy way to share my development environment logs with AI agents. So that's what is Teemux. A simple CLI program that aggregates logs, makes them available to you as a developer (in a pretty UI), and makes them available to your AI coding agents using MCP. There is one implementation detail that I geek out about: It is zero config and has built-in leader nomination for running the web server and MCP server. When you start one `teemux` instance, it starts web server, .. when you start second and third instances, they join the first server and start merging logs. If you were to kill the first instance, a new leader is nominated. This design allows to seamless add/remove nodes that share logs (a process that historically would have taken a central log aggregator). A super quick demo: npx teemux -- curl -N https://ift.tt/BohAs4K

Thursday, 22 January 2026

New top story on Hacker News: Show HN: Bible translated using LLMs from source Greek and Hebrew
Show HN: Bible translated using LLMs from source Greek and Hebrew
15 by epsteingpt | 15 comments on Hacker News.
Built an auditable AI (Bible) translation pipeline: Hebrew/Greek source packets -> verse JSON with notes rolling up to chapters, books, and testaments. Final texts compiled with metrics (TTR, n-grams). This is the first full-text example as far as I know (Gen Z bible doesn't count). There are hallucinations and issues, but the overall quality surprised me. LLMs have a lot of promise translating and rendering 'accessible' more ancient texts. The technology has a lot of benefit for the faithful, that I think is only beginning to be explored.

Wednesday, 21 January 2026

Monday, 19 January 2026

Saturday, 17 January 2026

Friday, 16 January 2026

Wednesday, 14 January 2026

Tuesday, 13 January 2026

Monday, 12 January 2026

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Friday, 2 January 2026

Thursday, 1 January 2026

New top story on Hacker News: Show HN: Feature detection exploration in Lidar DEMs via differential decomp
Show HN: Feature detection exploration in Lidar DEMs via differential decomp
3 by DarkForestery | 0 comments on Hacker News.
I'm not a geospatial expert — I work in AI/ML. This started when I was exploring LiDAR data with agentic assitince and noticed that different signal decomposition methods revealed different terrain features. The core idea: if you systematically combine decomposition methods (Gaussian, bilateral, wavelet, morphological, etc.) with different upsampling techniques, each combination has characteristic "failure modes" that selectively preserve or eliminate certain features. The differences between outputs become feature-specific filters. The framework tests 25 decomposition × 19 upsampling methods across parameter ranges — about 40,000 combinations total. The visualization grid makes it easy to compare which methods work for what. Built in Cursor with Opus 4.5, NumPy, SciPy, scikit-image, PyWavelets, and OpenCV. Apache 2.0 licensed. I'd appreciate feedback from anyone who actually works with elevation data. What am I missing? What's obvious to practitioners that I wouldn't know?