Show HN: Hover – IDE style hover documentation on any webpage
9 by sampsonj | 1 comments on Hacker News.
I thought it would be interesting to have ID style hover docs outside the IDE. Hover is a Chrome extension that gives you IDE style hover tooltips on any webpage: documentation sites, ChatGPT, Claude, etc. How it works: - When a code block comes into view, the extension detects tokens and sends the code to an LLM (via OpenRouter or custom endpoint) - The LLM generates documentation for tokens worth documenting, which gets cached - On hover, the cached documentation is displayed instantly A few things I wanted to get right: - Website permissions are granular and use Chrome's permission system, so the extension only runs where you allow it - Custom endpoints let you skip OpenRouter entirely – if you're at a company with its own infra, you can point it at AWS Bedrock, Google AI Studio, or whatever you have Built with TypeScript, Vite, and the Chrome extension APIs. Coming to the Chrome Web Store soon. Would love feedback on the onboarding experience and general UX – there were a lot of design decisions I wasn't sure about. Happy to answer questions about the implementation.
Sunday, 4 January 2026
Saturday, 3 January 2026
New top story on Hacker News: Show HN: FP-pack – Functional pipelines in TypeScript without monads
Show HN: FP-pack – Functional pipelines in TypeScript without monads
3 by superlucky84 | 1 comments on Hacker News.
Hi HN, I built fp-pack, a small TypeScript functional utility library focused on pipe-first composition. The goal is to keep pipelines simple and readable, while still supporting early exits and side effects — without introducing monads like Option or Either. Most code uses plain pipe/pipeAsync. For the few cases that need early termination, fp-pack provides a SideEffect-based pipeline that short-circuits safely. I also wrote an “AI agent skills” document to help LLMs generate consistent fp-pack-style code. Feedback, criticism, or questions are very welcome.
3 by superlucky84 | 1 comments on Hacker News.
Hi HN, I built fp-pack, a small TypeScript functional utility library focused on pipe-first composition. The goal is to keep pipelines simple and readable, while still supporting early exits and side effects — without introducing monads like Option or Either. Most code uses plain pipe/pipeAsync. For the few cases that need early termination, fp-pack provides a SideEffect-based pipeline that short-circuits safely. I also wrote an “AI agent skills” document to help LLMs generate consistent fp-pack-style code. Feedback, criticism, or questions are very welcome.
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
4 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?
4 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?
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