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New best story on Hacker News: User Interfaces of the Demo Scene

User Interfaces of the Demo Scene 424 by zdw | 74 comments on Hacker News.
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New best story on Hacker News: Show HN: I was tired of opening 2 tabs for every HN link, so I made a userscript

Show HN: I was tired of opening 2 tabs for every HN link, so I made a userscript 408 by twalichiewicz | 114 comments on Hacker News. HN is great for the links people share, but a big part of the value I get comes from reading the discussion around them. I realized I was always opening the article in one tab and the comments in another, constantly switching back and forth. I figured there was probably a simpler way, so I threw together this userscript to merge the two. 1. Clicking a link from Hacker News opens the article with a side panel containing the discussion. It doesn't require your credentials, is resizable, and is easy to tweak if you want to customize it. 2. If you land on an article that has previously been shared on HN, the script finds the existing discussion and adds a button in the top-right to open the panel. Feedback welcome.

New best story on Hacker News: Show HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac

Show HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac 425 by gitpusher42 | 145 comments on Hacker News. Hi HN, I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal. I have always adored on-device AI. It feels like magic that you can run a powerful NN on your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory. The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are included. The trick is to keep the shared part of the model and the KV cache in RAM, then stream only the routed experts needed for each token from SSD. An SSD is way slower than RAM, so the runtime uses a small expert cache and bounded parallel `pread`. While those reads ...