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Show HN: Crayon, the web's first personalized newspaper (1995), relaunched

来源: Show HN ·

In 1995 I co-built CRAYON (CReAte Your Own Newspaper) - the first personalized news service on the web. 650k people made papers. The Perl CGI site ran, barely, for 31 years. I've now rebuilt it: stories ranked by independent-source count instead of an editor or engagement algorithm, every side of every story on one page. Your 1996 password still works, and every subscriber can open their original paper as a time capsule - dead sources marked "lost to time." Comments URL: https://news.ycombinator.com/item?id=50449339 Points: 1 # Comments: 0

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Show HN: Crayon, the web's first personalized newspaper (1995), relaunched is a 工具 discovery curated from Show HN. In 1995 I co-built CRAYON (CReAte Your Own Newspaper) - the first personalized news service on the web. 650k people made papers. The Perl CGI site ran, barely, for 31 years. I've…

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Show HN: Cerulion – ROS 2-compatible robotics runtime with multi-process replay

Hi HN, I'm Lakshay. Raveen and I built Cerulion, an open-source Rust robotics runtime that deterministically replays recorded multi-process runs against new code and runs existing ROS 2 nodes unmodified. Repo: https://github.com/cerulion-inc/cerulion During my MIT PhD in off-road autonomy, a robot with two lidars and six cameras had too much latency between ROS processes. ROS 2's loaned messages need POD data, which rules out the standard sensor types like Image and PointCloud2. We ended up using a single process, which is very brittle. And when it broke, I couldn't reproduce the heisenbug rel

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Show HN: RealtimeVAD – trading false positives for 110ms earlier end-of-speech

Hey guys. I recently watched the latency reports for my local voice assistant, which show a waveform plus silero probability for voice activity. I noticed silero lags visibly on the speech off signal. There had to be a method to get this signal closer to what I felt is optimal. My idea was to use a 10 ms energy detector with a threshold that adapts to environment or room noise. Which worked amazingly well. It created more false positives but in my speculative generation pipeline that would not matter. If it's wrong and the user continues speaking I can abort the pipeline fast. I benchmarked my

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