Wed Jun 3 21:31:22 2026
(*4297a328*):: https://x.com/howtoai_/status/2062213124007297330?
*** How To AI (@HowToAI_) on X
*** Someone open-sourced a fully private Perplexity clone that runs 100% locally.
It’s called Vane. 32.4k stars. MIT license. Replaces a $20/mo subscription with a single command.
It’s called Vane. it’s a full perplexity replica that does real-time web search + cited answers
*** X (formerly Twitter)
HJ5zosnakAAJ94c.jpglarge
(*53e37792*):: https://stacker.news/items/1501247 +public!
*** LLM weights small enough to fit in a bitcoin tx \ stacker news
*** I wanted to see if you could make a language model small enough to fit in a standard bitcoin transaction (so smaller than 400kb). technical details to follow but if you want to skip to the punchline it came out pretty good! The inference code runs locally in your browser and the ui (which contains the inference code) and weights are both onchain via inscriptions. You can try it out here: Most modern language models are in the billions (or trillions) of parameters. This one is in the hundreds of thousands. Custom model written in pytorch, with 4 transformer layers, 4 heads, embedding dimension of 112. Has a context length of 128 tokens and a vocab of 2000 tokens (plus one for unknowns). Those hyperparameters were found by picking some defaults and then doing an autoresearch loop to figure out the right combination to maximize readability (same process with the sampler parameters). The weights were then quantized to 4 bits and compressed, getting an artifact just under 400kb. The training corpus was around 1M tokens. One of the problems i ran into is the model is so small it had trouble learning grammar for longer sentences. So part of the data prep was rewriting the corpus into shorter claims and statements that still kept the substance of the material but was shorter and used a more constrained vocabulary. Corpus processing, model tuning, sampler tuning was a super iterative process. Took about three weeks of on and off work to get something i was happy with. Hope you enjoy! [1 comment]
*** Stacker News
1501247.octet-stream