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MICA Flame-W preview
A language model with no neural net
Flame-W writes sentences from learned integer rules on a cellular automaton. No attention, no floating point, no neural net at inference. Same prompt, same text, on any CPU. Research demo, not an assistant.
I built MICA because I wanted a language model that is not a neural net at inference.Flame-W 0.3.3 is a learned integer-rule cellular automaton. No attention, no floating point, no network in the generator. Same prompt, same text, any CPU. The 0.3.2 release added a second integer memory past 64 words. 0.3.3 blends a short memory bank for the first 64 symbols. The automaton itself did not change.It is not an assistant. Many sentences still do not make sense. On public Tiny Theory-of-Mind it scores 636/2,000 (31.80%), chance is 25%. The 0.3.2 score was 632/2,000. That gap is inside the noise.Playground: https://mica-ai-ten.vercel.app
Code: https://github.com/Vovala14/Mica-Ai
Weights: https://huggingface.co/vynly/mic... for research. Commercial use is forbidden. If you try a prompt, tell me the first sentence that goes wrong. That is the useful comment.
About MICA Flame-W preview on Product Hunt
“A language model with no neural net”
MICA Flame-W preview was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #129 on the daily leaderboard. Flame-W writes sentences from learned integer rules on a cellular automaton. No attention, no floating point, no neural net at inference. Same prompt, same text, on any CPU. Research demo, not an assistant.
On the analytics side, MICA Flame-W preview competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how MICA Flame-W preview performed against the three products that launched closest to it on the same day.
Who hunted MICA Flame-W preview?
MICA Flame-W preview was hunted by Vova Lavrik. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of MICA Flame-W preview including community comment highlights and product details, visit the product overview.
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