17:T41d,We’ve been working on an open runtime architecture that stabilizes long-horizon reasoning in large language models.

Sigma Runtime defines the execution model for attractor-based cognition - integrating symbolic density, drift regulation, and recursive coherence into a unified cognitive runtime layer.

It’s like a Linux-style runtime for cognitive processes in LLMs: models act as hardware, and frameworks like LangChain become drivers on top of it.

→ Documentation: https://wiki.sigmastratum.org → GitHub: https://github.com/sigmastratum/documentation → DOI: https://doi.org/10.5281/zenodo.17703667

Would love technical feedback and collaboration from those working on LLM runtimes, recursive reasoning, or cognitive architectures.18:T4cd,Hey HN! Over the weekend (leaning heavily on Opus 4.5) I wrote Jargon - an AI-managed zettelkasten that reads articles, papers, and YouTube videos, extracts the key ideas, and automatically links related concepts together.

Demo video: https://youtu.be/W7ejMqZ6EUQ

Repo: https://github.com/schoblaska/jargon

You can paste an article, PDF link, or YouTube video to parse, or ask questions directly and it'll find its own content. Sources get summarized, broken into insight cards, and embedded for semantic search. Similar ideas automatically cluster together. Each insight can spawn research threads - questions that trigger web searches to pull in related content, which flows through the same pipeline.

You can explore the graph of linked ideas directly, or ask questions

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