Txtos for LLMs – 60 SEC setup, long memory, boundary guard, MIT
i built TXTOS because my models kept forgetting and bluffing. i wanted a portable fix that works across providers without code or setup. TXTOS is a single .txt you paste into any LLM chat. it boots a small reasoning OS that gives you two things by default: a semantic tree memory that survives long threads, and a knowledge boundary guard that pushes back when the model is out of scope. what it is plain text. no scripts, no trackers, no api calls. MIT. the file encodes a protocol for reasoning, memory, and safety. you can diff it and fork it. it is not “a clever prompt”. it behaves like a tiny…
What it does
In the maker’s words, at launch
i built TXTOS because my models kept forgetting and bluffing. i wanted a portable fix that works across providers without code or setup. TXTOS is a single .txt you paste into any LLM chat. it boots a small reasoning OS that gives you two things by default: a semantic tree memory that survives long threads, and a knowledge boundary guard that pushes back when the model is out of scope. what it is plain text. no scripts, no trackers, no api calls. MIT. the file encodes a protocol for reasoning, memory, and safety. you can diff it and fork it. it is not “a clever prompt”. it behaves like a tiny OS that the model follows. why it exists after debugging a lot of RAG and agent stacks, the same failures kept coming back. memory broke across sessions. the model answered outside its knowledge without warning. i wanted a zero install layer that i can carry between models and keep the same behavior. what you get * semantic tree memory. it records ideas and relations, not just tokens. it can recall earlier branches, avoid repetition, and keep tone stable. * knowledge boundary test. ask something impossible, then run the built in check. it flags risk and proposes a safe path instead of hallucinating. * simple rules. cite then explain. stop when sources or offsets are missing. show a short audit trail. keep answers compact when you ask for compact. try in 60 seconds 1. download TXTOS.txt, open a fresh chat with any model you like 2. paste the file content, then type: hello world 3. test memory: ask three related questions, ask it to recall the first one exactly, then switch topics and come back 4. test boundary: ask for something unknowable or very recent, then type kbtest. watch how it handles the boundary 5. optional: restart the chat and paste the same file. see how the tree helps you rebuild state fast what to expect less overtalking. better recall of your own constraints and tone. refusal on missing citations. early warnings when your question is outside scope. the file is small by design, so even weaker models can use it. stronger models show bigger gains. not just marketing TXTOS came out of real failures. it was built to stop two user facing problems that cost time and trust. forgetting, and unearned confidence. since it is plain text, the community can audit it and improve it. looking for feedback * what did the memory tree get right or wrong for your workflow * which boundary cases still slip through * what small operator would you add to the OS so it helps you daily url is in the link field above. thanks for reading. if you break it, even better. tell me how you did it and i will ship the fix.
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