Continual Learning with .md
I have a proposal that addresses long-term memory problems for LLMs when new data arrives continuously (cheaply!). The program involves no code, but two Markdown files. For retrieval, there is a semantic filesystem that makes it easy for LLMs to search using shell commands. It is currently a scrappy v1, but it works better than anything I have tried. Curious for any feedback!
Continual Learning with .md launched on April 13, 2026 with 34 votes, #926 of 21,248 launches that month and more than 95% of that year's launches.
In plain words
Continual Learning with .md is a system for managing long-term memory in language models when new data arrives continuously. It consists of two Markdown files and a semantic filesystem that allows LLMs to search information using shell commands. Designed for developers working with LLMs, the tool addresses memory retention challenges without requiring code. Currently in early version, it offers an alternative approach to existing solutions for continuous data integration.
written from the facts on this page · September 2026
From the sources
Turn your files into a memory filesystem for AI agents. - SunAndClouds/ReadMe
It doesn't make sense to ask your agent about your codebase if it cannot read the code; it needs context. Similarly, an agent can't give you good responses if it doesn't know about you. ReadMe turns your files into a "codebase," so that next time you ask something, it can look up the relevant context about you before answering. codex exec --dangerously-bypass-approvals-and-sandbox --ephemeral - < Init.md Or paste Init.md into Codex CLI/App. Source traces: ├── ~/.codex/sessions/ ├── ~/.claude/sessions/ ├── ... ├── ~/Downloads/ └── ~/Projects/ Builds ~/.codex/user_context/ ~/.codex/user_context/ └── 2026_agentic_rag_memory_systems/ ├── year.md └── 2026-Q2_prompt_programs_context_retrieval/…from github.com
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