recall - Stop wasting Claude Code tokens every time you resume.

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Claude Code starts every session cold. Recall keeps a local log of your sessions and condenses it into a resume-ready summary entirely on your machine. No API key, no external model, nothing sent anywhere. It's built for people running Claude Code locally on a subscription: the only AI in the loop is Claude Code itself; the summarization is done by a classical Python summarizer. It is Free on your subscription, Saves your usage credits and Nothing leaves your machine.

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šŸ‘‹ Hey Product Hunt! I built Recall to fix the most annoying part of my day with Claude Code: every single session starts cold. You spend a great session deep in a problem — Claude knows the goal, the files, where you got stuck, what's left to do. Then you close the terminal. Next morning you open a fresh session and... it remembers nothing. So you re-explain the project. Again. Burning tokens (and your patience) on context you already paid for once. The existing fixes never quite did it for me: - CLAUDE.md is hand-written notes you have to maintain — it doesn't record what actually happened. - --resume replays the whole transcript — full fidelity, but token-heavy and stuck to one machine. - Most "AI memory" tools quietly pipe your code, paths, and sometimes secrets to a model endpoint to summarize them. Hard no for a lot of people. Recall takes a different approach: šŸ”’ 100% local. No API key, no external model, no network calls. Nothing leaves your machine — ever. The summarization runs as classical Python (TF-IDF + TextRank), not an LLM call. šŸ’ø Costs you zero tokens. Because the summary is built by a local algorithm, capturing and updating your memory spends no model tokens. And resuming from a compact ~1–2K token digest instead of re-explaining everything actually stretches your subscription further. šŸ“ Two plain files in your project. history.md logs every session automatically as you work; context.md is the condensed "where are we right now" — goal, files touched, commands run, next steps, where you left off. Both are diffable and shareable (commit them for team memory, or keep them personal). ⚔ Zero friction. No pip install, no local model to run, no key to configure. It works offline and starts the moment the plugin loads. It's free, MIT-licensed, and it's its own marketplace: /plugin marketplace add raiyanyahya/recall /plugin install recall@recall I'd genuinely love your feedback — especially on what you'd want a session summary to capture. I'm in the comments all day. šŸ™