I replaced a memory app with two Markdown files and a Git repo

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I got tired of re-explaining myself to AI tools every session. Claude forgets me. Cursor forgets me. Switch from one to another and you're back to zero. Existing solutions (Nowledge Mem, Mem0) want you to install apps, configure MCP servers, run local LLMs, and set up plugins per tool. For what? To remember that I prefer tabs over spaces? Mem-Forever is a GitHub template repo. You click "Use this template", set it to private, and open it with any AI tool. The repo contains instruction files that every major tool auto-reads: Claude Code reads CLAUDE.md, Cursor reads .cursorrules, Codex reads AGENTS.md, Copilot reads .github/copilot-instructions.md, Gemini CLI reads GEMINI.md. On first use, the AI asks you a few questions and builds your profile. After that, it saves decisions, lessons, and preferences -- committed and pushed after every update, not batched to session end. Switch tools? Give the new one your repo URL and a PAT. One sentence, full context. No server. No app. No account. No vendor lock-in. Your data lives in your private GitHub repo. Free, MIT licensed. https://github.com/ilang-ai/Mem-Forever

Visit product View on Hacker NewsApr 26, 2026

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, model

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

20%20% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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