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Project Journal – Give AI coding assistants persistent memory

Hacker News

Project Journal – Give AI coding assistants persistent memory

I created a simple technique to solve AI assistant context loss. The idea: Maintain a PROJECT_JOURNAL.md file that documents your project's story - decisions, progress, status, next steps. The AI reads it at session start (full context) and updates it at session end (persistent memory). I've been using this building Vibe CMS. Results: - Zero time wasted re-explaining - AI maintains consistency - Natural documentation - Better decisions (you write WHY) Made a free template (CC0): https://github.com/CursorWP/ai-project-journal Works with Claude, ChatGPT, Copilot, any AI. Would love HN's feedback on this approach!

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, context · Missing: mac, agents, macos
85%85% 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: created · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
25%25% 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
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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