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Persistent memory for Claude Code using Mem0

Hacker News

Persistent memory for Claude Code using Mem0

Hey HN, I built this because I was frustrated re-explaining my setup every Claude Code session and hitting context limits faster than needed. The plugin uses mem0.ai to: - Automatically retrieve relevant memories before each prompt (UserPromptSubmit hook) - Store conversation context when sessions end (Stop hook) - Semantic vector search so only relevant context is injected Example: Claude now knows I prefer Python, my tech stack, and project context - without me repeating it. Technical details: - Pure Python, no dependencies except mem0ai - ~120 lines of code per hook - Works with Claude Code's official plugin system - MIT licensed Just submitted a PR to the official Anthropic plugin registry: https://github.com/anthropics/claude-plugins-official/pull/52 Happy to answer questions about the implementation or Claude Code plugins in general.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, user, 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 · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% 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
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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