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Cursor IDE now remembers your coding prefs using MCP

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Cursor IDE now remembers your coding prefs using MCP

Hi, I'm Daniel from Zep. I've integrated the Cursor IDE with Graphiti, our open-source temporal knowledge graph framework, to provide Cursor with persistent memory across sessions. The goal was simple: help Cursor remember your coding preferences, standards, and project specs, so you don't have to constantly remind it. Before this integration, Cursor (an AI-assisted IDE many of us already use daily) lacked a robust way to persist user context. To solve this, I used Graphiti’s Model Context Protocol (MCP) server, which allows structured data exchange between the IDE and Graphiti's temporal knowledge graph. Key points of how this works: - Custom entities like 'Requirement', 'Preference', and 'Procedure' precisely capture coding standards and project specs. - Real-time updates let Cursor adapt instantly—if you change frameworks or update standards, the memory updates immediately. - Persistent retrieval ensures Cursor always recalls your latest preferences and project decisions, across new agent sessions, projects, and even after restarting the IDE. I’d love your feedback—particularly on the approach and how it fits your workflow. Here's a detailed write-up: https://www.getzep.com/blog/cursor-adding-memory-with-graphi... GitHub Repo: https://github.com/getzep/graphiti -Daniel

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, cursor, model · Missing: mac, agents, macos
99%99% 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
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% 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 · Strong signals: way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
38%38% 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
14%14% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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