AI memory and project management (open source)

Hacker News

Hey HN, I'm Namanyay and I've been using Cursor since 2 years, but it seems to make mistakes as the project size grows. This caused me to have to repeat myself a lot and it used to break my flow. I realized it's because Cursor (and other AI tools) are not able to keep an overview of the project. Plus, Cursor does not have a "memory" layer to remember important things that the user has already mentioned. I solved this by building an open-source MCP which maintains project and memory information within the user's file system. It's simple and installation takes 30 seconds. Github: https://github.com/namanyayg/giga-mcp Demo: https://www.youtube.com/watch?v=BWMHCwg7Pwo Would love feedback! Are you using AI already? Do you encounter issues like this?

Visit product View on Hacker NewsApr 28, 2025

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

94%94% 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: mistakes · Missing: supports, reddit linkedin, podcasting

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

53%53% 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

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: plus · Missing: platform, intuitive, reviews

34%34% 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

17%17% 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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