Me

Memsprout – share AI context with your teammates

Hacker News

Memsprout – share AI context with your teammates

I’m an architect at my company and everyone on my team is now using Claude/Codex agents for their work. Managing context in claude.md/agents.md files is fine but so much of the knowledge required doesn’t fit cleanly in one repo or the other. Plus, much of the team (product, design, support) does not work in repos but still needs a place to share context. I searched for existing solutions to this but only found products meant for managing deployed AI agent memory for use in AI applications. I wanted something that would help my team share the context that lives in our collective heads. I first tried solving this problem by creating a directory structure with an MCP layer on top of it, but that became a pain to maintain. So I started tinkering with creating a platform for context sharing that works for the whole team. For this to work I wanted to prioritize two things above all else: 1. Easy to store valuable context. If it feels like maintaining docs, no one will use it. 2. Easy to share context with the right people. Access controls, read/write permissions, etc. This turned into memsprout. The build out is not revolutionary by any means but I tried to make it as approachable as possible to use. With memsprout your agent can capture memories and you can decide if and when to share them to your teammates in dedicated spaces with access controls, permissions, etc. nto specific buckets So now I am able to store memories for bits of context I define right from my agent conversation as I work and easily share them with my teammates. Q: Do you guys also see this kind of context sharing as a challenge? How have you solved it in your work?

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
98%98% 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: started · Missing: supports, reddit linkedin, podcasting
67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, platform · Missing: intuitive, reviews, host
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, lua, existing · Missing: https docs, excited, just released
28%28% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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