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LLM Memory Notes – semantic memory layer for AI agents (MCP)

Hacker News

LLM Memory Notes – semantic memory layer for AI agents (MCP)

Hi HN, I’ve been building LLM Memory Notes (LLMMN), a hosted semantic memory layer that gives AI agents persistent, searchable memory via the Model Context Protocol (MCP). You can create “Memories” (containers) and save “Notes” (entries) that are indexed using AI embeddings; agents can then search by meaning (e.g., searching “database error” will find relevant notes about “PostgreSQL timeout” or “MySQL connection failed”) . To get started, sign up on llm‑memory.com, create a Memory in the web UI, and obtain your API token . Add the server to your .mcp.json and use your MCP client’s ReadMcpResourceTool and AddNoteTool to query or add notes ; the docs have a full Quick Start guide . LLMMN is a hosted service (not open source), currently in a public beta with free early access; final pricing is still being determined . We’d love feedback from anyone building agent tooling or experimenting with persistent context layers!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
96%96% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: started · Missing: supports, reddit linkedin, podcasting
48%48% 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: open source, ide, io · Missing: https docs, excited, just released
35%35% 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
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
18%18% predicted probability of success on AppSumo, 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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