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CoreMem – Portable context for AI agents

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CoreMem – Portable context for AI agents

CoreMem lets you build collections of context, called a mem, and share it with any AI agent via URL, a Chrome extension, MCP, Cursor/VS Code plugins, a skill, and more. Instead of re-explaining your project or goal when you switch agents or start new sessions, CoreMem keeps your context centrally organized so that any AI tool can read it. This originally started as a CLI I built that kept pieces of context (Project A/B/C details, my writing style, preferred tech stacks, coding style, etc) in a SQLite database. I could instruct various agents to “use my `coremem` CLI to retrieve details about [project A] before we get started.” It solved a problem for me b/c I am continually bouncing around between different projects and chat agents, and having to re-explain myself every time became an exercise in either repeating myself or copy/pasting summaries I’d saved from previous sessions. I decided to make this a little more robust and portable, so I turned that original CLI into a SaaS. Tl;dr: You can create a “mem”, which is a collection of 1 or more pieces of related context, and share that mem with any agent to quickly get them up to speed. Right now I’ve got integrations in the form of revokable share links, a Chrome Plugin, Cursor Plugin, Cursor/VS Code extension, Claude Code plugin, ChatGPT/Claude/Gemini/et al via MCP. Since I mostly work from the CLI, I use the Claude Code plugin or create 5-min share links I can drop into a chat, but I’ve tried to make this useful to people who mainly work from a browser or an IDE. I’ve been coding for 30+ years, and I vibed most of this. I was able to use CoreMem to help it built itself as I jumped between various coding agents, having them grab context then start a new task. I’m sure my architecture and engineering experience helped, but building this in a few weeks confirmed for me that the barrier for someone to build a tool they need to solve a problem is incredibly low. The rush I used to get from coding has mostly faded, but I’m getting similar rushes managing different agents to build things now.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, cursor · Missing: mac, macos, model
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, gemini · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, saas · Missing: mrr, revenue, profit
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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