I built a pocket dimension for notes and context in LLM-space

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I've been building a bunch of little apps with AI tools for the last few months and find myself constantly passing research, plans, and working frameworks between tools and projects. At this point it's become one of the more time-consuming parts of my creative process and gets in the way of the parts I enjoy, so I built a tool to make it easier. It's up where anyone can use it and it's free — if people find it useful and want more than the basic level of daily usage I can afford to cover myself, I'll think about offering a paid tier. Here's how it works: 1. You sign up and get your own notes space (using github for ID, not code access) 2. You connect your tools (this is one-click for Cursor and VS Code, copy-paste for Claude Code, and a couple of clicks for Claude) 3. Use your tools as usual, letting them know when and what to store in your space 4. Any of your connected tools can then look for relevant notes while they work (or you can tell them to reference specific notes anytime) It's pretty flexible, so I'm still exploring what it's best for. Right now I'm using it to store and retrieve .rules files and commands I re-use regularly, pass project plans between the Claude (where I brainstorm throughout the day) and Cursor on my laptop (where I write and generate code), and have llms working in separate codebases store shared knowledge they both need. The stack for the service includes... - Supabase for auth + storage - Next.js + shadcn for the web app - A MCP server to handle LLM<->DB communication Happy to answer any questions folks have and would love to hear what anyone who gives it a try thinks (good and bad)

Visit product View on Hacker NewsSep 24, 2025

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

97%97% 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: para · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, month, way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

48%48% 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

32%32% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

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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