AI coding agents need a searchable, private corpus of all their conversations (Claude, Copilot, Ollama, etc.) to prevent repeating context and hitting usage limits.

Target User

Software developers and prompt engineers using multiple AI coding agents

Product Idea

A local CLI tool that captures all agent output streams, indexes them into a local vector store, and provides a search command to feed relevant context back into the current agent session.

Evidence

• “I built scritty because I got tired of agents making me repeat myself. Deep into a problem on Claude, I'd hit the usage limit mid thought or debugging, so then I would go over to Copilot which is now +”

  https://www.producthunt.com/products/scritty?utm_campaign=producthunt-api&utm_medium=api-v2&utm_source=Application%3A+11+%28ID%3A+280766%29

Why this opportunity matters

This opportunity surfaced from multi where Software developers and prompt engineers using multiple AI coding agents are actively describing the problem in their own words. We classified the demand intensity as high — meaning users are not just complaining, they are openly looking for paid solutions and willing to switch tools to find one. When users self-select to post a problem on a public platform like multi, they are usually already trying to solve it — which makes them strong candidates for the right paid alternative.

How to validate this before you build

Before writing a single line of code, three lightweight steps will tell you whether this opportunity is worth pursuing:

1. Read the original conversation. The evidence links above lead to the exact threads where users described this problem in their own words. Read every comment — what people argue about in the replies often matters more than the original post.
2. Talk to five real users. Find five people who match the target user description above and ask how they handle this today. You are looking for two signals: a consistent workaround they hate, and a willingness to pay for something better. If both show up in three of five conversations, the demand is real.
3. Test willingness to pay before building. Put up a one-page landing site describing the proposed product, with a deposit or pre-order checkout. If even a small fraction of traffic converts to a real payment, you have validated more than most products that ship to production.