When pitching AI services to local businesses, your proposals need to be more than some basic static site. It needs to flow well and describe all the things you're going to do and why. https://tronix-auto.amber-chord-3544.chatgpt.site

If you build with Claude Code or Codex, you've probably hit your usage limit in the middle of something. That kept happening to me, so I stopped guessing and built a small tool to find out why. It's called Token Leak Finder. It reads the session logs Claude Code and Codex already save on your computer, then gives you a one-page report. The report ranks the habits that use up the most of your limit and gives you a fix for each one. What it found on my last 30 days: - About 26% of my usage was avoidable. - More than half (51.3%) came from one habit: keeping the same chat going too long without compacting, so the AI re-reads a huge conversation on every message. - The fix was one line in my CLAUDE.md: when the chat gets near 150k tokens, write a short handoff, then /compact or start fresh. How to run it (you need Node.js installed). Open a terminal and paste: npx token-leak-finder --open The report opens in your browser. Add --redact if you want to share a screenshot without your project names showing. It's free and open source (MIT). It runs only on your computer: no signup, no network calls, and nothing gets uploaded. GitHub: https://github.com/RomeoJuliett/token-leak-finder npm: https://www.npmjs.com/package/token-leak-finder X post with the full breakdown: https://x.com/roughneckactual/status/2116646833930060121 Try it and reply with your biggest leak. I'm curious what shows up for you.

Before recording, decide what the viewer should be able to do afterward. One useful lesson is enough for one post. - Explain one problem. - Give a clear example using a neutral demo. - Show the next step. - Make the title and cover match what you actually say. - Check the existing queue before scheduling. Space the videos out so each lesson gets room. You can teach the process without showing your private project, accounts, or product plans.

A dashboard with no new issues doesn't settle whether a change is working. I want to know how many people actually reached the changed feature. Before expanding a release, write down: - The behavior that changed. - How much real use that behavior received. - The problems you're watching for. - What would make you stop and investigate. If very few people tried the feature, you haven't learned much about it yet. A quiet dashboard is useful. Enough relevant use is what gives it meaning.

These are different milestones. Saved means your change exists in the console. Submitted means you've sent it into the review process. Approved means the store accepted it. Available means people can get it. Check each platform separately. Don't let one status stand in for the whole release. Before announcing that an update is live, check the public listing and availability. The state that matters is the one your user sees.