Arafat Siddiqui

Hey hunters

We keep hearing from AI Context Flow users that ChatGPT forgets what they told it, so we dug into why.

Short version: it s almost always memory being off or full, instructions living in a one-off chat instead of Custom Instructions or a Project, or a thread that outgrew the context window. Most of these take under 2 minutes to fix.

The part no setting fixes: ChatGPT s memory never reaches Claude, Gemini, Cursor or Claude Code. That cross-tool gap is why we built AI Context Flow, a shared memory layer (Chrome extension + MCP server) that every tool reads from.

When UnleashX was handling a few thousand conversations a month, I used to read through a lot of them myself, and I had a pretty good feel for where the agents did well and where they needed work. Once we got into millions, that just wasn t possible anymore, and it completely changed how we think about building.

Speed was the first thing we had to get right, because on a voice call even a one-second pause feels strange, and it has to stay that fast on the busiest Monday morning too. We also stopped trying to review everything and built a way to flag the conversations that actually need a human to look at them, like an unhappy customer or a booking that didn t go through. And we learned that small improvements matter a lot more at this size, since getting even 1% better means thousands more people get what they needed the first time.
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The biggest lesson, though, is that customers don t really care how many calls we ve handled. They care that their call went well, and that s what we try to keep in mind every day.

Dashboards are loud. The signals that matter are usually quiet, and I'd like to collect yours.

I've written here before about how our most active users at Murror turned out not to be our happiest ones. That changed how I read numbers. Sessions, streaks and retention curves all measure that people came back. None of them tell you whether coming back was good for them.

Since then I've been more interested in small, quiet signals that are hard to put on a chart. Things like a user who stops using your product because they no longer need it. Someone who writes in to correct a detail, which means they read closely. A person who recommends it with a caveat, because they're being honest about who it is and isn't for.

My own rule of thumb is that a signal is worth trusting when it would still look good if you were wrong about your product. A big engagement number can rise because people love something, or because it nags them. A quiet signal like "they told a friend, unprompted" is harder to fake, even by accident.