I've spent the last several months building an AI memory system on nights and weekends, and the most valuable thing I learned has nothing to do with AI.
It's this: the moment you let what you hope is true override what you measured, you stop doing engineering and start doing marketing.
I caught myself doing it more than once. I had a headline result I loved - and the data quietly didn't support it. I had a clever feature I'd already written up as the fix - and when I finally measured it, it made things 25% worse. Each time, the honest move was to kill the thing I was attached to.
That's uncomfortable. It's also the only thing that makes a result trustworthy.
I'm going to spend the next 90 days here writing about building AI you can actually trust - the failures included, because the failures are where the truth is. If you work in AI, in financial services, or you're building something hard on the side, I'd love for you to follow along.
What's a result you were sure of that the data later overturned?
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