Blog
Notes from the team
Things we have learned building software and working with data. No filler, just what has worked for us.
How we keep our AWS bill from quietly creeping up
An AWS bill rarely jumps in one go. It creeps, a little each month, until someone finally looks. Here are the habits that keep ours honest.
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The small frontend details that make an app feel finished
The difference between an app that feels rough and one that feels solid is usually a handful of small things nobody notices when they are done right. Here are the ones I always check.
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Putting an LLM into production is mostly the boring parts
The demo is the easy bit. Making an AI feature reliable, affordable and honest is where the real work sits. Here is what I keep running into.
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Our Docker images got smaller once we stopped overthinking them
A big, slow Docker image is usually a sign of a few habits, not one big mistake. Here is how we get ours small and our builds quick, without anything fancy.
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Coming to the frontend from Java, what caught me off guard
I started out in Java and moved into frontend work. A lot of it transferred fine. A few things genuinely surprised me. Here are the ones worth knowing about.
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Long AI jobs need a proper tool, not a scheduled task and a prayer
Long, multi-step AI jobs fail partway through all the time. Here is why we use a tool built for that instead of stitching queues together and hoping.
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Five signs your nightly data job should run live instead
Nightly data jobs are simple and cheap, right up until they are not. Here are five signs it is time to move to live data, and how to do it without starting over.
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Running a GraphQL API on AWS Lambda, the good and the annoying
Running a GraphQL API on AWS Lambda keeps costs near zero when it is quiet and scales on its own. Here is what works, what bites, and how we keep it fast.
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