The eval problem nobody wants to talk about
Benchmarks are quietly broken, and most teams are shipping on numbers that don't mean what they think. Here's how to build evals you can actually trust.
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5 MIN
Every weekday morning, Inference turns the firehose of AI news into a five-minute read. No hype, no fear — just what actually shipped and why it matters to you.
A new release can hold a goal across hours of autonomous work without losing the plot. We break down what changed, what it means for agents, and the one limitation nobody's mentioning.
The same dependable structure every day — so you always know where to look, and you can stop reading the second you've got what you need.
Benchmarks are quietly broken, and most teams are shipping on numbers that don't mean what they think. Here's how to build evals you can actually trust.
When a capable model is free to run, the build-versus-buy decision flips overnight. We walk through the new economics with real numbers.
Long-running agents fail in predictable ways. We map the five most common failure modes — and the cheapest guardrail for each one.
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Before Inference, Dana spent eight years as a machine learning engineer — building recommendation systems, shipping models that didn't always work, and reading far too many papers at 2am.
Inference started in 2022 as a Slack thread for a handful of colleagues who kept asking the same question: "wait, what actually happened in AI this week?" It's now read by 182,000 people, and it's still written by one person, by hand, every weekday before 7am.
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