Maybe it's because I'm obsessed with the topic, but it feels like everyone in AI is suddenly talking about verification 🤓. Not all verification is the same, though. Most of what labs ship today is probabilistic: agents cross-checking each other, one model grading another's work. It scales to fuzzy, human problems, and honestly, it's the only thing that does. But you get confidence levels, never guarantees. At @AIPredictable, we're betting on deterministic verification. Mathematical methods that give you a binary answer: does the code do what the spec says? Yes or no. We generate specs, translate them into Lean, and formally verify every possible theorem about expected behavior. Not just the happy path. It doesn't apply to everything. Ask it a philosophical question, and it's useless. But for code in banking, pharma, or healthcare, where "probably correct" doesn't cut it, the game changes. So, honest question: is probabilistic verification enough for what you're building, or do you need the math?

Your compiler trusts you. It shouldn't. C# compiles a recursive loop that will slowly shove your database off a cliff. No warnings, no errors. Lean refuses to compile the exact same logic until you prove it terminates. 🧵

Predictable Code is alive!

When AI generates your code, we prove it does what you specified. Our first case study is live: full walkthrough on a real Java codebase, with a recording of every step. predictablemachines.com/blog…

The one question I keep coming back to with AI-generated code: does it actually do what I specified? Our first @PredictableCode case study answers that end-to-end on a real Java codebase, with a full recording of the workflow. predictablemachines.com/blog…

We'll be at @seattlefloworg Startup Day 2026 on May 15th, demoing @PredictableCode at the Startup Fair. Come see what it looks like to formally verify AI-generated code. Not "passes review," actually proven correct. 👌 Tickets 👉 seattleflow.org/startupday

AI writes the code. AI reviews the code. A developer clicks "merge" in under 10 minutes. In that single click, all the legal and regulatory liability concentrates in one human, for code they didn't write and couldn't fully read.

Every team has a doc that's quietly become a lie. The README wasn't updated when the API changed. The Notion page from six months ago. We used to shrug at it. Then we started pasting it into Claude as context for the next feature.

Code that runs, compiles, passes tests, and still isn't doing what you asked. Same requirement, three AI sessions, three incompatible implementations. The second piece of our pain points series is up. predictablemachines.com/blog…

Ten developers, ten AI sessions, ten slightly different ideas of what the codebase is supposed to do. First of a short series on the pain points we're solving with Predictable Code. predictablemachines.com/blog…