We are at @seattlefloworg sharing what we’re building at @AIPredictable.
We'll be at the Startup Fair. Live demos of @predictablecode, great conversations, and a lot more coming soon ❤️
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.
https://t.co/B21DHOwXFe
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.
https://t.co/2E14KNqwpy
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 👉 https://t.co/5eusyEE8ty
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.
https://t.co/025A3QEuT0
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.
https://t.co/U71O2VS2zo