Ùgò Chúkwú

Prompting is an interface design problem. The prompt is the contract between your application and the model’s capabilities. Good interfaces have explicit contracts, handle edge cases, behave predictably. Build your prompts the same way. 👇 x.com/ChukwuAugustus…

Prompt iteration without evals = random walk. You don’t know what changed, don’t know if it helped, can’t reproduce improvements. Version. Test. Measure. The model is the capability. The prompt is the interface. Build it right. New opinion piece → x.com/ChukwuAugustus…

The universe is designed with you in mind.

Prompt "engineering" done without version control, evals, or systematic iteration isn’t engineering. It’s guessing with extra steps. Here’s the system that makes prompts actually maintainable at scale: x.com/ChukwuAugustus…

We’re excited to announce that the new Ohere Credit App V2 is now live on both Google Play Store and Apple App Store. Download it to experience upgraded features and benefits. Google Play Store: play.google.com/store/apps/det… Apple App Store: apps.apple.com/ng/app/ohere-c…

The discourse around model size has a skew problem. The people writing about frontier models are the people building them, benchmarking them, or covering them. Not the people running 50,000 classification requests a day watching their API bill. x.com/ChukwuAugustus…

Unpopular opinion: most production AI use cases don’t need frontier models. The 7B models that run on a laptop have gotten quietly excellent, and most teams never needed GPT-5.5 to begin with. x.com/ChukwuAugustus…

@Simeon_Cps That’s why they push updates everyday. Have you ever shipped and maintained a working app that hundreds or even thousands of users use everyday?

I was on track to become a Claude Code hater until I spent a day using Codex

Trust the path you choose and it will protect you, even in darkness.

If you figure out how a large multi-agent system can autonomously coordinate to maximum effect, you’ll win a Nobel Prize, a Turing Award and a trillion-dollar fortune all in one.

@sama good work 👍

The gap between prototype AI and production AI is mostly output engineering. A 1% JSON failure rate = 10 broken documents per 1,000. At scale, that’s not acceptable. Here’s how to make structured outputs actually reliable: x.com/ChukwuAugustus…

we love seeing our users win. we want to give you the best tools, lots of compute, and watch you do the magic.

Hot take: the most common AI system design mistake isn’t choosing the wrong model. It’s using the context window as a database. Here’s why that’s a problem, and what to do instead 👇 x.com/ChukwuAugustus…