7 months ago,
@Karpathy called AI agents "slop".
Yesterday, he joined Anthropic.
The game has changed - the capabilities of frontier models and swarms of AI agents are growing incredibly fast.
This massive capability growth has shifted the work of developers: boilerplate is easier than ever before, but great engineering architectural thinking may be more valuable than ever.
Mojo, and
@Modular, are embracing the new era of agentic engineering. We've designed the language to be easy for AI agents and humans alike. We've written out structured kernels series to show you how to port kernels to Mojo, achieving increased simplicity, optimization, and hardware portability. We've built out skills examples to make it easier than ever for you to use LLMs to build with Mojo.
@ehsanmok took our skills for a test drive to prove what's possible - solo building 10 libraries and a production pastebin in a few weeks using Mojo and AI agents.
The dependency graph across all 11 repos has only 3 compile-time edges. That's not what happens when you turn agents loose without a plan, it's effective architecture applied by agents.
The result is a 1.1 MB binary serving sub-millisecond requests on a free-tier
Fly.io VM, with property-based fuzzing that caught real bugs in HTTP header parsing before any of them shipped. Ehsan built that production system solo while the agent handled most of the typing.
What changes when one developer can do that is the shape of the job itself. Boilerplate has become simple. Implementation isn't a moat anymore. The remaining differentiation lives in the design choices that came before the agent touched a keyboard: which libraries should exist, what their boundaries should be, what they should refuse to do, how they fit together. Teams that invest in that judgment will compound. Teams treating agents as a faster way to ship the same mediocre architecture will discover that bad code at agent speed is still bad code, just more of it.
This is the bet Modular is making with Mojo, the structured kernels series, the published skills, and everything else we are doing to create an open, portable AI software stack that runs on NVIDIA, AMD, and Apple silicon from one codebase. The languages and toolchains best suited for agentic engineering will be the ones that give agents tight feedback loops, deterministic builds, and clear patterns to imitate.
Agentic engineering will compound. It's why you're seeing top AI talent, like Karpathy, joining the best agentic engineering companies in the world.
Ehsan's ecosystem is the early proof the bet is working.
Read the full breakdown of his work:
modular.com/blog/how-i-built…
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