When Anthropic introduced its powerful new model, Claude Mythos , this spring, companies and countries freaked out . The general-purpose model, its creators claimed, could discover software vulnerabilities that no one knew existed.

Rather than release Mythos to the world, the company gave it to cybersecurity experts at major companies that build or maintain critical software infrastructure and told them to use it to find and fix bugs before Anthropic unleashed it on the world.

But less than two weeks later, 22-year-old developer Kye Gomez made educated guesses about the core design that makes Claude Mythos so powerful and published OpenMythos , a public project that approximates Anthropic’s breakthrough. Gomez’s code raced through the research community like a prairie fire.

This story has several startling implications : if a self-taught developer can reverse-engineer the structural innovation of a multi-billion-dollar lab in a matter of days, then the proprietary moat around AI architecture may already be gone. Gomez’s OpenMythos, which allows developers to train and run efficient versions of these models on laptops, also calls into question long-term reliance on the massive, energy-intensive data centers now under construction. Finally, and most importantly, governments can no longer count on AI secrets staying in the hands of a few. If thousands of individuals and small teams around the world can reverse engineer the work of the biggest AI companies, global safety enforcement will be increasingly complex, if not impossible, as advanced capabilities spread.

Gomez’s background does not follow the standard route into machine learning. He grew up near Miami and began coding as a kid. He left high school early and never went to college. Instead, he built a track record through code.

He also created Swarms , a framework for coordinating large numbers of AI agents, and launched Agora AI Labs , a loose research collective. Together, these projects form a small ecosystem built outside traditional institutions.

The Core Idea Behind OpenMythos

OpenMythos centers on Gomez’s hypothesis that Claude Mythos uses a new model paradigm that has been developing since 2022 and became reliably trainable at scale earlier this year . Rather than stacking neural network layers to give a model depth, researchers proposed looping data repeatedly through a smaller stack – in effect, giving the model depth through time.

Another way to describe it is giving the model time to think, with each loop improving its output.

As Gomez, himself, wrote on GitHub, the code repository favored by developers, “Claude Mythos is suspected to be a Recurrent-Depth Transformer (RDT) — also called a Looped Transformer (LT). Rather than stacking hundreds of unique layers, a subset of layers is recycled and run through multiple times per forward pass. Same weights. More loops. Deeper thinking.”

That has led to claims about smaller systems matching larger ones, claims that have yet to be proven. Gomez’s project does not yet include trained weights or full evaluations and has only trained a relatively small model (770 million parameters). He has written instructions to scale to 1 trillion parameters, but that would take tens of millions of dollars to actually do.

Still, the impact has been immediate. Developers are experimenting with the architecture, writing tutorials and debating whether this kind of design could shape the next generation of models.

Whether OpenMythos matches Anthropic’s design is almost beside the point. The project shows how quickly ideas can spread once enough clues are made public. It also highlights a shift in where experimentation happens. Independent developers now have the tools to explore architectural ideas that once stayed inside major labs.

Even if Gomez’s “theoretical implementation” is not what Anthropic is doing, it gives researchers something concrete to test. That alone can shape how the field evolves.

OpenMythos is another example of how fast cutting‑edge AI techniques propagate and how badly our safety and governance mechanisms lag.

In January 2025, DeepSeek , a relatively small Chinese lab, produced a reasoning model that rivals the performance of the biggest Western systems at a fraction of the cost. It has since been copied into downstream systems around the world.

In November 2025, a single developer created what is now known as OpenClaw, an open-source autonomous AI agent with none of the institutional safety machinery that usually wraps that kind of power.

The result is big‑tech autonomy without big‑tech safety, part of an emerging world of fast‑moving, bottom‑up hackers that clone and release frontier capabilities.

Gomez has said he plans to train larger versions of the model and connect them with his agent framework. Whether that leads to a serious competitor or a series of experiments will depend on resources and execution.

For now, OpenMythos has already done one thing clearly. It has shown that a single developer can operate at a level once dominated by large companies — and that the next breakthrough may come from anywhere.