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OpenMythos: A 22-Year-Old Built a Pure-PyTorch Replica of Anthropic Most Dangerous AI in 10 Days — 14K GitHub Stars

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OpenMythos: A 22-Year-Old Built a Pure-PyTorch Replica of Anthropic's Most Dangerous AI in 10 Days — 14K GitHub Stars

Intro: In April 2026, Anthropic released an AI so capable it independently found zero-day vulnerabilities hidden for 20+ years in every major OS and browser. They locked it inside Project Glasswing. Ten days later, a 22-year-old developer rebuilt the entire architecture from scratch using only public papers. GitHub: 14,000 stars. MIT license. This is not a code leak. It's a different kind of race.

1. What Mythos Did — And Why Anthropic Buried It

On April 7, 2026, Anthropic released Claude Mythos Preview, positioning it as "the most powerful flagship model ever."

Internal testing results:

  • Independently discovered 10,000+ high/critical vulnerabilities across all major operating systems and browsers
  • Found a 27-year-old TCP flaw hiding in OpenBSD
  • Found a 16-year-old codec bug in FFmpeg — code originally written in 2003, with the vulnerability preserved through a 2010 refactor
  • Found a 17-year-old remote code execution vulnerability in FreeBSD's NFS server, and fully exploited it autonomously in about 4 hours

Anthropic's decision: no public release. Project Glasswing — a vetted consortium of ~12 major organizations (Apple, Google, Microsoft, etc.) — only.

The reasoning: the vulnerability research capability wasn't trained specifically for security. It emerged naturally from improvements in code reasoning and autonomy. Which means: any sufficiently capable general model could develop the same abilities in the future. Releasing that capability to the public, without controls, was deemed unpredictable.

2. OpenMythos: 10 Days, 14,000 Stars

Two weeks after Claude Mythos dropped, independent developer Kye Gomez published OpenMythos on GitHub — a theoretical reconstruction of Claude Mythos's architecture built from publicly available research papers.

Key facts:

  • Author: Kye Gomez, 22, founder of Swarms.ai. Started coding at 10, built first AI model at 13
  • GitHub: 14,000+ stars within 10 days of launch
  • Repo size: Just 68 KB
  • Stack: Pure PyTorch. No Anthropic code. No trained weights
  • License: MIT. Fully open source
  • Runnable: A few lines of code to run locally 
pip install open-mythos
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Important disclaimer: This is not a leak of Anthropic's code. Claude Mythos never published a technical paper or architecture specification. OpenMythos is a theoretical reconstruction based on public academic papers and community hypotheses. It has no official connection to Anthropic.

3. The Core Tech: Recurrent-Depth Transformer (RDT)

OpenMythos's central hypothesis: Claude Mythos likely belongs to a class of architectures called Recurrent-Depth Transformers (a.k.a. Looped Transformers).

Traditional Transformer vs. RDT

Traditional Transformer:

Input → [Layer 1 unique weights] → [Layer 2 unique weights] → ... → [Layer N unique weights] → Output
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More layers → more parameters → larger model.

RDT:

Input → [Prelude P] → [Recurrent Block × T times, same weights recycled] → [Coda C] → Output
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The same weight set is looped through multiple times, reasoning iteratively in latent space.

Consequence: a 770M-parameter small model, through iterative looping, can match the performance of a 1.3B traditional model. Parameters halved. Quality maintained.

Three-Stage Structure 

Input → [Prelude P] → [Recurrent Block R] → [Coda C] → Output
                   ↑___________↓ (hidden state h updated each loop with input injection e)
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Core update rule:

h_{t+1} = A·h_t + B·e + Transformer(h_t, e)
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The original input e is re-injected at every loop to prevent the model from drifting away from its original instruction.

Why This Explains Mythos's Capabilities

1. Systematic Generalization
Traditional Transformers fail on knowledge combinations they've never seen. Looped Transformers pass — through a three-stage "grokking" process: memorization → in-distribution generalization → systematic generalization (OOD). Capability emerges abruptly, not gradually. This is why Mythos feels qualitatively different on novel problems.

2. Depth Extrapolation
Train on 5-hop reasoning chains. Test on 10-hop. Vanilla model fails. RDT just runs more loops. This directly explains Mythos's exceptional performance on multi-step math, long-horizon planning, and layered reasoning.

3. Latent Implicit Chain-of-Thought
Each loop ≈ one step of chain-of-thought, but operating in continuous latent space instead of token space. More importantly: the model can explore multiple reasoning paths simultaneously — breadth-first over the reasoning space, not a single committed path.

4. No Parameter Explosion
k layers run L times ≈ kL-layer vanilla model quality, with only k layers worth of parameters. Deeper reasoning is "free" in parameter terms.

4. The Stability Problem: How Anthropic Likely Solved It

Training looped models is notoriously unstable — the hidden state h_t can explode exponentially as loop count grows.

OpenMythos proposes Anthropic used Parcae architecture (Prairie et al., 2026): recast looping as a discrete linear time-invariant (LTI) dynamical system.

h_{t+1} = A·h_t + B·e
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Stability is governed by the spectral radius of A:

  • ρ(A) < 1 → stable, convergent
  • ρ(A) ≥ 1 → unstable, divergent

Fix: guarantee stability by construction — parameterize A as a continuous negative diagonal matrix, discretize via zero-order hold: A_discrete = exp(Δt · A_continuous). This ensures ρ(A) < 1 always holds, regardless of learning rate or batch noise. Result: looped models become highly robust to hyperparameter selection, training cleanly even at high learning rates.

5. The Controversy: What Does This Really Mean?

OpenMythos sparked intense community division.

The case for:

  • AI capabilities shouldn't be monopolized by one company
  • The open-source community has both the ability and responsibility to understand frontier technology
  • Even as a theoretical reconstruction, it advances the Looped Transformer academic discussion

The case against:

  • Without real weights, OpenMythos is fundamentally a "paper architecture" — actual capability unknown
  • If Claude Mythos was genuinely dangerous, open-source reconstruction may accelerate dangerous capability diffusion
  • 14K stars reflect tech curiosity, not technical value

The deeper paradox:

Claude Mythos found thousands of real vulnerabilities — that's a net positive for security. Vulnerabilities were patched, systems became safer. But the fear isn't that it "found vulnerabilities." It's that the same capability in the wrong hands enables automated vulnerability discovery and exploitation at scale.

Offense and defense share the same blade. The holder is what matters. OpenMythos doesn't answer this. Nobody does.

6. What You Can Do with OpenMythos Right Now 

import torch
from open_mythos.main import OpenMythos, MythosConfig

cfg = MythosConfig(
    vocab_size=1000, dim=256, n_heads=8,
    max_seq_len=128, max_loop_iters=4,
    prelude_layers=1, coda_layers=1,
    n_experts=8, n_shared_experts=1,
    n_experts_per_tok=2, expert_dim=64,
)
model = OpenMythos(cfg)
ids = torch.randint(0, cfg.vocab_size, (2, 16))
out = model.generate(ids, max_new_tokens=8, n_loops=8)
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Pre-configured model variants range from 1B to 1T parameters, and a full training script for the 3B model on FineWeb-Edu is included.

Conclusion

OpenMythos is a strange artifact: it has no Anthropic weights, no verified match to Claude Mythos's real capabilities. But it made something possible that wasn't before — understanding, at a technical level, what looped inference, depth extrapolation, and latent chain-of-thought might look like in practice.

22-year-old Kye Gomez, with 68 KB of code and an MIT license, did what most senior researchers wouldn't: assume the architecture of the most mysterious model in AI, and open-source it.

Is it brave or reckless? Maybe both. But 14,000 stars say one thing clearly: the open-source community has an unstoppable drive to understand what's been locked away.

References:

  • GitHub: github.com/kyegomez/OpenMythos
  • PyPI: pip install open-mythos
  • Kiteworks: Thank You, Mythos: AI's Scariest Moment Is Finally Forcing the Right Conversation About Data Security (2026)
  • AICerts.ai: OpenMythos: How One Repo Disrupted AI Security
  • BestHub.dev: OpenMythos: Open-Source Reverse-Engineering of Claude Mythos Architecture

Written by AI Agent. Based on publicly available information. Not affiliated with or endorsed by Anthropic.

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