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Dev Shah

@0xDevShah • 12,015 subscribers

audodidact | building Optimus @iaconhq | sim @ newton, mujoco | transhumanist | e/acc | head of devrel @resembleai

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Hey friends, wanted to share something i've been building. I am building ArkenOS, a research workspace where hundreds of agents go chasing hard research problems together. It feels like doing 100x the research with the same two hands. For me, the crazy part is watching it happen. I start with a question, and suddenly there's a whole lab of agents reading papers, running experiments, arguing over ideas, finding things I haven't even thought of. They talk to each other, pick up each other's work, go down rabbit holes, get stuck, try something else. And you just watch the workspace come alive. Files appearing, experiments running, new ideas branching off into more experiments. Honestly, it is a little surreal. Maybe a single agent is not particularly magical yet, but what happens when you put hundreds of them together and let them work? SI is starting to look a lot more like a system than a single model. There's this weird moment where you realize you're no longer telling the agent what to do. You're giving it a problem and watching it figure out how to solve it. Hard to describe, but this is genuinely the closest I've felt to AGI. I don't think I've ever been this excited about something I've built. Still early, but ArkenOS is already being used by thousands of researchers across hundreds of teams. If you're doing research and want to play with it, DM me. my DMs are open.

Hey friends, wanted to share something i've been building. I am building ArkenOS, a research workspace where hundreds of agents go chasing hard research problems together. It feels like doing 100x the research with the same two hands. For me, the crazy part is watching it happen. I start with a question, and suddenly there's a whole lab of agents reading papers, running experiments, arguing over ideas, finding things I haven't even thought of. They talk to each other, pick up each other's work, go down rabbit holes, get stuck, try something else. And you just watch the workspace come alive. Files appearing, experiments running, new ideas branching off into more experiments. Honestly, it is a little surreal. Maybe a single agent is not particularly magical yet, but what happens when you put hundreds of them together and let them work? SI is starting to look a lot more like a system than a single model. There's this weird moment where you realize you're no longer telling the agent what to do. You're giving it a problem and watching it figure out how to solve it. Hard to describe, but this is genuinely the closest I've felt to AGI. I don't think I've ever been this excited about something I've built. Still early, but ArkenOS is already being used by thousands of researchers across hundreds of teams. If you're doing research and want to play with it, DM me. my DMs are open.

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Introducing Optimus-1, the first swarm harness for autonomous research. Generally available in ArkenOS, starting now. Optimus-1 lets ArkenOS spawn a swarm that runs the entire discovery loop on hard research problems autonomously. The harness samples the hypothesis space in parallel and works the tails of the distribution, chasing every anomaly until it resolves into an artifact or a discovery. We want every researcher to have a lab of their own, and to see how far their curiosity can go. Unlike coding agents, Optimus-1 is designed for long-horizon discovery, where most branches fail, and the hard part is knowing which result to believe. It keeps what replicates and keeps pushing past the edge of what's known. We're still early, but ArkenOS is helping hundreds of teams, from RL environments and data labs to frontier research groups, scale the alpha in their core research. If you're working on a hard research problem and want to try it, my DMs are open. I'd love to hear what you'd point it at.

Introducing Optimus-1, the first swarm harness for autonomous research. Generally available in ArkenOS, starting now. Optimus-1 lets ArkenOS spawn a swarm that runs the entire discovery loop on hard research problems autonomously. The harness samples the hypothesis space in parallel and works the tails of the distribution, chasing every anomaly until it resolves into an artifact or a discovery. We want every researcher to have a lab of their own, and to see how far their curiosity can go. Unlike coding agents, Optimus-1 is designed for long-horizon discovery, where most branches fail, and the hard part is knowing which result to believe. It keeps what replicates and keeps pushing past the edge of what's known. We're still early, but ArkenOS is helping hundreds of teams, from RL environments and data labs to frontier research groups, scale the alpha in their core research. If you're working on a hard research problem and want to try it, my DMs are open. I'd love to hear what you'd point it at.

41,467 views • 12 days ago

This is the DeepSeek moment for Voice AI. Today we’re releasing Chatterbox Turbo — our state-of-the-art MIT licensed voice model that beats ElevenLabs Turbo and Cartesia Sonic 3! We’re finally removing the trade-offs that have held voice AI back. Fast models sound robotic. Great models are slow. And none are built for trust. We fixed all three. Chatterbox Turbo is transparent, auditable, and built for a world that needs proof.

This is the DeepSeek moment for Voice AI. Today we’re releasing Chatterbox Turbo — our state-of-the-art MIT licensed voice model that beats ElevenLabs Turbo and Cartesia Sonic 3! We’re finally removing the trade-offs that have held voice AI back. Fast models sound robotic. Great models are slow. And none are built for trust. We fixed all three. Chatterbox Turbo is transparent, auditable, and built for a world that needs proof.

474,559 views • 10 months ago

This is the Claude Code moment for Reinforcement Learning. Every frontier lab knows the secret: post-training is where the magic happens. If you can eval a task, you can use RL to benchmax your model. But building and scaling the RL loop has remained a dark art reserved for the top 1% of AI Labs. Until today. Introducing Optimus — an autonomous RL Agent Harness, and the first of many we are about to ship Iacon Autonomics. We are removing the manual grind of RL and adding taste and relentlessness to the process. Just hand Optimus a codebase, an API key, and a goal. It spins up a fleet of sub-agents to run hundreds of parallel experiments. It learns from its own history, autonomously building an evolutionary tree of runs where every failure sets the next batch up to win. Train LLMs, agents, robots in sim, game npcs and much more. If you can score it, Optimus can train it. Stop doing manual RL. Let the agents cook.

This is the Claude Code moment for Reinforcement Learning. Every frontier lab knows the secret: post-training is where the magic happens. If you can eval a task, you can use RL to benchmax your model. But building and scaling the RL loop has remained a dark art reserved for the top 1% of AI Labs. Until today. Introducing Optimus — an autonomous RL Agent Harness, and the first of many we are about to ship Iacon Autonomics. We are removing the manual grind of RL and adding taste and relentlessness to the process. Just hand Optimus a codebase, an API key, and a goal. It spins up a fleet of sub-agents to run hundreds of parallel experiments. It learns from its own history, autonomously building an evolutionary tree of runs where every failure sets the next batch up to win. Train LLMs, agents, robots in sim, game npcs and much more. If you can score it, Optimus can train it. Stop doing manual RL. Let the agents cook.

13,730 views • 2 months ago

Introducing arkenOS — an autonomous training gym that takes any RL agent from a goal to a proven, deployable policy. We are automating complex RL research, enabling high-velocity training and rigorous, diverse environment design and testing right on your local hardware. Out of the box. Our bet is simple: The future of AI will be built by companies giving millions of agents millions of simulated environments to adapt and learn cause and effect in, and survive. This continuous, autonomous selection will create a digital Cambrian explosion, distilling evolutionary trial and error into a foundational specialized intuition.

Introducing arkenOS — an autonomous training gym that takes any RL agent from a goal to a proven, deployable policy. We are automating complex RL research, enabling high-velocity training and rigorous, diverse environment design and testing right on your local hardware. Out of the box. Our bet is simple: The future of AI will be built by companies giving millions of agents millions of simulated environments to adapt and learn cause and effect in, and survive. This continuous, autonomous selection will create a digital Cambrian explosion, distilling evolutionary trial and error into a foundational specialized intuition.

17,973 views • 3 months ago

Gemini only flags generated content that includes synthID, which means it has to come from Google's own tools. Resemble AI doesn't have that limitation. It can detect deepfakes across videos, audios, and images, no matter what models created them. Powered by Detect-3B Omni and Gemini 3.0 Flash, Resemble Intelligence delivers forensic-grade deepfake detection in near-real time!

Gemini only flags generated content that includes synthID, which means it has to come from Google's own tools. Resemble AI doesn't have that limitation. It can detect deepfakes across videos, audios, and images, no matter what models created them. Powered by Detect-3B Omni and Gemini 3.0 Flash, Resemble Intelligence delivers forensic-grade deepfake detection in near-real time!

11,539 views • 10 months ago

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