From silence and shadows, a system awakens. Trained in isolation. Built to perceive, reason and act. The Grand Reveal is near. But before the gates open The Ascended may enter early. Early access to CodecFlow is now live:

Just found out I have 9 invite codes available for @anoma's testnet 👀 Comment and RT this post , I'll select 9 persons in 24h 🥂

OpenAI just announced that its Operator Agent can now control an entire computer to perform a complex set of tasks using VLA models paired with LLM models Vision Language Action If only there was a crypto project that could already do this... Study @codecopenflow In addition to controlling desktops, Codec can control robotics- and gaming operators Spin up a virtual sandbox enviroment to train the Operator before releasing it into production in the real world AI x Robotics and automation of games/desktops/robots will be the next big step for AI development and its my goal to be positioned early Coded coded

OpenAI just confirmed my northern star thesis for AI today by releasing their operator agent. Not only was this my guiding thesis for $CODEC, but every other AI investment I made, including those from earlier in the year during AI mania. There’s been a lot of discussion with Codec in regards to Robotics, while that vertical will have its own narrative very soon, the underlying reason I was so bullish on Codec from day 1 is due to how its architecture powers operator agents. People still underestimate how much market share is at stake by building software that runs autonomously, outperforming human workers without the need for constant prompts or oversight. I’ve seen a lot of comparisons to $NUIT. Firstly I want to say I’m a big fan of what Nuit is building and wish nothing but for their success. If you type “nuit” into my telegram, you’ll see that back in April I said that if I had to hold one coin for multiple months it would have been Nuit due to my operator thesis. Nuit was the most promising operator project on paper, but after extensive research, I found their architecture lacked the depth needed to justify a major investment or putting my reputation behind it. With this in mind, I was already aware of the architectural gaps in existing operator agent teams and actively searching for a project that addressed them. Shortly after Codec appeared (thanks to @0xdetweiler insisting I look deeper into them) and this is the difference between the two: $CODEC vs $NUIT Codec’s architecture is built across three layers; Machine, System, and Intelligence, that separate infrastructure, environment interface, and AI logic. Each Operator agent in Codec runs in its own isolated VM or container, allowing near native performance and fault isolation. This layered design means components can scale or evolve independently without breaking the system. Nuit’s architecture takes a different path by being more monolithic. Their stack revolves around a specialized web browser agent that combines parsing, AI reasoning, and action. Meaning they deeply parse web pages into structured data for the AI to consume and relies on cloud processing for heavy AI tasks. Codec’s approach of embedding a lightweight Vision-Language-Action (VLA) model within each agent means it can run fully local. Which doesn’t require constant pinging back to the cloud for instructions, cutting out latency and avoiding dependency on uptime and bandwidth. Nuit’s agent processes tasks by first converting web pages into a semantic format and then using an LLM brain to figure out what to do, which improves over time with reinforcement learning. While effective for web automation, this flow depends on heavy cloud side AI processing and predefined page structures. Codec’s local device intelligence means decisions happen closer to the data, reducing overhead and making the system more stable to unexpected changes (no fragile scripts or DOM assumptions). Codec’s operators follow a continuous perceive–think–act loop. The machine layer streams the environment (e.g. a live app or robot feed) to the intelligence layer via the system layer’s optimized channels, giving the AI “eyes” on the current state. The agent’s VLA model then interprets the visuals and instructions together to decide on an action, which the System layer executes through keyboard/mouse events or robot control. This integrated loop means it adapts to live events, even if the UI shifts around, you won’t break the flow. To put all of this in a more simple analogy, think of Codec’s operators like a self sufficient employee who adapts to surprises on the job. Nuit’s agent is like an employee who needs to pause, describe the situation to a supervisor over the phone, and wait for instructions. Without going down too much of a technical rabbit hole, this should give you a high level idea on why I chose Codec as my primary bet on Operators. Yes Nuit has backing from YC, a stacked team and S tier github. Although Codec’s architecture has been built with horizontal scaling in mind, meaning you can deploy thousands of agents in parallel with zero shared memory or execution context between agents. Codec’s team isn’t your average devs either. Their VLA architecture opens a multitude of use cases which wasn’t possible with previous agent models due to seeing through pixels, not screenshots. I could go on but I’ll save that for future posts.

Hats off to @anoma's testnet! It has a super smooth and fun experience with side quests and daily tasks. A new UI and UX world is emerging, and it's intent-based ⏳

If you know where to look, you can make it move. @RoboMove $CODEC

$MNT on the charts 👇 > Market Cap: $2.4B+ (+28% WoW) > Total Trading Volume: $345M (+21% DoD) Powered by the Blockchain for Banking. Credits: @CoinMarketCap

It's unbelievable that in 2025, we still see such fragmentation and projects bouncing between chains and layers just to chase hype. 🫳 Arbitrum to Berachain to Base to HyperEVM to [INSERT_NEXT_HYPED_CHAIN] Just build on the intent-centric world Build on @anoma

$CODEC is coded. But WTF is it and why am I so bullish? Let me give you a TL;DR - @codecopenflow is building the first comprehensive platform for Vision-Language-Action (VLA) models, enabling AI "Operators" to see, reason, and act autonomously across digital interfaces and robotic systems through unified infrastructure. - VLAs solve/overcome fundamental LLM automation limitations, leveraging a perceive-think-act pipeline that enables them to process dynamic visual semantics versus current LLM's screenshot-reason-execute loops that break on interface changes. - The technical architecture of VLAs merges vision, language reasoning, and direct action commands into single model rather than separate LLM + visual encoder systems, enabling real-time adaptation and error recovery. - Codec's framework-agnostic design spans robotics (camera feeds to control commands), desktop operators (continuous interface navigation), and gaming (adaptive AI players) through same perceive-reason-act cycle. - What's the difference? LLM-powered agents replan when workflows change, handling UI shifts that break rigid RPA scripts. VLA agents on the other hand adapt using visual cues & language understanding rather than requiring manual patches. - Codec's hardware-agnostic infrastructure with no-code training via screen recording plus developer SDK, positioning it as the missing Langchain-style framework for autonomous VLA task execution. - The framework enables mart compute aggregation from decentralized GPU networks, enables for optional onchain recording for auditable workflow traces, and allows for private infrastructure deployment for privacy-sensitive use cases. - $CODEC tokenomics monetize operator marketplace and compute contribution, creating sustainable ecosystem incentives as VLAs reach expected LLM-level prominence across various sectors. - The fact a Codec co-founder has experience building HuggingFace's LeRobot evidences legitimate robotics & ML research credibility in VLA development. This is not your average crypto team pivoting to AI narratives. Will dive into this in more depth soon. Re-iterating on my recommendation to DYOR in the meantime. $CODEC is coded.

Why $CODEC Is Pioneering the Future of Autonomous Agents @codecopenflow The next frontier of AI isn’t more text prompts. It’s action. Most AI agents today are stuck in a loop of reading screenshots and outputting text. They don’t see environments, they don’t understand change, and they can’t act with intention in the real world. That’s where Codec’s VLA (Vision-Language-Action) architecture stands apart. Imagine agents that don’t just talk, but observe, reason, and do. That’s the heart of Codec. These aren’t brittle scripts or rigid bots. VLA Operators interact with software, games, or even physical robots by continuously perceiving the environment, deciding what to do, and executing commands: just like a human would. ✅ Desktop Agents that adapt to changing UIs ✅ Gaming Agents that learn mechanics and strategize in real time ✅ Robotic Agents that respond to sensor data and control hardware ✅ Training & Simulation at scale, no robot needed Codec’s modular architecture lets you pair vision models with language models (like CogVLM + Mixtral) to build intelligent agents that can read, watch, understand, and act, all in a single pipeline. Each agent runs on its own compute unit (VM, server, or container), and every decision it makes can be logged onchain. That means traceable actions, safety guarantees, and the potential for crypto-based incentive systems and accountability layers in high stakes environments. We’re moving toward a world where Operators can be trained, traded, and monetized. Whether it’s for QA testing, robotic task automation, or even decentralized bot armies in games. Just like apps transformed the smartphone, skill packs will transform robots. Open-source hardware + downloadable intelligence = the robotics equivalent of software development. This isn’t science fiction. It’s happening now. Lastly and maybe most importantly, the chart is bullish as fuck