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Trissy @Cryptotrissy on x 15.5K followers
Created: 2025-07-18 08:10:10 UTC
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 X 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.
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Related Topics virtual robotics codec investment coins ai open ai $codec