2026 is the breakout year of robotics and no one is paying attention. Modular tech, practical usecases, insane levels of abstraction, zero VC and currently on sale; $CODEC might be the free-est trade in robotics rn Read further if you care about front-run the incoming liquidity 👇🏾 👇🏾 1) VC investment in robotics startups has exploded, rising from under $7B in 2023 to over $35B in 2025. Powerhouses like ScaleAI and Figure raised a combined $15.3B last year. 2) At the same time, Amazon is aggressively expanding automation, targeting up to 75% robotics integration across its operations by 2033 to meet projected growth in sales. 3) The era of prototypes is gone. Startups are moving from demos to commercial deployment this year. 4) Even $VIRTUALS is actively expanding into robotics with massive investment and vision The shift is clear and 2026 seems to be the inflection point. Despite the breakthroughs and progress so far, many demos still struggle with clumsiness, high power consumption and safety. More importantly, intelligence is siloed. Most humanoid robotics companies maintain proprietary stacks: their own models, training pipelines, datasets, and deployment layers. Even with a modular tech stack, intelligence is still isolated. So, new entrants are forced to repeatedly rebuild the same foundations from scratch instead of advancing intelligence across board Codec is rewriting the rules of robotics development @codecopenflow allows teams build robotic intelligence once and run it across machines so that perception, decision making and control can move between robots instead of isolated proprietary platforms. It works through four core primitives: ➛Unified data: A common data schema standardizes sensor inputs, giving robots and AI agents a shared understanding of their environment. ➛Composable operators: A marketplace for modular reusable skills (also known as operators). Robots across industries can transfer capabilities without retraining from scratch. ➛Fabric and marketplace: A distributed compute router ensures that these operators run on any hardware or cloud, while the marketplace handles discovery and royalties. ➛Compounding Intelligence: When an operator improves, the performance gains propagate across every connected robots… adoption drives more contributions, creating a compounding feedback loop (shared intelligence) Think of it like an internet for robots. Your robot plugs in, search for am operator (skill), pays for it with x402 rails, installs it and Instantly gains a new capability. Some companies like figure already aggregate learning across fleets, but it happens within closed, monolithic architectures. Figure robots (general-purpose robots btw) , once trained and deployed, are able to aggregate experience from every unit across various industries into a central model (a shared brain). New capabilities learned in one context can quickly transfer to robots in another industry, accelerating deployment of features across different industries like a hivemind while also solving the data scarcity problems. Codec amplifies this approach at scale. With an opensource model that allows anyone contribute developers, researchers, and startups can fork, tweak, and push upgrades back into the network Closed systems can’t compete with that velocity. Codec is amplifying intelligence through collective learning, community driven innovation and data aggregation. Picture this: You’re hosting a goth party with the foids, but you need decoration done by your clueless general purpose robot. All you have to do is look up “goth-themed decoration” operator on Codec marketplace, pay for the skill and install it. Applications extend to corpo workflow, manufacturing assembling, retail chain, hospitality and so on. Consider an SMB (small & medium Business) application for instance. A warehouse robot struggling with irregularly shaped packages under varying warehouse lighting can be upgraded remotely with an operator. The manager only needs to post a bounty for an operator : “adaptive package grasping in low-light conditions” A freelance robotics engineer then submits a solution: A Vision-Language-Action (VLA) fine-tune trained on their home-simulated data (e.g., using household items as proxies). The applications are endless thanks to contributors in the ecosystem that make robot functionalities infinitely scalable. “But training robot operation in the real world requires heavy use of simulation, large scale AI training and continuous learning loops” Well it used to. On the 16th of February, @codecopenflow introduced SimArena A browser-based simulation engine that allows contributors to train robots with near zero compute, cost and technical abilities With SimArena, contributors can: ➛Run full robotics simulations directly in the browser( no local setup or heavy configs) ➛Generate complete 3D simulation worlds with physics from simple text prompts (powered by World Labs) ➛Drop in your own robots, hardware, and sensors to mirror real-world conditions ➛Contribute simulation data that can be reused to train and improve robotics models Now you have: ➛VLA agents that can perceive, think and act ➛An open intelligence layer that compounds instead of isolating intelligence ➛A skill marketplace for instant automation ➛A browser-native simulation engine We haven't gone over: ➞the partnerships lineup ➞ Codec live products ➞Live usecases we’re already seeing ➞Token—ecosystem flywheel The bear market gives the best entries, you just have to pay attention to who’s building regardless of market condition

The horrors persist but so do I

$DUPE <$10M makes absolutely no sense They're trying to shove the next shiny thing down your throat its all psyop. Wake up