What I’ve been working on this past month with the $CODEC team: - Researching the entire Robotics sector and technical architecture from a birds eye perspective with @unmoyai (latest developments, best practices etc) - Understanding where and how Codec’s product positions in every single one of them (addressing the pain points) - What specific use cases and narratives this unlocks - The biggest value layer for the tooling and where major capital is flowing - Comparing web2 tooling and what led to the success of AI szn (Virtuals & ai16z) - what are the core components to spark developer activity? - Tokenomic flywheels and utility The team has done a great job with technical articles, although I still believe they’re only brushing the surface of explaining how important their tooling really is. My aim is to help build frameworks and processes to capture the narratives more succinctly while highlighting the true features the SDK unlocks. As it stands, there’s still nothing even in web2 which offers the same type of abstraction that Codec is working towards. Open source contribution is the way forward and leading foundation models like Nvidia’s Issac Gr00t are already building with this in mind as data and task training is still so early on. You can’t use AI text strings to train robots, there’s no “internet of robotics”. Each of these humanoids and robots you’re seeing are built with full stack monolithic architecture, there’s no carry over for task training or ways to add new components (extra sensor or camera to the back of the head) without having to rewrite the entire codebase. Instead of building data pipelines and simulations for singular monolithic architecture, they’re taking a modular approach where instead of building tasks for whole systems, It breaks each part of the robot down into core components (motors, sensors, actuators, eyes etc). Meaning it can easily plug into any type of robot/humanoid no matter its system and instruct it to carry out requirements based on individual parts. Similar to what we saw with Eliza and Virtuals, devs didn’t need to code their entire framework and had GPT models with all the plug-ins (twitter, news feed, dexscreener API’s etc) at their finger tips. All they needed was personal context for their Agents inputs, then it was purely a matter of fine tuning. The goal for Codec is very similar, a developer hub where devs don’t need to worry about building their own “game engine”, the SDK toolkit is what Unreal Engine/Unity is to game development. Myself and @0xdetweiler have been doing a lot of hidden work in the background to help achieve this. This work has taken a significant amount of my time away from trading and why you haven’t seen me writing as frequently on Twitter or Telegram. For those who’ve read my content, you know how big I am on not sacrificing trading time as all it takes is one good trade to change your trajectory. The potential, narrative and market share Codec is going for is so large it convinced me to sacrifice my time as the pay off could be the next ai16z. The tech alone isn’t enough, the reason I’m writing this is due to how important it is to build in public and have constant communication about direction and core positioning (what my 30 page masterdoc consists of). Today we saw the tip of the ice berg for Humanoids with the 1X release, this is only going to accelerate with more teams releasing their robots onto the market over the next couple months. Robotics will have the largest encapsulation, growth and mindshare of any narrative we’ve ever seen. Don’t bet against the future.