In the early days of the internet and web development, everything was hand written in raw JavaScript or HTML.

Each web app was its own silo. As they grew more complex, the community invented tools like Webpack and Vite which are module bundlers and build systems that automatically handle different file types and assets.

Meaning a developer could import an image, font or a TypeScript file into their project and the bundler’s loaders would know how to process it (convert TS to JS, inline the image etc).

The browser itself only understands HTML/CSS/JS but these toolkits encode/decode other formats into browser friendly output.

Even though this was a small tweak to how data was formatted, it revolutionized web development.

Since it unlocked faster iteration loops and better experimentation, you could use higher level languages (like TypeScript or frameworks) because the build tools would translate them for you.

Which resulted in an explosion of web apps and a much denser ecosystem, since developers weren’t wasting time reinventing build pipelines for each project.

Similarly, Robotics is in a pre Webpack stage where many teams still “hand code” their data pipelines.

A toolkit like $CODEC for Robotics would do for robot data what Webpack did for web assets.

This is the vision @unmoyai has and is the raw definition of “codec”.

It would allow robotics developers to more easily incorporate new data sources or formats without months of custom engineering.

Leading to much faster iteration cycles. What used to take a team 6 months could shrink to a few weeks or less.

When you compress the idea to experiment timeframe by an order of magnitude, you enable far more innovation. Developers can try new ideas without the huge upfront cost, allowing them to also fail and learn quickly.

We’ve already seen how faster iteration has transformed software with vibe coding. If you told developers a few years ago you'd be able to tell a prompt window to code you a super app in a single message prompt, they would have spat in your face. Now that’s become a reality.

The same friction is currently sitting with physical AI. Roboticist's are busy dealing with hardware:software compatibility instead of working towards the bigger problem set of more effective humanoids.

Once the repetitive grunt work is abstracted away, the focus can then turn towards design behaviours and fine tuning AI brains.

Until then, there’s a massive value pie for the team that unlocks these data pipelines, enabling developers to create without constraint.