It really is like being an ML engineer - how much compute to spend? - when outputs are stochastic, how to measure & test? - how do I run experiments? - looking at data to form better hypotheses ML engineers are so back

The moat in robotics is the data flywheel. This is one of the most intellectually honest interviews in the space, given by Karol Hausman. His core thesis, borrowed from "The Inner Game of Tennis": you can't program intelligence by writing rules. You have to learn it from data.

x.com/i/article/2035…

2/ Encourage demos and evals over docs. Claude Code with Opus 4.6 has lowered the barrier to build a working prototype to showcase an idea. It's faster and higher fidelity than trying to convey the idea in a doc.

4/ Do the simple thing With agentic systems, failures compound with system complexity. Find the simplest thing that works.

You can now schedule recurring cloud-based tasks on Claude Code. Set a repo (or repos), a schedule, and a prompt. Claude runs it via cloud infra on your schedule, so you don’t need to keep Claude Code running on your local machine.

claude-code can’t use discord bot and Chrome extension at the same time. Had some issues with chrome devtools MCP as well. Claude Desktop is the only reliable way to use Chrome extension now This is where openclaw system is showing its complexity, all the plumbing, different

Interesting perspective

Qwen is not shy of letting you know

Put all the vague posters and iconoclasts in the conference, then everyone will start talking and arguing

Model speciation is an interesting one, it’ll emerge naturally from use cases, similar to evolution. That’s what happened with coding

This assumes scientific progress is still human-centric. It’s already shifting toward system-level discovery: humans, models, and tools co-evolving. The question isn’t “AI vs humans by 2035”, it’s whether we’re ready for a world where Einstein is a system, not a person.

Terence Tao spent a year at the Institute for Advanced Study - no teaching, no random events of committees, just unlimited time to think. But after a few months, he ran out of ideas. Terence thinks that mathematicians and scientists need a certain level of randomness and

This is why RedHat existed. There will be a whole slew of companies that support all the open source projects for the agentic engineering explosion coming to enterprises.