Introducing the MegaBrain Science blog

We’re launching a new blog about AI and science. We’ll share work happening at MegaBrain and elsewhere, our collaborations with external researchers and labs, and practical workflows for scientists using AI in their research.

Increasing the pace of scientific progress is part of why we build MegaBrain. The bottleneck in modern research is rarely the idea — it’s the wrangling: cleaning data, wiring up tools, re-running an analysis five different ways, and tracing every number back to the code that produced it. That work is exactly what an agentic workbench can take on.

What we’ll cover

This blog will run three kinds of posts. Features are detailed case studies of AI applied to a real research problem. Workflows are practical, reproducible guides you can follow in MegaBrain Science. And Field notes round up what’s happening across AI and science, at MegaBrain and beyond.

Science at MegaBrain

MegaBrain Science is a local-first research workbench: a live kernel the agent shares, running on your machine so your data never has to leave it. Every result carries an auditable history — the exact code, the environment, and the full message trail that produced it — and an independent reviewer checks the citations and the numbers before you trust them. The models come through the MegaBrain Gateway, so you pick the right one for each step.

We think the most interesting questions ahead aren’t only technical. As core research tasks become things you can hand to a machine, the day-to-day of science changes — how projects start, how results get checked, and who gets to move fast. We’ll write about those questions here too.