
A beginner-friendly field guide to AI world models: what they are, how they evolved, who is building them, where funding is going, and why they matter for robotics, autonomous vehicles, gaming, 3D worlds, and physical AI.

A Nature Medicine study found that general-purpose frontier models outperformed specialised clinical AI tools. The real lesson is not that vertical AI is doomed — it is that specialisation only matters when it makes adoption easier and outcomes better.

Databricks’ Omnigent is not another AI agent. It is a control layer for managing many agents at once — a signal that the next AI infrastructure battle may be about orchestration, governance, and enterprise trust.

AI startups aren't differentiated by which model they use anymore. The real decision is the stack around it — how they access a model, layer retrieval, routing, and fine-tuning on top, and manage the cost, data, and trust trade-offs that follow.

A clear, data-led look at whether UK regulation helps or holds back deeptech startups, covering energy, medtech, grid queues, late-stage capital, and the gap between policy ambition and deployment reality.

A practical Series 101 guide to building a small AI image creator agent: map the workflow, split it into specialist agents, create the folder structure, generate images, critique outputs, control cost, and improve the system through iteration.

YC still looks software-led, but the signal underneath is shifting. Fintech is accelerating, physical AI is becoming more visible, and founder attention is moving toward infrastructure-heavy markets.

From the chip in your phone to the servers running AI — a beginner-friendly breakdown of how the semiconductor industry works: the types of chips, how they're made, the value chain, who dominates and why, and where startups are finding opportunity.

Ferveret is tackling one of AI’s less glamorous but increasingly critical bottlenecks: cooling the physical infrastructure beneath smarter models.