Sreenivas Makam's Blog

I built a daily AI digest using Jev to classify posts and Claude to summarize the best ones—for about four cents a day.

Token price, benchmark scores, model size, and context windows can all be misleading.

We created Model Academy to connect concepts that are often taught separately—model weights, inference systems, and GPU execution.

Deterministic CI tools should produce the evidence.

I tested three AI code review approaches on the same pull request defects to understand what each option gives you, what you must build yourself, and where the real tradeoffs are.

AI-native coding assessments should not ban AI or allow unlimited delegation.

The first article in this series, Enterprise AI Adoption: Requirements for Security, Governance, and Scale, defined the problem: enterprises consume AI through coding tools, standalone hosted applications, custom agents, and enterprise knowledge systems,…

In the first article in this series, Enterprise AI Adoption: Requirements for Security, Governance, and Scale, we identified four ways enterprises consume AI and seven requirements that apply across them.

AI adoption inside enterprises rarely begins with a coordinated platform strategy.

A perspective from the data layer of humanoid robot training I recently spent time working on egocentric video data — first-person footage of humans doing everyday things with their hands — and how it could be used to train humanoid robots.

AI assistants are no longer just chat windows.