The archive
Guides and explainers on artificial intelligence, newest first. Pick a topic to narrow things down.
Google opened its AI & Economy ATLAS to public exploration and published research on how scientists use AI. The headline figures are shares of usage within countries, not adoption rates.
5 min read
Google introduced two real-time voice models: Gemini 3.8 Live for cost-efficient conversation and an Extended Thinking variant that reasons mid-sentence across 97 languages.
5 min read
MIT researchers report a deployment-time method that forces pretrained flow-matching models to satisfy hard safety constraints, with no retraining required.
5 min read
AI bias rarely comes from a single bad decision. It builds up across data, labels, design choices, and deployment. This guide traces each source and the practical steps teams use to reduce it.
8 min read
Concrete habits for getting better code from AI coding assistants: giving context, working in small steps, reviewing like a skeptic, and keeping tests in charge.
7 min read
A plain-language definition of AI agents: the loop that powers them, the parts they are built from, and how they differ from chatbots that only answer questions.
8 min read
A practical framework for placing people inside AI workflows: where to require approval, how to escalate, and why reversibility should drive oversight decisions.
8 min read
A step-by-step approach to evaluating AI agents: building task suites, defining success criteria, reviewing trajectories, and catching regressions before users do.
8 min read
How multi-agent AI systems split work between orchestrators and workers, how handoffs pass context, what they cost, and when a single agent is the better choice.
7 min read
What embeddings are, how vector spaces capture similarity, and how teams use them for search, recommendations, clustering, and deduplication.
8 min read
Language models sometimes state false things with total confidence. Here is why that happens mechanically, and the practical controls teams use to catch and reduce it.
8 min read
How language models call tools: writing schemas, running the request-execute-return loop, handling errors, and scoping permissions so models act safely.
8 min read