Lessons
Prompt injection: the con that hijacks AI agents
Prompt injection is when hidden instructions in the content an AI reads trick it into ignoring its real orders, the core security problem of any AI that browses, reads email, or uses a computer.
Distillation: how a small AI learns from a big one
Distillation trains a smaller, cheaper model to imitate a larger, smarter one, the idea behind both efficient deployment and the 'copying' accusations now driving AI geopolitics.
Synthetic Data: When AI Makes Its Own Training Material
The internet is running out of fresh text to train on, so the most advanced models increasingly learn from data that other AI made or shaped. Here is how that works, why it helps, and how it can quietly poison a model.
Mixture of Experts: The Committee Inside a Giant Model
Why the biggest AI models are not really one big brain but a large team of specialists, only a few of whom wake up for any given word -- the trick that lets a model be huge and fast at the same time.
Recursive self-improvement: when AI starts building AI
The idea that an AI good enough at AI research could improve itself, and the improved version could improve itself again, faster each round. Here's what it actually means, why a major lab now says we're getting close, and why "close" is not the same as "here."
AI Persuasion: When Machines Get Good at Changing Your Mind
Why language models have quietly become powerful persuaders, how they do it, and why researchers treat 'superpersuasion' as a safety problem rather than a marketing feature.
How AI Gets Benchmarked — and Why the Leaderboard Can Lie
Every 'this AI is now #1' headline rests on a benchmark. Here's how those tests actually work, why a top score doesn't always mean what you think, and how to read a leaderboard like a skeptic.
Open vs. closed AI models — what "open weights" really means
Some AI models you can only rent through a company's interface; others you can download and run yourself. That difference — open weights vs. closed — shapes privacy, research, cost, and who controls the technology.
Scaling laws — does bigger always mean better?
For years, AI progress ran on a simple recipe: make the model bigger, feed it more data, get a better model. That pattern is real and predictable — but it has limits and surprises. Here's what scaling laws actually say.
What is a context window?
A model's context window is how much text it can hold in mind at once — its working memory. Bigger is useful, but a long window isn't the same as a good memory. Here's how it works and where it breaks.
Why does AI make things up?
Language models sometimes state false things with total confidence — a behavior called hallucination. It isn't a bug they'll simply patch out; it falls out of how they're built. Here's why it happens and how people fight it.
What makes an AI an "agent"?
An AI agent doesn't just answer questions — it takes actions: calling tools, running steps, and reacting to what it finds. Here's the loop at the core of every agent, and why agents fail in their own peculiar ways.
What does it mean for AI to grade AI?
We increasingly use one AI model to evaluate another's answers — because human grading doesn't scale. Here's how 'AI as a judge' works, why it's everywhere, and the traps that make it unreliable.
Mechanistic interpretability & sparse autoencoders
What people mean by "reading a model's mind" — finding human-understandable features inside a neural network, the tools that do it, and where those tools fall short.
Reward-based fine-tuning (RLHF and RLVR)
After a model is first trained, it gets "polished" by rewarding good answers. Here's what that phase is, why it works, and the failure mode where models get repetitive and dull.
What are diffusion language models?
Most AI writes one word at a time and can never go back. Diffusion language models start from noise and clarify it iteratively — and some versions can revise any word at any step. A growing alternative to the standard left-to-right approach.
What are world models?
A world model is an AI system's internal understanding of how an environment works — not just what it sees right now, but what will happen after an action, and what would have happened differently. Central to planning, robotics, and the next generation of physical AI.