ai

I ran the same question through 6 AI tools and scored them against peer-reviewed evidence. Every tool got something wrong that another got right.

Nobody practises arithmetic speed anymore. The same thing is happening to prose, research, and analysis — and it changes what humans should get good at.

From AppleScript's rigid English-like syntax to LLM tool-calling — what changes when the computer actually understands you.

Automation that collects without learning is just a cron job. The difference is a feedback signal — a number that goes up or down.

Karpathy's autoresearch and every useful AI tool share the same pattern: the code is trivial, the feedback loop is the product.

You need the tool to build the tool. The answer is: build the dumb version first, use it once, then have it build its replacement.

Every AI lab builds single-vendor orchestration. The cross-vendor layer is a gap — and it's a gap for a reason.

Multi-agent AI orchestration frameworks are commodity. The competitive advantage is knowing which agent to use when, what breaks, and how to recover.

When pattern-matching feels like wisdom, what are we actually experiencing?

When AI conversations feel insightful because the language model is good at producing insight-shaped text

The things that matter most in production are the things that get benchmarked least

When the paper becomes more interesting than the answer you set out to find

The missing layer in enterprise AI isn't smarter models — it's structured memory that humans can actually review.

In AI agent architecture, calling something a 'cross-cutting concern' without naming an owner and a gate is just a polite way of saying nobody owns it.

When red-teaming a document with multiple AI models, the second review — run on the edited version — consistently finds more than the first. Here's why, and what it means for how many rounds to run.

The retrieval pipeline was built for systems that couldn't reason about their own information needs. Agents can.

An open-source AI agent framework became the fastest-growing project in GitHub history — mostly in China. The pattern is the same as last year. So is the security panic.

I spent an afternoon researching AI tools for personal life management. The conclusion was that I should stop looking.

One of the architects of modern deep learning just raised $1B on the thesis that token prediction can't reach real reasoning. Here's what he's proposing instead — and why it matters even if he's wrong.

An AI agent ran unsupervised for two days and found twenty improvements to another model's training. Not an AGI claim. A rate claim.

The shift from agents you summon to agents that watch. Enterprise AI workflows are becoming continuous loops — and the failure modes are different.

Not better function calling — decoupling. When tools expose MCP servers, any agent can compose any system freely. The heterogeneity problem becomes a configuration problem.

We called them language models and spent years confused about why they could reason. The name stuck to the interface, not the mechanism.

Ask an AI to write from scratch and you get the average of the corpus. Give it something rough and it amplifies what's already there. The workflow implications are significant.

Not productivity. Something stranger — the cognitive texture of days when the bottleneck shifts from execution to articulation.

The comfort trap is about effort. This one is about epistemics — and it's harder to see.

The right test for any AI interaction isn't 'did it help me?' but 'am I more capable after it?'

AI coding agents didn't just make developers faster. They changed who gets to have a bespoke system.

The moment I stopped building scheduling into my tools, everything got simpler.

Running 10 real queries through 5 tools revealed that theoretical routing rules have systematic gaps — and the surprises were more useful than the confirmations.