From simplifying workflows to connecting knowledge and rules, coordinating AI agents, and delivering measurable business results.
Using spatstat raster layers and point process models to ask how neurons, astrocytes and microglia sit relative to each other
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Exploring what happens when neural networks learn functions instead of weights.
Embeddings know that "broken" means "defective". They may not notice that ZX-4821 is not ZX-4812.
I replayed correct model decisions through four deliberately broken browser actions, and a simple page check missed half of the wrong clicks, which shows that noticing a fault and recovering from it are different skills.
In the Age of Borrowed Thinking, How To Become More Valuable as AI Gets Smarter
Artificial IntelligenceA framework for staying relevant in the age of artificial intelligence
And that’s not even the worrying part.
And how we can turn that feeling into insights
Neural networks won’t settle the measurement problem — but they are quietly changing how we see, predict and protect quantum superpositions.
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Exploring what happens when neural networks learn functions instead of weights.
Embeddings know that "broken" means "defective". They may not notice that ZX-4821 is not ZX-4812.
In the Age of Borrowed Thinking, How To Become More Valuable as AI Gets Smarter
Artificial IntelligenceA framework for staying relevant in the age of artificial intelligence
And how we can turn that feeling into insights
Claude Code vs. Cursor: What a Field Test Shows That the Feature Comparisons Miss
Large Language ModelsOne benchmark says Claude Code used 33,000 tokens where Cursor used 188,000. Another says the exact reverse. Here is the field test that actually settles it for your team.
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Sorting through the good, bad, and ambiguous aspects of vibe coding
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Deep Dives
Beyond basic ReAct loops: six production-oriented architectures for building autonomous, reliable, and scalable LLM agents.
How statistical moments connect the mean, the variance, and higher powers of a distribution
First in a series on probabilistic forecasting for physical signals. Next: what happens when you roll the forecast forward more than one step.
When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work.
Why the skill-inflation panic is aimed at the wrong thing, and what it costs to make agent knowledge a build artifact instead of a file.
A real Weave project that regression-tests three OpenAI models against the exact reply format your app depends on.
