Research-oriented articles on symbolic systems, model tooling, and technical architecture.
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The customers that created Nvidia's AI boom are becoming large enough to build their own silicon. Hugging Face points to where Nvidia expects the replacement demand to come from.
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Go gives you errors as values and, since 1.21, structured logging in the standard library. The two do not meet: context is known deep in the call stack, and the decision to write a log record belongs at the boundary. A small type closes ...
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Claude Fable 5 was public for three days before an export-control letter made it unreachable. That week settled four arguments I'd been having since January, and exposed the borrowed floor that holds the price of everything else down.
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Thoughts on collaboration, authorship, and the tools that extend our capacity to think and write
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Vulnerability discovery is becoming a function of targeted AI compute, turning software security into a fire hose problem of spend, access, and remediation capacity.
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A toolchain for testing whether the Exp-Minus-Log representation gives symbolic regression a useful search space.
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Alphabet's AI problem is structural, not mainly competitive. If AI systems weaken Google's control over intent capture, they threaten the advertising engine that funds the company while the time left for a serious response keeps shrinking.
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As frontier models get stronger, access may become more stratified. The token divide is a market structure in which the best models, highest limits, and most productive agent workflows are divided by price.
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Anthropic's Mythos may be too dangerous for public release. Project Glasswing raises the harder question: when defensive access is scarce, who gets protected first, and who waits outside the perimeter?
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Google’s Gemma 4 release does more than expand an ecosystem. It changes which AI companies are exposed when model access becomes less scarce.
