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When AI Infrastructure Becomes a Geopolitical Target

Iran's threat to OpenAI's Abu Dhabi data center reveals how AI infrastructure has become intertwined with international power dynamics.

GitHub's Performance War: How Simplifying React Components Made Diffs 10x Faster

GitHub gutted their diff viewer's React tree and won big on performance. Here's what happens when you stop overengineering your components.

GitHub's Performance Overhaul: Why Simplicity Beats Clever Architecture

How GitHub slashed heap usage by 10X and made massive pull requests usable again by questioning every abstraction and DOM node.

The Axios Attack: When Social Engineering Becomes Your Supply Chain's Weakest Link

A sophisticated social engineering attack compromised Axios maintainer credentials through fake job interviews. Every open source maintainer needs to know this.

When Machines Write Code, Humans Must Learn to Judge

As LLMs generate more code, teams face cognitive surrender and debt proliferation. The future isn't about writing code, it's about verification.

Meta's KernelEvolve: When AI Writes Its Own Performance Code

Meta's KernelEvolve system uses AI agents to automatically optimize low-level hardware kernels, achieving 60% performance gains in hours instead of weeks.

The Axios Attack: Why Social Engineering is Now the Biggest Threat to Open Source

A sophisticated supply chain attack on Axios used fake job interviews to install malware. Every open source maintainer needs to understand this threat.

When Agents Write Code, We Judge It: The Verification Economy

As LLMs generate code at scale, our job shifts from writing to verifying. What does this mean for how we organize teams and think about programming?

Meta's Adaptive Ranking Model: The Real Cost of Serving Trillion-Parameter Ads

Meta scaled ads recommendations to LLM complexity while keeping latency under a second. Here's why their inference trilemma solution matters beyond advertising.

Treating AI Instructions as Infrastructure, Not Documentation

How encoding team standards as versioned AI instructions solves the consistency problem that plagues AI-assisted development workflows.

Making Team Standards Executable: Infrastructure for AI-Assisted Development

AI coding tools produce wildly different results based on who's prompting. Treating team standards as versioned, executable instructions solves the consistency problem.

Why Your AI Benchmark Is Probably Wrong: The N,K Trade-off

Google Research reveals why using 3-5 human raters per item isn't enough for reproducible AI evaluation. The depth vs breadth problem explained.

Tests Are the Real Safety Net: Why Your AI Specs Need Executable Validation

Writing specs for LLMs is trendy. But without automated tests, you're flying blind. Here's why the spec document isn't your safety net.

Meta's AI is Reshoring American Concrete, One Mix at a Time

How Bayesian optimization is helping U.S. concrete producers ditch imported cement and redesign mixes in days instead of months.

Sora's Shutdown: When Burning $1M Daily Isn't Worth the Hype

OpenAI killed Sora after six months, ditching a $1B Disney deal. The real story isn't about data grabs, it's about brutal economics and losing ground to Claude.

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