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#2077: Why Big Armies Hate Their Best Soldiers
From WWII's fish oil raids to modern Green Beret teams, discover the real mechanics of elite military units.
#2076: Is Pure NLP Dead? The Hidden Scaffolding of AI
Modern AI didn't appear from nowhere. Discover how decades of linguistic rules and statistical models built the foundation for today's LLMs.
#2075: AI Agents for Israel: Hyper-Local Skills in Action
How reusable AI "skills" are solving real Israeli problems—from shelter navigation to tax compliance.
#2074: Generative Social Science: When AI Agents Develop Theory of Mind
See how a new framework models 10,000 virtual citizens to test policies before spending a dime.
#2072: When the Downed Pilot Becomes the Forward Air Controller
A downed WSO in Iran directed Reaper strikes from a mountain crevice while awaiting rescue—here's the tech and tactics that made it possible.
#2071: Git Can't Handle AI Agents—Yet
Three AI agents in one repo is pure chaos. Here's why Git's design causes collisions—and how worktrees and locks can save your sanity.
#2070: SemVer, Changelogs, and the Social Contract of Code
Stop breaking the internet. Learn the exact system developers use to release software without causing chaos.
#2069: The Vibe Coding Trap: Why Your Agent Skills Keep Breaking
Stop guessing at the agentskills.io spec. Learn the exact YAML fields, directory structure, and authoring patterns to make Claude Code skills that ...
#2068: Is Safety a Filter or a Feature?
External filters vs. baked-in ethics: the architectural war for LLM safety.
#2067: MoE vs. Dense: The VRAM Nightmare
MoE models promise giant brains on a budget, but why are engineers fleeing back to dense transformers? The answer is memory.
#2066: The Transformer Trinity: Why Three Architectures Rule AI
Why did decoder-only models like GPT dominate AI, while encoders and encoder-decoders still hold critical niches?
#2065: Why Run One AI When You Can Run Two?
Speculative decoding makes LLMs 2-3x faster with zero quality loss by using a small draft model to guess tokens that a large model verifies in para...
#2064: Why GPT-5 Is Stuck: The Data Wall Explained
The "bigger is better" era of AI is over. Here's why the industry hit a data wall and shifted to a new scaling law.
#2063: That $500M Chatbot Is Just a Base Model
That polite chatbot? It started as a raw, chaotic autocomplete engine costing half a billion dollars to build.
#2062: How Transformers Learn Word Order: From Sine Waves to RoPE
Transformers can’t see word order by default. Here’s how positional encoding fixes that—from sine waves to RoPE and massive context windows.
#2061: The Memory Bottleneck That Drives Attention Design
Attention is the engine of modern AI, but it’s also a memory hog. Here’s how MQA, GQA, and MLA evolved to fix it.
#2060: The Tokenizer's Hidden Tax on Non-English Text
Why does a simple greeting in Mandarin cost more to process than in English? It's the tokenizer's hidden inefficiency.
#2059: When Your AI Agent Runs Stale Code
npx is silently running old versions of your AI tools. Here's why your updates vanish into a cache black hole.
#2058: How Stuxnet's Code Physically Broke Iran's Centrifuges
Stuxnet didn't just infect computers—it rewrote PLC logic to spin uranium centrifuges into self-destruction while faking normal readings.
#2057: How Agents Break Through the LLM Output Ceiling
The output window is the new bottleneck: why massive context doesn't solve long-form generation.