AI
Artificial intelligence, machine learning, and everything LLM
#2168: What Serious Agentic AI Developers Actually Need to Know
Python, TypeScript, LangGraph, and the frameworks reshaping how agents work. A technical map of the skills and concepts that separate prototypes fr...
#2167: Sync vs. Async: Architecting Agents for Scale
Why most enterprise AI agents fail in production has less to do with models and more to do with whether they're built synchronously or asynchronously.
#2166: Code vs. Canvas: How Developers Pick Their Tools
LangGraph or Flowise? The honest answer isn't obvious. Developers gain speed and integrations with visual builders—but lose version control, testin...
#2165: Strip Your Agent to Bash
The frameworks matter less than you think. What separates a working agent from a failing one is the harness—the orchestration, memory, and tool des...
#2164: Why Bigger Context Windows Don't Fix Attention
Frontier models have million-token context windows, but attention degrades well before you hit the limit. New research reveals why bigger isn't bet...
#2163: Designing Autonomy Boundaries for AI Agents
Production data reveals a surprising truth: fully autonomous AI agents waste 98% of their context window on tool descriptions. Here's why the indus...
#2162: When Knowledge Work Stops Being Safe
The knowledge economy promised safety from automation. Then AI arrived. Here's how we got here—and why the disruption this time is different.
#2160: Claude's Latency Profile and SLA Guarantees
Claude is measurably slower than competitors—and Anthropic's SLA promises are even thinner than the latency numbers suggest. What enterprises actua...
#2158: Claude Managed Agents: Brain Versus Hands
Anthropic's new Managed Agents service runs your agent loop on their infrastructure. Here's what you gain, what you lose, and who it's actually for.
#2155: Public Affairs vs. Lobbying: Shaping the Battlefield
Lobbying is just one tool. Public affairs shapes the entire regulatory battlefield—from AI laws to supply chains.
#2153: How Lobbying Actually Works in DC
Federal lobbying hit $6B in 2025. Here’s what a lobbyist actually does all day—and why the system regulates itself.
#2146: The AI Wargame's Flat Hierarchy Problem
AI wargames treat NGOs and nuclear powers as equals. That's a dangerous flaw for real-world policy planning.
#2144: AI Wargaming: One Model or Many?
Should geopolitical AI simulations use one model or many? We debate the pros and cons of a single-model approach.
#2142: The Nervous System of Multi-Agent Systems
We break down the plumbing that lets a parent agent know exactly when a subagent finishes, from message passing to lifecycle events.
#2141: Choosing Your Durable Execution Platform
Why building AI agents means managing infrastructure. We explore durable execution backends like Temporal and AWS Step Functions.
#2139: AI Wargame Memory: Beyond the Context Window
Why simply extending context windows fails in multi-agent simulations, and how layered memory architectures preserve strategic fidelity.
#2137: Wargaming's Methodology, Not Magic
Most AI wargames are just expensive role-play. Here's the professional methodology they're missing.
#2136: The Brutal Problem of AI Wargame Evaluation
Most AI wargame simulations skip evaluation entirely or rely on token expert reviews. This is the field's biggest credibility problem.
#2135: Is Your AI Wargame Signal or Noise?
Monte Carlo methods promise statistical rigor for AI wargaming, but the line between genuine insight and sampling noise is thinner than you think.
#2134: The Fog-of-War Problem in AI Wargaming
Why shared AI brains make secret-keeping a nightmare, and the four architectural patterns researchers use to fix it.