AI Reality Checks
Is it actually working? Demo-vs-reality posts, hype audits, 'what they're not telling you' takes on model releases and tool launches.
AI Setup Porn: The Pattern Killing Builder Productivity
AI setup porn is the new productivity trap: configuring agent frameworks for hours while shipping nothing. Here's the pattern and where it comes from.
The Post-Prompting Era: How AI Agents Are Shifting From Reactive to Proactive
AI is moving from chat interfaces to always-on background agents. Here's what the post-prompting era means for how you build and use AI workflows.
What Is the Post-Prompting Era? How AI Agents Are Moving From Reactive to Proactive
The post-prompting era means AI acts without being asked. Learn what this shift means for automation, agents, and how you build workflows today.
How to Spot Setup Porn in Your AI Workflow (And Escape It)
A practical checklist for spotting setup porn in your AI workflow — and the simpler, ship-first patterns to use when agent frameworks aren't earning their keep.
AI Job Displacement: What the Data Actually Shows About White-Collar Employment
Dario Amodei predicts AI could eliminate 50% of entry-level white-collar jobs. Here's what the Stanford, MIT, and Federal Reserve data actually shows.
Coding Agents Skipped RAG — RAG Still Wins on Large Docs
RAG isn't dead — it's mismatched for code. Here's the nuanced view: where coding agents win without vectors, and where RAG still earns its place for documents.
ARC AGI 3 Adds Interactive Games — All Frontier Models Failed
ARC AGI 3 introduced an interactive video game benchmark that broke every frontier model. Here's how the format works and why fluid intelligence is still hard.
What Is ARC AGI 3? The Interactive AI Benchmark Humans Solve at 100%
ARC AGI 3 is the first interactive AGI benchmark where AI scores under 1% while humans hit 100%. Here's how it works and what it reveals about generalization.
7 AI Skills That Are Actually in Demand: What Employers Are Hiring For in 2026
Based on hundreds of AI job postings, these 7 skills are what employers can't find: specification precision, evaluation, task decomposition, and more.
AI Agent Failure Pattern Recognition: The 6 Ways Agents Fail and How to Diagnose Them
Context degradation, specification drift, sycophantic confirmation, tool errors, cascading failure, and silent failure: the 6 agent failure modes explained.
Why Cursor, Claude Code, and Devin Use grep, Not Vectors
Cursor, Claude Code, and Devin lean on grep, find, and direct file reads — not vector search. Why agentic coding tools dropped RAG and where it still wins.
Four Types of AI Agents Explained: Coding Harnesses, Dark Factories, Auto Research, and Orchestration
Not all AI agents are the same. Learn the four distinct agent types used in production, when to use each, and why mixing them up leads to failure.
What Is Context Rot in Claude Code Skills? How Bloated Skill Files Degrade Agent Performance
Context rot happens when skill.md files grow too large and flood the context window. Learn how to keep skills lean and outputs sharp.
What Is the MCP Server Trap? Why Wrapping an API Is Not Enough for Agent-Readable Data
Shipping an MCP server doesn't make your company agent-readable. Here's why clean data architecture matters more than the interface layer on top of it.
What Is the Remote Labor Index? Why AI Agents Complete Only 2.5% of Real Freelance Work
Scale AI's Remote Labor Index tested frontier agents on 240 Upwork projects. The 97.5% failure rate reveals the gap between task execution and real jobs.
What Is Context Rot in Claude Code? How to Keep Your AI Agent Sharp
Context rot happens when stale information floods your agent's context window. Learn how to use /clear, /compact, and lean skill files to prevent it.
AI Job Market Impact: What the Data Actually Shows About White-Collar Employment
White-collar job openings hit a 10-year low. Here's what the Anthropic AI Exposure Index, Gartner forecasts, and real layoff data reveal.
What Is the Anthropic AI Exposure Index? How to Find Out If Your Job Is at Risk
Anthropic's AI Exposure Index maps 800+ occupations against real Claude usage data. Here's how to read it and what it means for your career.
AI Agent Failure Modes: 4 Ways Your Agent Knows the Answer But Says the Wrong Thing
Research from Mount Sinai reveals 4 AI agent failure modes including reasoning-action disconnect and social anchoring bias. Learn what to watch for.
What Is the Inverted U Failure Pattern in AI Agents?
AI agents perform best on routine middle-of-distribution cases and worst on high-stakes edge cases. Learn why aggregate accuracy metrics hide this problem.
What Is Factorial Stress Testing for AI Agents? The Mount Sinai Method
Factorial stress testing runs the same scenario across controlled variations to expose anchoring bias and guardrail failures in AI agents. Here's how it works.
Context Rot in AI Coding Agents: What It Is and How to Fix It
Context rot happens when your AI coding agent's window fills up and performance degrades. Learn what causes it and how to prevent it in your workflows.
Public Sentiment Toward AI Is Negative: What It Means for Builders and Businesses
AI has a net favorability of -20 in recent polls, worse than ICE and Trump. Here's what the backlash means for how AI tools and products should be positioned.
What Is the AI Coordination Overhead Problem? Why Talented People Work at 25% Capacity
Most high performers spend 75% of their time on coordination—meetings, syncs, emails. Here's how AI agents eliminate that overhead and unlock real output.