Deconstructing AI engineering through the lens of systems thinking.
We explore the systems behind intelligent solutions — so you can build with clarity, ship with confidence, and scale what truly matters.
Deep insights
Unpack complex AI systems with practical, long-form analysis and frameworks.
Built for builders
Actionable guidance for engineers and technical leaders, not just theorists.
Better outcomes
Make smarter trade-offs, avoid hidden risks, and create scalable impact.
Explore ideas and frameworks for building intelligent systems.
In-depth essays and practical notes on AI engineering, systems thinking, and the real-world trade-offs behind building with intelligence.
Tested in production
Honest technical reviews of coding assistants, agent frameworks, vector databases, and the workflows that actually scale.
See the whole loop
Causal diagrams, feedback structures, and second-order thinking applied to the messy reality of building software with AI.
Better decisions, better systems
Frameworks for reasoning clearly, avoiding traps, and designing AI systems that deliver real impact.
Notes from the lab
Reflections, experiments, and behind-the-scenes notes from building, shipping, and learning in public.
The signal in the noise.
The Expanding Attack Surface: Security Risks When AI Agents Run Your Business
As AI agents gain autonomous tool access — browsing, writing, executing code — the attack surface expands dramatically. A rigorous breakdown of prompt injection, data exfiltration, and agentic risk for individuals and organizations.
Long-form, obsessively researched.
AI in 2026: A Clear-Eyed Map of Real Opportunities and Real Risks
A rigorous, evidence-grounded analysis of where AI creates genuine leverage and where it introduces real risk in 2026 — without the hype or the alarm. Decision frameworks for engineers, founders, and knowledge workers.
The 19% Problem: Why Your AI-Assisted Code Is Slower Than You Think
METR's RCT found developers 19% slower with AI despite predicting 20% faster. A rigorous look at the 39-point perception gap and what it means for your workflow.
The 5.5% Inconvenient Truth About Enterprise AI ROI
McKinsey found 88% of orgs use AI but only 5.5% achieve real business impact. A structural analysis of why most enterprise AI investment produces no measurable value.
The Deskilling Ledger: What You Lose Every Time You Let AI Think For You
CHI 2025 found 40% of AI-assisted tasks involved zero critical thinking. A framework for understanding cognitive offloading costs — and when the tradeoff is worth it.
When AI Becomes a Crutch: The Mental Health Data No One Wants to Talk About
1.2M users/week discuss suicide with ChatGPT. MIT/OpenAI RCT shows AI use correlates with loneliness. A careful look at the evidence — without alarmism or dismissal.
Hinton vs LeCun: How to Think When Experts Completely Disagree
Hinton gives 10-20% odds AI destroys humanity. LeCun calls it complete B.S. A framework for forming calibrated views when credentialed experts reach opposite conclusions.
Writing at the
intersection of
machines & minds.
"We don't need more AI hype. We need clearer thinking about what these tools actually do — and what they ask of us in return."
Tech&MindSet Lab is a reader-supported publication studying the overlap of cognitive psychology and production AI systems.