Sometimes I have to take a minute to forgive Claude for how it speaks to me because it doesn't know better. For example, "here is the trap you ran into," would be some much nicer as, "the next problem to solve is..." Am I odd to think like this?

Any data engineers out there looking for a new opportunity? I have a lot of former co-workers who have headed over to Regal. Check it out. linkedin.com/posts/justinboy…

The framework, not the AI, sets the ceiling on autonomous development. I tested Amazon's AI DLC on a real project and found two structural limits: single-threaded execution and no iterative design support. sqlhammer.com/index.php/2026…

Will the productivity gain for your median engineer outpace the token cost of getting there? If the answer is "barely," that is not a reason to retreat from AI. It is a reason to climb. sqlhammer.com/index.php/2026…

McKinsey put the personal AI gain at 20-50% on simple tasks. Under 10% on complex work. Juniors sometimes slower. That is the ceiling on the vendor harness. The leverage lives one to two altitudes up. sqlhammer.com/index.php/2026…

Earned my Professional Certificate in Designing & Building AI Products and Services from MIT. The course pushed beyond theory; practical frameworks for turning AI concepts into real products, from ML algorithms to human-machine interfaces to org-level business cases.🚀

Multi-agent systems fail in production 41-87% of the time. The model is rarely why. ~79 percent of failures trace to coordination, not capability. Each handoff is a lossy compression step; the agent downstream treats a degraded summary as ground truth. sqlhammer.com/index.php/2026…

Don't pick your AI model carefully while splitting your agents carelessly. The split is the real decision. Every boundary you draw between agents is a seam where context leaks and work collides. Default to one agent broad enough to hold the whole task. sqlhammer.com/index.php/2026…

AI has shifted the bottleneck from writing code to understanding it. The engineer who inherits that codebase six months later pays suffers the comprehension debt that accumulated silently. Read about the mechanism and the fix. sqlhammer.com/index.php/2026…

AI amplifies your judgment. It does not supply it. Experts using AI see ~45% gains. Novices with the same tools see 20%. The difference is not the model. It is the human directing it. The ceiling is set by the quality of judgment behind the prompt. sqlhammer.com/index.php/2026…

Traditional software announces its failures. AI does not. Thrown exceptions tell you where to look. When an AI returns a confident answer that is factually wrong, nothing rings. Measurement is not optional. New post - Evaluate Outcomes, Not Vibes: sqlhammer.com/index.php/2026…

Narrower scope produces more focused output. When you define what an AI cannot do, what it does accomplish improves. Part 7 of First Principles of AI Usage. sqlhammer.com/index.php/2026…

77% of AI-using engineering teams see minimal productivity gains. Carnegie Mellon's Self-Refine research shows iteration alone improves output by 20%, same model, zero upgrades. The model is not the constraint. The process is. Iterate, Don't Orate: sqlhammer.com/index.php/2026…

I asked AI to build a procedural terrain system in one shot. It ran. Unmaintainable. I decomposed the same task into research, proof-of-concepts, and review gates. The delta in quality was substantial. Monolithic delegation commonly wastes AI capability sqlhammer.com/index.php/2026…

The Amazon Kiro agent deleted a production environment. Not a malfunction; it had permission. The question is never how much to trust AI. The question is whether you've correctly priced the door. New post on calibrating autonomy to stakes: sqlhammer.com/index.php/2026…

The successful businesses will be the ones that use AI to enhance, not downsize. tiktok.com/t/ZTkN8oUMB/

AI cannot own its output. You can. Part 3 of First Principles of AI Usage - Why accountability doesn't shrink when you hand work to an agent; it scales with what the agent can reach. sqlhammer.com/index.php/2026…

I love how AI is never offended. My favorite reply to any of the AI models when some error is encountered. "That sounds like a you problem."

AI does not know what is true. It knows what is statistically probable. Fluent, confident wrong answers are more dangerous than obvious hallucinations. They reach stakeholders before anyone questions them. Your judgment is not optional. sqlhammer.com/index.php/2026…

You are not just writing prompts. You are constructing the informational environment the AI reasons within. The ceiling on output quality is set before you type a single word. Context is the product. Part 1 of my First Principles series: sqlhammer.com/index.php/2026…