Every week someone posts about AI hallucination like it's a mystery. It's not. A 2025 Frontiers in AI study measured it: vague, multi-objective prompts hallucinate 38.3% of the time. Structured, single-focus prompts? 18.1%. That's a 20-point accuracy gap from how you write

Most AI coding tools ship as monoliths. One big system prompt, one set of capabilities, one-size-fits-all. That works fine for general software engineering. It falls apart the moment you need domain-specific workflows that vary by role, by team, and by engagement. I build presales systems at Presidio — client research, SOW

Everyone's writing better prompts. Few are building better context. That's the gap. Prompt engineering treats AI like a search box — craft the perfect query, get the perfect answer. Context engineering treats AI like a new team member — give them the right docs, the right access, and

AWS just launched two autonomous AI agents — Security Agent and DevOps Agent — and they're both generally available now. These aren't chatbots with polished wrappers. They're persistent, autonomous systems that run for hours or days without human oversight, doing work that previously required dedicated teams.

Everyone's Buying GPUs. Almost Nobody's Ready to Feed Them. The enterprise AI conversation has a blind spot the size of a data center. Every budget meeting I've sat in over the past 18 months has the same shape: GPU allocation gets 70% of the

The Cloud Repatriation Nobody Expected: Why Enterprise AI Is Pulling Compute Back from the Cloud The original pitch for cloud computing was simple: stop buying servers, rent someone else's. For most workloads over the past fifteen years, that trade worked. But AI infrastructure has rewritten the economics, and

Most enterprise AI projects die quietly. No dramatic failure, no post-mortem email chain. They just... stop. The prototype gets a demo, leadership nods approvingly, and then six months later the Slack channel goes silent and the budget gets reallocated. Roughly 80% of enterprise AI projects fail — double the failure rate

Addy Osmani dropped a piece last week that's been making the rounds: "Comprehension Debt — The Hidden Cost of AI-Generated Code." His thesis is sharp. Teams are shipping AI-generated code faster than anyone can understand it. Tests pass, PRs look clean, and nobody notices the growing gap

The Production Gap: Why Nobody Teaches the Infrastructure That Actually Matters Every bootcamp, CS program, and YouTube tutorial series teaches you how to build features. Almost none of them teach you what happens when those features meet real traffic, real failure modes, and real users who do things you never