Blog
Hire an AI & Full-Stack Consultant for One Conversation — or the Whole Build
You don't need a signed contract to get a straight answer about your AI or engineering roadmap. From a single architecture review to a fully shipped agent, RAG pipeline, or platform, here's how to work with me — and where to start.
I Let an AI Agent Run My Job for 30 Days. Here's What It Couldn't Do
I build production AI agents for a living, so I turned the experiment on myself for 30 days — and the moment that taught me the most was the moment the agent confidently did the wrong thing.
One Person, Five AI Agents, One SaaS: My Solo "Team" Experiment
I ran five role-scoped AI agents like a tiny team to build and ship a small SaaS by myself — a spec agent, a builder, a reviewer, a QA agent, and an ops agent. The leverage was real. So was the fact that I never stopped being the manager.
Vibe Coding Is Dead. Spec-Driven Development Is What Comes Next
Vibe coding's failure mode was always the same: nobody could review what the AI actually built. Spec-driven development fixes that by making the spec the artifact you own — and the code the disposable part.
Production RAG Implementation: How I Build Retrieval Systems at Million-Document Scale
Most RAG demos fall apart past a few thousand documents. Here's what I actually build for clients — a hybrid retrieval architecture proven past a million documents, with the production practices that keep it fast and reliable.
Bounded Agents: The Only Multi-Agent Pattern That Actually Works in Production
Everyone wants one AI agent that does everything. The teams getting real value in 2026 built the opposite — many small agents with narrow jobs and a human holding the loop. Here's why boring wins.
How Developers Actually Use AI Coding Agents in 2026
The pitch is an AI that writes your whole app while you sleep. The real 2026 workflow is smaller and more supervised — and a study showing experienced developers 19% slower with AI tools explains exactly why that's the smart way to work.
Agentjacking: The New Prompt-Injection Attack Hiding in Your Error Logs
A June 2026 disclosure shows attackers hiding prompt injection inside fake error reports — a source coding agents read constantly, automatically, and almost never review raw. Here's how agentjacking works and how to gate it.
Why Gemini 3.5 Pro Got Delayed: The Real Cost of Long-Horizon Agent Tasks
Sundar Pichai promised Gemini 3.5 Pro for June 2026. It slipped to July because enterprise testers flagged excessive token burn on long agentic tasks. Simply explained: why that's a genuinely hard engineering problem, and why delaying to fix it was the right call.
The Agent Leap: Why 73% of Workplaces Now Run AI Agents (Up From 34%)
Workplace AI adoption more than doubled in a year, from 34% to 73%. The interesting part isn't the stat — it's the gap between having adopted an agent and actually getting value from one.
Repo-Level AI Agents: How Coding Assistants Learned to Reason Across a Whole Codebase
Autocomplete finishes your line. A repo-level agent reads the project, follows the dependency trail, and edits five files that have never been open in the same tab. Simply explained: how that actually works, and why it isn't magic.
Your AI Agent Just Got Fired: Why Agentic AI Still Can't Handle Real Business
The demo worked perfectly. The agent browsed the web, sent emails, called APIs. Then you put it near an actual business process and it fell apart in under an hour. Here is why that keeps happening.
The Real Cost of AI Agents: Security, Prompt Injection, and Trust
Every component in your agent stack either spends trust or earns it. Once you see the attack surface through that lens, the defenses become obvious — and so do the gaps.
The AI Bubble Isn't Popping — It's Leaking. And That's Better for Everyone
Bubbles pop when pressure builds faster than value can form underneath. The dot-com crash happened because there was nothing underneath. AI is different — the value is real, it's just not where the money went. What's happening now isn't a pop. It's a pressure release.
DeepSeek Changed Everything: What Silicon Valley Won't Admit About Chinese AI
DeepSeek-R1 was trained for ~$6M. GPT-4 cost an estimated $100M+. DeepSeek matched or beat it on most benchmarks. The uncomfortable explanation is not geopolitics — it's that the compute moat was never the moat.
Multimodal AI Is Finally Real: Building Apps That See, Hear, and Act
A receipt hits your system. An LLM reads the image, a voice memo patches a line item, and a tool call pushes the result to QuickBooks — without a handoff between any of them. Here is how to build it.
From Prompt Engineer to Agent Architect: The Career Shift Happening Right Now
The job description changed. The title didn't. Here's the diff.
Why Your AI Strategy Should Be 'Small Models, Big Impact' in 2026
Most teams start their AI strategy at GPT-5 and optimize down when cost bites. That's backwards. Here is the framework for starting small and earning your way up.
Stop Fine-Tuning GPT-5. A 7B Open-Source Model Will Beat It on Your Use Case
GPT-5 is trained to be good at everything, which makes it mediocre at your specific thing. Here's why a fine-tuned 7B beats it on narrow tasks at 1/50th the cost.
Terraform + MCP + AI Agents: The New Infrastructure Stack Nobody's Talking About
Three technologies you already use. One pattern nobody has named yet. Here is the stack that makes AI agents safe to run against real cloud infrastructure — and the one line you must not let the agent cross.
MCP and Agentic AI Have Crossed the Infrastructure Threshold
MCP has 97 million monthly SDK downloads, governance under the Linux Foundation, and first-class support from every major AI vendor. That is not a popular open-source project. That is infrastructure. Here is what that transition actually changes for developers building AI systems.
The State of AI Benchmarks in 2026
Classic benchmarks are saturated, contaminated, and increasingly useless for choosing a model. A practitioner's guide to what frontier evals actually measure, why leaderboards lie, and how to build the evals that matter for your specific use case.
The Skeptic's Reality Check: What AI Is Actually Delivering in 2026
Goldman Sachs found no economy-wide productivity impact from AI. MIT Media Lab says 95% of organizations see no measurable returns. $650B in capex is meeting a very short list of demonstrated results. Here is what the numbers say.
AI in Science & Hardware: The Two Curves Reshaping Everything
AI is collapsing scientific discovery timelines while hardware bifurcates into massive training chips and ultra-efficient edge silicon. The thread connecting both stories is energy — and the engineers who ignore it will be caught flat-footed.
AI Security & Sovereignty: The Gap Nobody Has Actually Closed
Most organizations have solved data residency — where data sits. Almost none have solved data sovereignty — who controls where data is processed, trained, and inferred. That distinction is now a regulatory and geopolitical fault line.
MLOps Is Just DevOps With More Humility
MLOps extends DevOps principles into machine learning systems — but ML introduces a new class of silent, world-driven failure modes that demand an entirely new posture of epistemic humility.