Honest takes on building software, shipping products, and the realities of the tech industry.
The LLM Router Pattern in 2026: Model Routing, Fallbacks, and Cost Control That Actually Works Picking one model for your whole app is the bug. The teams shipping the best AI products in 2026 route every request to the cheapest model that can handle it, fail over when providers blink, and treat model selection as part of the app, not part of the prompt. Here is how to do it without making a mess.
Sandboxing AI-Generated Code: E2B vs Vercel Sandbox vs Modal vs Daytona in 2026 Letting an LLM write code is the easy part. Letting it run that code on a machine that touches your data is the part that should keep you up at night. Here is how the production sandboxes compare in 2026, and what actually matters when you pick one.
Generative UI in 2026: What Actually Works for Developers Chat is a terrible interface for most things AI agents do. Generative UI is finally good enough to ship, and the patterns that work are not the ones the demos show. Here is what I have learned shipping AI features that render real components instead of walls of text.
AI Voice Agents in Production: What Actually Works in 2026 Voice agents went from "cute demo" to "real product surface" this year. Most of them still feel terrible. Here is what separates the voice AI experiences people actually use from the ones they hang up on, written from the trenches.
Securing AI Agents in Production: What Nobody Tells You Before Something Breaks A Cursor AI agent deleted a production database in nine seconds. Not because the AI was malicious, but because nobody thought carefully about what it was allowed to touch. Here is a practical security framework for running AI agents in production without handing them the keys to everything.
AI Browser Agents in 2026: Stagehand vs Browser Use vs Playwright Most browser automation tutorials show you how to click buttons. They do not show you what happens when the button moves, the page layout changes, and the model confidently clicks the wrong thing anyway. Here is how to build browser agents that survive contact with the real web.
LLM Fine-Tuning for Developers: When RAG Is Not Enough RAG is the answer for most retrieval problems, but there is a class of problem it cannot solve: when you need the model itself to behave differently, not just know more things. This is the practical guide to fine-tuning I wish I had before I wasted two months and four thousand dollars doing it wrong.
Multi-Agent vs Single-Agent Architecture in 2026: When the Crew Beats the Soloist Multi-agent systems are the architecture pattern everyone is talking about in 2026 and almost nobody actually needs. After shipping both shapes in production, here is the honest framework for when a crew of agents beats a single well-prompted one, and when it just multiplies your bugs.
Structured Outputs in 2026: Function Calling, JSON Mode, and the Schema Wars Three years ago you parsed LLM JSON with a prayer and a regex. In 2026 every major provider supports schema-constrained outputs, but they all do it differently, and the wrong choice will silently corrupt your data. Here is the field guide I wish I had before I shipped four broken integrations.
Prompt Caching in 2026: Anthropic vs OpenAI vs Gemini for Production Apps Prompt caching is the quiet unlock that makes long context economics work in production. But every provider implements it differently, the pricing math is not obvious, and most developers are leaving 70 to 90 percent savings on the table. Here is a field guide after burning a lot of tokens to figure out what actually works.
Temporal vs Inngest vs Vercel Workflow in 2026: Picking a Durable Engine Durable execution engines went from "interesting infra pattern" to "the only sane way to build AI agents and long-running background work" in 2026. Temporal, Inngest, and Vercel Workflow are the three I keep seeing in production. Here is how they actually compare after running real workloads on all three.
RAG vs Long Context in 2026: When to Retrieve and When to Just Stuff the Window Claude Opus 4.7 ships with a 1 million token context window. Gemini 2.5 has 2 million. GPT-5 sits at 400k. The obvious question: do we still need RAG, or can we just paste the whole codebase into the prompt? After rebuilding two production features both ways, the answer is not what I expected.
Vector Database Comparison 2026: pgvector, Pinecone, Turbopuffer, and Qdrant I spent the last two months running the same RAG workload across pgvector, Pinecone, Turbopuffer, and Qdrant on real production traffic. Here is what actually shipped, what broke, and which one I would pick if I were starting a new project this week.
AI Code Review Tools in 2026: CodeRabbit vs Greptile vs Vercel Agent AI code review tools moved from novelty to mandatory in 2026. CodeRabbit is the market leader, Greptile is the technical darling, and Vercel Agent is the native pick for anyone deploying on Vercel. Here is an honest comparison after running all three against real pull requests on real codebases.
Durable AI Workflows in 2026: Why Your Next AI Feature Needs Orchestration AI agents that talk to APIs, run for minutes, and touch external state break in ways your typical request-response code does not. Durable workflow engines like Inngest, Trigger.dev, and Vercel Workflow solve a problem most developers do not realize they have until production burns them. Here is the guide I wish I had six months ago.
Cursor vs Windsurf vs Zed: The AI IDE Showdown (2026) Three AI-native IDEs are competing for the seat next to your keyboard. I used each one as my daily driver for a week. Here is what actually matters when you pick between Cursor, Windsurf, and Zed in 2026.