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A note on building reliability infrastructure for AI agents and why post-incident debugging matters more than pre-flight validation.

A few weeks ago I started building SafeRun — inline reliability infrastructure for AI agents in production. The temptation, when you're building something in the agent reliability space, is to lead with validation. Block the bad action before it happens. Stop the runaway loop. Enforce the policy. The failure mode no one talks about Why observability tools don't solve this The four-step loop, and why Replay is the foundation The Stripe boolean problem What we shipped, in order: Phase 0: Working p

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Claude Code vs Cursor AI: Which Should You Use in 2026?

Claude Code is better at understanding legacy code you didn't write. Cursor is better at generating net-new features fast. That's the whole comparison — everything else is details. Claude Code reads context better than anything I've tested. My authentication middleware was failing intermittently. I pasted the error. Claude traced it through four middleware layers, found the race condition in my session store, and explained why it only happened under load. Cursor's autocomplete is magic when you'

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"I Tried Google Antigravity 2.0 — Here's Why It's Not Just Another Cursor Clone"

For 50 years, Brooks' Law was gospel. An ironclad truth no engineering team dared challenge. Then, at Google I/O 2026, Google casually broke it in a live demo. 93 AI agents. 12 hours. Under $1,000 in compute. One fully functional operating system — capable of running Doom. That wasn't a magic trick. That was Google Antigravity 2.0 — and it's the most important developer tool announcement of the decade. If you missed the original launch back in November 2025, here's the short version: Google rele

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Introducing QAC: a commit message specification for AI agents

AI agents already write code, create files, refactor modules, and make commits. This is the daily workflow of anyone using Cursor, Claude Code, Copilot, Kiro, or any IDE with an integrated agent. The problem is that when you review the git history afterwards, you cannot distinguish what the agent did on its own from what you asked it to do — and you cannot determine why it made each change. I work in QA and software quality engineering. Part of my work is ensuring traceability — knowing who did

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Quick Tip: Benchmarking Multimodal APIs in Under 10 Minutes

Look, I’m a backend engineer. I don’t have time to read through 40 pages of model cards before picking an API. I just need to know: which multimodal model handles my use case without breaking the bank or my sanity? So I spent a weekend testing every model I could get my hands on via a unified endpoint (shout-out to Global API for not making me manage ten different provider keys). Here’s what I found, some code you can steal, and the honest trade-offs. I stuck with the same lineup that’s been flo

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The Developer's Guide to Picking the Right AI Code Model in 2026 (I Spent $500 So You Don’t Have To)

I’ve been building backend systems for over a decade. I’ve seen AI code generators go from “cute party trick that crashes your CI” to “legitimately useful pair programmer.” But in 2026, the landscape is a jungle of model names, pricing tiers, and benchmark claims. So I did what any sane engineer would do: I blew a budget on 10 different models, ran them through a gauntlet of real-world coding tasks, and tracked every dollar spent. The result? DeepSeek V4 Flash at $0.25/M tokens is the no-brainer

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DeepSeek vs Qwen vs Kimi vs GLM: Which AI API Actually Wins in 2026? (A Cost-Optimizer’s Verdict)

Let me start with a confession: I’m obsessed with getting the most bang for my buck. Whenever I see a new AI API price list, I immediately start calculating cost per token, comparing it to GPT-4o, and wondering if I could replace half my infrastructure with something that costs 90% less. So when I got access to four Chinese AI models via Global API, I spent a weekend stress-testing them with one question: Which one saves me the most money without sacrificing quality? Here’s the thing: these aren

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