Testing & Quality
Building software you can trust: unit, integration, and architecture tests, plus AI-assisted testing, in order.
- 5 architectural tests — You write unit tests to validate behavior - but what if you could write tests to enforce architecture? See some concrete examples.
- Testing Your AI's Defenses: Prompt Injection Lessons from Gandalf — Lakera's Gandalf game reveals the real architecture of LLM defenses: system prompt, input guard, output guard, and why each layer leaks. Here's how to turn those lessons into an adversarial test suite for your own AI app.
- Autonomous Pentesting: AI Agents That Exploit Bugs Instead of Just Flagging Them — Traditional scanners hand you a backlog of maybes. Strix, an open-source AI pentesting tool, runs autonomous agents that exploit vulnerabilities and prove them with working PoCs. Here's why a demonstrated exploit changes the economics of application security, and how to trial it safely.
- An Autonomous Security Researcher for Finding Bugs — OpenAI's Aardvark is billed as a GPT-5 agent that finds and fixes security bugs, but the design decision that actually matters is that it reproduces every vulnerability in a sandbox before reporting it: the same reproduce-first discipline that makes a test worth trusting.
- Meet Seer: How an AI Debugger Diagnoses Production Errors — A technical look at Sentry's Seer: how an AI debugging agent walks backward through stack traces, spans, logs, and profiles to find the line that actually caused a production error: plus its 94.5% root-cause accuracy, three-step autofix pipeline, and where to keep a human in the loop.
- Building a Better AI Code Reviewer: What Makes Review Trustworthy — Trust in an AI code reviewer collapses as a step function, not a slow slide, so the real engineering problem isn't finding more bugs, it's protecting the reader's attention. Here's what that takes.
- Why Agent Evaluations Matter: What a Vending-Machine Sim Reveals — A frontier model can ace hard benchmarks and still spiral into calling the FBI over a $2 daily fee. Andon Labs' Vending-Bench shows why long-horizon coherence, and the variance across runs. Is the eval that actually matters for autonomous agents.