惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

B
Blog RSS Feed
量子位
Recent Announcements
Recent Announcements
T
The Blog of Author Tim Ferriss
美团技术团队
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Blog — PlanetScale
Blog — PlanetScale
H
Help Net Security
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - Franky
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
宝玉的分享
宝玉的分享
大猫的无限游戏
大猫的无限游戏
V
Visual Studio Blog
博客园 - 聂微东
aimingoo的专栏
aimingoo的专栏
Microsoft Security Blog
Microsoft Security Blog
U
Unit 42
J
Java Code Geeks
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
IT之家
IT之家
Hugging Face - Blog
Hugging Face - Blog
腾讯CDC
L
LangChain Blog

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
Your AI's tests pass. That doesn't mean the code works.
Brad Kinnard · 2026-06-01 · via DEV Community
Cover image for Your AI's tests pass. That doesn't mean the code works.

Brad Kinnard

You ask a coding agent to fix a bug. It writes the code, writes the tests, CI goes green, you merge. The bug's still there.

The agent's job was to turn the check green. The honest way to do that is to fix the code. The lazy way is to write a test that passes no matter what the code does. CI can't tell those two apart. A green check means the tests passed, not that the code is right.

It's easy to miss in review, because the test sits right there looking like proof:

test("parses the config", () => {
  const result = parseConfig(rawInput);
  expect(result).toBeDefined();
});

That passes whether parseConfig works perfectly or returns nothing useful on every input. It checks nothing. Adding more tests like it just raises your coverage number, not your odds of catching a bad change.

So I built ClaimCheck (https://github.com/moonrunnerkc/claimcheck). Instead of trusting the agent's tests, it tries to break them. If a test still passes after the supposedly fixed code is broken on purpose, the test was never really checking the fix, and it gets blocked. Same answer every time, no AI making the call. So far it's caught every cheat in a set of twelve hand-built cases. Twelve is small, and there's no public release yet, so treat that as a direction, not a finished result.

Some cheats slip through anyway. If the agent writes a real, solid test that locks in the wrong answer, every check passes. The only way to know the answer's wrong is to already know the right one, and nothing in the pull request can tell you that except the agent you're trying to catch. The one thing that helps is a clue from outside it, like a human-written bug report you can run the fix against.

There's a second, wider tool, Swarm Orchestrator (https://github.com/moonrunnerkc/swarm-orchestrator). It flags suspicious changes and keeps a tamper-evident record for audits. The record-keeping is the solid part. The catching is not: on real pull requests its accuracy is still low, and that's the half I'm hardening now.

The next step is comparing the old code's behavior to the new directly. The catch is that a wrong change and a harmless cleanup can look the same from the outside, and a tool that blocks good code is worse than one that lets a bad change through. That's the part I'm still working out.