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

P
Privacy & Cybersecurity Law Blog
WordPress大学
WordPress大学
Last Week in AI
Last Week in AI
腾讯CDC
人人都是产品经理
人人都是产品经理
小众软件
小众软件
V
Visual Studio Blog
S
Secure Thoughts
J
Java Code Geeks
V
V2EX
量子位
The Hacker News
The Hacker News
酷 壳 – CoolShell
酷 壳 – CoolShell
Security Latest
Security Latest
博客园_首页
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
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Spread Privacy
Spread Privacy
博客园 - 叶小钗
T
Threat Research - Cisco Blogs
Security Archives - TechRepublic
Security Archives - TechRepublic
T
Tailwind CSS Blog
Cloudbric
Cloudbric
S
SegmentFault 最新的问题
AI
AI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Application and Cybersecurity Blog
Application and Cybersecurity Blog
IT之家
IT之家
T
Tenable Blog
S
Security @ Cisco Blogs
月光博客
月光博客
雷峰网
雷峰网
博客园 - 【当耐特】
Know Your Adversary
Know Your Adversary
C
Cybersecurity and Infrastructure Security Agency CISA
Exploit-DB.com RSS Feed
Exploit-DB.com RSS Feed
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Hugging Face - Blog
Hugging Face - Blog
爱范儿
爱范儿
Attack and Defense Labs
Attack and Defense Labs
博客园 - 三生石上(FineUI控件)
Hacker News - Newest:
Hacker News - Newest: "LLM"
有赞技术团队
有赞技术团队
N
News and Events Feed by Topic
阮一峰的网络日志
阮一峰的网络日志
TaoSecurity Blog
TaoSecurity Blog
宝玉的分享
宝玉的分享
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
The Cloudflare Blog
K
Kaspersky official 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 Common SOC 2 Failures (Real World) Stop Vibe-Checking Your AI App: A Practical Guide to Evals How to Use SonarQube and SonarScanner Locally to Level Up Your Code Quality Your Next To-Do App Is Dead — I Replaced Mine with an OpenClaw AI Sign a Nostr event in 60 lines of Python using coincurve — no nostr-sdk, no nbxplorer, no rust toolchain ITGC Audit Explained Like You’re in Big 4 Patch Tuesday abril 2026: Microsoft parcha 163 vulnerabilidades y un zero-day en SharePoint Stop scraping everything: a better way to track competitor price changes Listing on MCPize + the Official MCP Registry while routing payments OUTSIDE the marketplace — how I kept 100% of my x402 revenue Building an AI-Powered Risk Intelligence System Using Serverless Architecture Why We Ripped Function Overloading Out of Our AI Toolchain Testing AI-Generated Code: How to Actually Know If It Works SaaS Churn Is Killing Your Business. Here Is What to Do About It (Without a Support Team) The Speed of AI Is No Longer Linear - And Self-Improving Models Are Why How to Implement RBAC for MCP Tools: A Practical Guide for Engineering Teams From Standard Quote to Persuasive Proposal: AI Automation for Arborists I built a CLI that scaffolds complete multi-tenant SaaS apps Axios CVE-2025–62718: The Silent SSRF Bug That Could Be Hiding in Your Node.js App Right Now The dashboard that ended our friendship Data Pipelines Explained Simply (and How to Build Them with Python) The Hidden Cost of AI Systems Nobody Talks About. undefined vs undeclared, and how typeof behaves Switching from file-based jobs to NATS/Kafka in Rust without changing code io_uring Adventures: Rust Servers That Love Syscalls Why Agentic AI is Killing the Traditional Database The POUR principles of web accessibility for developers and designers Quantum Neural Network 3D — A Deep Dive into Interactive WebGL Visualization How To Install Caveman In Codex On macOS And Windows Automation Pipeline Reliability: Why Your Workflow Breaks When Nobody Is Watching I Built an 'Open World' AI Coding Agent — It Works From ANY Folder From Freelancing to Product: A Tech Service Company's SaaS Transformation China's AI Giants: Adding Tencent Hunyuan & ByteDance Doubao to AI University (74 Providers) On the Vibe Coders and Their Lies clerk: Auto-Summarize Your Claude Code Sessions AI Weekly — 2026/04/10–04/17 | The Model Lockdown Is Here, but the Toolchain Is the Real Battleground AI 週報 — 2026/04/10–2026/04/17 模型封鎖潮來了,但工具鏈才是真戰場 Maybe this is how Open-Source apps are born... 🚀 Fine-Tune LLMs with LoRA and QLoRA: 2026 Guide tRPC v11 + Next.js App Router: End-to-End Type Safety Without the Boilerplate ShadCN UI in 2026: Why I Stopped Installing Component Libraries and Started Owning My Components SaaS Billing in React Server Components: Stripe + Supabase Without a Single `useEffect` Join our DEV Weekend Challenge — $1,000 in Prizes Across TEN winners! Submissions Due April 20 at 6:59 AM UTC. Implementing FSRS Spaced Repetition in Flutter + Supabase — Adding Memory Science to an AI Learning App "I Texted My Localhost From the Train — Claude Code Fixed the Bug Before I Got Home" I Built a Sales Prep AI and It Went Deeper Than Expected Design to Code #2: One JSON, Eleven Outputs Solving the 100M-Row Problem: A Summary Table Pattern for High-Volume Push Notification Logs Flutter Web With Wasm: What Actually Changes For Developers I Built 50 Royalty-Free Soundtracks for My Side Project in a Weekend Using AI Music Generation The Vibe Coding Security Checklist: 7 Things to Check Before You Ship Stop Letting Googlebot Guess Fix Your React App's SEO Right Desconstruindo o Streaming do LinkedIn: Como Criar um Engine de Extração de Vídeo de Alta Performance com HLS e FFmpeg (EDA Part-1) EDA (Exploratory Data Analysis) Explained With Real Life — Why Looking at Your Data Is the Most Important Step in Machine Learning Brand Relationship Management at Scale: Our 4-Touch Outreach System for 200+ Brands Why String.fromEnvironment() Might Return an Empty String in Dart JGuardrails 1.0.0 — Hardening Java LLM Apps Against Jailbreaks, Toxicity, and Prompt Injection Plan and Schedule a Full Week of Threads Content From One Claude Conversation Coding Cat Oran Ep3, Five Tables Changed Everything Updated: BFF Pattern I'm done watching freelancers get buried by 200 proposals. So I'm building the alternative. This is my first post BFS Algorithm in Java Step by Step Tutorial with Examples Tracking LLM Pricing Monthly: An Open Dataset for 22 AI Models How We Measure Content ROI on a Comparison Site: Revenue Attribution Without Perfect Data Introducing Nova AI Ops: The AI-Native Operating System for SRE Teams I built a free desktop video downloader for Windows — Grabbit How Talkie OCR Helps Vision-Impaired & Dyslexic Users Read the World Around Them VRCFaceTracking安装和iPhone面捕配置教程,有bug Even CrowdStrike Can't See Your Agents The Automation Gold Rush: What n8n Workflows and Claude Are Opening Up for Developers Right Now
Why I built an AI repair loop that stops after one fix on purpose
Noumenon AI · 2026-05-30 · via DEV Community

Noumenon AI

A few weeks ago I watched my AI coding agent successfully fix a bug.

The tests passed. The patch looked clean. The agent reported success. I shipped it.

Three hours later I noticed the agent had also "improved" four unrelated files in the same session. One of those improvements quietly removed a guard clause that I had spent a full evening writing two months earlier. There was no test covering that guard clause because it was the kind of thing you write defensively — the bug it prevents doesn't show up in the suite, it shows up in production at 4am.

The patch passed every check. The patch was also wrong.

This is the part of AI coding that nobody is talking about clearly. The model isn't dumb. The tests aren't broken. The CI didn't fail. The agent didn't lie. Everything in the loop reported green. And the repo was a little bit worse than before.

I've now lost count of how many times this exact pattern has played out across my projects — a multi-agent build engine, an e-commerce site, a trading bot fleet. Different stacks, different agents (Claude CLI, Codex CLI, both), same failure mode. The agent makes a "successful" patch that quietly breaks something three files over, and the only reason I catch it is because I happen to read the diff before bed.

So I built AutoMaxFix. And the core design choice is one most people would call a step backwards: it stops after one ticket.

The autonomy trap

Every AI coding tool on the market right now is racing toward more autonomy. Multi-step agents. Long-running loops. "Just describe the bug and walk away." The marketing screenshots show 50 commits done overnight while you slept.

I think this is the wrong direction. Not because autonomy is bad in principle, but because we don't have the reliability floor to justify it yet.

Here's the math that nobody runs:

If each step in an autonomous loop has a 90% chance of being correct (which is generous), then a 10-step autonomous run has a 35% chance of being fully correct. A 20-step run has a 12% chance. The error doesn't compound linearly — it compounds geometrically. And the agent doesn't know which step was wrong. It just reports green on the last one.

Worse: the failures that slip through aren't random. They're systematically the ones that don't have test coverage, because if they did the agent would have caught them. So autonomous loops are biased toward producing exactly the bugs your test suite can't see.

What stopping after one ticket actually means

AutoMaxFix is a Python CLI. You point it at a failing pytest run (or jest, vitest, mocha, go test, cargo test). It parses the failure output into a structured ticket. It generates a reproduction brief. It hands one ticket — one — to your local Claude CLI or Codex CLI. It validates the diff against a strict safety contract. It applies the patch only after you approve. It runs the targeted tests plus a regression sweep. It writes a report. Then it stops.

Not "stops until tomorrow." Stops. The loop is over. If there are 14 more failing tickets, that's 14 more runs you initiate when you're ready.

The reason is simple: every additional ticket inside the loop is another place where the agent can make a "successful" patch that quietly degrades the codebase. Stopping after one ticket means every patch has your eyes on it before the next one lands. It means the regression of one fix can't hide inside the success of another.

It also means AutoMaxFix is slower than every competitor. Intentionally. That's the trade.

The safety floor is infrastructure, not prompting

The other choice that matters: every safety rule in AutoMaxFix is enforced before the agent sees a prompt, not asked of the agent inside one.

Patches that touch .git, .env*, secrets*, .venv, or node_modules are rejected at validation. Diffs containing rm -rf, sudo, curl | bash, wget | bash, or package install commands are rejected. Binary patches are rejected. Mode-change-only patches are rejected. Anything exceeding the configured max_files_changed cap is rejected. The workspace must be clean (no uncommitted changes) before apply. Ticket files carry an integrity hash verified on load. Credential-shaped strings in ticket content are redacted before write.

None of that is in a prompt. The agent literally cannot do those things because the validation layer rejects the diff before apply. Prompts get ignored. Validation doesn't.

This is the part I learned the hard way. I spent weeks early on writing increasingly elaborate prompts trying to make the agent "be careful." The agent was careful 95% of the time. The 5% was unbounded. The only fix that actually held was moving safety out of the prompt entirely and into the infrastructure.

What it is not

AutoMaxFix does not call any hosted API. It drives whichever local agent CLI you already have configured. If you have a Claude or Codex subscription with CLI access, you have everything you need — no API costs, no separate billing.

It does not run unattended. There's a --yes flag for CI use, but the default everywhere else is human approval.

It does not chain tickets. I cannot say this often enough. If you want multi-ticket autonomy, AutoMaxFix is the wrong tool. Go use a different one and good luck.

It does not install packages. It does not run shell commands. It does not touch your secrets. It is the boring, paranoid version of an AI coding tool, and I'm comfortable with that.

Why open source

I'm releasing AutoMaxFix as MIT because I want the safety floor to be a thing that exists in the world, not a thing locked behind my paywall. The model where "be careful about your repo" is a premium feature is, frankly, dystopian. Every developer using AI agents should have these guardrails. Mine just happens to be the version I built for myself.

If it's useful, fork it. If it's broken, file an issue. If the design choice of "one ticket per run" sounds wrong to you, I'd genuinely like to hear why — I've been wrong before and will be again.

Where to find it

https://github.com/Noumenon-ai/AutoMaxFix

GitHub Actions composite action included. Works with pytest, jest, vitest, mocha, go test, cargo test, and a generic format for anything else.

Built because I needed it. Shipped because maybe you do too.