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

酷 壳 – CoolShell
酷 壳 – CoolShell
D
DataBreaches.Net
C
Check Point Blog
雷峰网
雷峰网
小众软件
小众软件
GbyAI
GbyAI
美团技术团队
P
Proofpoint News Feed
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
罗磊的独立博客
大猫的无限游戏
大猫的无限游戏
WordPress大学
WordPress大学
MyScale Blog
MyScale Blog
The Cloudflare Blog
阮一峰的网络日志
阮一峰的网络日志
Apple Machine Learning Research
Apple Machine Learning Research
Y
Y Combinator Blog
Jina AI
Jina AI
爱范儿
爱范儿
Last Week in AI
Last Week in AI
MongoDB | Blog
MongoDB | Blog
I
InfoQ
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
博客园 - 司徒正美

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
How to build a production-ready app with Claude Code
John Pitarresi · 2026-06-28 · via DEV Community

John Pitarresi

Claude Code can build most of an app in a day. The hard part isn't getting something that runs. It's getting something you'd confidently put in front of real users. The gap between those two is where nearly every project I've shipped with it started to unravel.

The fix isn't a cleverer prompt. It's process. Here's what actually moved my builds from demo to something I'd deploy.

Write the thing down before you write any code. Not a sentence in a prompt, an actual page: what it is, who it's for, what data it touches, the threat model, and what's explicitly out of scope. This feels like procrastinating. It isn't. A clear brief is the only reason the agent makes consistent choices across a few hundred files instead of contradicting itself by file 40. If you can't write it down in a page, the agent can't build it coherently.

Decide the rules that must always hold, up front. "Every query is scoped to the tenant." "Money is integer cents, never a float." "No endpoint returns another user's row." Write those down before the build, because then they're things you can check instead of things you hoped it remembered. Pin them in a file the agent reads, or it'll quietly invent three different patterns for the same thing across three sessions.

Build in a loop, not one shot. Implement a piece, test it, run it through real checks: typecheck, the test suite, a security pass, a smoke test that actually clicks through the feature. The part that matters is what happens on a failure: it goes back and gets fixed, and then everything re-runs. A build earns "done" by passing after the last change, not by passing once at the start.

And do not trust what it tells you. This is the step everyone skips and the one that bites. An agent on a long task will report things as finished that aren't there. It'll mark a control "done" in its notes while the code behind it was never written. So you need something independent, ideally a fresh context with no memory of the plan, to grep the actual code for every control it claims. I've watched that catch encryption that three documents swore was applied and that did not exist.

The upside nobody mentions: do it this way and you don't get a black box. You get a real repo. Real stack, real tests, an architecture you can read. You own it, extend it, deploy it wherever you want. That's the whole case over a no-code generator, which is great right up until you need it to do the one thing it wasn't built for.

None of this is push-button. You're still driving, still deciding, still on the hook. The model writes the code. The process is what makes the code trustworthy.

(I build MDLC, which packages this loop so you don't reassemble it every project. The workflow works without it too.)