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

罗磊的独立博客
Recent Announcements
Recent Announcements
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
有赞技术团队
有赞技术团队
J
Java Code Geeks
T
The Blog of Author Tim Ferriss
MyScale Blog
MyScale Blog
人人都是产品经理
人人都是产品经理
aimingoo的专栏
aimingoo的专栏
U
Unit 42
The GitHub Blog
The GitHub Blog
云风的 BLOG
云风的 BLOG
T
Tailwind CSS Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 三生石上(FineUI控件)
Apple Machine Learning Research
Apple Machine Learning Research
小众软件
小众软件
Hugging Face - Blog
Hugging Face - Blog
博客园 - 司徒正美
腾讯CDC
I
InfoQ
GbyAI
GbyAI
博客园_首页

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
The Death of the Pull Request: Why Manual Code Reviews ar...
Dmitry Amelc · 2026-05-02 · via DEV Community

Dmitry Amelchenko

The industry is suffering from a collective delusion. We’ve treated the manual Pull Request (PR) as a sacred ritual of "quality," yet it has become the single greatest bottleneck in the modern delivery pipeline.

As we move into an era of Generative AI and agentic workflows, the traditional code review isn't just slow—it’s redundant. If you want to maximize delivery efficiency, you need to stop policing lines of code and start orchestrating intelligence.

The "Cargo Cult" of Manual Review

Most teams perform code reviews because of "cargo culting"—they do it because they’ve always done it. When you ask a senior engineer why they are reviewing a specific PR, the answers are usually vague: "To catch bugs" or "To ensure quality."

The reality? Humans are statistically terrible at spotting bugs in a diff. We are great at spotting missing semicolons or naming inconsistencies—things that a linter should have caught before the code was even committed.

The Efficiency Delta: If your delivery is stalled for 24 hours waiting for a human to "LGTM" a change that passed CI, you aren't ensuring quality; you're manufacturing latency.

Gen AI: The Ultimate "Rubber Duck"

The argument for manual review often centers on the need for a "second pair of eyes." In the past, this meant Pair Programming or PRs. Today, Generative AI has fundamentally shifted this landscape.

Gen AI serves as a sophisticated rubber duck. It allows for "Embarrassment-Driven Refactoring"—the process of iterating with an LLM to tighten a domain model or simplify logic before a human ever sees it.

How AI Supplements Pairing:

• Technology Adjacent Execution: AI excels at writing the "glue" code—the Node.js boilerplate, the CSS, the WordPress hooks—that you understand but don't want to spend cognitive cycles on.
• Iterative Feedback Loops: Instead of waiting for a scheduled pairing session, you can "pair" with an agent in real-time, challenging its trade-offs and evolving the design iteratively.
• Contextual Scanning: AI is better than humans at scanning for "janky" patterns across thousands of lines of code, identifying global variable leaks or CSS duplication in seconds.

Three Reasons to Stop Reviewing (And What to Do Instead)

To achieve high-leverage execution, you must categorize code changes by their "In Order To" and automate the path to production.

Category The Old Way (Manual) The New Way (Automated/AI)
Policy Manual gatekeeping for "rules." Automated Enforcement. Linters, static analysis, and AI agents verify compliance.
Knowledge Blocking PRs to "share info." Asynchronous Show & Tell. Merge first; share the delta in a team feed for awareness.
Critique Nitpicking variable names. Design Evolution. Use AI to rubber-duck the architecture, then ship.

The Efficiency Thesis

If we are generating ten times the code using AI agents, we cannot require ten times the human review. That math doesn't scale.

Delivery efficiency is found by inverting the power structure:

  1. Automate the "What": Use CI/CD and AI to verify that the code works and follows the rules.
  2. Focus Humans on the "Why": Reserve human intervention for high-level architectural critiques—not for checking if a feature branch is "ready."
  3. Trust the Pipeline: If the tests pass and the AI-driven "policy" check clears, the code should be in production.

The future of software engineering isn't about reading more code; it's about writing better prompts and building more robust automated gates. The manual PR is dead. Long live the pipeline.


       [ START ]
           |
           v
+-------------------------+
|   Write Code with AI    | <-----------+
|   (The "Rubber Duck")   |             |
+-------------------------+             |
           |                            |
           v                            |
+-------------------------+             |
|  Automated Policy Check |             |
| (Security/Lint/AI Scan) | -- FAIL ----+
+-------------------------+             |
           |                            |
          PASS                          |
           |                            |
           v                            |
+-------------------------+             |
|     Automated Tests     |             |
|   (Unit/Integration)    | -- FAIL ----+
+-------------------------+
           |
          PASS
           |
           v
+-------------------------+
|       PRODUCTION        |
|    (Immediate Deploy)   |
+-------------------------+
           |
           v
+-------------------------+
|  Async Knowledge Share  |
|   (Post-Merge Review)   |
+-------------------------+

Enter fullscreen mode Exit fullscreen mode

Summary for the High-Agency Engineer

Stop using humans as expensive compilers. If you are still blocking merges for "sanity checks," you are holding the tool wrong. Leverage Gen AI to handle the "adjacent" complexity, automate your policy, and focus your team on the vector of the work, not the syntax.