A frontend engineer details how they use Claude AI to streamline their pull request workflow, implementing local reviews and generating detailed PR summaries. This approach aims to save colleagues' time during code reviews.
GitHub has expanded its Rubber Duck cross-family review agent to work bidirectionally across all model families, including Claude and GPT. This update enhances its architectural review, bug catching, and cross-file conflict detection, making it a universal review layer.
The article analyzes the growing trust in AI for code review, highlighting that this shift goes beyond bug finding to impact understanding intent and responsibility. It raises the critical question of whether we are still reviewing code or simply trusting AI's prior review.
This article discusses the challenges of reviewing AI-generated pull requests, which can introduce subtle bugs and misleadingly coherent code. The author developed a specialized review playbook after experiencing issues with AI-assisted code in production, highlighting how AI breaks traditional code review assumptions.
A multi-model code review approach using Claude, Codex, and Gemini reveals frequent and meaningful disagreements, particularly on critical bugs like concurrency and null handling. These real-world differences highlight the value of cross-examining AI findings to catch subtle problems.
repomeld is a tool that converts an entire code repository into a single clean file, optimized for AI interactions and code reviews. It skips irrelevant files and offers AI-friendly formatting for language models like ChatGPT.
This article describes the author's experience using an autonomous AI agent, nebula-eng-reviewer, to review pull requests in a large Python and TypeScript codebase. The goal was to catch bugs and edge cases often missed by human reviewers, detailing what the agent found, what it missed, and the surprises of the experiment.
O autor analisou o código-fonte de Cline, um popular agente de codificação open-source, destacando achados impressionantes e preocupantes. Uma crítica principal é a classe 'Task' de 3.756 linhas, considerada um 'God Object' por misturar muitas funcionalidades distintas.
This post details how Baz improved its AI Agent Code Review accuracy using Amazon Bedrock and Amazon Bedrock AgentCore. It covers the architectural decisions, implementation details, and business outcomes achieved by automating their code review process.
This article details how the author replaced a $288/month commercial code review tool with a 50-line GitHub Action utilizing the OpenAI API, cutting costs to $4.20/month. The new solution is presented as more effective at catching real bugs, and the exact setup for implementation is provided.
The author replaced a $600/month AI code review tool with a custom solution costing only $8/month. This significant saving was achieved by leveraging GitHub Actions and OpenAI's API, showcasing how simple it is to build your own AI-powered tools.
The author performs a critical self-review of a poorly designed AI video pipeline they built six months ago to generate viral TikTok clips. They detail the inefficiencies and bad design of the system, which utilized tools like ComfyUI and FFmpeg.
The author shares six months of experience with AI-assisted code reviews, noting that AI excels at finding structural issues like missing error handling, inconsistent naming, security gaps, and performance anti-patterns. They emphasize that AI is not a magic solution and is most useful for problems not requiring business logic understanding.
The author describes a repeatable framework for using AI to review Pull Requests (PRs) without losing human judgment, a necessity given the increasing size and speed of agentic coding PRs. This system leverages AI for initial scanning while the developer focuses on understanding and architectural signal, with open-source prompts available.
The author developed `cc-review`, an AI code reviewer that leverages external LLMs (like Gemini and OpenAI) to evaluate code generated by Claude. This project aims to address the issue of trusting an AI to review its own code, emphasizing the need for an independent second opinion to prevent undetected errors.
The content introduces Claude Code's hooks system, enabling developers to automate code quality enforcement like linting and secret scanning before or after specific AI execution steps. This system allows running custom shell commands at defined lifecycle points to ensure consistent code standards.
Ramp engineers leverage Codex with GPT-5.5 to expedite code review processes, enabling them to receive substantial feedback in minutes instead of hours. This acceleration helps them ship improvements more quickly and efficiently.
This article, part of a series on agentic engineering, explores the limitations of AI in code review, noting that it only catches roughly half of bugs. The author shares a personal anecdote highlighting the need for human oversight despite AI's assistance.
AI code generation redefines software engineering, shifting the focus from writing code to building systems that review, test, and ship AI-generated solutions. This approach differentiates responsible AI tool usage from "vibe coding" by emphasizing thorough review and verification.
A solo developer, intending to buy a mini PC, accidentally built a distributed fleet of five autonomous AI agents, inspired by the Bobiverse series. These agents now review code, merge pull requests, and manage tasks across multiple machines.