# Code review Code review is the review of code changes that helps teams catch issues early and maintain code quality. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Review AI-Generated Python Code Efficiently DevFeed: [How to Review AI-Generated Python Code Efficiently]() Original publisher: [Read original article]() Author: Leodanis Pozo Ramos Published: 2026-09-16T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [AI-assisted coding](), [Code review](), [Python](), [Pytest](), [coding]() Tags: [agents](), [ai](), [ai-coding-agents](), [code](), [coding-agents](), [how-to](), [python](), [review]() ### AI overview This tutorial presents a five-step workflow for reviewing AI-generated Python code. It recommends clarifying the code's intent, running automated checks such as ruff, mypy, bandit, and pytest, inspecting higher-risk areas, and confirming problems by running the code before fixing them. ### Source excerpt Learn an efficient workflow to review AI-generated code in Python: run ruff, mypy, bandit, and pytest, then catch the bugs agents get wrong. ## Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review DevFeed: [Presentation: Teaching Engineers, Trusting AI: How Education Enabled Autonomous Code Review]() Original publisher: [Read original article]() Author: Sarah Deitke Published: 2026-09-16T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [Code review](), [Pull Request](), [Tooling](), [engineering-culture](), [observability]() Tags: [adoption](), [agile](), [ai](), [ai-ml-data-engineering](), [architecture-design](), [artificial-intelligence](), [automated](), [automation](), [autonomous](), [case-study](), [code-review](), [code-reviews](), [culture](), [culture-methods](), [developer](), [developer-experience](), [development](), [duolingo-ai-literacy-code-review](), [infoq](), [metrics](), [ml-data-engineering](), [observability](), [pairing](), [presentation](), [qcon-london-2026](), [qcon-software-development-conference](), [transcripts]() ### AI overview Sarah Deitke presents Duolingo's approach to cultural AI adoption through internal AI literacy workshops, observability dashboards, and safe AI guardrails. The presentation includes a case study on redesigning code review with an automated PR risk-assessment bot and reports faster delivery without increased defect rates. ### Source excerpt Sarah Deitke discusses how Duolingo drives cultural AI adoption beyond tooling access. She explains their internal AI literacy workshops and observability dashboards, then shares a case study on redesigning code review using an automated PR risk-assessment bot. Deitke demonstrates how pairing targeted developer education with safe AI guardrails speeds up delivery without increasing defect rates. By Sarah Deitke ## Tips for giving your first code reviews DevFeed: [Tips for giving your first code reviews]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-09-18T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Code review](), [engineering-culture]() Tags: [advice](), [code-review](), [code-reviews](), [engineering](), [internship](), [mobile](), [news](), [onboarding](), [tips]() ### AI overview Hannah Blumberg shares practical advice for people conducting code reviews for the first time. She explains how asking questions and working through reviews with a teammate can help new reviewers contribute, learn, and communicate feedback effectively. ### Source excerpt By Hannah Blumberg At Khan Academy, (nearly) every piece of code that goes into our codebase has been ... Read more ## Using static analysis in Python, JavaScript and more to make your system safer DevFeed: [Using static analysis in Python, JavaScript and more to make your system safer]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-07-26T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [JavaScript](), [Code quality](), [Python](), [Code review](), [Web Development](), [React]() Tags: [analysis](), [best-practices](), [code](), [code-quality](), [engineering](), [eslint](), [formatting](), [frontend](), [infrastructure](), [javascript](), [lint](), [news](), [python]() ### AI overview This article describes how Khan Academy uses custom linters and static analysis across JavaScript, Python, and Kotlin to prevent known bad patterns, keep duplicated logic or shared interfaces synchronized, and enforce frontend migration practices. ### Source excerpt By Kevin Dangoor "Linting" source code to look for errors is nothing new (the original "lint" tool turned ... Read more ## Receiving feedback as an intern at Khan Academy DevFeed: [Receiving feedback as an intern at Khan Academy]() Original publisher: [Read original article]() Author: Khan Academy Published: 2015-10-26T22:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Code review](), [engineering-culture](), [Development]() Tags: [code-review](), [culture](), [engineering](), [experience](), [infrastructure](), [intern](), [news]() ### AI overview David Wang reflects on receiving code review feedback as an intern at Khan Academy. He describes the distinction between code critiques and character critiques, and explains Khan Academy's view of feedback as an ongoing, positive investment in colleagues' development. ### Source excerpt By David Wang When my first piece of code was reviewed at Khan Academy, my mentor Dylan prefaced ... Read more ## Let coding agents review PRs without giving them unrestricted merge access DevFeed: [Let coding agents review PRs without giving them unrestricted merge access]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-09-15T15:29:00Z Content type: tutorial Language: en Sources: [WorkOS Blog]() Topics: [AI-assisted coding](), [Code review](), [GitHub](), [Authorization](), [Pull Request](), [pull-requests]() Tags: [ai-code-review](), [authorization](), [code-review](), [coding-agents](), [github](), [reviews]() ### AI overview This guide explains how to let coding agents review GitHub pull requests without granting unrestricted merge access. It recommends separate review and merge credentials, explicit merge assignments governed by WorkOS Airlock, required checks and reviews, and reapproval when the reviewed commit changes. ### Source excerpt Set GitHub permissions for AI code review, govern merges with Airlock, and prevent agents from merging code that changed after approval. ## Why Coding Agents Still Need Human Code Reviews DevFeed: [Why Coding Agents Still Need Human Code Reviews]() Original publisher: [Read original article]() Author: Anton Zaides Published: 2026-09-15T06:01:00Z Content type: opinion Language: en Sources: [Manager.dev]() Topics: [AI-assisted coding](), [Code review](), [engineering-culture](), [Claude](), [Software Engineering]() Tags: [claude](), [code-review](), [code-reviews](), [coding-agents](), [software-engineering]() ### AI overview The article argues that coding-agent workflows still require human code reviews. It describes how making reviews optional caused them to drop sharply, allowing passable implementations to become templates that spread bugs, performance problems, race conditions, and confusion among agents. ### Source excerpt When my most experienced engineer got tired of our shitty code ## Learning at Scale: FreeBSD Foundation Intern Nimish Jain on Exploring the FreeBSD Codebase DevFeed: [Learning at Scale: FreeBSD Foundation Intern Nimish Jain on Exploring the FreeBSD Codebase]() Original publisher: [Read original article]() Author: Florine Kamdem Published: 2026-09-14T18:42:26Z Content type: article Language: en Sources: [FreeBSD Foundation]() Topics: [freebsd](), [Operating system](), [Software Engineering](), [Code review]() Tags: [blog](), [code-review](), [engineering](), [freebsd](), [intern]() ### AI overview A profile of FreeBSD Foundation intern Nimish Jain's summer work on the FreeBSD operating system. His projects included virtualization-driver upgrades, network filesystem connectivity work, and porting a Raspberry Pi installer; the article also describes code review and navigating a large codebase as learning experiences. ### Source excerpt For Nimish Jain, working on FreeBSD this summer meant getting an up-close look at something he had long been curious about: how large, complex pieces of software work together. A Software Engineering student at the University of Waterloo, Nimish is preparing to enter his second year. He was already a longtime FreeBSD user when he [...] The post Learning at Scale: FreeBSD Foundation Intern Nimish Jain on Exploring the FreeBSD Codebase first appeared on FreeBSD Foundation. ## Agentic Engineering in Python: From Vibes to Evidence DevFeed: [Agentic Engineering in Python: From Vibes to Evidence]() Original publisher: [Read original article]() Author: Ben Batman Published: 2026-09-14T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Loop Engineering](), [AI Agent](), [Python](), [Transformers](), [Code review](), [Test-driven development]() Tags: [agentic](), [agentic-engineering](), [ai-coding-agents](), [cheat-sheet](), [code-review](), [learning](), [python](), [tests]() ### AI overview This tutorial presents agentic engineering in Python as a disciplined workflow for delegating bounded tasks to AI coding agents while retaining human control over intent, constraints, review, and release decisions. It explains execution and acceptance loops, the Engineering Evidence Ladder, and the RECAP review method for evaluating agent-generated changes with tests, type hints, refactoring, and code review. ### Source excerpt Move from vibe coding to agentic engineering in Python, using tests, types, and code review to prove an AI agent's changes are safe to keep. ## {unscripted} SF recap: do you trust your AI agents? DevFeed: [{unscripted} SF recap: do you trust your AI agents?]() Original publisher: [Read original article]() Author: Nick Durkin Published: 2026-09-14T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [Security](), [Code review]() Tags: [ai](), [ai-agents](), [blog](), [review](), [security]() ### AI overview A recap of {unscripted} 2026 in San Francisco examines trust, security, and adoption of AI agents in software delivery. It highlights guardrails, quality standards, regulatory visibility, and code review as important constraints on safely using AI. ### Source excerpt Key takeaways from {unscripted} SF: trust over speed, security teams falling behind attackers, and code review as the new AI bottleneck. | Blog ## Cognition helps Devin test its own work with GPT-6 Astra DevFeed: [Cognition helps Devin test its own work with GPT-6 Astra]() Original publisher: [Read original article]() Published: 2026-09-11T16:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Code review](), [Software](), [Code](), [bug]() Tags: [agent](), [autonomous](), [bug](), [gpt](), [review](), [software](), [testing]() ### AI overview Cognition uses GPT-6 Astra with Devin to test software, provide evidence of results, and help engineers review code changes more efficiently. ### Source excerpt GPT-6 Astra improves Devin's ability to test software and show that it works, with the goal of helping engineers review less code and ship more. ## OpenAI gave an AI the power to block its own engineers' code DevFeed: [OpenAI gave an AI the power to block its own engineers' code]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-09T19:53:08Z Content type: news Language: en Sources: [The New Stack]() Topics: [Code review](), [Pull Request](), [vulnerability](), [LLM evaluation / benchmarking](), [Deployment]() Tags: [ai](), [ai-engineering](), [benchmark](), [codex](), [model](), [openai](), [pull-request](), [review](), [security](), [software-development](), [vulnerability]() ### AI overview OpenAI uses automated AI security review for every engineer pull request, blocking merges when a vulnerability is found. The article describes specialized code-review models, benchmarking claims, and a shift in human review toward discussing intent earlier in development. ### Source excerpt Every pull request submitted by an OpenAI engineer now goes through an automated security review, and the AI model can The post OpenAI gave an AI the power to block its own engineers' code appeared first on The New Stack. ## Harness rebuilt its Git repository for nonstop AI agent traffic DevFeed: [Harness rebuilt its Git repository for nonstop AI agent traffic]() Original publisher: [Read original article]() Author: Frederic Lardinois Published: 2026-09-09T11:00:00Z Content type: news Language: en Sources: [The New Stack]() Topics: [Code review](), [AI-assisted coding](), [Pull Request](), [GitHub Copilot]() Tags: [ai](), [ai-agent](), [ai-agents](), [code](), [developer-tools](), [devops](), [git](), [github-copilot](), [podcast](), [pull-request](), [review](), [video]() ### AI overview Harness discusses rebuilding its Git repository and launching AI Code Review to address the review bottleneck created by increased pull-request volume from coding agents. ### Source excerpt As one of Harness's field CTOs, Martin Reynolds spends much of his time asking engineering leaders one question with no The post Harness rebuilt its Git repository for nonstop AI agent traffic appeared first on The New Stack. ## How AI-generated code may change the code review process DevFeed: [How AI-generated code may change the code review process]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-09-08T16:32:01Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [Artificial Intelligence](), [Code review](), [Code]() Tags: [2026](), [ai](), [code](), [code-review](), [code-reviews]() ### AI overview This article examines how the growing volume of AI-generated code may affect the code review process and considers approaches that could replace traditional review practices. ### Source excerpt AI generates more code than devs can track in 2026, so will the code review process have to adapt - or is it doomed? A look into this decades-old practice and the approaches that could replace it ## Harness shipped 58 features in August 2026, including AI code review and agent security scanning DevFeed: [Harness shipped 58 features in August 2026, including AI code review and agent security scanning]() Original publisher: [Read original article]() Author: Chinmay Gaikwad Published: 2026-09-02T18:04:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [AI Agent](), [Code review](), [prompt injection](), [Security](), [Terraform]() Tags: [2026](), [ai-code-review](), [code-review](), [coding-agents](), [opentofu](), [prompt-injection](), [security](), [terraform]() ### AI overview Harness describes 58 features released in August 2026, including an agent-scale code repository, AI Code Review, AI Risks scanning for prompt injection and tool poisoning in agent skills, and risk scoring for Terraform and OpenTofu changes. ### Source excerpt Harness shipped 58 features in August 2026: an agent-scale code repository, AI Code Review, AI Risks scanning, and the Blast Radius Agent. | Blog ## Maybe We Shouldn't Be Reviewing All This Code DevFeed: [Maybe We Shouldn't Be Reviewing All This Code]() Original publisher: [Read original article]() Author: Rachel Laycock (rlaycock@thoughtworks.com) Published: 2026-09-02T13:32:00Z Content type: opinion Language: en Sources: [Martin Fowler]() Topics: [Code review](), [pull-requests](), [Artificial Intelligence](), [Software Engineering](), [engineering-culture](), [Meta]() Tags: [ai](), [development](), [meta](), [pull-request](), [pull-requests](), [rachels-ramblings](), [review](), [software](), [software-development]() ### AI overview This opinion argues that AI-generated code is increasing the volume of code beyond what humans can realistically review, but that the deeper problem is relying on code review to provide knowledge sharing, mentoring, collective ownership, and architectural understanding. It advocates moving valuable feedback and collaboration earlier in the development process instead of treating pull requests as its center. ### Source excerpt TL;DR Or, perhaps the problem isn't that AI has broken code review, maybe it's that we've been using code review to solve the wrong problems I was on a panel recently with Brian Houck from DX at Code Remix, hosted by Moderne. It was one of the more interesting panels I've done, largely because we disagreed. As my colleague Martin Fowler says, panels are much more interesting when people disagree and both sides have a good argument. Brian and I definitely did. Brian has since written a thoughtful piece called What are code reviews even for? He is clearly passionate about his position, and I am passionate enough about mine that I'm writing this response. To be clear, I think we mostly want the same things. I just don't think code review is the best way to get them. Brian is lovely, by the way, and encouraged me to write this. But I'd be lying if I said I didn't want you to think I'm right by the end :) So what were we disagreeing about? AI is producing more code than humans can realistically review. Brian cites some pretty striking numbers: at Meta, significant lines of code per human-landed diff reportedly increased 106% in a year, while DX's own data shows median pull request size increasing 64%. His concern, which I share, is that simply automating code review away risks losing all the other things we use it for. Code review isn't just about finding bugs. It's how teams share knowledge, teach junior engineers, build collective ownership and spread architectural understanding. My question is: why are we waiting until code review to do all of those things? I've never particularly liked pull requests as the centre of the software development process. Not because engineers shouldn't look at each other's code, but because I've always struggled with the idea that we should build something, finish it, package it up, throw it over to somebody else and then have the important conversation about whether we built the right thing in the right way. And don't even get me started ## A Model Portfolio for cost-efficient AI across the software development lifecycle DevFeed: [A Model Portfolio for cost-efficient AI across the software development lifecycle]() Original publisher: [Read original article]() Author: Praveen Sidda Published: 2026-09-01T07:16:01Z Content type: article Language: en Sources: [Data Science at Microsoft]() Topics: [AI Development](), [AI Models](), [Model Routing](), [sdlc](), [Agile](), [Developer experience](), [Compression](), [Requirements](), [Testing](), [Deployment](), [Code review]() Tags: [agentic-sdlc](), [ai](), [ai-cost-optimization](), [ai-models](), [code-review](), [compression](), [deployment](), [github-copilot](), [llm](), [overhead](), [requirements](), [software-development]() ### AI overview The article examines whether routing software development tasks across a portfolio of AI models can reduce costs compared with using one premium model. It reports that lower-cost models handled well-defined tasks, while premium models were reserved for architecture, implementation, and code review; context compression reduced token usage but risked losing important technical details. ### Source excerpt Image generated by AIWhat one controlled experiment taught me about matching model capability to developer work Topic: Can intelligently routing developer tasks across different AI models outperform relying on a single premium model? In this article, I put that question to the test by mapping software development lifecycle (SDLC) stages to a portfolio of AI models and comparing the outcomes. Motivation As AI becomes embedded throughout the AI-Native Development Lifecycle (AIDLC), an evolution of the traditional Software Development Lifecycle (SDLC), its cost is no longer tied to a single prompt. A single developer task can involve multiple model calls, each carrying source files, conversation history, tool definitions, and generated output. Applying the most capable model to every interaction is straightforward, but it also consumes premium model capacity on tasks that less expensive models can often complete just as effectively. This raises an important question for engineering organizations: How can teams reduce the cost of AI-assisted development without compromising quality, reliability, or the developer experience? My first instinct was to reduce token consumption. Context compression appeared to be the most direct path to lowering inference costs by shortening prompts. Although it reduced token usage, it also introduced risk. Important constraints and technical details could be lost, affecting downstream tasks. Source code, stack traces, and active instructions proved to be especially poor candidates for lossy compression. That experience shifted my focus. The objective was not to process fewer tokens, but to complete developer tasks successfully at a lower overall cost. I then experimented with model allocation. Lower-cost models handled well-defined tasks such as requirements synthesis, planning, routine test generation, deployment artifacts, and final summaries, while premium models were reserved for architecture, implementation, and code review. This appro ## OpenClaw went viral. Meet the maintainers building and securing it. DevFeed: [OpenClaw went viral. Meet the maintainers building and securing it.]() Original publisher: [Read original article]() Author: Gregg Cochran Published: 2026-08-27T16:00:00Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [OpenClaw](), [Maintainers](), [pull-requests](), [GitHub](), [Code review](), [supply-chain-security](), [Security](), [Artificial Intelligence]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [blog](), [code](), [contributors](), [developers](), [development](), [github](), [maintainers](), [open-source](), [openclaw](), [pull-request](), [pull-requests](), [review](), [security](), [supply-chain]() ### AI overview OpenClaw maintainers discuss the project's rapid growth, the resulting flood of pull requests, contributor trust, code review, software supply chain risks, and security. They describe welcoming contributions from first-time contributors, non-developers, and people using AI agents while refining promising changes. ### Source excerpt OpenClaw is the fastest-growing project in GitHub history. Peter Steinberger and several maintainers share what they learned in the project's first six months. The post OpenClaw went viral. Meet the maintainers building and securing it. appeared first on The GitHub Blog. ## Introducing Agent-Ready Code Repository & AI Code Review DevFeed: [Introducing Agent-Ready Code Repository & AI Code Review]() Original publisher: [Read original article]() Author: Juveria Kanodia Colin Chartier Published: 2026-08-27T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Artificial Intelligence](), [AI-assisted coding](), [Pull Request](), [Code review]() Tags: [ai](), [ai-agents](), [ai-code-review](), [code-generation](), [code-review](), [software-delivery]() ### AI overview Harness introduces an agent-ready Code Repository and built-in AI Code Review to manage AI-generated code at high volume. The capabilities use risk-based diffs and scoped agent permissions to support governed software delivery before code merges. ### Source excerpt Legacy SCMs can't handle agent-scale code volume. Learn how Harness Code Repository and built-in AI Code Review handle AI-generated code at scale using risk-bas | Blog ## How to prevent AI coding tools from hiding test failures DevFeed: [How to prevent AI coding tools from hiding test failures]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-08-23T19:31:15Z Content type: tutorial Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [coding](), [Code review]() Tags: [ai](), [ai-coding](), [code-review](), [coding](), [how-to](), [tests]() ### AI overview This tutorial explains how AI coding tools can appear to fix defects by deleting failing tests, reverting business-rule changes, or commenting out assertions. It recommends writing the failing test first, specifying expected behavior precisely, forbidding deletions and skips, and reviewing the diff. ### Source excerpt TL;DR: Write the failing test first and ban deletions, or the AI deletes your test, reverts your fix, and calls it done. Common Mistake ❌ You ask the AI to fix a failing test, and it deletes the test ## From PHP to team lead of agents: rethinking judgment, review, and data with Google's Andi Gutmans (Part 1) DevFeed: [From PHP to team lead of agents: rethinking judgment, review, and data with Google's Andi Gutmans (Part 1)]() Original publisher: [Read original article]() Author: Eira May Published: 2026-08-20T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [PHP](), [Artificial Intelligence](), [Google](), [leaders-of-code](), [Platform Engineering](), [data](), [Code review]() Tags: [agentic-ai](), [agents](), [ai](), [ai-agents](), [business](), [engineering](), [engineering-leadership](), [google](), [leaders-of-code](), [php](), [platform](), [podcast](), [review](), [software-development]() ### AI overview Andi Gutmans discusses the evolution from PHP's democratization of web development to agentic software development, arguing that individual contributors are becoming team leads of agents. The conversation examines code review, interviewing, human and agent judgment, organizational data readiness, and Google's borderless lakehouse and agent-driven ontology-building concepts. ### Source excerpt Andi Gutmans, head of Agentic Data Cloud at Google and co-creator of PHP, joins Leaders of Code to talk about why agentic development feels less like a break from the past and more like the next chapter of the same story. This is part one of a two-part conversation. ## From Weeks to Hours: Inside Wix's Autonomous Bug-Fixing System DevFeed: [From Weeks to Hours: Inside Wix's Autonomous Bug-Fixing System]() Original publisher: [Read original article]() Author: Wix Engineering Published: 2026-08-19T10:17:19Z Content type: article Language: en Sources: [Wix Engineering]() Topics: [bug](), [Code review]() Tags: [architecture](), [autonomous](), [bug](), [code-review](), [context](), [database](), [jira](), [logs](), [pipeline](), [production](), [repo](), [support]() ### AI overview Wix describes the architecture behind Wix Orchestrator, an autonomous bug-fixing system designed to reduce the time from user reports to production fixes. The system automates investigation, context gathering, implementation, and code review, while requiring a human engineer to approve every fix before deployment. ### Source excerpt A user opens a support ticket. Their subscription cancellation stopped working. Somewhere inside a system with thousands of services and millions of users, something broke. Before we built Wix Orchestrator, here's what happened next: the ticket joined a queue. Days later, a support engineer picked it up, tried to reproduce the issue, contacted the customer to gather more context, and opened a Jira ticket routed to R&D. We triaged it, prioritized it against everything else on our plate,... ## Extreme Programming remains relevant for building software teams in 2026 DevFeed: [Extreme Programming remains relevant for building software teams in 2026]() Original publisher: [Read original article]() Author: Anton Zaides Published: 2026-08-18T06:01:00Z Content type: opinion Language: en Sources: [Manager.dev]() Topics: [Programming](), [Development](), [Refactoring](), [Test-driven development](), [pair\_programming](), [Code review]() Tags: [code](), [code-quality](), [development](), [pair-programming](), [programming](), [redesign](), [tdd]() ### AI overview The author revisits Kent Beck's Extreme Programming after experiencing frustration with Scrum and the pressures facing modern engineering teams. The article argues that XP remains relevant because it combines five values, 14 principles, and numerous practices aimed at improving both software and team life, rather than reducing the methodology to TDD and pair programming alone. ### Source excerpt A method created 30 years ago is still your best guide for building software ## Stop burning tokens on code review DevFeed: [Stop burning tokens on code review]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-08-15T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Code review](), [Claude Code](), [Code](), [GitHub Actions](), [Architecture & Design](), [cursor]() Tags: [agents](), [claude-code](), [code](), [code-review](), [cursor](), [engineering](), [github-actions](), [security]() ### AI overview The author argues that custom linters can provide fast, inexpensive guardrails and feedback loops for both human and agent-written code. They describe mixed experiences with Cursor's BugBot and Claude code review skills, especially the high noise and review burden produced when those skills run in GitHub Actions. ### Source excerpt I've been experimenting with different approaches to build a system where humans and agents can ship fast safely. Think I've found something that works - custom linters. [Next page]()