# code review Published articles for code review. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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 ## 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 ## Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent DevFeed: [Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent]() Original publisher: [Read original article]() Author: TNS Staff Published: 2026-09-15T16:21:45Z Content type: article Language: en Sources: [The New Stack]() Topics: [SRE](), [incident](), [Azure](), [observability](), [DevOps](), [Redis]() Tags: [agent](), [ai-agents](), [ai-operations](), [azure](), [code-review](), [deployment](), [devops](), [incident](), [microsoft-azure](), [monitoring](), [observability](), [post](), [redis](), [sponsor-microsoft-azure](), [sponsored](), [sponsored-post](), [sre](), [telemetry]() ### AI overview The article describes Azure SRE Agent as a system that analyzes telemetry, correlates deployment and monitoring data, investigates incidents, identifies root causes, recommends or prepares fixes, and supports mitigation and other operational tasks under human approval. It cites examples involving Microsoft service teams and InEight, including a recommendation to scale Redis. ### Source excerpt What if engineers could spend their time building and optimizing systems rather than maintaining them? It's 3 a.m., and the The post Agents operate, humans govern: Scale your operations and reduce toil with Azure SRE Agent appeared first on The New Stack. ## 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. ## Any Nix package, live in your browser DevFeed: [Any Nix package, live in your browser]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-10T23:44:15Z Content type: article Language: en Sources: [Simon Willison's Weblog]() Topics: [Nix](), [WebAssembly](), [qemu](), [x86](), [GitHub Actions](), [Pull Request]() Tags: [browser](), [code-review](), [code-review-16](), [github-actions](), [github-actions-72](), [linux](), [linux-54](), [qemu](), [webassembly](), [webassembly-131](), [x86]() ### AI overview The article describes trynix.dev, which runs an x86_64 Linux virtual machine in the browser using qemu-wasm and WebAssembly. It can boot Nix packages from the past 13 years, including Python 3.6.2, and supports URL-addressable interactive shells. It also highlights trynix-preview, a GitHub Action that links pull requests to browser-based builds without servers. ### Source excerpt Any Nix package, live in your browser Farid Zakaria calls this his "magnum opus of Nix work", and I can see why. trynix.dev provides a qemu-wasm powered x86_64 Linux virtual machine running entirely in your browser through WebAssembly. That VM can then be booted with any Nix package from the past 13 years. They are URL addressable, so you can navigate to this page: https://trynix.dev/?pkg=python3%403.6.2 Then click "Load" and get an interactive shell against a virtual machine running Python 3.6.2 from 2017. Farid is building all sorts of neat things on top of this. One recent example: Review a pull request by booting it introduces trynix-preview, described like this: GitHub action that comments a link on a pull request which lets you boot the PR's build in the browser using https://trynix.dev. No servers, just browsers. Via Lobste.rs Tags: code-review, linux, webassembly, github-actions ## 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 ## 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 ## 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 ## Automating Code Modernization with OpenRewrite - By Stefano Dalla Palma, Adyen DevFeed: [Automating Code Modernization with OpenRewrite - By Stefano Dalla Palma, Adyen]() Original publisher: [Read original article]() Author: Adyen Published: 2026-08-22T06:45:48Z Content type: article Language: en Sources: [Adyen Tech]() Topics: [Refactoring](), [Jackson, Java Dates, Exception, LocalDate](), [migration](), [Tooling](), [Development](), [Software Engineering]() Tags: [code-review](), [internal-developer-platform](), [jackson](), [migration](), [modernization](), [refactoring](), [software](), [software-engineering](), [tech](), [tooling]() ### AI overview An Adyen engineering team explores using OpenRewrite to automate code modernization and reduce the manual effort of framework, API, and static-analysis updates. The article describes a completed migration of an internal Jackson 2 wrapper to Jackson 3 across hundreds of modules and discusses integrating the tool into the build and CI workflow. ### Source excerpt Automating Code Modernization with OpenRewriteBy Stefano Dalla Palma - Development Tooling Engineer, Adyen In a large-scale engineering organization such as Adyen, where hundreds of developers produce hundreds of merge requests per day across thousands of modules, continuously evolving code to meet the latest standards is essential for long-term platform health and performance. However, executing routine updates manually, such as adopting new framework patterns, keeping up with API enhancements, or fine-tuning static analysis rules can introduce friction that takes developers away from building core features. When I joined Adyen's internal developer platform team, I wanted to understand whether any of that friction could be automated away. Around that time, I came across an episode of Software Engineering Radio (link) where the creator of OpenRewrite made an argument that stuck with me: "We are asking framework authors to start taking responsibility for providing recipes when they make breaking changes." I loved that framing. The hassle of keeping up shouldn't fall entirely on the consumers of a library, but on the people who changed the contract in the first place. While we couldn't change how external maintainers worked, we could adopt that mindset internally. I saw an opportunity: if a tool could absorb the friction of a breaking change, we could handle the cleanup on behalf of our developers. But my enthusiasm wasn't going to convince anyone. The engineers I pitched to wanted to see it work before they'd trust it, and I don't blame them. The only way to earn trust was to do the work: pick a real use case, drive it to completion, and let the results speak. One team had a ticket to migrate an internal Jackson 2 wrapper to Jackson 3, a tricky refactoring with deep caller dependencies across hundreds of modules, exactly the kind of high-effort, lower-urgency work that teams understandably prioritize behind customer-facing delivery. I asked if I could take a shot at ## 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,... ## Coding Challenge #131 - Automated Code Review Agent DevFeed: [Coding Challenge #131 - Automated Code Review Agent]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-08-15T08:01:51Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Code Challenge](), [GitHub Copilot code review](), [pull-requests](), [Pull Request](), [Orchestration](), [GitHub](), [Artificial Intelligence](), [YAML](), [engineering-culture]() Tags: [agent](), [ai](), [automated](), [code-review](), [coding](), [collaboration](), [github](), [orchestration](), [pull-request](), [run](), [tools](), [triggers](), [workflow](), [yaml]() ### AI overview Coding Challenge #131 asks readers to build an automated code review agent that reviews pull requests against team coding guidelines and posts findings as comments. The challenge covers retries, event triggers, concurrency control, large pull requests, static analysis tools, and lookup capabilities. ### Source excerpt This challenge is to build your own code review agent. ## 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. ## Practical Loop Engineering DevFeed: [Practical Loop Engineering]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-14T14:30:53Z Content type: opinion Language: en Sources: [Elevate]() Topics: [Loop Engineering](), [AI Agent](), [Claude Code](), [codex](), [Code review](), [Scripting, bash]() Tags: [agent](), [agents](), [bash](), [claude-code](), [code-review](), [codex](), [engineering](), [loops](), [workflows]() ### AI overview An opinion article about practical loop engineering for AI-assisted software development. It describes running multiple agents in parallel, delegating bounded tasks with explicit stopping conditions, and using goal- and timer-based loops in Claude Code and Codex. It emphasizes careful supervision and well-defined goals and constraints, especially for complex or high-risk codebases. ### Source excerpt Goals, loops, and the discipline of not delegating your judgment ## Practical Loop Engineering DevFeed: [Practical Loop Engineering]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-14T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Loop Engineering](), [AI Agent](), [Claude Code](), [codex](), [Bash]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [bash](), [claude-code](), [code-review](), [codex](), [complexity](), [engineering](), [loops](), [workflows]() ### AI overview The article explains loop engineering as an autonomous, self-correcting feedback cycle in which an AI agent acts, tests results, and adjusts its approach toward a defined goal. It discusses Claude Code and Codex primitives, earlier Bash-based loops, and the need to define goals and constraints carefully before delegating work, especially in complex or high-risk codebases. ### Source excerpt Goals, loops, and the discipline of not delegating your judgment ## Agentic Code Quality DevFeed: [Agentic Code Quality]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-08T14:31:15Z Content type: opinion Language: en Sources: [Elevate]() Topics: [Code quality](), [Testing](), [mutation-testing](), [Code review]() Tags: [agentic](), [code-quality](), [code-review](), [mutation-testing](), [testing]() ### AI overview The article argues that code quality for agent-generated software depends on constraints enforced by the surrounding harness, environment, and operating system. It describes quality gates including unit, property, and acceptance tests, mutation testing, code-quality metrics, and checks on proposed changes, while noting risks from autonomy, ambiguity, brittle environments, nondeterministic builds, missing permissions, and weak tests. ### Source excerpt Quality now depends on the constraints you set around your agents. ## How credential issuance controls software built by legal teams with agent CLIs DevFeed: [How credential issuance controls software built by legal teams with agent CLIs]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-06T16:29:26Z Content type: opinion Language: en Sources: [WorkOS Blog]() Topics: [Authorization](), [GitHub Copilot CLI](), [Security](), [Command-line interface](), [Code review](), [GitHub]() Tags: [agent](), [code-review](), [copilot](), [github](), [security](), [tools]() ### AI overview This commentary examines how legal and other non-engineering teams use agent CLIs to build software, arguing that credential issuance and access controls are more important security control points than code review for tools that may never enter a pull-request workflow. ### Source excerpt Agent CLIs turned every department into a builder. The control point for the tools they ship is credential issuance, not code review. Here's the paved path. ## Experiments with AI Code Review DevFeed: [Experiments with AI Code Review]() Original publisher: [Read original article]() Author: Austin McKee Published: 2026-08-03T23:34:10Z Content type: article Language: en Sources: [Wealthfront]() Topics: [AI-assisted coding](), [Code review](), [Artificial Intelligence](), [Claude Code](), [Large Language Model](), [engineering-culture]() Tags: [ai](), [ai-code-review](), [ai-coding](), [anthropic](), [claude](), [claude-code](), [code-review](), [google](), [llm](), [openai](), [wealthfront-engineering]() ### AI overview Wealthfront describes a multi-year experiment using AI to augment code review. The article explains how an LLM caught a missed SQL filter that had passed automated testing and human code review, and why Wealthfront chose to build a structured review harness using models from Anthropic, OpenAI, and Google. ### Source excerpt Part of a series on Wealthfront's AI Developer Tooling Why Code Review? At Wealthfront, our arc of AI adoption has followed a familiar pattern: from skepticism to experimentation to deep adoption in the code lifecycle. Just a few years ago, in the dark era of "AI coding is just fancy autocomplete," we heard rumors of... Read more ## We Became Editors-in-Chief, and Nobody Trained Us DevFeed: [We Became Editors-in-Chief, and Nobody Trained Us]() Original publisher: [Read original article]() Author: Freek Van der Herten (freek@spatie.be) Published: 2026-08-03T14:56:25Z Content type: opinion Language: en Sources: [freek.dev - all blogposts]() Topics: [Artificial Intelligence](), [Development](), [Code]() Tags: [ai](), [career](), [code](), [code-review](), [developer-experience](), [development](), [essay](), [soft-skills](), [writing-code]() ### AI overview An essay examines how AI-assisted development shifts engineers from writing code toward reviewing AI-generated diffs. It argues that this change creates cognitive fatigue and may widen the gap between senior and junior engineers. ### Source excerpt An essay on how AI-assisted development turns engineers into full-time reviewers. We spend our days evaluating AI-generated diffs instead of writing code, and that shift carries a new cognitive fatigue and may widen the senior/junior gap. Read more [Next page]()