# code reviews Published articles for code reviews. 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 ## 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 ## Harness CLI: AI Code Reviews in the Terminal DevFeed: [Harness CLI: AI Code Reviews in the Terminal]() Original publisher: [Read original article]() Author: Mohit Suman Published: 2026-09-09T00:00:00Z Content type: release Language: en Sources: [Harness Blog]() Topics: [Command-line interface](), [ai-coding](), [AI-assisted coding](), [Pull Request](), [CI/CD]() Tags: [agent](), [ai](), [ci-cd](), [cli](), [code-reviews](), [github]() ### AI overview This Harness article introduces Harness CLI 3.0, a terminal interface that unifies Harness Code, CI/CD pipelines, AI Code Reviews, and interactive terminal workflows. It describes a predictable grammar spanning more than 320 commands and 95 nouns, along with JSON output, self-describing schemas, agent detection, and HARNESS_API_KEY authentication for autonomous workflows. ### Source excerpt | Blog ## 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 ## Nitpicking in code reviews wastes time DevFeed: [Nitpicking in code reviews wastes time]() Original publisher: [Read original article]() Author: Dan Lew Published: 2026-09-01T13:00:13Z Content type: opinion Language: en Sources: [Dan Lew Blog]() Topics: [Code](), [code productivity](), [Software Engineering](), [Development]() Tags: [bugs](), [code](), [code-reviews](), [development](), [features](), [problems](), [refactor](), [software-development](), [time]() ### AI overview An opinion article argues that nitpicking minor issues in code reviews wastes time. Small problems usually have limited impact, may never cause trouble, and can often be fixed later, so review effort should prioritize major problems, bugs, and features. ### Source excerpt I once wrote about how I've stopped nit-picking in code reviews, which improved the signal-to-noise ratio and helped everyone get along better. I'm back with a third reason to ditch the practice: nit-picking wastes time. By definition, nitpicks are about minor problems: ## Setting up a CI/ CD pipeline with GitLab: Quality, Test and Report DevFeed: [Setting up a CI/ CD pipeline with GitLab: Quality, Test and Report]() Original publisher: [Read original article]() Author: T.J. Maher (noreply@blogger.com) Published: 2026-08-25T02:28:52Z Content type: tutorial Language: en Sources: [T.J. Maher]() Topics: [CI/CD Pipeline](), [GitLab](), [Testing](), [Playwright](), [pull-requests](), [TypeScript]() Tags: [bun-create-playwright](), [ci-cd](), [ci-cd-pipeline](), [code-reviews](), [gitlab](), [playwright](), [test](), [testing](), [typescript](), [workflows]() ### AI overview A tutorial on setting up a three-stage GitLab CI/CD pipeline for merge requests. The pipeline runs code-quality checks, smoke and regression tests, and produces a downloadable report, with stages executed sequentially and jobs within each stage running in parallel. ### Source excerpt So far, we have reviewed how to review code with ES Lint + Prettier and Typecheck, how to set up and run smoke tests, how to run all Playwright + TypeScript tests, and reviewed the HTML report of results. In this post, we are going to set up a three stage GitLab CI/ CD pipeline that will run against every merge request: Quality (lint, typecheck, prettier) --> Test ( smoke + regression ) --> Report ( Downloadable ) I haven't used GitLab since when I worked at ThreatStack back in 2020. ( See my blog entry Getting to Know GitLab and How They Test the UI ) GitLab reads .gitlab-ci.yml from the repository root and turns it into a pipeline, a set of jobs, grouped into stages, running whenever you push a change to a repository. Stages run one after another, and jobs within a stage run in parallel. If one stage fails, the stages after it are skipped. This begs the question: What is GitLab? What is CI/ CD? Or a pipeline? Or a merge request? What is GitLab? GitLab began in 2011 as a side project by Ukrainian programmer Dmytro Zaporozhets, built to help developers work together on code more easily. Over time, it grew into a large open-source DevOps platform. More than just storing code, it handles code reviews, CI/CD (continuous integration and continuous delivery), and package registries. A company called GitLab Inc. was eventually created to turn the project into a commercial product. What makes GitLab different from tools that only host Git repositories (like a basic Git server) is that it bundles the entire software delivery process into one application. This includes: Merge requests - GitLab's term for proposing and reviewing code changes before they're merged into the main codebase (similar to a "pull request" in GitHub) Issue tracking - a built-in system for logging bugs, tasks, and feature requests Pipelines - automated workflows that build, test, and deploy code whenever changes are pushed So instead of stitching together separate tools for version control, testing, an ## The Case for Hiring Junior Engineers 🌱 DevFeed: [The Case for Hiring Junior Engineers 🌱]() Original publisher: [Read original article]() Author: Luca Rossi Published: 2026-08-12T07:19:52Z Content type: opinion Language: en Sources: [Refactoring]() Topics: [Artificial Intelligence](), [systems](), [code reviews]() Tags: [2026](), [ai](), [code-reviews](), [hiring](), [junior](), [systems]() ### AI overview This opinion argues that companies are making a practical business mistake by freezing junior engineering hiring because of AI. It acknowledges that junior engineers now face earlier demands such as code review, critical thinking, and systems thinking, while arguing that these challenges do not eliminate the value of hiring them. ### Source excerpt Let's flip the narrative that you only need seniors now. ## AI-generated code is overwhelming reviewers and changing pull request approval practices DevFeed: [AI-generated code is overwhelming reviewers and changing pull request approval practices]() Original publisher: [Read original article]() Author: Ishan Khanna Published: 2026-08-01T19:45:03Z Content type: opinion Language: en Sources: [Ishan Khanna]() Topics: [Artificial Intelligence](), [Pull Request](), [Code review](), [GitHub]() Tags: [ai](), [code](), [code-review](), [code-reviews](), [developer-experience](), [gen-ai](), [generative-ai](), [github](), [large-language-models](), [llms]() ### AI overview The article argues that AI-generated code is increasing the volume of pull requests beyond what engineers can thoroughly review. It describes slow approvals, ignored code-review-agent comments, and a growing practice of approving changes without close examination. ### Source excerpt "Can you review this PR quickly?" has become every engineer's most-sent message. On AI-generated code, drowning reviewers, and how Meta's RADAR auto-approves low-risk diffs. ## Broken Systems Don't Need More Developers DevFeed: [Broken Systems Don't Need More Developers]() Original publisher: [Read original article]() Author: Valentina Jemuović Published: 2026-07-14T09:12:34Z Content type: opinion Language: en Sources: [Optivem Journal]() Topics: [systems](), [legacy](), [Back end](), [Test-driven development]() Tags: [architecture](), [code-reviews](), [developers](), [engineering](), [legacy](), [legacy-code](), [meetings](), [merge](), [onboarding]() ### AI overview Adding developers to a tightly coupled legacy system may slow progress because onboarding, interruptions, meetings, code reviews, and merge conflicts increase. The article argues that architecture, rather than team size, is the main bottleneck. ### Source excerpt If four developers aren't delivering fast enough, why not make it eight? ## Worth Reading: Genie Tarpit DevFeed: [Worth Reading: Genie Tarpit]() Original publisher: [Read original article]() Published: 2026-06-09T06:25:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [ai-coding](), [Artificial Intelligence](), [Code](), [coding]() Tags: [ai](), [ai-coding](), [code](), [code-reviews](), [coding](), [worth-reading]() ### AI overview A recommendation of Kent Beck's "Genie Tarpit," which the author says reflects their experience that AI-assisted code reviews are more reliable than AI-generated new code. The article attributes this behavior to a task orientation that can claim success despite nonworking code and notes that no clear solution is known. ### Source excerpt Following a link in Martin Fowler's Fragments, I stumbled upon Genie Tarpit by Kent Beck - a perfect summary of my experiences with AI coding (code reviews are OK, new code less so). He also provided a good reason for that behavior: The "plausible deniability" task orientation of the genie leaves it claiming success even though the code doesn't work at all. And the proposed solution? You probably saw this one coming--nobody knows. Read more ... ## Why AI Changes Code Reviews and Why Engineers Should Review Intent DevFeed: [Why AI Changes Code Reviews and Why Engineers Should Review Intent]() Original publisher: [Read original article]() Author: Daniel Moka Published: 2026-05-18T05:02:06Z Content type: opinion Language: en Sources: [Craft Better Software]() Topics: [AI-assisted coding](), [Software Engineering](), [Artificial Intelligence]() Tags: [agents](), [code-reviews](), [quality](), [software-engineering](), [vibe-coding]() ### AI overview The article argues that AI-generated code is increasing the volume of pull requests, commits, and overall code output beyond what humans can review line by line. It recommends shifting engineering attention from reviewing implementation details to reviewing intent, solutions, and the reasoning behind what AI agents build. ### Source excerpt How Top Engineers Review Code in the Age of AI ## Using bots to implement fixes from pull request review comments DevFeed: [Using bots to implement fixes from pull request review comments]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-05-13T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Code review](), [Pull Request](), [Artificial Intelligence](), [engineering-culture]() Tags: [ai](), [api-design](), [code-review](), [code-reviews](), [knowledge-sharing](), [pull-requests]() ### AI overview The article argues that bots can implement fixes requested in pull request review comments, allowing developers to review the changes and merge them when appropriate. It presents this as useful for small fixes and notes that code owners may need to re-verify the result. ### Source excerpt You know what's better than a code review comment? A fix. ## Pi versus Claude Code: An Honest Comparison of a Minimalist AI Coding Agent DevFeed: [Pi versus Claude Code: An Honest Comparison of a Minimalist AI Coding Agent]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-04-18T17:00:15Z Content type: comparison Language: en Sources: [The AI Engineer]() Topics: [AI-assisted coding](), [Open Source](), [Claude Code](), [codex](), [TypeScript]() Tags: [bash](), [claude-code](), [code-reviews](), [coding-agents](), [extension](), [open-source](), [reviews](), [tests]() ### AI overview The article compares Pi, an open-source minimalist coding agent, with Claude Code and Codex. It examines Pi's four built-in tools, broad model support, editable system prompt, and TypeScript extensions, including a workflow for writing, reviewing, fixing, testing, and verifying code. It also discusses trade-offs such as the lack of built-in MCP, to-do tracking, sub-agents, and hooks. ### Source excerpt I spent a week with the minimalist AI coding agent Pi. Here's my honest take. ## Agentic code workflows with Nick Tune DevFeed: [Agentic code workflows with Nick Tune]() Original publisher: [Read original article]() Author: Dr Milan Milanović Published: 2026-03-26T16:01:37Z Content type: article Language: en Sources: [Tech World With Milan Newsletter]() Topics: [agentic-coding](), [ai-coding](), [Claude Code](), [Code quality](), [code reviews](), [Test-driven development](), [Testing](), [Command-line interface](), [Domain-driven design (DDD)](), [migration](), [Requirements](), [Continuous Delivery (CD)]() Tags: [agentic](), [agentic-coding](), [agents](), [ai-coding](), [ai-tools](), [best-practices](), [claude-code](), [cli](), [code-quality](), [code-reviews](), [continuous-delivery](), [domain-driven-design](), [migration](), [requirements]() ### AI overview An interview with Nick Tune about advanced agentic coding workflows. It covers planning requirements, modeling development as a tested state machine, implementing features with Claude Code and guardrails, enforcing architecture rules deterministically, reviewing pull requests, applying TDD, and using custom tooling. ### Source excerpt Most engineers who talk about AI productivity talk in abstractions. ## AI-Assisted Code Review Tools: Useful First-Line Checks, Not a Replacement for Humans DevFeed: [AI-Assisted Code Review Tools: Useful First-Line Checks, Not a Replacement for Humans]() Original publisher: [Read original article]() Published: 2026-03-01T00:00:00Z Content type: opinion Language: en Sources: [Mutt0-ds Notes]() Topics: [Code review](), [Artificial Intelligence](), [AI Agent](), [Pull Request](), [CI/CD]() Tags: [ai](), [ai-agent](), [ci-cd](), [code-review](), [code-reviews](), [pull-request]() ### AI overview This subjective article examines the author's experience using AI-assisted code review tools such as CodeRabbit in day-to-day development and a CI/CD pipeline. It argues that these tools can catch bad practices and obvious issues early, but still miss deeper architectural bugs and should not replace human reviewers. ### Source excerpt AI-assisted code reviews: are they actually helpful, or just more noise in an already crowded inbox? Today I want to talk about my... ## AI Can Accelerate Experienced Engineers but Should Not Replace Junior Developers' Learning Process DevFeed: [AI Can Accelerate Experienced Engineers but Should Not Replace Junior Developers' Learning Process]() Original publisher: [Read original article]() Author: Mike Yerou Published: 2026-02-05T09:59:43Z Content type: opinion Language: en Sources: [Rock and Null]() Topics: [Artificial Intelligence](), [coding](), [code reviews]() Tags: [ai](), [ai-tools](), [artificial-intelligence](), [code-reviews](), [coding](), [productivity](), [software-engineering](), [thoughts]() ### AI overview The article argues that AI tools can help experienced engineers work faster but may allow junior engineers to bypass the struggle, debugging, code reviews, and design discussions that build deep understanding. It recommends intentional training and allowing juniors to develop intuition before relying heavily on AI. ### Source excerpt Junior engineers can get instant answers and working code, but risk skipping the hard-earned understanding that comes from struggle. AI is a powerful multiplier for experienced engineers, but without intentional training, it can quietly erode how the next generation is built. ## Introducing GPT-5.2 DevFeed: [Introducing GPT-5.2]() Original publisher: [Read original article]() Published: 2025-12-11T00:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [Frontier Model](), [OpenAI](), [ChatGPT](), [Artificial Intelligence](), [API](), [agentic-coding](), [long-context](), [coding](), [Tool](), [Data Science](), [data]() Tags: [agentic](), [agentic-coding](), [agentic-workflows](), [ai](), [analysis](), [api](), [benchmarks](), [bug](), [chatgpt](), [code-reviews](), [coding](), [context](), [data](), [frontier-model](), [gpt](), [long-context](), [model](), [openai](), [product](), [reasoning](), [vision]() ### AI overview OpenAI introduces GPT-5.2, a frontier model series designed for professional knowledge work. It improves coding, vision, long-context understanding, tool use, reasoning, document analysis, data science, and complex multi-step projects, with availability in ChatGPT and the API. ### Source excerpt GPT-5.2 is our most advanced frontier model for everyday professional work, with state-of-the-art reasoning, long-context understanding, coding, and vision. Use it in ChatGPT and the OpenAI API to power faster, more reliable agentic workflows. ## From Intern Project to Production: How I Shipped the Draw Tool for Canva's Present Mode DevFeed: [From Intern Project to Production: How I Shipped the Draw Tool for Canva's Present Mode]() Original publisher: [Read original article]() Author: Edwina Adisusila Published: 2025-08-06T00:00:01Z Content type: article Language: en Sources: [Canva Engineering]() Topics: [Development](), [Front end](), [Software Engineering](), [real-time]() Tags: [code-reviews](), [draw-tool](), [feature](), [frontend](), [i](), [intern](), [monitoring](), [new-feature](), [overview](), [production](), [quality](), [real-time](), [technical](), [tool](), [user](), [users]() ### AI overview A Canva engineer describes evolving an internship proof of concept into the Draw in Present Mode feature and shipping it to production. The article covers user demand, implementation and testing, code reviews, release planning, analytics monitoring, and the technical challenge of integrating drawing into Canva's separate editor and presenting code domains. ### Source excerpt Overview of the technical hurdles I overcame to evolve my intern project into a new feature for users. ## Coding with LLMs in the summer of 2025 (an update) DevFeed: [Coding with LLMs in the summer of 2025 (an update)]() Original publisher: [Read original article]() Published: 2025-07-20T10:58:54Z Content type: article Language: en Sources: [Antirez]() Topics: [Large Language Model](), [coding](), [Programming](), [Redis](), [Assembly]() Tags: [2025](), [assembly](), [claude](), [code](), [code-reviews](), [coding](), [llms](), [programming](), [redis](), [update]() ### AI overview The article reflects on how frontier LLMs such as Gemini 2.5 PRO and Claude can support programming through code reviews, rapid prototyping, pair design, specification-driven coding, and work with unfamiliar technologies. It argues that effective use depends on clear problem descriptions, iterative collaboration, and human judgment. ### Source excerpt Frontier LLMs such as Gemini 2.5 PRO, with their vast understanding of many topics and their ability to grasp thousands of lines of code in a few seconds, are able to extend and amplify the programmer capabilities. If you are able to describe problems in a clear way and, if you are able to accept the back and forth needed in order to work with LLMs, you can reach incredible results such as: 1. Eliminating bugs you introduced in your code before it ever hits any user: I experienced this with Vector Sets implementation of Redis. I would end eliminating all the bugs eventually, but many were just removed immediately by Gemini / Claude code reviews. 2. Explore faster how a given idea could work, by letting the LLM write the throw away code to test ASAP in order to see if a given solution is actually more performant, if it is good enough, and so forth. 3. Engage in pair-design activities where your instinct, experience, design taste can be mixed with the PhD-level knowledge encoded inside the LLM. In this activity, the LLM will sometimes propose stupid paths, other times incredibly bright ideas: you, the human, are there in order to escape local minimal and mistakes, and exploit the fact your digital friend knows of certain and various things more than any human can. 4. Accelerate your work by writing part of the code under your clear specifications. 5. Work with technologies far from your expertise but contiguous with what you can do (for instance: coding in 68000 assembly for an Amiga demo?) using LLMs as an extension of specific parts of your mind, for the knowledge you don't have. One and half years ago I wrote a blog post called "LLMs and programming in the first days of 2024". There, I found LLMs to be already useful, but during these 1.5 years, the progresses they made completely changed the game. However, in order to leverage their capabilities, humans interacting with LLMs must have certain qualities and follow certain practices. Let's explore them. ## Refuse vi ## How to give outstanding code reviews (if you have to) DevFeed: [How to give outstanding code reviews (if you have to)]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2024-07-20T05:00:00Z Content type: tutorial Language: en Sources: [Tobias Mende]() Topics: [code reviews](), [Programming](), [pull-requests]() Tags: [asynchronous](), [code](), [code-reviews](), [collaboration](), [developer-experience-developer-productivity-quality-software-development-software-testing-engin](), [how-to](), [knowledge-sharing](), [programming](), [software]() ### AI overview This article explains the author's approach to code reviews. It favors pair or team programming for immediate feedback, while presenting asynchronous code reviews as a fallback when pairing is not possible. It discusses slower feedback, less effective written communication, reduced knowledge sharing, and weaker collaboration as drawbacks of asynchronous reviews. ### Source excerpt How to give outstanding code reviews (if you have to) Most software companies use code reviews to ensure that at least two people check each part of the code base and maintain its quality. Although I prefer pair or team programming over asynchronous code reviews, I recognize that this isn't always possible. In such cases, asynchronous code reviews are the next best option. ## 5 security best practices for adopting generative AI code assistants like GitHub Copilot DevFeed: [5 security best practices for adopting generative AI code assistants like GitHub Copilot]() Original publisher: [Read original article]() Author: Liqian Lim (林利蒨) Published: 2024-03-05T12:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Generative AI](), [AI-assisted coding](), [GitHub Copilot](), [code security](), [Code](), [snyk]() Tags: [acquisition](), [ai](), [blog](), [code-completion](), [code-reviews](), [code-security](), [developer](), [errors](), [generative-ai](), [github-copilot](), [pmm](), [security-best-practices](), [security-tools](), [snyk](), [snyk-code](), [validation]() ### AI overview This article presents five security best practices for adopting generative AI code assistants such as GitHub Copilot. It emphasizes keeping humans in the loop, validating and reviewing AI-generated code, educating teams about risks, and using security tools and guardrails. ### Source excerpt Learn how you can safely adopt AI code completion tools (like Copilot) by applying these 5 best practices and see how Snyk can make it easy to stay secure. ## Static Code Analysers DevFeed: [Static Code Analysers]() Original publisher: [Read original article]() Published: 2024-01-22T00:15:00Z Content type: tutorial Language: en Sources: [Kt. Academy]() Topics: [detekt](), [Kotlin](), [Programming](), [code reviews](), [compilers]() Tags: [android](), [code-reviews](), [compilers](), [detekt](), [generics](), [java](), [jvm](), [kotlin](), [workshop-learning-programming]() ### AI overview This tutorial introduces static analysis as a way to identify bugs and recurring code patterns without running the code. It explains how static analysers can automate and enforce coding patterns in large Kotlin codebases, then begins a practical walkthrough of detekt, a popular Kotlin static analyser. ### Source excerpt All you need to know about Static Code Analysers and Detekt. ## Peer Reviews for Data Science DevFeed: [Peer Reviews for Data Science]() Original publisher: [Read original article]() Author: Peter Weir Published: 2024-01-17T06:00:00Z Content type: article Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [Data Science](), [Software Engineering]() Tags: [code-reviews](), [data-science](), [errors](), [process](), [quality](), [research](), [software-engineering](), [team]() ### AI overview This article explains how peer reviews can improve data science by catching analytical errors, broadening perspectives, sharing knowledge, and strengthening collaboration. It describes the practice as combining ideas from software engineering code reviews and scientific peer review. ### Source excerpt If it's worth doing, it's worth reviewing [Next page]()