# pull-requests A proposal to merge code changes into a project for discussion and review before merging. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Libreboot Git repositories now on Codeberg (RIP Notabug) DevFeed: [Libreboot Git repositories now on Codeberg (RIP Notabug)]() Original publisher: [Read original article]() Author: Leah Rowe Published: 2026-09-17T04:32:50.666044Z Content type: news Language: en Sources: [News about Libreboot releases and development]() Topics: [forgejo](), [Git](), [ci](), [pull-requests](), [Gitea]() Tags: [bios](), [canoeboot](), [ci](), [coreboot](), [forgejo](), [free-software](), [git](), [go](), [libre](), [libreboot](), [opensource](), [pull-requests](), [repositories](), [uefi]() ### AI overview Libreboot is moving its advertised Git repositories to Codeberg because Notabug has become unreliable and frequently returns HTTP 500 errors. Notabug remains available as a backup, while Codeberg provides features including issue search and built-in CI through Woodpecker. ### Source excerpt Article: Libreboot Git repositories now on Codeberg (RIP Notabug) Web link: https://libreboot.org/news/codeberg.html ## AI Agent Governance: Why It Belongs in Your Platform DevFeed: [AI Agent Governance: Why It Belongs in Your Platform]() Original publisher: [Read original article]() Author: Prateek Mittal Published: 2026-09-16T20:28:57.610955Z Content type: article Language: en Sources: [Harness Blog]() Topics: [AI Agent](), [Artificial Intelligence](), [observability](), [audit](), [test](), [Deployment](), [Monitoring](), [pull-requests]() Tags: [ai](), [ai-agent](), [audit](), [deployment](), [governance](), [monitoring](), [observability](), [pull-requests](), [test]() ### AI overview Engineering leaders from Yelp, Platformable, and Harness argue that AI agent governance must be automated, continuously enforced, and built into the platform rather than left to policy documents. The article discusses audit trails, agent-to-agent access controls, experiment tracking, testing, latency monitoring, rollback paths, and observability for agent-driven changes. ### Source excerpt Engineering leaders from Yelp, Platformable, and Harness explain why AI agent governance has to be built into the platform, not a policy doc. | Blog ## Investigating whether LLMs disproportionately use "spine" in GitHub pull request titles DevFeed: [Investigating whether LLMs disproportionately use "spine" in GitHub pull request titles]() Original publisher: [Read original article]() Author: Hillel Wayne Published: 2026-09-16T19:58:30Z Content type: opinion Language: en Sources: [Newsletter feed for Hillel Wayne's Newsletter]() Topics: [Large Language Model](), [Artificial Intelligence](), [GitHub](), [pull-requests](), [anthropic](), [cursor]() Tags: [2026](), [ai](), [claude](), [cursor](), [github](), [llms](), [pull-requests]() ### AI overview The article investigates whether large language models disproportionately use the word "spine" in GitHub pull request titles. Using GitHub Search as a proxy, it finds a much larger increase in "spine" pull requests than overall pull requests, but does not establish that the pattern comes from one specific model; sampled pull requests were coauthored by Claude or Cursor. ### Source excerpt Earlier this year I worked on a couple of AI-generated TLA+ specifications, and one thing I've noticed was that they all used the word "spine" somewhere. Then I saw some non-TLA+ public projects use "spine" too and got curious if it was a new LLMism (LLisM?). If it is, we should see it appear in a discontinuous jump in the number of code projects that use "spine". The proper way to thoroughly explore this would be via the GitHub archive data dump but that'd take forever and I have a full time job now. Instead, I'm going to use GitHub Search. We can't filter code samples by date but can filter pull requests, which seems like an acceptable enough proxy. Here's a graph of the number of public PRs each year with "spine" in the title: I stopped 2026 at 2026-09-01, meaning the first nine months of this year have seen 20 times more "spine" PRs as all of 2025. Now it could be the case that that GitHub grew, like, a lot in the last year. Maybe the 20x increase is due there being 20x as many pull requests. We can account for this by comparing it against all public PRs:1 If "spine" wasn't an LLisM, we would expect only a 1.5xish increase from 2025, not a 20x increase. One other possible confounder could be that LLMs don't like the word spine but PRs Georg submitted 15,000 changes to a chiropractor repo. I will deal with this issue by ignoring it. Can we figure out which models specifically are obsessed with spines? Let's try breaking the change down by month: Looking at LLM timeline, the only widely-used model released in May was Opus 4.8, which came out too late in the month to explain the spike. GPT 5.5 came out in April, which could be the cause. But I'm not convinced by this. For one, the spine count is still 1.5xing each month before April, and I definitely remember seeing spines as early as February. Also, I spot checked and many of the PRs that use "spine" are coauthored by Claude or Cursor. So I think it's not specific to one LLM, but could be convinced otherwise. Othe ## Zed launches Delta public beta for collaborative code review with developers and coding agents DevFeed: [Zed launches Delta public beta for collaborative code review with developers and coding agents]() Original publisher: [Read original article]() Author: Paul Sawers Published: 2026-09-16T19:45:30Z Content type: article Language: en Sources: [The New Stack]() Topics: [GitHub](), [AI-assisted coding](), [pull-requests](), [Developer Platform](), [Tooling]() Tags: [ai](), [ai-agents](), [beta](), [coding-agents](), [collaboration](), [developer](), [developer-tools](), [github](), [pull-requests](), [review](), [software-development]() ### AI overview Zed has announced the public beta of Delta, a collaborative environment where developers and coding agents work, review, and revise code together in shared threads rather than pull requests. The article presents Delta as a response to the volume of agent-generated code and limitations of the pull-request model. ### Source excerpt Something of a consensus has emerged from the developer fraternity in 2026 -- GitHub, a platform built substantively for human The post "Everyone's in a race to replace GitHub": Zed launches Delta because agents made pull requests obsolete appeared first on The New Stack. ## Migrating to a Mobile Monorepo for React Native DevFeed: [Migrating to a Mobile Monorepo for React Native]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-05-29T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [monorepo](), [React Native](), [Mobile](), [Submodules](), [Android](), [iOS](), [pull-requests](), [Jenkins]() Tags: [also](), [android](), [engineering](), [ios](), [jenkins](), [mobile](), [monorepo](), [news](), [react-native](), [submodules]() ### AI overview Khan Academy describes moving its React Native-related iOS and Android repositories from separate repositories and submodules into a single mobile monorepo. The change was intended to reduce synchronization problems, coordinate bridge changes across platforms, make pull requests and reviews more coherent, and simplify verification between codebases. The article also outlines repository-merging steps and anticipated trade-offs. ### Source excerpt By Jared Forsyth Over the past few months, we've been adding some React Native to our existing iOS ... Read more ## Delta launches public beta for collaborative coding with agents and code review DevFeed: [Delta launches public beta for collaborative coding with agents and code review]() Original publisher: [Read original article]() Author: Nathan Sobo Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Zed Industries - Blog]() Topics: [coding](), [pull-requests](), [GitHub](), [browser]() Tags: [beta](), [github](), [mobile](), [pull-requests](), [tooling]() ### AI overview Delta is now available in public beta as a collaborative environment for coding with agents and reviewing their work. It supports shared agent conversations, isolated review worktrees, and access from macOS, Linux, Windows, the web, and mobile browsers. ### Source excerpt Delta is now in public beta. Replacing pull requests is our first step toward replacing GitHub. ## 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. ## Your Donations at Work: One Year of Sponsored Servo Development DevFeed: [Your Donations at Work: One Year of Sponsored Servo Development]() Original publisher: [Read original article]() Author: The Servo Project Developers Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Servo Blog]() Topics: [servo](), [Maintainers](), [pull-requests](), [Development]() Tags: [contributions](), [development](), [documentation](), [flaky-tests](), [maintainers](), [pull-requests](), [rust](), [servo](), [servo-engine](), [servo-rendering-engine](), [sponsored](), [web-browser](), [web-browser-engine](), [web-engine](), [web-rendering-engine]() ### AI overview A Servo maintainer reviews a year of part-time work funded by monthly donations, including nominating new maintainers, reviewing pull requests, filing contributor-focused issues, writing documentation, improving flaky tests, and supporting JavaScript engine integration work. ### Source excerpt Last September, the Servo project announced that long-time maintainer Josh Bowman-Matthews (@jdm) would work part-time on improving the Servo contributor experience, entirely funded by the monthly donations on OpenCollective and GitHub. In his own words, here is a look back over the past year! First of all, I am enormously grateful to everyone who financially supports Servo, as those donations have enabled me to devote significant time to a project that I care a lot about. Some highlights from that funded work that I'm proud of: I nominated 8 new maintainers I reviewed 1150 pull requests I filed 114 issues targeted at newer contributors (92% of them have been fixed) I wrote new documentation about borrow hazards, experimental features, the AI policy, finding things to do, and fixing stable and intermittent test failures On top of that, I spent time diagnosing unexpected failures in others' PRs and fixed numerous intermittent test failures that made merging PRs more difficult for everyone. A few pieces of work from this period that stand out to me: supporting a large scale rewrite of Servo's JS engine integration to address intermittent panics related to garbage collection--I reviewed lots of pull requests, but also filed many issues that enabled the work addressing the panics to be spread across many other contributors getting tagged in to help understand test failures, uncovering our broken window.open behaviour, and eventually making a lot of flaky tests more stable supporting another contributor's grant proposal to work on Servo that was approved! This role I've carved out means a lot to me--I've found a healthy balance that allows me to spend time with my family as well as make meaningful contributions to Servo, and I get to spend a lot of time looking for ways to make the project more accessible for others. A big thank you to everybody supporting the project and my work; each individual monthly donation makes a big difference! I'm excited to see what's possible i ## How to Catch Security Vulnerabilities in Code Before They Reach Your Pull Requests DevFeed: [How to Catch Security Vulnerabilities in Code Before They Reach Your Pull Requests]() Original publisher: [Read original article]() Author: Umair Mirza Published: 2026-09-14T22:23:01Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [Application Security](), [Security](), [Vulnerabilities](), [Secret Scanning](), [Git](), [pull-requests](), [ci](), [Command-line interface](), [.NET](), [Python](), [coding]() Tags: [application-security](), [ci](), [cli](), [code](), [devsecops](), [git](), [net](), [pull-requests](), [python](), [sast](), [secret-scanning](), [security](), [security-vulnerabilities](), [software-development](), [testing](), [vulnerabilities]() ### AI overview This tutorial explains how to shift security checks earlier in development by running SAST locally with Git pre-commit hooks. It demonstrates using the DevSkim CLI to detect insecure coding patterns, pairing it with Gitleaks for dedicated secret scanning, validating the setup with an intentional failure, and enforcing the checks in CI. ### Source excerpt Security reviews are most effective when developers receive feedback while the code is still fresh in their minds. Waiting until a pull request, CI build, or penetration test to find exposed credentia ## A Daily Digest of What Actually Got Merged, Built from MQTT and a Local LLM DevFeed: [A Daily Digest of What Actually Got Merged, Built from MQTT and a Local LLM]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-09-14T07:00:00Z Content type: article Language: en Sources: [Philipp Lehmann]() Topics: [pull-requests](), [MQTT](), [Large Language Model](), [SQLite]() Tags: [architecture](), [ci-cd](), [llm](), [local-llm](), [mqtt](), [observability](), [pull-requests](), [python](), [sqlite]() ### AI overview This article describes a daily digest that uses MQTT merge events, a SQLite subscriber, and a locally run LLM to summarize merged pull requests by repository without sending diffs to a cloud API. ### Source excerpt paho-mqtt subscriber to SQLite plus a local LLM: a daily digest that summarises merged pull requests per repository from diff stats, no cloud API. ## Void Linux Maintainer Orphans 100+ Packages Over AI Policy Dispute DevFeed: [Void Linux Maintainer Orphans 100+ Packages Over AI Policy Dispute]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-12T15:11:26Z Content type: news Language: en Sources: [Phoronix]() Topics: [Linux](), [Artificial Intelligence](), [Large Language Model](), [pull-requests]() Tags: [ai](), [desktop-linux](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [llm](), [open-source-graphics](), [opencode](), [packages](), [phoronix](), [phoronix-test-suite](), [policy](), [pull-request](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview A Void Linux maintainer orphaned 113 packages after a dispute over the project's policy on using generative AI. The dispute followed the use of GLM-5.3-Flash with OpenCode to generate comparison text for a Go package update without disclosing the AI usage. ### Source excerpt A Void Linux maintainer has orphaned more than 100 packages he maintained over a dispute of the project's AI policy for LLM-generated text... ## How Every Team Can Build in Port at Scale, Without the Bottleneck DevFeed: [How Every Team Can Build in Port at Scale, Without the Bottleneck]() Original publisher: [Read original article]() Author: Guy Berman Published: 2026-09-03T14:39:07Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Authorization](), [Scalability](), [sdlc](), [pull-requests](), [Security]() Tags: [agent](), [agentic](), [compliance](), [dashboards](), [development](), [production](), [pull-requests](), [scalability](), [scale](), [sdlc](), [security](), [workflow]() ### AI overview The article explains how every team can build and own a scoped part of Port at scale. It presents fine-grained permissions, role-based access controls, and controlled promotion across development, staging, and production environments as ways to reduce platform-team bottlenecks while limiting risk. ### Source excerpt How every team can build in Port at scale: scoped permissions, dynamic policies, and safe rollouts across environments. ## 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 ## Documentation as a Repository: Publishing a Wiki.js Site from Reviewed Markdown DevFeed: [Documentation as a Repository: Publishing a Wiki.js Site from Reviewed Markdown]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-09-01T07:00:00Z Content type: opinion Language: en Sources: [Philipp Lehmann]() Topics: [Documentation](), [Markdown](), [pull-requests](), [Git](), [Wiki](), [Open Source]() Tags: [architecture](), [ci-cd](), [documentation](), [git](), [markdown](), [open-source](), [pull-request](), [testing](), [wiki]() ### AI overview The article argues that keeping documentation as reviewed Markdown in a repository makes changes visible, reviewable, and enforceable alongside code changes. It presents OpenTaberna's Wiki.js documentation workflow as an example. ### Source excerpt Wiki.js Git storage for docs as code: publish reviewed Markdown and fail a pull request when src/ changes without docs/, checked with git diff. ## 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. ## GitHub Copilot app for Beginners: Automate Dependabot pull request triage DevFeed: [GitHub Copilot app for Beginners: Automate Dependabot pull request triage]() Original publisher: [Read original article]() Author: Christopher Harrison Published: 2026-08-26T20:12:53Z Content type: tutorial Language: en Sources: [GitHub Engineering]() Topics: [GitHub Copilot app](), [pull-requests](), [Automation](), [GitHub Copilot](), [GitHub](), [Vulnerabilities]() Tags: [ai-ml](), [automation](), [copilot](), [developer](), [github](), [github-copilot](), [github-copilot-app](), [github-copilot-app-for-beginners](), [pull-request](), [workflow]() ### AI overview Learn how to create a daily automation in the GitHub Copilot app to triage Dependabot pull requests. The automation reviews open pull requests, groups updates by risk, checks CI status, and returns a summary highlighting changes ready to merge or requiring closer attention. ### Source excerpt Managing library updates can be tedious at times. Learn how the GitHub Copilot app can handle this type of repetitive task. The post GitHub Copilot app for Beginners: Automate Dependabot pull request triage appeared first on The GitHub Blog. ## 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 ## Human judgment doesn't leave the software factory. It relocates. DevFeed: [Human judgment doesn't leave the software factory. It relocates.]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-21T14:31:12Z Content type: article Language: en Sources: [Elevate]() Topics: [Software](), [Testing](), [event driven](), [Architecture & Design](), [GitHub](), [pull-requests](), [mutation-testing](), [Security]() Tags: [agent](), [architecture](), [claude-code](), [codex](), [event-driven](), [github](), [mutation-testing](), [pull-requests](), [quality](), [security](), [software](), [testing]() ### AI overview This field guide explains how to build a repeatable software factory while preserving human judgment and ownership. It recommends human involvement in product intent, system design, quality standards, code review, and merge decisions, supported by automated checks and deliberate experimentation. The article argues that a factory is most useful when work must be repeatable and event-driven across queues such as GitHub issues, Slack, Linear, or a backlog. ### Source excerpt A field guide to building a software factory that still has an owner. ## What Replaces Git When Agents Write the Code DevFeed: [What Replaces Git When Agents Write the Code]() Original publisher: [Read original article]() Published: 2026-08-19T00:00:00Z Content type: opinion Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Git](), [GitHub](), [pull-requests](), [ci](), [Code](), [audit]() Tags: [agents](), [audit](), [ci](), [code](), [context-window](), [git](), [github](), [logs](), [pull-requests]() ### AI overview The article argues that Git and GitHub's human-centered workflow does not scale when many software agents modify the same codebase. It identifies four resulting problems: code review bottlenecks, blocking merge conflicts, unstructured CI output that consumes agent context, and missing provenance for auditing and rollback. ### Source excerpt . [What Replaces Git When Agents Write the Code](what-replaces-git-when-agents-write-the-code-cover... ## Remediation Agents, Demystified: Why Fixing Beats Finding DevFeed: [Remediation Agents, Demystified: Why Fixing Beats Finding]() Original publisher: [Read original article]() Author: Snyk Team Published: 2026-08-19T00:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Application Security](), [Vulnerabilities](), [AI-assisted coding](), [pull-requests](), [Responsibility & Safety](), [Artificial Intelligence]() Tags: [agentic](), [ai](), [ai-agents](), [application-security](), [aspm](), [awareness](), [ci-cd](), [code-security](), [community](), [developer](), [devops](), [devsecops](), [enablement](), [interest](), [javascript](), [live-stream](), [node-js](), [pull-requests](), [security](), [snyk-code](), [snyk-open-source](), [snyk-platform](), [validation](), [vs-code](), [vulnerabilities]() ### AI overview Snyk's Remediation Agent is presented as a public-preview solution for the growing security-issue backlog. The article explains how coding agents can increase vulnerabilities, why deterministic remediation advice has not been enough, and how validation helps turn fixes into mergeable pull requests. ### Source excerpt See how Snyk's Remediation Agent uses security intelligence, breakability analysis, and validation to turn vulnerabilities into mergeable pull requests. ## Cursor Origin vs GitHub: What changes for developers DevFeed: [Cursor Origin vs GitHub: What changes for developers]() Original publisher: [Read original article]() Author: Aditya Oberai Published: 2026-08-18T00:00:00Z Content type: comparison Language: en Sources: [Appwrite Blog]() Topics: [cursor](), [GitHub](), [pull-requests](), [AI-assisted coding](), [hosting](), [Development]() Tags: [ai-assisted-coding](), [coding](), [comparisons](), [cursor](), [cursor-origin](), [development](), [github](), [hosting](), [pull-requests](), [workflow]() ### AI overview This comparison explains how Cursor Origin, an early-beta code hosting platform built into Cursor, differs from GitHub and what it changes for developers. Origin combines repositories, pull requests, code browsing, GitHub sync, and agent workflows in one editor, but initially targets paid Cursor accounts and is not positioned as an immediate replacement for GitHub. ### Source excerpt Cursor Origin vs GitHub: how Cursor's new code hosting handles repos, pull requests, and GitHub sync, and what actually changes in your daily workflow. ## Cursor Origin review: An engineer's perspective DevFeed: [Cursor Origin review: An engineer's perspective]() Original publisher: [Read original article]() Author: Matej Bačo Published: 2026-08-18T00:00:00Z Content type: opinion Language: en Sources: [Appwrite Blog]() Topics: [cursor origin](), [cursor](), [Git](), [GitHub](), [pull-requests](), [Command-line interface]() Tags: [ai](), [cli](), [cursor](), [cursor-origin](), [git](), [github](), [pull-request](), [review](), [switching]() ### AI overview A review of Cursor Origin, Cursor's Git hosting service, examines its repository, code browsing, search, pull request, and GitHub mirroring features. It finds that core repository and pull request workflows work, while account switching, repository creation flows, and repository management remain rough in the early beta. ### Source excerpt A detailed review of Cursor Origin, covering Git hosting, GitHub sync, cloud agents, current limitations, and whether it's worth switching. ## AI Coding Tip 032 - Build a Dark Factory Pipeline DevFeed: [AI Coding Tip 032 - Build a Dark Factory Pipeline]() Original publisher: [Read original article]() Author: Maxi Contieri Published: 2026-08-16T14:54:12Z Content type: opinion Language: en Sources: [Maximiliano Contieri - Software Design]() Topics: [ai-coding](), [pull-requests](), [coding]() Tags: [agents](), [ai](), [ai-coding](), [audit](), [production](), [pull-request](), [quality-control](), [review]() ### AI overview This article recommends an automated pull-request pipeline in which one model builds from a specification, a separate model from another vendor or family adversarially verifies the result, and flagged changes are fixed before human review. A defined sample of merged pull requests is randomly audited by humans, with overrides logged as correction signals and merges blocked when either gate is skipped. ### Source excerpt TL;DR: Run your pipeline like a dark factory: automated, sampled, and policed by an adversarial model. Common Mistake ❌ You let one model write a pull request, then hand the same model (or a suspicio ## 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. [Next page]()