# pull-requests Published articles for pull-requests. 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. ## 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. ## 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. ## A Week of Symfony #1028 (September 7-13, 2026) DevFeed: [A Week of Symfony #1028 (September 7-13, 2026)]() Original publisher: [Read original article]() Author: Javier Eguiluz Published: 2026-09-13T07:05:00Z Content type: news Language: en Sources: [Symfony Blog]() Topics: [Code](), [OAuth 2.0](), [OpenID connect (OIDC)](), [Security]() Tags: [cache](), [code](), [development](), [docs](), [http](), [pull-requests](), [security](), [symfony]() ### AI overview Symfony's weekly roundup covers standalone framework bundles, Docker-style console subcommands, expanded OAuth2 and OpenID Connect support, and SymfonyCon Warsaw 2026 announcements. It also reports merged pull requests, closed issues, and selected development fixes. ### Source excerpt This week, Symfony introduced standalone bundles for the framework features (MessengerBundle, HttpClientBundle, PropertyInfoBundle, etc.), added Docker-style console subcommands, and expanded OAuth2 and OpenID Connect support. In addition, we announced the... ## A Week of Symfony #1027 (August 31 - September 6, 2026) DevFeed: [A Week of Symfony #1027 (August 31 - September 6, 2026)]() Original publisher: [Read original article]() Author: Javier Eguiluz Published: 2026-09-06T07:46:00Z Content type: news Language: en Sources: [Symfony Blog]() Topics: [bug](), [ci](), [Command-line interface](), [pull-requests]() Tags: [bug](), [ci](), [command-line](), [diagnostics](), [pull-requests](), [symfony]() ### AI overview Symfony released versions 6.4.45, 7.4.18, and 8.1.6 with bug fixes and hardening improvements. The update also introduces the symfony lsp:check command for CI diagnostics covering unknown routes, missing templates, and configuration errors. ### Source excerpt This week, Symfony 6.4.45, 7.4.18, and 8.1.6 were released with numerous bug fixes and hardening improvements. Meanwhile, we introduced symfony lsp:check, a new command that brings Symfony-aware diagnostics to your CI pipeline, catching unknown routes, missing... ## OpenAI GPT-6 Astra Hits GA in Microsoft Foundry: Computer Use, Agentic Execution, and $10 to $75 per Million Tokens DevFeed: [OpenAI GPT-6 Astra Hits GA in Microsoft Foundry: Computer Use, Agentic Execution, and $10 to $75 per Million Tokens]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-05T17:09:34Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Microsoft](), [OpenAI](), [computer-use](), [Azure](), [Generative AI](), [Frontier Model](), [Security](), [Authorization](), [cloud-infrastructure](), [Software Engineering](), [pull-requests](), [dashboards]() Tags: [access-control](), [agentic](), [ai](), [apis](), [azure](), [computer-use](), [enterprise](), [frontier-model](), [generative-ai](), [governance](), [microsoft](), [openai](), [pull-requests](), [security]() ### AI overview Microsoft announced the general availability of OpenAI GPT-6 Astra in Microsoft Foundry on Azure. The frontier model is intended to support autonomous agentic workflows through multi-step planning, decision support, cross-application tool execution, and computer use. It can operate approved software interfaces, generate structured business artifacts, and assist with software engineering, dashboard creation, record updates, form processing, and interface testing. Microsoft Foundry provides scoped credentials, role-based access control, human-approval checkpoints, and identity controls for containment. ### Source excerpt Microsoft announced the general availability of GPT-6 Astra within Microsoft Foundry on Azure. The frontier model is engineered to transition enterprise generative AI from interactive chat interfaces toward autonomous agentic workflows, providing multi-step planning, deliberate decision support, and cross-application tool execution. OpenAI calls Astra its most aligned model to date, and Microsoft says it is The post OpenAI GPT-6 Astra Hits GA in Microsoft Foundry: Computer Use, Agentic Execution, and $10 to $75 per Million Tokens appeared first on StorageReview.com. ## GHarchive data has become unreliable for measuring GitHub activity DevFeed: [GHarchive data has become unreliable for measuring GitHub activity]() Original publisher: [Read original article]() Author: KD (noreply@blogger.com) Published: 2026-09-03T16:00:00Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [data](), [datasets](), [GitHub](), [Crawler](), [GitHub API](), [Open Source](), [GraphQL]() Tags: [collect](), [data](), [dataset](), [datasets](), [github](), [google](), [google-open-source](), [graphql](), [open-data-sets](), [open-source](), [oss](), [pipeline](), [pull-requests](), [retention](), [stream](), [volume]() ### AI overview This article examines the reliability of open source data, focusing on GHarchive's coverage of GitHub events. It argues that GHarchive should not be used for real-time or volume-based metrics because event retention has declined and some activity is omitted by the GitHub Event stream and API limitations. ### Source excerpt by Sophia Vargas, Google Open Source & Andrew Nesbitt, Ecosyste.ms Every second, open source contribution quietly shapes the software we rely on, and yet our view of this open ecosystem is surprisingly opaque. Open source development is performed in public spaces -- we can see the commits, issues and comments, the APIs and endpoints are free to use -- the logs are just sitting there, so why can't we just collect all of the data? ...Said every researcher, everywhere. However in most cases of open source related data, we are only looking at part of the whole. Why am I writing this post? Because many of us (including many business decision-makers) are too comfortable with unsubstantiated data. We've gotten used to it. Our models assume that it's smelly and we adjust the logic and weights to compromise. When it comes to open source, our confidence is even lower, even though our resulting decisions can directly impact individuals whom we collectively depend on. Let's consider one of my favorite datasets: GHarchive. Started as a hobby project in 2011, this crawler has amassed more than 15 years of event data from GitHub. While this source provides a historical record of open source development on GitHub, as a real-time or comprehensive source of metrics, it's unreliable and should not be a source for volume-based metrics. In 2025, GHarchive captured 14% fewer events than in 2024, despite steady growth in platform adoption. Since 2025, we estimate that data retention in GHarchive has fallen to ~50% and in 2026 it may be as low as 20% for some event types (see figure below). Prior to 2025, you could make the general assumption that the majority of events would be represented in this pipeline. Since 2025, we must now assume we may be missing at least half of events and possibly more -- not to mention all of the additional activity that's left out of the event API (see GitHub's GraphQL API.) The crawler logic behind this dataset is simple: give me all the events from the GitHub Ev ## 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 ## Using coding agents on a migration: Three practices that mattered DevFeed: [Using coding agents on a migration: Three practices that mattered]() Original publisher: [Read original article]() Author: Chandler Ortman Published: 2026-09-02T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [migration](), [clickhouse](), [AI-assisted coding](), [coding](), [configuration](), [Deployment](), [Environment Variables]() Tags: [cleanup](), [clickhouse](), [coding-agents](), [deployment](), [environment-variables](), [migration](), [prompt](), [pull-requests](), [temporal-voices]() ### AI overview The article examines coding agents used during a migration of Temporal Cloud usage and billing data to ClickHouse. It finds that the agents were most useful for cleanup and removal work, with durable cleanup instructions and exhaustive searches helping make that work practical. ### Source excerpt Three lessons from using coding agents during a ClickHouse migration, from cleanup automation to writing durable instructions that stay useful over time. ## Today I will... review GitHub PRs DevFeed: [Today I will... review GitHub PRs]() Original publisher: [Read original article]() Author: Aaron Powell Published: 2026-09-01T16:00:27Z Content type: tutorial Language: en Sources: [Visual Studio Blog]() Topics: [Pull Request](), [GitHub](), [GitHub Copilot](), [Syntax Highlighting]() Tags: [copilot](), [git](), [github](), [productivity](), [pull-requests](), [review](), [tooling](), [visual-studio](), [visual-studio-2026]() ### AI overview A tutorial on reviewing GitHub pull requests from Visual Studio. It covers opening PRs, examining diffs, adding buffered inline comments, approving or merging, and using Copilot during review. ### Source excerpt Today I will show you how we can tackle one of the most common tasks when working in a development team or managing an open-source project, and that is working with Pull Requests. While we're going to be using the GitHub tooling within Visual Studio for this, the approach would be very similar with Azure [...] The post Today I will... review GitHub PRs appeared first on Visual Studio Blog. ## Gitea v1.27.3 Ships 18 Security Hardening Fixes DevFeed: [Gitea v1.27.3 Ships 18 Security Hardening Fixes]() Original publisher: [Read original article]() Author: Christian Rakoot Published: 2026-09-01T06:31:36Z Content type: article Language: en Sources: [Self Host Lab]() Topics: [Gitea](), [Security](), [Authorization](), [API](), [Vulnerabilities](), [Pull Request](), [GitHub](), [GitLab](), [Maven](), [Swift]() Tags: [api](), [changelog](), [code](), [git](), [gitea-v1-27-3-ships](), [github](), [maintainers](), [network-security](), [network-security-news](), [news](), [pull-requests](), [release](), [security](), [vulnerabilities]() ### AI overview Gitea v1.27.3 introduces 18 security hardening fixes focused primarily on access control. The release narrows package and API-token access, restricts repository and attachment exposure, strengthens pull-request and artifact trust boundaries, and limits several migration and metadata inputs. It follows earlier Gitea releases that addressed numbered vulnerabilities, but these fixes do not carry dedicated CVE identifiers. ### Source excerpt Gitea v1.27.3 landed August 29, 2026 with an unusually long SECURITY section: 18 separate access-control hardening fixes, none carrying a CVE identifier. The changes tighten package API scope, attachment paths, repository enumeration, and more. It's the third Gitea security story here in three weeks, following the CVE-2026-59774/60004 patches and CISA's active-exploitation confirmation. Update on your normal schedule. ## Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration DevFeed: [Troubleshoot and secure your code faster with Datadog's Bitbucket Cloud Source Code integration]() Original publisher: [Read original article]() Author: Eric Metaj; Mark Azer Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Developer Tools](), [iac-security](), [Vulnerabilities]() Tags: [apm](), [bitbucket](), [ci-visibility](), [cloud](), [code](), [code-profiling](), [code-security](), [devsecops](), [error-tracking](), [infrastructure-as-code](), [integration](), [observability](), [open-source](), [pull-requests](), [secret-scanning](), [security](), [software-delivery](), [test-optimization](), [vulnerabilities]() ### AI overview Datadog's Bitbucket Cloud Source Code integration links selected repositories with Datadog so teams can investigate production issues with source context and review security findings before merging code. ### Source excerpt Connect Bitbucket Cloud to Datadog to troubleshoot with source code in context and surface test, quality, and security feedback in pull requests. ## 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. ## How we shipped a production API out of one Slack thread DevFeed: [How we shipped a production API out of one Slack thread]() Original publisher: [Read original article]() Author: WorkOS Published: 2026-08-27T14:09:14Z Content type: article Language: en Sources: [WorkOS Blog]() Topics: [API](), [Slack](), [CRUD]() Tags: [agents](), [api](), [merge](), [production](), [pull-requests](), [rest](), [security](), [slack]() ### AI overview WorkOS describes how its IT Contact API was shipped from a single Slack thread. Humans made product and design decisions and gated the merge, while agents handled tickets, pull requests, review responses, and merges. The discussion shaped the API from an initial RPC proposal toward REST and resolved security, deletion, and duplicate-creation questions. ### Source excerpt The WorkOS IT Contact API went live on August 18 out of a single Slack thread. Humans made the decisions and gated the merge; agents did everything else. ## AI Software Factory: What It Is, Why You Need One, Who Owns It DevFeed: [AI Software Factory: What It Is, Why You Need One, Who Owns It]() Original publisher: [Read original article]() Author: Zohar Einy Published: 2026-08-25T12:53:22Z Content type: article Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Artificial Intelligence](), [sdlc](), [Platform Engineering](), [DevOps]() Tags: [ai](), [devops](), [incident](), [platform-engineering](), [pull-requests](), [sdlc](), [testing]() ### AI overview The article argues that AI coding assistants alone do not improve end-to-end delivery because review, testing, and incident work can remain bottlenecks. It proposes an AI software factory that orchestrates agents, shared context, governance, and measurement across the SDLC. ### Source excerpt Discover what an AI software factory is, why it beats coding assistants, and who owns it in your engineering organization today. ## Day to day PostgreSQL on Kubernetes: Resize, Scale, and Upgrade DevFeed: [Day to day PostgreSQL on Kubernetes: Resize, Scale, and Upgrade]() Original publisher: [Read original article]() Author: Hans-Jürgen Schönig Published: 2026-08-25T06:16:00Z Content type: tutorial Language: en Sources: [CYBERTEC PostgreSQL | Services & Support]() Topics: [Kubernetes](), [PostgreSQL](), [postgresql clusters]() Tags: [capacity](), [commands](), [declarative](), [github](), [how-to](), [kubectl](), [kubernetes](), [operations](), [patroni](), [pg-operator](), [postgresql](), [postgresql-on-cloud](), [pull-requests](), [rolling-update](), [scale](), [upgrade]() ### AI overview A tutorial demonstrates resizing, scaling, and minor version upgrades for PostgreSQL running on Kubernetes with the CYBERTEC PG Operator. Using end-to-end minikube tests, it shows declarative manifest changes, rolling updates, Patroni failover, and scaling while the cluster remains available. ### Source excerpt This blog is about the resizing, scaling PostgreSQL in Kubernetes. This blog helps you learn various operation related to operations of PostgreSQL in Kubernetes. The post Day to day PostgreSQL on Kubernetes: Resize, Scale, and Upgrade appeared first on CYBERTEC PostgreSQL | Services & Support. ## The end of credential sprawl for agents DevFeed: [The end of credential sprawl for agents]() Original publisher: [Read original article]() Author: Dima Voytenko Published: 2026-08-25T04:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Vercel](), [Security](), [OAuth](), [OpenID connect (OIDC)](), [Authorization](), [SDKs](), [API](), [pull-requests]() Tags: [agents](), [connectors](), [github](), [oidc](), [pull-requests](), [sdk](), [security](), [shopify]() ### AI overview Vercel Connect is generally available as a way for applications and agents to obtain short-lived, task-scoped credentials at runtime. It supports connectors for services such as Slack, GitHub, Snowflake, Shopify, and OAuth providers, uses deployment OIDC identity for authentication, refreshes tokens automatically, and avoids storing provider secrets in applications. ### Source excerpt Every useful agent reaches beyond your codebase. It posts to Slack, opens pull requests, queries Snowflake, or calls an internal API. That reach is what makes it valuable, and it's also where the risk lives, because for years, granting it meant provisioning a long-lived token and hoping it never leaked. Vercel Connect replaces long-lived tokens with ones your code requests at runtime, scoped to the task and expiring on their own. During the public beta, we've grown the ecosystem past 100 connectors, unified how they work, and added the governance capabilities teams need in production. Today, Vercel Connect is generally available. Vaults don't fix long-lived tokens Managing credentials has become its own workload. Teams write rotation scripts, copy secrets across environments, and share tokens between users. Putting a token in a vault made it harder to steal, but no less dangerous once stolen. It never expires, and no vault limits what a leaked credential can do. Agents compounded the problem by touching more systems with greater autonomy, more often. Yet the tools to contain a secret haven't changed. With Vercel Connect, your app never stores credentials. It requests one: You register a connector once for a provider like Slack, GitHub, Snowflake, Shopify, or your own OAuth service You attach it to the projects and environments that need it Your code requests a token at runtime, and it refreshes automatically Your app has no provider secret to commit by accident Requesting a token doesn't require another secret. Every deployment on Vercel carries an OIDC identity, and the SDK uses it to prove who's asking. What changes when access becomes a request The difference shows up in the properties of the credential: Property Stored token Vercel Connect Lifetime Never expires Short-lived, refreshed automatically Reach Everything the agent could need Scoped to the task in the request Identity One shared bot for every user App or a specific named user Rotation Mint, update copi ## 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. [Next page]()