惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

TaoSecurity Blog
TaoSecurity Blog
V2EX - 技术
V2EX - 技术
K
KPMG report finds enterprise disconnect between AI and its ROI | CIO
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
S
Secure Thoughts
Forbes - Security
Forbes - Security
Engineering at Meta
Engineering at Meta
Microsoft Azure Blog
Microsoft Azure Blog
Apple Machine Learning Research
Apple Machine Learning Research
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Webroot Blog
Webroot Blog
W
WeLiveSecurity
Blog — PlanetScale
Blog — PlanetScale
G
Google Developers Blog
Last Week in AI
Last Week in AI
月光博客
月光博客
H
Help Net Security
PCI Perspectives
PCI Perspectives
Security Archives - TechRepublic
Security Archives - TechRepublic
Hugging Face - Blog
Hugging Face - Blog
Hacker News - Newest:
Hacker News - Newest: "LLM"
有赞技术团队
有赞技术团队
T
Troy Hunt's Blog
Google DeepMind News
Google DeepMind News
Hacker News: Ask HN
Hacker News: Ask HN
Microsoft Security Blog
Microsoft Security Blog
N
News and Events Feed by Topic
Project Zero
Project Zero
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
L
LangChain Blog
P
Privacy & Cybersecurity Law Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
V
V2EX
I
Intezer
H
Hacker News: Front Page
Recent Announcements
Recent Announcements
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
博客园 - Franky
T
Threat Research - Cisco Blogs
Spread Privacy
Spread Privacy
博客园 - 【当耐特】
美团技术团队
Schneier on Security
Schneier on Security
D
Docker
Scott Helme
Scott Helme
L
LINUX DO - 最新话题
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
aimingoo的专栏
aimingoo的专栏
L
Lohrmann on Cybersecurity

Swift for Visual Studio Code comes to Open VSX Registry | InfoWorld

Notion courts developers with a platform for AI agents and workflow automation Using continuous purple teaming to protect fast-paced enterprise environments A better way to work with SQL Server Evidence-driven workflows: Rethinking enterprise process design AWS debuts Graviton-powered Redshift RG instances to cut analytics costs SAP’s AI promises last year? Most are still rolling out First look: Lemonade serves up local AI with limitations GitLab CEO sees developer tool bill increasing 100-fold Red Hat adds support for agentic AI development What’s new and exciting in JDK 26 Kill the loading spinner with local-first data and reactive SQL A networking revolution at AWS Tokenmaxxing is super dumb How to add AI to an existing product (without annoying users) Your AI doesn’t need another database What happens when engineering teams reorganize around AI agents Python isn’t always easy When cloud giants meddle in markets 12 model-level deep cuts to slash AI training costs The best new features in Python 3.15 Teradata launches platform for enterprise AI agents moving beyond pilots Three skills that matter when AI handles the coding MongoDB targets AI’s retrieval problem Building AI apps and agents with Microsoft Foundry Designing front-end systems for cloud failure No, AI won’t destroy software development jobs Diskless databases: What happens when storage isn’t the bottleneck Vibe coding or spec-driven development? The agentic AI distraction Vibe coding or spec-driven development? How to choose Cloud providers are blinded by agentic AI SAP to acquire data lakehouse vendor Dremio Small language models: Rethinking enterprise AI architecture Making AI work through eval hygiene Improving AI agents through better evaluations AI in the cloud is easy but expensive Running AI in the cloud is easy – and expensive Making AI work for databases Harness teams of agentic coders with Squad Harness teams of coding agents with Squad Oracle NetSuite announces AI coding skills for SuiteCloud developers Why it’s so hard to create stand-alone Python apps A new challenge for software product managers The hidden cost of front-end complexity GitHub shifts Copilot to usage-based billing, signaling a new cost model for enterprise AI tools OpenAI’s Symphony spec pushes coding agents from prompts to orchestration The front-end architecture trilemma: Reactivity vs. hypermedia vs. local-first apps Enterprise AI is missing the business core The best JavaScript certifications for getting hired Google begins putting the guardrails on agentic AI Why world models are AI’s next frontier Where to begin a cloud career Google pitches Agentic Data Cloud to help enterprises turn data into context for AI agents How open source ideals must expand for AI Is your Node.js project really secure? How I doubled my GPU efficiency without buying a single new card SpaceX secures option to acquire AI coding startup Cursor for $60B Google’s Gemma 4 shines on local systems – both big and small AI is upending the SaaS game How AI is upending SaaS tools Snowflake offers help to users and builders of AI agents From the engine room to the bridge: What the modern leadership shift means for architects like me Addressing the challenges of unstructured data governance for AI The cookbook for safe, powerful agents Enterprises are rethinking Kubernetes GitHub pauses new Copilot sign-ups as agentic AI strains infrastructure Best practices for building agentic systems Making agents dull Oracle delivers semantic search without LLMs When cloud giants neglect resilience Exciting Python features are on the way Ease into Azure Kubernetes Application Network The agent tier: Rethinking runtime architecture for context-driven enterprise workflows The two-pass compiler is back – this time, it’s fixing AI code generation MuleSoft Agent Fabric adds new ways to keep AI agents in line Salesforce launches Headless 360 to support agent‑first enterprise workflows Tap into the AI APIs of Google Chrome and Microsoft Edge Where will developer wisdom come from? The hyperscalers are pricing themselves out of AI workloads HTMX 4.0: Hypermedia finds a new gear Google Cloud introduces QueryData to help AI agents create reliable database queries Hands-on with the Google Agent Development Kit Are AI certifications worth the investment? AWS targets AI agent sprawl with new Bedrock Agent Registry Cloud degrees are moving online Swift for Visual Studio Code comes to Open VSX Registry AI agents aren't failing. The coordination layer is failing How Agile practices ensure quality in GenAI-assisted development Anthropic rolls out Claude Managed Agents Microsoft’s reauthentication snafu cuts off developers globally Meta’s Muse Spark: a smaller, faster AI model for broad app deployment Bringing databases and Kubernetes together Rethinking Angular forms: A state-first perspective Minimus Welcomes Yael Nardi as CBO to Facilitate Strategic Growth Microsoft announces end of support for ASP.NET Core 2.3 Get started with Python’s new frozendict type AWS turns its S3 storage service into a file system for AI agents Microsoft’s new Agent Governance Toolkit targets top OWASP risks for AI agents The winners and losers of AI coding GitHub Copilot CLI adds Rubber Duck review agent
GitHub adds Stacked PRs to speed complex code reviews
by Anirban Ghoshal Senior Writer · 2026-04-15 · via Swift for Visual Studio Code comes to Open VSX Registry | InfoWorld

Breaking up is hard to do when it comes to large pull requests, so GitHub is stacking things in favor of development teams with a new feature to facilitate code reviews and prepare for an AI-driven surge in code changes.

AI-aided development tools are churning out more lines of code than ever, presenting a challenge for reviewers who must review ever larger pull requests. After toying with the idea of closing the door to AI-aided code submissions, GitHub is now looking to help enterprises manage big code changes in a more incremental way. It says a new feature, Stacked PRs, can improve the speed and quality of code reviews by breaking large changes into smaller units.

“Large pull requests are hard to review, slow to merge, and prone to conflicts. Reviewers lose context, feedback quality drops, and the whole team slows down,” the company said, announcing GitHub Stacked PRs on its website.

With the new, stacked approach it aims to reduce the overhead of managing dependent pull requests by minimizing rebasing effort, improving continuous integration (CI) and policy visibility across stacked changes, and preserving review context to enhance code quality.

Stacked PRs tracks how requests in a stack relate to one other, propagating changes automatically so developers don’t have to keep rebasing their code  and letting reviewers assess each step in context, the company explained in the documentation.

The feature, GitHub wrote, is delivered through gh-stack, a new extension to GitHub CLI that manages the local workflow, including branch creation, rebasing, pushing changes, and opening pull requests with the correct base branches.

On the front end, all changes created via gh-stack are surfaced in the GitHub interface, where reviewers can navigate them through a stack map, with each layer presented as a focused diff and subject to standard rules and checks, the company added.

Developers can merge individual pull requests or entire stacks, including via the merge queue, after which any remaining changes are automatically rebased so the next unmerged PR targets the base branch.

Monorepos and platform engineering drive shift to modular development

For Pareekh Jain, principal analyst at Pareekh Consulting, Stacked PR is GitHub’s response to a structural shift being driven by large-scale monorepos and platform engineering, which are pushing teams toward more modular, parallel workflows.

“GitHub’s traditional PR model created a bottleneck where developers either waited long cycles for reviews or bundled work into large, hard-to-review PRs that increased risk and slowed merges. Stacking solves this by letting developers break a feature into smaller, dependent PRs such as database, API, and UI layers, so reviews happen incrementally while development continues in parallel,” Jain said.

“Stacked PRs is likely to see rapid adoption in mid-to-large enterprises, especially those managing monorepos. Its biggest impact is eliminating rebase hell — the manual effort of updating multiple dependent branches when the base changes,” Jain noted, adding that the feature’s integration into both the GitHub CLI and UI will also drive adoption as it removes the need for third-party tools.

Change management

The biggest obstacle to adoption of Stacked PRs will not involve changes to the code, but changes to coders’ habits, said Phil Fersht, CEO of HFS Research. “The constraint will not be the feature itself, but whether development teams adjust their workflow discipline to use stacking properly.”

That will involve them learning to organize large pull requests into neat stacks for the reviewer, which may be as challenging as reviewing a large PR.

That was echoed by Paul Chada, co-founder of agentic AI-based software startup Doozer AI: “Workflow shifts only happen when the pain of not changing exceeds the friction of learning,” he said.

AI-driven code velocity driving a new pressure point

The release of Stacked PRs comes amid a deeper structural shift in software development: the rise of AI-assisted coding. This is accelerating the pace of code generation, increasing the volume of changes and making traditional, linear review workflows harder to sustain.

“AI-assisted coding has changed the math. When humans wrote the code, big PRs were annoying but tolerable,” said Chada. “Now agents produce 2,000-line diffs across 40 files in seconds, and GitHub is staring down 14 billion projected commits this year versus 1 billion last year. That’s not a workflow problem, it’s a survival problem.”

GitHub appears to be betting that Stacked PRs changes the way development teams view a unit in software development by making it small, attributable, and revertible, regardless of  whether the author is a senior engineer or an agent, Chada said.

But, he cautioned, integrating Stacked PRs with coding agents risks adding to toolchain sprawl for enterprises.

“The current dev toolchain — IDE plus Copilot plus Claude Code plus Codex plus stacking tools plus review bots plus CI/CD plus security scanners plus MCP servers — is squarely in the Cambrian explosion phase,” Chada pointed out.

Competitive pressures

GitHub Stacked PRs isn’t an entirely novel idea: There are third-party tools that work with GitHub already offering the similar functionality.

Jain said GitHub’s addition of the feature will likely impact Graphite CLI, a GitHub-focused tool that allowed stacking PRs when the functionality wasn’t natively available.

“Graphite has been the market leader in this space. GitHub’s entry validates the Stacking category but poses an existential threat to Graphite’s core value proposition,” Jain said. “To survive, Graphite will likely need to double down on superior UI/UX, faster performance, and features GitHub won’t touch like cross-platform stacking for GitLab and Bitbucket.”

That competitive pressure also reflects a broader platform play.

Stacked PRs, Jain further noted, represents a “strategic move” to internalize a workflow long used by high-velocity teams at companies like Google, Meta, and Uber, referring to the stacked differential code review model popularized by tools like Phabricator.

Stacked differentials, much like Stacked PRs, are a series of small, dependent code changes reviewed individually but designed to build on each other and land as a cohesive whole.

In effect, this means that GitHub is trying to pull enterprises away from such tools by making it easier to adopt these advanced workflows natively within its own platform, reducing the need for external tooling.

There is also a quieter platform economics angle emerging, Chada pointed out.

“GitHub is effectively building out infrastructure to absorb a surge of machine-generated activity that does not yet translate into proportional revenue, from third-party coding agents that compete with its own GitHub Copilot to the very workflows those agents are accelerating,” Chada said.

In that light, Stacked PRs looks as much like a scaling response as a developer experience upgrade — one that could foreshadow a shift in how GitHub monetizes its AI layer, with Copilot pricing likely to move toward more usage-based models over time, Chada added.