Cursor has officially thrown its hat into the code-hosting ring with Origin, a Git-compatible platform built for a world where AI agents generate the commits.

The beta launch announced late on Monday comes two months after Tomas Reimers took to the stage at Cursor’s developer conference in San Francisco to tease its agent-focused GitHub alternative. That happened to be on the very same day that Elon Musk’s SpaceX confirmed it had tabled a $60 billion bid to acquire Cursor outright, and with that deal formally closing on August 14, Origin becomes the first product Cursor has shipped as a fully owned SpaceX subsidiary.

Notably, however, Origin landed on the same day that GitHub itself went down worldwide, turning what might have been a routine beta rollout into a “case in point” on why Cursor was building Origin to begin with. But while the 8-hour outage may have seemed like fortuitous timing on the surface, GitHub going offline when it did wasn’t great for Cursor, given that Cursor needs a fully operational GitHub for new users to get the ball rolling. As SpaceXAI’s Matt Palmer acknowledged on X: “We were going to ship this earlier, but GitHub was down. Importing your GitHub repos as a first onboarding step is non-optimal if GitHub is down.”

Still, GitHub’s troubles predate this particular blackout. As The New Stack reported in June, the platform has logged hundreds of incidents over the previous 12 months as commit volume jumped from 1 billion a year to 1.4 billion a month, with AI agents alone generating more than 17 million pull requests monthly — growth GitHub traced to infrastructure bottlenecks like MySQL contention and webhook overload.

“Cursor has joined a slew of technology companies looking to rebuild version control for a world where agents work around the clock.”

In an interview with The New Stack at the time, GitHub COO Kyle Daigle discussed the scaling problem: “It’s not just about normal scaling,” he said. “It’s now making sure we can scale at 30 or 40 times” annual growth, as opposed to doubling each year, which GitHub had historically planned around.

Fast-forward to today, and Cursor has joined a slew of technology companies looking to rebuild version control for a world where agents work around the clock, querying and pushing to repositories faster than any human team ever could.

Origin story

Origin marks a fairly significant expansion of Cursor’s ambitions. Until now, its agents have largely operated on code hosted elsewhere; with Origin, Cursor is pushing to own more of the underlying development infrastructure itself.

At launch, that starts with the basics. Users can create and host Git repositories directly inside Cursor, with the new Codebase tab acting as the home for Origin repos.

Those repositories still behave like Git repos outside Cursor. Developers can clone them locally, add an Origin remote, and push code from the command line — essentially putting Cursor in the role normally occupied by a service such as GitHub.

Cursor isn’t demanding an all-or-nothing migration, either. Existing GitHub repositories can be synced into Origin and displayed alongside Cursor-hosted repos, while GitHub remains the source of truth for projects that started there.

Pull requests are built in too, including diffs, comments, checks and merging. Cursor’s agents sit directly alongside that code: from the browser, users can ask questions about what they’re viewing, have an agent make changes, update a PR or push a branch.

That combination is arguably the more consequential part of Origin: the repository, pull request and coding agent now all live inside the same product — giving Cursor more control over the environment in which code is stored, reviewed and changed.

Rob Whiteley, CEO of Coder, a cloud development platform built for enterprises, sees Origin as a “smart play” — most of the industry’s energy has gone into the tools that write code, he argues, while comparatively little has gone into what happens to that code after it’s produced.

“GitHub is starting to crack under the weight of agentic code development, and an agent-native source code forge is needed,” Whiteley tells The New Stack. “Everyone is integrating the ‘writing code’ stack, from editor and chat to agents, tools and LLMs. No one else is really integrating the ‘managing code’ stack, where code gets stored, versioned, reviewed and merged.”

For now, Origin’s restricted to Cursor’s Pro, Teams and Enterprise plans — nothing on the free tier, it seems — and the rollout itself is staged, so not everyone will have access to it quite yet.

How is Origin different to GitHub?

For now, there’s no escaping the fact that there isn’t a great deal that’s different from trusty ol’ GitHub, a fact that wasn’t entirely lost on the online community. And Origin’s own team isn’t shying away from that, either.

Tomas Reimers, the Origin engineer who co-founded Graphite, a code-review startup Cursor acquired in early 2026, fielded questions directly from developers on Hacker News after the launch.

“We’re intentionally releasing this as a GitHub alternative where we meet them toe-to-toe on functionality.”

Asked what set Origin apart from GitHub beyond uptime, Reimers concedes that it’s very “very little,” in all honesty. “We’re intentionally releasing this as a GitHub alternative where we meet them toe-to-toe on functionality,” he writes.

He does note that more is on the way: in the coming weeks, Reimers explains, Origin should start shipping deeper agent integrations, tooling that can make sense of agent-written code, and automation that pushes pull requests toward a mergeable state on their own.

“Expect a lot more from us,” he continues. “We wanted to release a beta so people could start experimenting with our scalability and extensibility themselves. Over the next few weeks, you can expect a handful of features starting to change source control to better understand and work with agents.”

Several in the online community also highlighted the timing of Origin’s launch. Gergely Orosz, author of The Pragmatic Engineer newsletter and an investor in Graphite, initially took to X to complain about GitHub’s ongoing performance issues, despite the number of engineers it has at its disposal.

Incredible how GitHub’s eng team knows reliability (having zero nines) is their #1 problem for ~6 months now and seemingly not being able to get a handle on it. Despite so many solid engineers working on it

My bet: arch decisions years ago bite back v hard now https://t.co/wHbCpugD6X

— Gergely Orosz (@GergelyOrosz) August 17, 2026

Within an hour, however, Orosz was back to comment on Origin. “Cursor could not have timed their launch announcement of their hosted code service better either,” he writes. “If GitHub was stable, these alternatives would not be as interesting / popular!”

“These alternatives,” as Orosz puts it, are already forming an orderly queue in GitHub’s shadow.

The ‘GitHub alternative’ surge

The most direct comparison to Origin is perhaps Entire, a distributed Git network founded by Thomas Dohmke, who stepped down as GitHub’s CEO last August. Entire, essentially, mirrors repositories across regional nodes, with GitHub remaining the source of truth for now — though that could change once teams start creating repos natively on the platform. The company raised a $60 million seed round in February, with investors including Microsoft’s venture arm, and it formally went to market in July.

“Cursor could not have timed their launch announcement of their hosted code service better either. If GitHub was stable, these alternatives would not be as interesting / popular!”

GitLab, meanwhile, is also rethinking source control for an agent-heavy world, announcing a private beta of “Next Generation Source Code Management” — internally called Project Switch — back in June. Instead of agents cloning an entire repository to read or change a handful of files, the system lets them query the server for exactly what a task needs, with each agent’s visibility capped at the minimum required.

Elsewhere, code editor startup Zed has also been teasing a new approach to version control since last year, and in early August the company finally debuted Delta, a “multiplayer environment for coding with agents and reviewing what they build,” as the company puts it.

“Delta keeps code and conversations connected, so developers and agents can work together with the full context of how the code came to be,” Zed co-founder and CEO Nathan Sobo wrote at the beta launch.

Underneath, Delta runs on DeltaDB, which keeps a live copy of conversations and in-progress work synced across a team. It sits alongside a project’s existing Git repository, and works with agent tools including Claude Code. What that changes in real terms: comments stay attached to the code they refer to as an agent keeps editing it.

Despite GitHub’s persistent reliability problems, Whiteley doesn’t see Cursor’s version becoming a major enterprise play any time soon, mostly due to the pain of switching.

“Most [enterprises] have already spent a lot of pain and money standardizing on GitHub, and moving again would mean a lot of pain for limited ROI today,” he says. “That could change as ‘vibe coding’ generates an order of magnitude more code. If Cursor commits to keeping Origin open enough for enterprises to trust and integrate with, it could become much more appealing over time.”

So while there is a clear flurry of activity in the “GitHub alternative” realm, it’s still too early to say whether any of them will cut it in the long term. GitHub has an 18-year head start: it launched in 2008, popularized the pull-request review model most of these newcomers are trying to disrupt, and was snapped up by Microsoft in 2018. It also set off the current wave of AI coding tools itself with the launch of Copilot in 2021 — the same wave now generating the volume its own infrastructure is struggling to absorb.

For now, GitHub remains the only one of these platforms actually handling this kind of developer activity at scale. However, at a lofty $2 trillion valuation, SpaceX is one of the world’s most valuable companies, which puts Cursor in a strong position when it comes to investment — not just in its own AI models, but in the infrastructure underneath them.

The post “If GitHub was stable, these alternatives would not be as interesting”: Cursor launches Origin as GitHub goes dark appeared first on The New Stack.

For the past two years, the conversation about AI-assisted software development has been dominated by speed. A new GitLab survey of more than 1,500 developers and technology leaders found that 60% say AI coding ROI has already exceeded expectations, and 78% report their teams are writing and committing code faster since adopting AI tools. 

But speed without control is a liability.

Most organizations have pursued agentic engineering by adding AI coding tools on top of their existing infrastructure. Coding agents are delivering speed, but that speed isn’t showing up across the full software lifecycle: Only 21% of respondents report productivity gains beyond code generation itself.

“Speed without control is a liability.”

The infrastructure problem runs deeper. Git backends, toolchains, and governance frameworks were built for human-scale concurrency. Agents operate at machine scale, and that mismatch shows up fast. Platform reliability breaks down with millions of agent sessions hitting the same backend, security exposure widens as agents touch dependencies at scale, and cost overruns mount as agents consume tokens inefficiently on infrastructure that wasn’t built for them.

Agentic adoption outpaced governance

The adoption curve for AI coding tools outpaced the development of required guardrails, with 80% of organizations saying they adopted AI tools faster than they developed policies to govern them, and 82% reporting that AI-generated code risks creating a new form of technical debt that their organizations are not prepared to manage.

In practice, that means platform reliability challenges under agent load, security and compliance exposure that widens as agents touch dependencies at volume, and agents operating with artificial confidence because they lack full context. Only 28% of organizations say their software development lifecycle tools are fully integrated with shared data and workflows, which means most teams are trying to govern agent actions across a toolchain that was never designed for them.

Agentic engineering needs agentic infrastructure

Agentic engineering requires two things: agentic coding and agentic infrastructure. Most organizations have the first but lack the second.

Agentic infrastructure spans four areas: the execution layer, the context layer, the governance layer, and the orchestration layer working together.

The first is machine-scale execution. Git backends, CI/CD pipelines, and deployment systems were designed for human-paced development. In the agentic era, they need to handle millions of agent sessions without breaking. When a production incident occurs, the path from symptom back to origin should take minutes, not days.

The second is context that travels with code. As Bastian Stahmer, Business Owner of Vehicle Software Development Platform at Mercedes-Benz, put it on a panel recently, “An agent can only be as good as the context and semantics fed to it.” A context graph connecting code, work items, pipelines, security findings, and production signals is what makes agents genuinely useful at scale and keeps artificial confidence in check.

“An agent can only be as good as the context and semantics fed to it.”

The third is governance built into the flow. Agent actions need to be tied to an identity, logged against a policy, and provable to a reviewer. Low-risk changes move fast, while higher-risk changes trigger review. For Mercedes, operating under automotive regulatory standards that require full traceability and human accountability, GitLab is the control plane where that accountability lives.

The fourth is orchestration. Execution, context, and governance are only as effective as the system coordinating them. The orchestration layer coordinates agent actions across the full software lifecycle according to the policies teams define, determining which agents run, in what order, and how failures and handoffs are managed. Without it, agentic infrastructure is a set of independent capabilities rather than a working system.

What’s next

The next phase of AI in software will focus less on generating code and more on governing it, according to 85% of respondents. That shift reflects how enterprises are maturing their thinking about AI, from a productivity tool to a foundational capability that needs to be trusted, traced, and maintained at scale.

When governance is built into the platform, speed and control are no longer in tension. Traceability becomes a competitive advantage. Context becomes institutional memory. And the codebase, rather than accumulating invisible risk, becomes an asset that grows more reliable over time.

The post GitLab just surveyed 1,500 developers. Here’s why it matters for your codebase. appeared first on The New Stack.

Platform engineers are some of the most resourceful people in IT. Give them a problem, and they’ll automate their way toward a solution. But what happens when the automation itself becomes the problem?

This is the quiet crisis hiding inside many organizations today. In the race to reduce toil, teams have built what amounts to a mountain of automation. Scripts, layered on blueprints, layered on orchestration workflows, layered on tooling, APIs, GitOps, and infrastructure and label it a “platform.” This isn’t a platform; it’s complexity dressed up in a better outfit.

Trading one problem for another

Here’s the dirty secret about using automation to build your own platform stack: you don’t actually eliminate complexity; you just become responsible for it in a new way.

It starts well enough. You automate a painful workflow, ship it, and you move on to the next fire. But automation doesn’t maintain itself. Over time, the team that wrote it moves on. As they do, the context behind why it was built fades.

Nobody quite remembers what the edge cases were, why that snowflake script was written that way, or the original problem it solved. And when it breaks–and it will break–you’re not debugging an application. You’ll be performing an archaeological excavation of your own infrastructure, to decipher the intent of the team that originally constructed the automation.

When scripts outlive their authors

So you do what engineers do: you automate around it. You add new automation on top of old to address the gaps. Now you’re managing two mountains of automation instead of one. And here’s the impact that rarely makes it into the original business case – the platform team doesn’t get to walk away when it’s “done” because it’s never done.

These engineers can’t be reassigned without your platform decaying beneath the applications and services that make up the business’s backbone. You need a robust team to manage this indefinitely just to keep the lights on. We’re simply trading software costs for people costs. And often, you end up spending more to produce something less scalable and capable than what you had from day one.

“Automation may mask complexity but does not eliminate it, and mountains of automation makes diagnosis and repair exponentially harder when things go sideways.”

This is the real trap. Automation, at its best, is a productivity multiplier. At its worst, it’s lipstick on a pig; all the ceremonies of agility without the true benefits. Automation may mask complexity but does not eliminate it, and mountains of automation makes diagnosis and repair exponentially harder when things go sideways.

What a pre-engineered PaaS actually does

A true Platform as a Service (PaaS) isn’t a collection of automation. It’s a pre-engineered system where the underlying plumbing, services, security, and resilience are already integrated before you ever install and consume it. This is the ‘batteries included’ model where the platform is ready to use on Day 1 based on best practices and proven architectures. This kind of integration out-of-the-box is one of the things that makes a platform trustworthy and predictable at scale.

For example, an integrated platform includes ‘how’ applications are built and deployed is pre-wired out of the box and works consistently across application types. One of the more unique things Tanzu Platform does is build deployment packages, including the base image for developers. This means that when a security problem comes out–like Copy Fail or the flood of AI-discovered vulnerabilities–the platform engineer can rebuild and redeploy apps very quickly without reaching back into the software delivery lifecycle (SDLC). They simply “restage’ the application using a single command.

The consistency of deployment packages and base images also enables developer velocity in the every day. When every application is built, packaged, and deployed the same way, developers stop re-solving the same infrastructure problems, and start focusing on the code that actually matters. This is a distinction worth drawing clearly–assembling capable open source tooling like Terraform, ArgoCD, Kubernetes, cert-manager, OpenBao, and Istio gives you powerful building blocks, but it doesn’t give you a platform.

You still own the integration, automation, the opinions, lifecycle management, and the operational model that ties them together. A pre-engineered PaaS handles the myriad of decisions for you. With a PaaS, onboarding a new team or new application isn’t a one-off integration project; it’s a repeatable, predictable process. Standardization is a core outcome of a pre-engineered PaaS, not a side effect, and it’s precisely what makes it possible to push changes faster and with more confidence, regardless of the team, language, or application type.

Security built into the platform

Security works the same way. When your team makes the decision to pull from open-source components and stitch them together, you now own every security gap between them – data at rest, data in flight, and its running state. This seems like a worthwhile investment at first, but as recent research suggests, AI-assisted attacks are on the rise, and platform teams won’t be able to keep up with surging security vulnerabilities in the build-it-yourself model.

A pre-engineered PaaS standardizes the governance and compliance posture across the board. Updates, patches, and fixes from a trusted, first-party vendor mean you’re not reinventing governance from scratch each time you add a new component. The PaaS can help you uniformly and at scale apply the changes in a cascading fashion rather than relying on custom automation.

The calculus starts to shift when you see deployment, security, and onboarding issues in aggregate. Achieving all of this with custom automation means spanning organizational silos, coordinating teams, sustaining headcount, and continuously funding work that never actually finishes.

And worse yet, this work has no meaningful competitive advantage for your organization. Your competitors are working to solve the same problem, but you’re just burning more resources to accomplish the same end result. You own the problem entirely rather than leaning on a trusted, proven vendor. Embracing a PaaS allows you to focus on higher-value, differentiating initiatives for the business.

AI is the forcing function

The conversation around PaaS is urgent again, and AI is why. Code generation can speed up your development cycles, building and pushing features faster, but production delays will persist if you’re still deploying at the same speed as before. 

To avoid eroding the benefits of code generation, you need to deploy applications nearly as fast as they can be coded with AI. This requires streamlining each step of the path to production. In an era where more organizations are exploring the use of autonomous agents, they need a platform that doesn’t take weeks to rotate credentials, days to provision a database, or require access to JIRA’s MCP Server to accomplish their goals.

“To avoid eroding the benefits of code generation, you need to deploy applications nearly as fast as they can be coded with AI.”

The pace of AI innovation itself compounds the problem. Whether it’s shadow AI use, MCP servers, agentic harnesses, this week’s new foundation models, or whatever emerges next, the landscape is evolving fast enough that what’s bleeding-edge today may be table stakes in six months. When you build your own platform, you’re on the hook to evaluate each layer of these new technologies, determine how they fit into your stack, and then integrate them yourself, on top of everything else your platform team is already managing to keep the business going.

Organizations running VMware Tanzu Platform receive those innovations. With Tanzu Platform 10.4, for example, customers gained MCP Gateway, an expanded service marketplace where developers can publish their own MCP servers and services for cross-organization consumption, as well as an agent buildpack to streamline and secure the deployment of agentic AI applications.

These are capabilities that would have taken a DIY team months to evaluate, build, and harden. And this isn’t unique to AI. Every release brings new capabilities across the platform that customers simply inherit, without the integration tax. That’s the compounding return on your investment in a pre-engineered PaaS. The platform keeps moving forward, and so do you.

VMware customers: You’re closer than you think

Platform engineers are uniquely equipped for this moment. They have the pattern recognition, they have seen the technology waves before, they bring their hard-won operational instincts, and the critical thinking skills to know when a system is genuinely resilient versus cleverly disguised complexity.

That’s exactly why their role in curating and operating this next generation of PaaS matters more than ever. Platform Engineers applying these battle-tested experiences using a PaaS will shortcut the tedious automation pitfalls, jumping straight to safely delivering Agentic AI and GenAI application services.

If you’ve already built your stack on VMware Cloud Foundation you’re already in a position to add the value of a true PaaS. VMware Tanzu Platform layers a pre-engineered PaaS on top of your existing infrastructure APIs, running alongside your VMs and containerized workloads, without ripping out what you’ve built and budgeting for alternatives.

With an incremental step, you can leverage what you know and trust, and layer on the only private cloud PaaS for agents that offers direct integration with Private AI services.

The post The DIY platform trap that’s burning out engineering teams appeared first on The New Stack.

Last week, I visited AWS Hong Kong User Group with my team. Hong Kong has a small but strong community, and their energy and passion are high. They recently started a new AI user group, and we hope more people will join. I was able to strengthen my bond with the community through great food and conversation.

This week, I’ll first take a closer look at some of the key launches.

AWS DevOps Agent and Security Agent GA
At the last re:Invent, we introduced the concept of frontier agents that work autonomously across multiple steps to achieve outcomes, operating continuously until the job is done. The first two—AWS DevOps Agent and AWS Security Agent—are now generally available after the preview.

AWS DevOps Agent helps you run cloud operations—investigating incidents, reducing time to resolution, and preventing issues before they happen. Customers like United Airlines, Western Governors University, and T-Mobile are already using DevOps Agent to accelerate incident response and simplify operations at scale. At WGU, resolution time dropped from hours to minutes, and in preview customers report up to 75% lower MTTR and 3 to 5 times faster resolution. Learn more in Sébastien’s preview blog post and GA announcement.

AWS Security Agent brings continuous, context-aware penetration testing into the development lifecycle. This agent operates like a human penetration tester. Customers including LG CNS, HENNGE, and Wayspring are seeing strong results. At LG CNS, teams estimate over 50% faster testing and ~30% lower costs, along with significantly fewer false positives. Learn more in Esra’s preview blog post and GA announcement.

Both are designed to work across AWS cloud, multicloud, and on-prem environments. You can have an always-available teammate that can handle the heavy lifting, so you can focus on what matters most.

AWS Service Availability Updates
When the availability of an AWS service or feature changes, we provide customers guidance in AWS Product Lifecycle Changes on available alternatives and support for migration so that disruptions to your operations are minimized. The following lifecycle changes were updated on March 31, 2026.

• Availability change guide for services in maintenance
  • AWS App Runner
  • AWS Audit Manager
  • AWS CloudTrail – Lake
  • AWS Glue – Ray jobs
  • AWS IoT FleetWise
  • Amazon Application Recovery Controller (ARC) – Readiness Check
  • Amazon Comprehend – Topic Modeling, Event Detection, and Prompt Safety Classification
  • Amazon Rekognition – Streaming Events and Batch Image Content Moderation
  • Amazon Simple Notification Service (Amazon SNS) – Message Data Protection (MDP)
• Availability change guide for services in sunset:
  • AWS Service Management Connector
  • Amazon RDS Custom for Oracle
  • Amazon WorkMail
  • Amazon WorkSpaces – Thin Client
• Services reaching in sunset:
  • Amazon Chime SDK – Proxy Sessions

We understand that changes in availability can impact your operations. For specific guidance, consult the relevant service documentation or contact AWS Support.

Last week’s launches
Here are last week’s launches that caught my attention:

• Amazon ECS announces Managed Daemons for ECS Managed Instances
• New AWS Sustainability console: Scope 1–3 reporting in one place
• Amazon Bedrock AgentCore Evaluations is now generally available
• AWS Transform custom announces general availability of automated codebase analysis
• Amazon CloudWatch launches OpenTelemetry Container Insights for Amazon EKS (Preview)
• New compute-optimized instance bundles for Amazon Lightsail with up to 72 vCPUs
• Amazon CloudFront now supports SHA-256 for signed URLs and signed cookies

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Additional updates
Here are some additional news items that you might find interesting:

• Architecting for agentic AI development on AWS
• Optimizing data transfer costs when using AWS Network Load Balancer
• Announcing AWS World Sports Innovation Cup – Will your idea change the game?
• 5 Techniques to Stop AI agent hallucinations in production
• Explore the global AWS Community through a 3D interactive globe

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS led in-person and virtual events, startup events, and developer-focused events as well as AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy