The closest thing to magic you can find right now: lower agentic cost, improve outcomes, speed up reasoning - GitLab Orbit.

Mythos class model support now available in Duo Agent Platform. about.gitlab.com/blog/mythos… I’m excited to be on stage with both Anthropic and Google in just a few minutes to show the incredible work we’re doing, together.

Selling this into some of the most rigorous engineering orgs in financial services. The price opens doors. The results in trials and on-demand seals the deal. Given the latest news in token pricing from our competitors, it’s only up from here 🚀

I expected Duo Agent Platform to beat Cursor, Claude, Copilot and Devin on price with our $0.25/review fixed price, I did not expect us to win all of the above on precision and recall, but check out this site to learn more, including how we rank against all the code review solutions in market. duo-review-bench-6f7260.gitl… This is the power of repo-side agents, and it’s just the beginning of more powerful, higher quality and lower cost agentic engineering. This is our structural advantage in action.

You’re not going to want to miss this event! Register today!

The new aspirational TAM for developer platforms - at least one order of magnitude higher than it has ever been.

Copilot customers, if you run out of credits on GitHub, consider checking out GitLab and Duo Agent Platform. It is free to sign up and every premium seat includes $12/month and every ultimate seat includes $24/month in promotional credits so you can experiment with a more powerful cloud neutral, model neutral agent platform for DevSecOps with zero cost. Learn more and get started here: about.gitlab.com/gitlab-duo-…

Jensen Huang just said the biggest prediction about AI was dead wrong. It’s not replacing software engineers. It’s making companies hire more of them. Huang: “If you can hire a software engineer and you could generate 9 trillion dollars worth of productive work, why wouldn’t you want to hire more software engineers?” Nine trillion dollars. That’s the number that collapses the entire narrative. Three years of headlines screaming that AI would erase the people who built it. Huang: “People talk about AI reducing jobs, complete nonsense.” And he’s right. Huang: “The number of engineers, software engineers is actually increasing.” Not plateauing. Not declining slower than expected. Increasing. When one person can move nine trillion dollars worth of output, you don’t cut headcount. You hire every engineer you can find. Huang: “If that line was flat, then obviously people will hire fewer software engineers. But because the output is so incredible, people want to hire more software engineers.” Cheaper never shrinks a field. It floods it. He’s right about all of it. And it is the smallest part of what he said. The number of engineers was never the real question. The real one is what the engineer is for. The ceiling on what one person could build was always the tool in their hand. That ceiling is gone. And when building costs nothing, only one thing stays rare. Knowing what is worth building. Leverage does not make you valuable. It makes you visible. It takes whatever is already inside you and multiplies it. Vision turns into force. Hollowness turns into scale. For all of history, work was the great blur. The lazy and the brilliant both showed up. Both looked busy. Both put in the hours. That blur was the closest thing we ever had to equal. And it is burning off. When the doing is free, all that remains to measure is the mind that chose what to do. The machine was never coming for your job. It was coming for the place you were hiding. Huang: “This is going to show up in our economy somehow soon.” It shows up in people first. We spent three years afraid the machines would learn to think. The real fear runs the other way. They will give us everything we ask for. And leave us alone with the size of what we wanted.

Anthropic models have been the Duo default since inception and we’re happy to share Opus 4.8 is now available, same day release. about.gitlab.com/blog/claude…

Watching the AI ROI conversation get real this week. The honest answer to “what did we get for the spend” isn’t available in most platforms yet. We’ve been building our agent platform knowing it was just a matter of time with three meaningful answers, that keep getting better over time: We’re using fixed pricing on the use cases where ROI is proven. Example: basic code review at $0.25 instead of $15 . Customers know the unit cost before they consume a credit. We ship our usage dashboards with detailed telemetry and auditing on every agent action tied to budget controls down to the individual user. Finance gets the same visibility engineering has had on cloud spend for a decade. We’re providing an AI impact dashboard that correlates consumption with engineering cycle outcomes. Actual movement in the work. The first wave of AI buying was faith-based. The second wave will be evidence-based, and the vendors who can show the link between spend and shipped software will be the ones to bet on.

Really excited to bring this new product to market, now in public beta!

Supply chain security is on everyone’s mind, as attackers use LLMs to accelerate their own nefarious activities. Dependency scanning just got better in GitLab 19.0, helping you stay up to date and secure.

More models, more choice! It’s easy to see why regulated customers love Gitlab

One million developer hours saved per year! Read how one of the world’s largest, multinational banks uses GitLab Duo to go faster. Thank you Barclays! about.gitlab.com/customers/b…

From the same survey: “We’re at the death of code review. I used to do very deep code reviews where I’d take the time to understand the architecture. I have no motivation in spending that time to review a giant PR where it’s clear that even the original author didn’t bother to do that.” A lead engineer at a small company said that. He’s not wrong, and he won’t be the last to say it. Human code review of every line doesn’t scale when AI is producing the code. The math doesn’t work. The reviewer is the bottleneck, the reviewer is human, and humans tire quickly keeping up with machines. That’s why we fixed Duo Agent code reviews at $0.25 per review, so teams can afford to run it on every MR, before the human ever sees it. Automation is the only path that keeps quality from collapsing under volume.

Every org has AI-written code in their repo, and most can't tell you which agent wrote it. GitLab logs every agent action, runs every MR through your approval rules, and scans every diff before merge. See how teams are keeping agent code in check at about.gitlab.com.