Depot

the same standup can feel like an interrogation or the most valuable 15 minutes of your day. the difference isn't the format, it's the principle underneath: control vs. connection. watts on protecting the why. depot.dev/blog/why-your-…

When the token subsidies run out, this is what they will send to your door to collect payment.

sol is currently creating a video edit driving my laptop for a brand new @depotdev feature.

Depot Code is now in private beta. It's the git host we wanted after three years accelerating everything downstream of source control: a diskless git server that stores content in S3 and scales horizontally. Regular git under the hood. depot.dev/blog/now-avail…

Depot CI and Sandbox workloads are now running on Depot Metal, our new 4th-gen compute platform. Benchmarks show them running 30% faster, with nothing to migrate. Built on bare-metal EC2 with microVMs and dedicated NVMe storage. depot.dev/blog/announcin…

going to be a big week of announcements for @depotdev

two test failures can look different and still represent the same failure pattern. the hard part isn't collecting failures. it's knowing when two failures are actually the same. iris goes deep on how failure fingerprinting filters out noisy data while preserving the pieces that matter. depot.dev/blog/failure-f…

we just made snapshotting 30x faster for folks using depot ci. very powerful way to build reusable runner images to make your builds even faster ⚡ depot.dev/changelog/2026…

we just made snapshotting in depot ci blazing fast from 30 Mbps to 1 Gbps ⚡ probably going to bring this over to our sandbox sdk here shortly as well. depot.dev/changelog/2026…

A deeper dive: depot.dev/blog/staying-i…

AI can generate code in seconds. That doesn't mean the hard part is solved. Someone still has to understand the code, review it, maintain it, and debug it months later. Engineering guardrails like linting rules, reference tests, coding standards, and reviewable pull requests help make AI-generated code easier to review and maintain.

New case study with @PlanetScale . PlanetScale runs CI at massive scale. After moving their CI workloads to Depot, they cut CI time in half on many workloads, eliminated runner management, and stopped thinking about CI. Read the full story: depot.dev/customers/plan…

@depotdev is really cool

We're pumped to be powering @pantsbuild for all of their CI 🚀 "Depot hosted runners were exceptionally easy to set up, basically just a drop-in replacement for our GitHub hosted runners, but the performance and stability are substantially better."

You haven't manually re-run a job in weeks. That's the feature. When a Depot CI job fails, `depot ci diagnose` gives you the failed step and the error lines that matter. No log paging, no re-running just to see what broke. `depot ci diagnose --run <run-id>`

@dexhorthy you can also have the agent validate the changes against real ci before even committing if you use @depotdev ci

something cool is coming...

we have test results in beta inside of Depot now and it's really cool to see the big uptick in people trying it out. check it out here 👉 depot.dev/changelog/2026…

If agents write the code, they have to be able to drive CI too. Depot CI has a typed API: trigger, retry, and cancel runs, pull logs and metrics, read failure diagnoses. You, your scripts, and your agents read the same contract. depot.dev/docs/api/ci/re…

It's the shift every team adopting AI is hitting: agents generate PRs faster than CI can clear them. Queues back up, feedback loops stretch, and the gap between code written and code shipped widens.

Writing code stopped being the bottleneck. Validating it became one. When an agent ships a feature in 20 minutes, a 20-minute CI pipeline is the constraint. The code is done. The pipeline is what you're waiting on.