Announcing StarSling Review Runners 💫 The bottleneck for most engineering team is CI and code review. StarSling now offers agent-native infra for both! Learn more about review runners and our raise: starsling.dev/blog/pre-seed

CI is only half of the bottleneck for almost every engineering team we talk to. We took the runners built to make CI faster and cheaper and brought that to code reviews. We started with the hosted review bots like most teams, then built our own custom review system using OpenAI Codex. Now we use open source models to customize the reviews more and make reviews much cheaper. We’re opening up the same infra we built so anyone can get their own custom setup using Review Runners. Huge thanks to @BessemerVP @ycombinator and all the other great funds for believing in us!

StarSling has now done over 2M CI jobs in production, saved customers over 28K hours in compute, and sped up CI up to 6x for customers like @mastra @better_auth (@vercel) @bespokelabsai and more. Agents write code faster than it can be verified and shipped to prod. The bottleneck is CI and code review. StarSling now provides agent-native infrastructure for both. Today we’re announcing early access of Review Runners. starsling.dev/review-runners

Today we’re announcing our pre-seed round and a new product: @starslingdev Review Runners - code review agents that use your model key, your team’s skills and scripts, and reviewers you define, all running in GitHub Actions. We’ve raised $3M from @BessemerVP and @ycombinator to make code verification agent-native. code → verification (ci + code review) → production

How do you let AI agents improve production CI without letting experimentation become an operational risk? That’s the problem @starslingdev is solving with Novita Agent Sandbox. StarSling is an AI-native CI platform that analyzes pipeline performance, proposes optimizations, tests those changes, and turns successful improvements into production-ready pull requests. To make that loop practical, they use Novita Agent Sandbox as the execution layer for isolated microVMs, elastic compute, and reproducible experimentation. StarSling customers have seen CI become up to 6X faster and up to 13X cheaper. Read the full case study to see how StarSling is building self-improving CI with Novita Agent Sandbox.

Announcing StarSling GPU Runners: GitHub Actions runners backed by fast NVIDIA GPUs ⚡️ More and more teams need GPUs for their CI, and those GPUs have to be pulled from training runs or inference. Your GPUs should be used for product, not tests. With StarSling GPU Runners you get dedicated GPUs for your CI jobs with per minute billing. One of our customers is already seeing a 14x reduction in queue time for a GPU runner! starsling.dev/gpu-runners

Announcing GitHub Actions runners backed by NVIDIA GPUs ✨

CI Harness v2 - an agent loop that thinks like a performance engineer 💫 Our first production run produced four merged pull requests into Better Auth, including one test job that went from 24 seconds to 4.5s. Built on @mastra @ClickHouseDB @hatchet_dev @render ✨ starsling.dev/blog/ci-harnes…

Details on how we got agents to think like a real performance engineer to find CI speedup wins 🏎️

Today we shipped sling, an agent-first CLI for your GitHub Actions 💫 Right now, if you ask your agent why a CI job failed, it goes and pulls the entire set of GHA logs and eats up your context window. 🪵 > sling why @starslingdev computes the run's logs and metadata, along with the evidence lines it matched to tell you why a run failed If you want to know what your GHA usage is, you have to go to the GH UI and click around to find it. 💸 > sling bill tells you how many minutes, how much you're spending, and across which runner labels Trying to understand what your slowest job is? You'll need to hunt around the GHA UI and even then you only get an average. 📊 > sling top and sling usage tell you runner minutes and cost across all workflows and repos, with p50, p95, p99, queue wait and a week over week trend starsling.dev/cli

💫 sling - the agent-first CLI for GitHub Actions

Easily check your GitHub Actions against 10 attack vectors all backed by a named public incident or published security research with /ci-secure 🔐

So basically Wiz opened a GitHub issue titled with shell commands, the workflow ran them, and the Jira token leaked. And none of their repo's automated PR checks caught this. Our new skill catches this and 9 other types of attack vectors in your GHA (runs locally, uses gh cli): github.com/starslingdev/skil…

3 @github Actions skills shipped in the past month, more on the way 💫 starsling.dev/skills

Ran hpc-sandbox-benchmarks on @cursor_ai cloud agents... They give you: - 4 vCPU, 16GB RAM with 270GB disk - Intel Emerald Rapids chip - AWS us-east-2 (AS16509) - 4.2 Mbit/s up and down github.com/starslingdev/hpc-…

Our third agent skill is live! Check your repo's GitHub Actions for 10 attack vectors that all map to public security incidents. npx skills add starslingdev/skills

Thread on where each sandbox provider shines 👇

The latest version of hpc-sandbox-benchmarks is live! We’ve added 5 additional sandbox providers: @microsandbox @namespace run.cloud @RunloopAI and @vercel Today we’re also releasing an interactive results explorer that allows you to cut the data across the benchmarks to see p50, p95, and specs for CPU, disk I/O, memory, network, etc. Try out the explorer, we’d love your feedback! And keep the PRs coming :) starsling.dev/hpc-sandbox-be…