From GitLab to Kilo Code (Interview)
Episode
77 min
Read time
2 min
Topics
Productivity, Startups, Artificial Intelligence
AI-Generated Summary
Key Takeaways
- ✓Parallel Treatment Strategy: When doctors declared standard care exhausted, Sijbrandij combined multiple experimental treatments simultaneously rather than sequentially, prioritizing cure over research reproducibility. He assembled a tumor board including pathologists to ensure safe drug combinations that don't overload organs like kidneys.
- ✓FAP-Targeted Radioactive Therapy: Single-cell sequencing revealed high fibroblast content in his tumor, enabling targeted treatment in Germany using FAP binders combined with lutetium radioactive elements. This achieved 60% necrosis and 20% shrinkage with zero detectable side effects, detaching tumor from spinal cord to enable surgical removal.
- ✓Maximal Diagnostics Approach: Fresh-freezing tumor samples instead of standard FFPE preservation enables running hundreds of additional diagnostic tests later. Sijbrandij traveled to China for first-available B7-H3 scans, discovering highest expression levels seen in 20 patients, which directly informed drug development modifications to protect liver function.
- ✓Kilo's Multi-Model Architecture: The platform supports over 500 models including eight of ten most popular being free to use, charging exact list price for paid models without markup. Users create profiles combining specific models with custom prompts, enabling teams to share optimized agents for tasks like Java upgrades.
- ✓Agent Productivity Multiplier: Kilo engineers ship at speeds equivalent to seven-to-eight person teams from two years ago by parallelizing work across multiple AI agents. One engineer can manage multiple agents working simultaneously on separate tasks with small context windows, reducing costs while increasing speed and preventing context loss.
What It Covers
Sid Sijbrandij, GitLab founder, shares his bone cancer journey since 2022, including experimental treatments across three continents, single-cell sequencing diagnostics, and launching six biotech companies plus Kilo, an open-source all-in-one agentic coding platform.
Key Questions Answered
- •Parallel Treatment Strategy: When doctors declared standard care exhausted, Sijbrandij combined multiple experimental treatments simultaneously rather than sequentially, prioritizing cure over research reproducibility. He assembled a tumor board including pathologists to ensure safe drug combinations that don't overload organs like kidneys.
- •FAP-Targeted Radioactive Therapy: Single-cell sequencing revealed high fibroblast content in his tumor, enabling targeted treatment in Germany using FAP binders combined with lutetium radioactive elements. This achieved 60% necrosis and 20% shrinkage with zero detectable side effects, detaching tumor from spinal cord to enable surgical removal.
- •Maximal Diagnostics Approach: Fresh-freezing tumor samples instead of standard FFPE preservation enables running hundreds of additional diagnostic tests later. Sijbrandij traveled to China for first-available B7-H3 scans, discovering highest expression levels seen in 20 patients, which directly informed drug development modifications to protect liver function.
- •Kilo's Multi-Model Architecture: The platform supports over 500 models including eight of ten most popular being free to use, charging exact list price for paid models without markup. Users create profiles combining specific models with custom prompts, enabling teams to share optimized agents for tasks like Java upgrades.
- •Agent Productivity Multiplier: Kilo engineers ship at speeds equivalent to seven-to-eight person teams from two years ago by parallelizing work across multiple AI agents. One engineer can manage multiple agents working simultaneously on separate tasks with small context windows, reducing costs while increasing speed and preventing context loss.
Notable Moment
Sijbrandij received a text saying his PET scan was positive not subtle, revealing 50 metastatic lung sites that appeared terminal. After telling his team and receiving condolences from multiple doctors, the final specialist recognized it as COVID remnants, not cancer, based on lymph node patterns.
Episode Transcript
Welcome to the change log, where we have deep technical conversations with the hackers, leaders, and innovators of the software world. I'm Jared Santo. On this episode, Adam and I are joined by Sid Sabranage, founder of GitLab, who led the all in one coding platform all the way to IPO. In late twenty twenty two, Sid discovered that he had bone cancer. That started a journey he's been on ever since, a journey that he shares with us in detail. Along the way, Sid continued founding companies, including kilo code, an all in one agentic engineering platform, which he also tells us all about. But first, a big thank you to our partners at fly.io, the platform for devs who just want to ship. Build fast, run any code fearlessly at fly.io. Okay. Sid Zibbrandage back on the changelog. Let's do it. Well, friends, I'm here again with a good friend of mine, Kyle Golbraith, cofounder and CEO of depot .dev. Slow builds suck. Depot knows it. Kyle, tell me, how do you go about making builds faster? What's the secret? When it comes to optimizing build times to drive build times to zero, you really have to take a step back and think about the core components that make up a build. You have your CPUs, you have your networks, you have your disks. All of that comes into play when you're talking about reducing build time. And so some of the things that we do at Deepholm, we're always running on the latest generation for ARM CPUs and AMD CPUs from Amazon. Those in general are anywhere between 3040% faster than GitHub's own hosted runners. And then we do a lot of cache tricks, both for way back in the early days when we first started Depot. We focused on container image builds. But now we're doing the same types of cache tricks inside of GitHub Actions where we essentially multiplex uploads and downloads of GitHub Actions cache inside of our runners so that we're going directly to blob storage with as high of throughput as humanly possible. We do other things inside of a GitHub Actions runner, like we quarter off portions of memory to act as disk so that any kind of integration tests that you're doing inside of CI that's doing a lot of operations to disk, think like you're testing database migrations in CI. By using RAM disks instead inside of the runner, it's not going to a physical drive. It's going to memory, and that's orders of magnitude faster. The other part of build performance is the stuff that's not the tech side of it. It's the observability side of it, is you can't actually make a build faster if you don't know where it should be faster. And we look for patterns and commonalities across customers, and that's what drives our product road map. This is the next thing we'll start optimizing for. Okay. So when you build with Depot, you're …
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“SPONSORS: Fly.io”
“SPONSORS: Notion”
“SPONSORS: Depot”
“SPONSORS: Tigris Data”
“Sijbrandij...launching six biotech companies plus Kilo, an open-source all-in-one agentic coding platform.”
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