Introducing Red Planet Labs with $5M in funding
I’m excited to announce today Red Planet Labs, with funding from Initialized, Kindred, Rogue, Background, Max Levchin’s SciFi VC, and Naval Ravikant.
The goal of Red Planet Labs is to radically change the economics of software development — not just modest and incremental improvements in specific areas, but multiple orders of magnitude cost reduction for building end-to-end, scalable software applications.
The origins of Red Planet Labs go back to the beginning of my career. I worked as an engineer building scalable systems for many years, from startups like BackType to large companies like Twitter. I was always bothered by how far removed we were from the problems we were solving. So often we’d spend weeks or months tangled in the weeds of getting our database to do what we wanted, setting up deploys, instrumenting our systems, or any number of other arcane tasks. Even a simple thing like counting was so difficult to get right when you took into account fault-tolerance and scalability. Our architectures ended up being comprised of dozens of different tools, making changes complicated and time-consuming. This is exponentially worse at larger companies where functions are spread across many different teams, creating communication boundaries that make progress even slower.
When I created Apache Storm back in 2011, I was able to make the work related to realtime processing less arcane. But even though Storm was a huge improvement to what existed at the time, the resulting work was still far removed from the applications we were building. It was clear we needed higher level abstractions than the tools we were using. You can see as far back as when I was working on my book that this was on my mind.
In 2013 I had a big realization: thinking in terms of existing tooling is fundamentally limiting. Every tool that exists cares only about its narrow scope, and none of them exist within any sort of cohesive model of building end-to-end applications. In order to find those higher-level abstractions, I needed to start from first principles. And in 2013 I finally figured out the broad outlines of those high-level abstractions and how they should work.
I spent the next five years researching how to turn this big idea into a practical tool. It’s not just for the initial construction of an application, but also encapsulates deployment, monitoring, and maintenance. It implements the first truly cohesive model for building software applications — a set of abstractions that can build any application at any scale with greatly reduced engineering cost.
Joining Red Planet Labs as a founding engineer is Bryan Duxbury, an ultra-talented engineer with over a decade of experience building large-scale systems for companies like LiveRamp, Square, and StreamSets. He’s contributed to significant open-source projects like Apache HBase and led Apache Thrift as the PMC Chair. Most recently at StreamSets, he led the engineering team as VP of Engineering from Series A to Series C, more than doubling the size of the team during his tenure. His experience scaling engineering teams and bringing complex enterprise software products to market will be an enormous asset to Red Planet Labs.
Zooming out, working on such a leap forward to software infrastructure is exciting because of all the industries it will affect — healthcare, social networking, personal finance, analytics, AI, and so many more. These industries are enabled but also limited by the infrastructure they use. We believe the technological advancement we are creating will empower these industries to do amazing things that will benefit the lives of all.
Now with a fresh $5M in the bank from investors who have backed many impactful companies like Uber, Twitter, Coinbase, and Instacart, we’re focused on building a company that will stand the test of time. We’re looking for talented software engineers to join our distributed team— ideally highly proficient in Clojure — who are excited to push the boundaries of what’s possible with compilers, databases, and distributed systems.