Everybody is aboard the VS Code train, which has the hottest TikTok around. Cassidy recommends the MonoLisa font helping viewers read your code during a livestream.
On this sponsored episode of the podcast, we talk with Marcin Wyszynski, founder and CEO at Spacelift. Marcin says Spacelift aims to be for infrastructure-as-code what GitHub is to git. It centralizes everything about your IaC system: it runs code, deploys within CI/CD pipelines, tracks the progress of your infrastructure, and gives you insight into who made what changes and why. Today it works with the IaC tools already out there: Terraform, Cloud Formation, and Pulumi, with plans to add support for services like Ansible and Kubernetes in the future.
Like a lot of programmers, Marcin got into coding through games. Once he ran through the limited number of Commodore 64 games at his local shop in Poland, he learned to program his own. But he never thought of programming as a career, so when it came time to pick a college major, he followed a group of his peers into sociology. Sociology, with its heavy focus on statistics, brought him back to programming.
He landed his first job at Google reviewing copy for Ads, which lasted until he could automate himself out of it. Google gave him increasingly technical roles until he moved into an SRE position handling tape backups, a job that is mostly very boring until it becomes extremely exciting. After that, it was a stint at Facebook spinning up point-of-presence clusters around the world, then CTO at a startup that didn’t catch on as he’d hoped.
With this wealth of experience under his belt, he went into consulting. As a consultant, he had his bag of best practices, open-source tools, processes, and scripts that he brought with him, but he also built bespoke pieces of technology for every single one of his clients. One need his clients had in common was a way to manage the code that defined their infrastructure.
During Marcin’s career, there were many times when he built the thing he needed: games, automation, scripts. When his consulting clients would leave for a new organization, they would reach out to ask if he could provide them with the solution he had built for infrastructure as code. Realizing that he had created something which addressed a pain point common to many companies, he decided to turn this solution into a new company: Spacelift.
Spacelift aims to take the heavy lifting out of infrastructure-as-code, automate it, and make it auditable. When a change gets made, everyone can see it and comment on it. From the product manager to the junior dev, everyone knows what’s going on, even if an infrastructure change doesn’t fit the original architecture docs. Plus, the SRE team no longer need to go on archeological expeditions to find a database secretly running and costing the company five figures a month.
To learn more about Spacelift, check out their website at https://spacelift.io/, where you can start a free trial and see it in action.
Ceora wonders whether participants in a hypothetical, decentralized version of YouTube (a YouTube-like dApp) would need coding skills to contribute meaningfully.
Ben outlines how Solana has become the fastest-growing blockchain in the world by evolving the Ethereum concept to make it more scalable and less congested.
The inspiration for today's episode was a terrific article from The Guardian about the many ways in which the modern world, specifically the software we use every day, was designed to steal our attention.
During the episode, we discuss Mihaly Csikszentmihalyi, a professor know as the "father of flow" for his pioneering research on flow states. Sadly, Prof. Csikszentmihalyi passed away in 2021, but you can find a terrific tribute to him and his work here.
In the second half of the episode, we discuss "The California Ideology" and the ways in which hustle culture and libertarian ideals helped to shape Silicon Valley and the world of technology more broadly.
Will no one think of the maintainers? As The New Stack points out, watching millions of projects fail because of a bug in an open source library has become common enough that we shrug and reply, "Told you so." It's gotten so bad, big tech companies are visiting the White House to discuss the issue as a matter of national security.
There is a great post up on the Stack Overflow blog examining this issue, but it's not about color.js, it's about Log4J. Traffic to questions on this logging library grew more than 1000% percent after the recent revelations about a new vulnerability.
Also discussed in this episode: cryptographer and Signal creator Moxie Marlinspike stepped down from his role as CEO of the encrypted messaging service. That's news, but he actually made bigger waves in tech circles with an unrelated blog post detailing his first experience with Web3. Spoiler alert: it's not as decentralized or divorced from Web2 as you might have thought.
You can find Cassidy Williams on Twitter and her website.
There is a wealth of free courses available through the AWS training website, including Operations, Advanced Networking, Machine Learning, and Data Science.
Data scientists and engineers don’t always play well together. Data scientists will plan out a solution, carefully build models, test them in notebooks, then throw that solution over the wall to engineering. Implementing that solution can take months.
Historically, the data science team has been purely science-driven. Work on methodologies, prove out something that they wanted to achieve, and then hand it over to the engineering organization. That could take many months.
Over the past three to five years, they’ve been moving their engineering and data science operations onto the cloud as part of an overall Agile transformation and a move from being sales-led to being product-led. With most of their solutions migrated over, they decided that along with modernizing their infrastructure, they wanted to modernize their legacy systems, add new functions and scientific techniques, and take advantage of new technologies to scale and meet the demand coming their way.
While all of the rituals and the rigor of Agile didn't always facilitate the more open-ended nature of the data science work at 84.51°, having both data science and engineering operating in a similar tech stack has been a breath of fresh air. Working cross-functionally has shortened the implementation delay. At the same time, being closer to the engineering side of the house has given the data science team a better sense of how to fit their work into the pipeline.
Getting everyone on the same tech stack had a side effect. Between the increasing complexity of the projects, geographic diversity of the folks on these projects, a rise in remote work, and continued growth, locating experts became harder. But with everyone working in the same tech, more people could answer questions and become SMEs.
Of course, we’d be remiss if we didn’t tell you that 84.51° was asking and answering questions on Stack Overflow for Teams. It was helpful when Chris and Michael no longer had to call on the SMEs they knew by name but could suddenly draw more experts out of the woodwork by asking a question. Check out this episode for insights on data science, agile, and building a great knowledge base for a large, increasingly distributed engineering org.
