The Stack Overflow podcast is a weekly conversation about working in software development, learning to code, and the art and culture of computer programming. Hosted by Paul Ford and Ben Popper, the series features questions from our community, interviews with fascinating guests, and hot takes on what’s happening in tech. Founded in 2008, Stack Overflow is empowering the world to develop technology through collective knowledge. It’s best known for being the largest, most trusted online community for developers and technologists. More than 100 million people come to Stack Overflow every month to ask questions, help solve coding problems, and develop new skills.
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Finally, an AI bot that can ace technical interview questions
Learn more about AlphaCode here.Check out an amazing video essay critiquing the NFT market, The Line Goes Up.Read up on Josh Wardle, the developer who built Wordle for his partner to help pass the time during the pandemic, then sold it to the NY Times for a sweet seven figures.
An algorithm that optimizes for avoiding ennui
You can learn more about Clement's career on his LinkedIn and on Twitter (assuming you speak French).You can learn more about Dailymotion here and check out the roles they are hiring for here.You can find Cassidy Williams on Twitter and at her website. You can find Ceora Ford on Twitter and at her website.Our Lifeboat badge winner of the week is Swati Kiran, who helped solve an error causing permission problems in an angular app.
Column by your name: The analytics database that skips the rows
These days, every company looking at analyzing their data for insights has a data pipeline setup. Many companies have a fast production database, often a NoSQL or key-value store, that goes through a data pipeline.The pipeline process performs some sort of extract-transform-load process on it, then routes it to a larger data store that the analytics tools can access. But what if you could skip some steps and speed up the process with a database purpose-built for analytics?On this sponsored episode of the podcast, we chat with Rohit (Ro) Amarnath, the CTO at Vertica, to find out how your analytics engine can speed up your workflow. After a humble beginning with a ZX Spectrum 128, he’s now in charge of Vertica Accelerator, a SaaS version of the Vertica database. Vertica was founded by database researcher Dr. Michael Stonebreaker and Andrew Palmer. Dr. Stonebreaker helped develop several databases, including Postgres, Streambase, and VoltDB. Vertica was born out of research into purpose-built databases. Stonebreaker’s research found that columnar database storage was faster for data warehouses because there were fewer read/writes per request. Here’s a quick example that shows how columnar databases work. Suppose that you want all the records from a specific US state or territory. There are 52 possible values here (depending on how you count territories). To find all instances of a single state in a row-based DB, the search must check every row for the value of the state column. However, searching by column is faster by an order of magnitude: it just runs down the column to find matching values, then retrieves row data for the matches. The Vertica database was designed specifically for analytics as opposed to transactional databases. Ro spent some time at a Wall Street firm building reports—P&L, performance, profitability, etc. Transactions were important to day-to-day operations, but the real value of data came from analyses that showed where to cut costs or increase investments in a particular business. Analytics help with overall strategy, which tends to be more far-reaching and effective. For most of its life, Vertica has been an on-premises database managing a data warehouse. But with the ease of cloud storage, Vertica Accelerator is looking to give you a data lake as a service. If you’re unfamiliar, data lakes take the data warehouse concept—central storage for all your data—and remove limits. You can have “rivers” of data flowing into your stores; if you go from a terabyte to a petabyte overnight, your cloud provider will handle it for you. Vertica has worked with plenty of industries that push massive amounts of data: healthcare, aviation, online games. They’ve built a lot of functionality into the database itself to speed up all manner of applications. One of their prospective customers had a machine learning model with thousands of lines of code that was reduced to about ten lines because so much was being done in the database itself. In the future, Vertica plans to offer more powerful management of data warehouses and lakes, including handling the metadata that comes with them. To learn more about Vertica’s analytics databases, check out our conversation or visit their website.
Gen Z doesn’t understand file structures
It’s not news that, as Cassidy says, “remote has grown wildly fast”—but Remote has gone from about 25 employees in March 2020 to 900 now (a 3,500% increase).Ceora explains to Matt (oh, sweet summer’s child) what it means to get ratioed on Twitter.Inspired by a great read, the team discusses how Gen Z, having grown up without floppy disks, file folders, or directories, thinks about information.This week’s Lifeboat badge goes to user 1983 for their answer to the question Why can I not use `new` with an arrow function in JavaScript/ES6?.
China’s only female Apache member on the rise of open source in China
SphereEX builds distributed data systems, making it easier for organizations to load balance massive data stores across multiple servers. Now that open-source software has taken over Western software, it’s China’s turn. Even big companies like Baidu and Bytedance are opening up their projects. Trista is the only female Apache member in China, which is both an honor and a demonstration of how much work needs to be done to support women in STEM. This episode’s Lifeboat badge shoutout goes to swati kiran for her answer to Error: EACCES: permission denied, mkdir '/usr/local/lib/node_modules/node-sass/build'.
There’s no coding Oscars. Write software that works
Ceora has her second brain stored in Notion, complete with GIFs and pretty color to get that aesthetic.Ancient history in blog years: Cassidy talks about the perils of being bleeding-edge instead of cutting-edge: Apollo Mission: The pros and cons of being an early adopter of new technology 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.Today’s lifeboat goes to Bill the Lizard for Using IFF in Python.
Moving from CEO back to IC: A chat with Mitchell Hashimoto on his love for code
Neopets: A little-known gateway into a software career. (Nineties kids will remember.)Among the products Mitchell helped build at Hashicorp: Terraform, Vagrant, and Vault.Not many C-level execs return to IC roles, but you might be surprised how many managers move back to being individual contributors.Follow Mitchell on Twitter here.
A collaborative hub for infrastructure as code
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, Pulumi, and Ansible, with plans to add support for services like 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.
Next stop, Cryptoland?
The Twitter thread that brought Cryptoland to the team’s attention.Ceora wonders whether participants in a hypothetical, decentralized version of YouTube (a YouTube-like dApp) would need coding skills to contribute meaningfully.Why is Ethereum so expensive and so congested?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.
Using synthetic data to power machine learning while protecting user privacy
You can learn more about Gretel here. The company is hiring for numerous positions. Think your commits are anonymous? Think again: DefCon researchers figured out how to de-anonymize code creators by their style. We published an article about the importance of including privacy in your SDLC: Privacy is an afterthought in the software lifecycle. That needs to change.Our Lifeboat badge shoutout goes to 1983 (the year Ben was born) for their answer to Why can I not use `new` with an arrow function in JavaScript/ES6?
How to defend your attention and find a flow state
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.Congrats to our lifeboat badge winner of the week, UrbanoJVR, who answered the question: What is the difference between 'mvn verify' vs 'mvn test'?
Who's going to pay to fix open source security?
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.Ben Popper can be found on Twitter here.Ryan Donovan can be found on Twitter, or writing for the Stack Overflow blog.
A chat with the folks who lead training and certification at AWS
You can find Maureen here. You can find Scott here.There is a wealth of free courses available through the AWS training website, including Operations, Advanced Networking, Machine Learning, and Data Science.
Safety in numbers: crowdsourcing data on nefarious IP addresses
You can find Philippe on Twitter here and learn more about CrowdSec here.They recently put together a list of the IP addresses trying to exploit the new Log4j vulnerability.For a prescient view of today's cybersecurity challenges, Humeau recommends John Brunner's classic 1975 sci-fi novel, The Shockwave Rider.
Making Agile work for 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.