The Stack Overflow Podcast

You can find Tara on  Twitter here. 

Sam is on Twitter here.

You can learn more about Loveshark's latest games and the roles they are hiring for here.

Thanks to our lifeboat badge winner of the week, Elliott Frisch, for answering the question: Convert list of integer into comma separated string?

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You can find some fun video of Cassidy putting Copilot to the test here.

If you want to take the Jamstack survey, check it out here.

Our lifeboat badge of the week goes to Andomar, who answered the question: Will multiple calls to `now()` in a single postgres query always give same result?

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We discuss how Simões learned to code and the feature set that allowed Poker Now to differentiate itself in a crowded space. 

Simões shares the tech stack he used to craft the first version of Poker Now, and how he rebuilt the service after it crashed under the weight of a massive wave of new users. During the peak of lockdown, his site went from an average of 100 concurrent users to more than 10,000 at a time.

Lastly, we chat about the allure of leaving a regular job behind to work on a passion project, and about the challenges of maintaining a service and earning a living as a solo developer.

Today we're celebrating Divakar, who was awarded a lifeboat badge for answering the question: Searching a sequence in a NumPy array.

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If you want to catch up on the first half of the episode, you can find it here.

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The massive shift to remote work that so many companies undertook over the last year has pushed many to adopt an asynchronous, merge driven workflow that has been pioneered and perfected by software developers. With tools like Airtable, and Coda, the boundary between programming and other forms of media and knowledge work is beginning to blur. 

What happened to Google Wave? Can products with passionate fans get pushed into the Commons after they are sunset?

Peek under the hood, and it's spreadsheets all the way down. Some companies are now turning a simple spreadsheet into an interactive web app. 

Spreadsheets on steroids, what could go wrong?

No Lifeboat badge this episode, but tune in tomorrow, we'll have Part 2 of our live episode from the Fishbowl.

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You can learn more about Lightship, Niantic's AR SDK, here. They are hiring developers, and openings can be found here.

Richard can be found on LinkedIn here. 

Kelly can be found on LinkedIn here.

A big thanks to our lifeboat badge winner of the week, Karim, for answering the question: Check if value exists in Array object Javascript or Angular?

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As explained in this piece, "A headless CMS is a back-end only content management system (CMS) built from the ground up as a content repository that makes content accessible via a RESTful API or GraphQL API for display on any device." Shopify has leaned hard into GraphQL and APIs in general. 

The goal, as Coates describes it, is to allow developers to bring their own stack to the front-end, but provide them with the benefits of Shopify's back-end, like edge data processing for improved speed  at global scale. Shopify also offers a wealth of DevOps tooling and logistical support when it comes to international commerce. 

We also discuss Liquid, the flexible template language Shopify uses for  building web apps.

Our lifeboat badge of the week goes to chunhunghan for answering the question: How to customize the switch button in a flutter?

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If you're full up on technical content and just want funny retweets, follow Adam on Twitter here

If you're interested in learning more about tag pages, check out what the community created for Rust.

Thanks to Peter Cordes, our lifeboat badge winner of the week, for answering the question: How can I accurately benchmark unaligned access speed on x86_64?

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You can check out all the details about Collectives in our launch post here.

We detailed the user research that allowed our community to help shape this product in a Meta post here.

Teresa is on Twitter here and Jascha is on LinkedIn here.

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We chatted with three guests:

Miguel Jetté: Head of AI R&D

Josh Dong: AI Engineering Manager

Jenny Drexler: Senior Speech Scientist

When Jette was studying mathematics in the early 2000s, his focus was on computational biology, and more specifically, phylogenetic trees, and DNA sequences. He wanted to understand the evolution of certain traits and the forces that explain why our bones are a certain length or our brains a certain size. As it turned out, the algorithms and techniques he learned in this field mapped very well to the emerging discipline of automatic speech recognition, or ASR. 

During this period, Montreal was emerging as a hotbed for artificial intelligence, and Jette found himself working for Nuance, the company behind the original implementation of Siri. That experience led him to several positions in the world of speech recognition, and he eventually landed at Rev, where he founded the company’s AI department. 

Jette describes Rev as an “Uber for Transcription.” Anyone can sign up for the platform and earn money by listening to audio submitted by clients and transcribing the speech into text. This means the company has a tremendous dataset of raw audio that has been annotated by human beings and, in many cases, assessed a second time by the client. For someone looking to build an AI system that mastered the domain of speech to text, this was a goldmine. 

Jette built the earliest version of Rev’s AI, but it was up to our second guest, Josh Dong, to productize and scale that system. He helped the department transition from older technologies like Perl to more popular languages like Python. He also focused on practical concerns like modularity and reusable components. To combine machine learning and DevOps, Dong added Docker containers and a testing pipeline. If you’re interested in the nuts and bolts of keeping a system like Rev’s running at tremendous scale, you’ll want to check out this part of the show. 

We also explore some of the fascinating future and promise this technology holds in our time with Jenny Drexler. She explains how Rev is moving from a hybrid model—one that combines Jette’s older statistical techniques with Dong’s newer machine learning approach—to a new system that will be ML from end-to-end. This will open up the door for powerful applications, like a single system that can convert speech text across multiple languages in a single piece of audio. 

“One of the things that's really cool about these end to end models is that basically, whatever data you have, it can learn to handle it. So a very similar architecture can do sequence to sequence learning with different kinds of sequences. The model architecture that you might use for speech recognition can actually look very similar to what you might use for translation. And you can use that same architecture, to say, feed in audio in lots of different languages and be able to do transcription for any of them within one model. It's much harder with the hybrid models to sort of put all the right pieces together to make that happen,” explains Drexler.

If you’re interested in learning more about the past, present, and future of artificial intelligence that can understand our spoken language and learn how to respond, check out the full episode. If you want to learn more about Rev or check out some of the positions they have open, you can find their careers page here.

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