From search trees to neural nets, a deep dive into natural language processing

From search trees to neural nets, a deep dive into natural language processing

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.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Jaksot(896)

From punch cards to prompts: a history of how software got better

From punch cards to prompts: a history of how software got better

SPONSORED BY AWSRyan welcomes Darko Mesaroš, Principal Developer Advocate at AWS and all around computer history buff, to chat about history of software development improvements and how they made developers made more productive. They discuss the technologies and breakthroughs that created greater abstractions on the underlying bit manipulations and made software development more powerful. Episode notes:If you’re looking to take advantage of the breakthroughs mentioned in this episode, check out AWS Builder Center, a place for you to learn, build, and connect with builders in the AWS community.If you want to connect with Darko, find him on social media including LinkedIn. Congrats to Lundin for being curious and asking about Implicit type promotion rules.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

27 Elo 34min

Svelte was built on “slinging code for the sheer love of it”

Svelte was built on “slinging code for the sheer love of it”

Rich Harris, creator of Svelte and software engineer at Vercel, joins Ryan on the show to dive into the evolution and future of web frameworks. They discuss the birth and growth of Svelte during the rise of mobile, the challenges of building robust and efficient web applications, how companies can back more open-source community projects, and the dirty little secret about asynchronous operations and component frameworks. Episode notes:Svelte is a UI framework that uses a compiler to let you write components using HTML, CSS and JavaScript. It’s ranked as one of developer’s most admired web frameworks in this year’s Developer Survey. Keep up with the Svelte community on the Svelte Society page. Find Rich on Blue Sky and GitHub.Congrats to Paul Pladijs, who won a Populist badge for answering the question How can one change the timestamp of an old commit in Git?.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

26 Elo 35min

Learning in the flow: Unlocking employee potential through continuous learning

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In this episode of Leaders of Code, Stack Overflow CEO Prashanth Chandrasekar and Christina Dacauaziliqua, Senior Learning Specialist at Morgan Stanley, talk about the importance of experiential learning in fast-paced environments. They emphasize the value of creating intentional learning environments where innovative tools meet collaborative communities to support growth for both individuals and organizations. The discussion also:Explores why leaders need to model continuous learning to inspire their teams.Explains three practical principles to embed a culture of ongoing learning into everyday operations successfully.Touches on Morgan Stanley's multi-year strategic initiatives centred on talent excellence and how they empower employees through an intentional learning framework and metric tracking. Notes:Connect with Christina Dacauaziliqua and Prashanth Chandrasekar.Learn how to empower learning within your organization with Stack Overflow for Teams.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

22 Elo 33min

Robots in the skies (and they use Transformer models)

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Ryan welcomes Nathan Michael, CTO at Shield AI, to discuss what AI looks like in defense technologies, both technically and ethically. They cover how the Hivemind technology works in coordinating the autonomous decisions of drones in the field while keeping humans in the loop, whether Shield AI is building Terminators, and how software security works on an edge device that could fall into enemy hands. Episode notes:Shield AI produces Hivemind, a resilient autonomy platform intended to protect service members and civilians.Congrats to Great Answer badge winner tmdavison for dropping a 100 point plus answer on Set max value for color bar on seaborn heatmap.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

22 Elo 26min

The server-side rendering equivalent for LLM inference workloads

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Ryan is joined by Tuhin Srivastava, CEO and co-founder of Baseten, to explore the evolving landscape of AI infrastructure and inference workloads, how the shift from traditional machine learning models to large-scale neural networks has made GPU usage challenging, and the potential future of hardware-specific optimizations in AI. Episode notes:Baseten is an AI infrastructure platform giving you the tooling, expertise, and hardware needed to bring AI products to market fast.Connect with Tuhin on LinkedIn or reach him at his email tuhin@baseten.co. Shoutout to user Hitesh for winning a Populist badge for their answer to Cannot drop database because it is currently in use. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

19 Elo 21min

The future of Vue is you (and You)

The future of Vue is you (and You)

Ryan welcomes Evan You, the creator of Vue.js, to explore the origins of Vue.js, the challenges faced during its development, and the project’s growth over a decade. They dive into potential integrations for AI, future developments for Vue.js, and the sustainability of open-source projects. Episode notes: Vue.js is a progressive JavaScript framework that’s approachable, performant, and versatile for building web user interfaces.Check out what Evan and his team are doing to create the next generation of tooling at Void Zero. Vue was ranked as the eighth most-popular web framework in the 2025 Developer Survey. Congrats to Stellar Question badge winner Hari, who won for asking the question JavaScript - Track mouse position. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

15 Elo 29min

AI isn’t stealing your job, it’s helping you find it

AI isn’t stealing your job, it’s helping you find it

Wenjing Zhang, VP of Engineering, and Caleb Johnson, Principal Engineer at LinkedIn, sit down with Ryan to discuss how semantic search and AI have transformed LinkedIn’s job search feature. They explore the engineering efforts behind transitioning from keyword-based search and the impact of AI models on LinkedIn’s job seekers and employers.Episode notes: LinkedIn is the world’s largest professional network with more than one billion members.Connect with Caleb on LinkedIn.Connect with Wenjing on LinkedIn.Congrats to Mattias Larsson, who won a Populist badge for answering Singleton Class which requires some async call.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

12 Elo 37min

Python: Come for the language, stay for the community

Python: Come for the language, stay for the community

Ryan welcomes Paul Everitt, developer advocate at JetBrains and an early adopter of Python, to discuss the history, growth, and future of Python. They cover Python’s pivotal moments and rise alongside the internet, the increased adoption from transitions like Python 2 to Python 3, and the significant role Python plays in academia and data science today. Episode notes: JetBrains is improving the developer experience through a rich suite of tools. Connect with Paul on LinkedIn and X.Python is the fourth most-popular language in our 2025 Developer Survey. From the archives: Why is the migration to Python 3 taking so long? Today we’re shouting out a popular Python question, Fastest way to find the least amount of subsets that sum up to the total set in Python, asked by user Shaun Han. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

8 Elo 30min

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