Tracy Lee and Jerome Hardaway (Vets Who Code) embark on a series of defining the six steps of AI adoption. In this episode, they define the first step: destroying the bias, through which technologists must first acknowledge the transformative potential of AI across industries. Tracy and Jerome explore the role of AI in enhancing human capabilities, particularly in education and workflow improvement, and how developers can reframe their thinking about AI to more mindfully integrate AI into their daily work.

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Jay Phelps, a Senior Software Engineer at Netflix, alongside Ben Lesh, Tracy Lee, and Adam Rackis, explores challenges facing React developers, the problem with Vercel and Next.js, and the solutions that the web platforms teams should be focused on to improve the web for everyone, especially those that use third party scripts.

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Jack Herrington talks with Tracy Lee on the latest trends in JavaScript development and developer culture. They talk about evolving web development tools and philosophies and the abstractions that come with these new frameworks. Jack and Tracy talk about the potential of HTMX and Astro, highlighting trends towards simplicity in tech and the role of client-side libraries like HTMX in bridging front-end and back-end development.

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Join Ben Lesh, Adam Rackis, and Tracy Lee as they talk about observables landing in the browser and its potential impact on the use of RXJS. They also talk about the significance of listening to customers vs having a strong vision in software development, and explore the factors that contribute to the success or failure of frameworks. Finally, they discuss the potential impact of AI automation on job roles and the outsourcing of tech jobs.

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Atila Fassina is a devrel engineer at Crabbula and on the Solid DX team. He is passionate about creating content and in this show we talk about his involvement with SolidJS and developer experiences in general. Rob and Atila discuss how to improve DX, performance optimizations, and the nuances behind developer documentation and how to create a comprehensive toolkit for developers. Atila also talks about the upcoming stable release of Solid and its integration with Tauri V2 for mobile app development.

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Tracy Lee, Adam Rackis, Ben Lesh host Chance Strickland to discuss new web APIs, CSS, Tailwind and Atomic CSS pros and cons, React Server Component implementations, and so much more on this podcast.

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What is the current state of React and its influence from Vercel? What are the challenges of upgrading to React 18 and the impact on testing? The hosts also cover the potential for the runtime Bun to become the default for node development. The conversation explores the future of front-end development, the complexities of modern web development, and the influence of content creators in the industry.

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Loren Sands-Ramshaw, Developer Relations Engineer at Temporal joins Rob Ocel to talk about reliable application development. They introduce the topic of durable execution and talk about reliability in systems, unraveling common issues developers face and showcase the benefits that durable execution can bring to software development. They also talk about the challenges of traditional programming and the complexities of event-driven architecture.

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Rob Ocel interviews Dr. Joye Purser, the Global Lead Field CISO at Veritas Technologies. Dr. Purser explains the need for passionate problem solvers in the private sector and outlines the three key elements of security leadership. She discusses the rising threat of cyber security attacks in the private sector and what organizations are doing to address them, and what skillsets are needed to hire leadership in this area.

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Ever been curious about neural networks? Chris Gardner talks neural networks, covering fundamental concepts, historical development, and practical applications. What’s the difference between AI (artificial intelligence) and machine learning? Learn about the practical application of neural networks in solving complex problems with large datasets and the importance of understanding underlying concepts. The episode also explores the challenges of training neural networks and the ethical considerations and biases inherent in machine learning models.

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