LLMs evolve quickly. Their underlying architecture, not so much.
The decoder-only transformer architecture is one of the most fundamental ideas in AI research.
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The decoder-only transformer architecture is one of the most fundamental ideas in AI research.
Ben and Ryan are joined by Robin Gupta for a conversation about benchmarking and testing AI systems. They talk through the lack of trust and confidence in AI, the inherent challenges of nondeterministic systems, the role of human verification, and whether we can (or should) expect an AI to be reliable.
Product manager Ash Zade joins the home team to talk about the journey to OverflowAI, a GenAI-powered add-on for Stack Overflow for Teams that’s available now. Ash describes how his team built Enhanced Search, the problems they set out to solve, how they ensured data quality and accuracy, the role of metadata and prompt engineering, and the feedback they’ve gotten from users so far.
Ben and Ryan explore why configuration is so complicated, the right to repair, the best programming languages for beginners, how AI is grading exams in Texas, Automattic’s $125M acquisition of Beeper, and why a major US city’s train system still relies on floppy disks. Plus: The unique challenge of keeping up with a field that’s changing as rapidly as GenAI.
In the wake of the XZ backdoor, Ben and Ryan unpack the security implications of relying on open-source software projects maintained by small teams. They also discuss the open-source nature of Linux, the high cost of education in the US, the value of open-source contributions for job seekers, and what Apple is up to AI-wise.
The home team is joined by Michael Foree, Stack Overflow’s director of data science and data platform, and occasional cohost Cassidy Williams, CTO at Contenda, for a conversation about long context windows, retrieval-augmented generation, and how Databricks’ new open LLM could change the game for developers. Plus: How will FTX co-founder Sam Bankman-Fried’s sentence of 25 years in prison reverberate in the blockchain and crypto spaces?
Ben and Ryan talk about how tiny nations are making huge money from their domain names, the US government’s antitrust case against Apple, the implications of a four-day work week, Reddit’s IPO, and more.
Ben and Ryan are joined by Bill Harding, CEO of GitClear, for a discussion of AI-generated code quality and its impact on productivity. GitClear’s research has highlighted the fact that while AI can suggest valid code, it can’t necessarily reuse and modify existing code—a recipe for long-term challenges in maintainability and test coverage if devs are too dependent on AI code-gen tools.
The home team discusses the challenges (hardware and otherwise) of building AI models at scale, why major players like Meta are open-sourcing their AI projects, what Apple’s recent changes mean for developers in the EU, and Perplexity AI’s new approach to search.
Ben talks with Ryan Polk, Chief Product Officer at Stack Overflow, about our strategic partnership with Google Cloud, the importance of collaboration between AI companies and the Stack Overflow community, and why Stack Overflow’s Q&A format is so suitable for training AI models.
Machine learning scientist, author, and LLM developer Maxime Labonne talks with Ben and Ryan about his role as lead machine learning scientist, his contributions to the open-source community, the value of retrieval-augmented generation (RAG), and the process of fine-tuning and unfreezing layers in LLMs. The team talks through various challenges and considerations in implementing GenAI, from data quality to integration.
Ryan and Ben chat with Shivang Shah, Chief Architect, and Jon Fasoli, Chief Design & Product Officer, both of Intuit Mailchimp, about implementing GenAI and how all the pieces came together to make a better end user experience.
This is part two of our conversation with Roie Schwaber-Cohen, Staff Developer Advocate at Pinecone, about retrieval-augmented generation (RAG) and why it’s crucial for the success of your AI initiatives.
On this episode: Roie Schwaber-Cohen, Staff Developer Advocate at Pinecone, joins Ben and Ryan to break down what retrieval-augmented generation (RAG) is and why the concept is central to the AI conversation. This is part one of our conversation, so tune in next time for the thrilling conclusion.
On this home team episode: Discussions on Stack Overflow is a new feature that allows users to engage in open-ended conversations outside the site’s primary Q&A structure. The team explores deep-cut Stack Exchange questions about the nature of consciousness and the availability of corrective lenses for medieval knights. Plus: The psychology of downvoting and a recent FCC ruling on AI-generated robocalls.
On this episode: Eitan Worcel, CEO and cofounder of Mobb, a company that uses AI to automate security vulnerability remediation, talks about how AI can help reduce security backlogs and free up developers’ time, what security risks emerge with GenAI, and why we still need a human in the loop.
The home team chats with William Falcon, an AI researcher and creator of PyTorch Lightning, about developing tooling for the AI ecosystem, open-source contributions, what happens when widely hyped technology needs to scale, and why he’s bullish on experienced developers using AI but not so bullish on new devs doing the same.
On today’s home team episode: a new study confirms that AI isn’t putting us out of business, why tech layoffs have been good for share prices, and the programming students learning to code with Copilot.
In today’s episode of the podcast, sponsored by Intuit, Ben and Ryan talk with Shivang Shah, Chief Architect at Intuit Mailchimp, and Merrin Kurian, Principal Engineer and AI Platform Architect at Intuit. They discuss generative AI at Intuit, GenOS (the generative AI operating system that they built), and how GenAI can scale without sacrificing privacy.
The home team convenes to discuss AI deepfakes, the legal implications of generating an AI version of a dead comedian or a famous singer-songwriter, whether leaderboard rankings for AI models reflect reality, and the relationship between agile development and burnout.
The home team chats about machine learning and its applications beyond the hot topic of GenAI, what it means for models to unlearn data, the future of open source, and new frontiers in game development.
Ben and Ryan talk about the hacker who exposed a security vulnerability in AI-powered software, security risks of smart devices, symbolic deduction engines in AI, and the programming language that features time travel.
Ben and Ryan are joined by Ryan’s former colleague and current backend engineer at Spotify, Omar Delarosa, to talk about time-traveling programming languages, a keyboard that turns an iPhone into a Blackberry, and what it’s like working on everyone’s personal DJ.
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