The home team talks about the current state of the software job market, the changing sentiments around AI job opportunities, the impact of big players like Facebook and OpenAI on the space, and the challenges for startups. Plus: The philosophical implications of LLMs and the friendship potential of corvids.
Ben talks with Shane McAllister, lead developer advocate at MongoDB, Stanimira Vlaeva, senior developer advocate at MongoDB, and Miku Jha, director, AI/ML and generative AI at Google Cloud, about the challenges and opportunities of operationalizing and scaling generative AI models in enterprise organizations.
On this episode: Stack Overflow senior data scientist Michael Geden tells Ryan and Ben about how data scientists evaluate large language models (LLMs) and their output. They cover the challenges involved in evaluating LLMs, how LLMs are being used to evaluate other LLMs, the importance of data validating, the need for human raters, and more needs and tradeoffs involved in selecting and fine-tuning LLMs.
In this sponsored episode, Ben and Ryan are joined by Ria Cheruvu, an AI evangelist at Intel, to discuss the different approaches to incorporating AI models into organizations.
The home team convenes to discuss the XZ backdoor attack, what great software engineers have in common, how GenAI is changing the face of drug development, and the rise of managed service providers for AI.
We sit down with Jessica Clark, a senior data scientist at Stack Overflow, to discuss how our company approaches generative AI and data quality.
This new LLM technique has started improving the results of models without additional training.
Ben and Ryan are joined by Nick Heudecker, Senior Director of Market Strategy and Competitive Intelligence at Cribl, to discuss the state of data and analytics. They cover GenAI, the role of incumbents vs. startups, challenges of data storage and security, data quality and ETL pipelines, measures of data quality for GenAI, and Cribl’s role in the data and observability space.
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.
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.
Stack Overflow is on a journey to build a new era in the practice of AI: the era of social responsibility. All products based on models that consume public Stack Overflow data are required to provide attribution back to the highest relevance posts that influenced the summary given by the model.
Ryan and Ben chat with Raymond Lo, AI software evangelist at Intel, about the AI PC, the software that powers AI breakthroughs, and optimizing hardware and software in unison to improve generative AI performance.
If you’re building experimental GenAI features that haven’t proven their product market fit, you don’t want to commit to a model that runs up costs without a return on that investment.
On this home team episode: Massachusetts makes a welcome shift toward skills-based hiring, AI-generated content robs us of our appetite for mac and cheese, and large-scale crypto mining operations account for more than 2% of the US’s electricity generation. Plus: A PDF quite a bit bigger than Germany.
Intuit shares what they've learned building multiple LLMs for their generative AI operating system.
Ben and Ryan discuss how complex images (and maybe even interactive games) are being encoded in living cells, the latest trends in prompt engineering, and the educational benefits of gaming.
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 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.
Developers love automating solutions to their problems, and with the rise of generative AI, this concept is likely to be applied to both the creation, maintenance, and the improvement of code at an entirely new level.
The home team talks about Google’s new AI model, Gemini; the problems with regulating technology that evolves as quickly as AI; how governments can spy on their citizens via push notification; and more.
Insight into how IBM built their own LLM, data lakehouse, and AI governance system.
Ben and Ryan discuss how LLMs are changing the industry and practice of software engineering, a notorious Crash Bandicoot bug, and communication via series of tubes.
Anand Das, cofounder and CTO of Bito AI, joins Ben and Ryan for a conversation about the intersection of developer productivity and GenAI.
