Ryan is joined by Tobiko Data co-founders Toby Mao and Iaroslav Zeigerman to talk about the crucial role of rigorous data practices and tooling, the innovations of Tobiko Data’s SQLMesh and SQLGlot, and their insights into the future of data engineering with the rise of AI.
Ryan Donovan and Ben Popper sit down with Jamie de Guerre, SVP of Product at Together AI, to discuss the evolving landscape of AI and open-source models. They explore the significance of infrastructure in AI, the differences between open-source and closed-source models, and the ethical considerations surrounding AI technology. Jamie emphasized the importance of leveraging internal data for model training and the need for transparency in AI practices.
Diverse, high-quality data is a prerequisite for reliable, effective, and ethical AI solutions.
Snowflake customers can now easily enrich their AI applications and agentic systems with some of the most trusted, highest-quality data available while respecting our community members who provide this content with proper attribution.
Ryan is joined by Jeremy Edberg, CEO of DBOS, and Qian Li, co-founder of DBOS, to discuss durable execution and its use cases, its implementation using technologies like PostgreSQL, and its applications in machine learning pipelines and AI systems for reliability, debugging, and observability.
Money is pouring into the AI industry. Will software survive the disruption it causes?
Ben Popper chats with CTO Abby Kearns about how Alembic is using composite AI and lessons learned from contract tracing and epidemiology to help companies map customer journeys and understand the ROI of their marketing spend. Ben and Abby also talk about where open-source models have the edge and the challenges startups face in building trust with big companies and securing the resources they need to grow.
This post explores crucial lessons learned in the trenches of data licensing, drawing insights from Stack Overflow and the growing importance of socially responsible data practices in a changing internet landscape.
Avoiding bad data is just as important in AI; it can open you to fines, lawsuits, and lost customers.
Ryan chats with Dataiku CEO and cofounder Florian Douetteau about the complexities of the genAI data stack and how his company is orchestrating it.
Data has always been key to LLM success, but it's becoming key to inference-time performance as well.
Stack Overflow CEO Prashanth Chandrasekar sat down with Ryan at HumanX 2025 to talk about how Stack is integrating AI into its public platform, the enormous importance of a high-quality knowledge base in your AI journey, how AI tools are empowering junior developers to build better software, and much more.
Jeremy “Jezz” Kellway, VP of Engineering for Analytics and Data & AI at EDB (Enterprise Database), joins Ryan for a conversation about Postgres and AI. They unpack how Postgres is becoming the standard database for AI applications, the importance of managing unstructured data, and the implications of data sovereignty and governance in AI.
Minh Nguyen, VP of Engineering at Transcend, joins Ryan for a conversation about the complexities of privacy and consent in tech, from the challenges organizations face in managing data privacy to the importance of consent management tools to the evolving landscape of privacy regulations.
Ken Stott, Field CTO of API platform Hasura, tells Ryan about the data doom loop: the concept that organizations are spending lots of money on data systems without seeing improvements in data quality or efficiency.
Ben and Ryan sit down with public interest technologist Sukhi Gulati Gilbert, a senior product manager at Consumer Reports, for a conversation about digital data privacy. They talk about why digital privacy matters, the challenges consumers face in safeguarding their data, and the legislative gaps in privacy protection, along with the app Sukhi is working, Permission Slip, that helps users exercise their rights to digital data privacy. Plus: Why it might be worth reducing your digital footprint.
Ben and Ryan are joined by Matt Zeiler, founder and CEO of Clarifai, an AI workflow orchestration platform. They talk about how the transformer architecture supplanted convolutional neural networks in AI applications, the infrastructure required for AI implementation, the implications of regulating AI, and the value of synthetic data.
Or Lenchner, CEO of Bright Data, joins Ben and Ryan for a deep-dive conversation about the evolving landscape of web data. They talk through the challenges involved in data collection, the role of synthetic data in training large AI models, and how public data access is becoming more restrictive. Or also shares his thoughts on the importance of transparency in data practices, the likely future of data regulation, and the philosophical implications of more people using AI to innovate and solve problems.
Ben chats with Shayne Longpre and Robert Mahari of the Data Provenance Initiative about what GenAI means for the data commons. They discuss the decline of public datasets, the complexities of fair use in AI training, the challenges researchers face in accessing data, potential applications for synthetic data, and the evolving legal landscape surrounding AI and copyright.
In this episode, Ben interviews Jannis Kallinikos, a professor at Luiss University in Rome, Italy about his new book Data Rules: Reinventing the Market Economy, coauthored with Cristina Alaimo. They discuss the social impact of data, explore the idea that data filters how we see the world and interact with each other, and highlight the need for social accountability in data tracking and surveillance.
More and more of our lives are becoming data-driven. Is that a good thing?
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.
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.
AI systems obey the golden rule: garbage in, garbage out, Want good results, feed it good data.
