High Agency is the podcast for AI builders. If you’re trying to understand how to successfully build AI products with Large Language Models and Generative AI th...
How to build great AI products with Vanta Software Developer Noam Rubin
In this episode, Noam Rubin, a Software Developer at Vanta reveals how his team uses data-driven strategies to design, test, and improve cutting-edge AI features. Learn how customer insights, rapid prototyping, and iterative development transform raw ideas into tools that make compliance and security easier for businesses everywhere.Chapters:00:00 - Introduction02:47 - The process of building AI products at Vanta04:51 - The role of customer feedback in product development06:59 - Integrating AI into security and compliance workflows08:06 - Using data specifications to guide product development10:10 - Collaborating with subject matter experts to refine AI models12:14 - Iterative testing and refining AI features14:10 - Quality control and ensuring AI accuracy16:00 - The importance of dogfooding and internal feedback loops18:23 - Scaling AI features and rolling them out to wider audiences20:50 - Educating engineers and democratizing AI at Vanta22:20 - Key lessons learned from building AI products24:12 - Maintaining AI quality through continuous feedback26:00 - The future of AI in business and product development
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40:57
Predictions for AI in 2025 I Ex-OpenAI, Ex-Stripe researcher Stanislav Polu
In this episode of High Agency, former OpenAI researcher Stan Polu shares his journey from AI research to founding Dust, an enterprise AI platform. Stan offers a contrarian view on the future of AI, suggesting we may be hitting a plateau in model capabilities since GPT-4. He discusses why startups should focus on product-market fit before investing in GPUs, shares practical lessons for building AI products, and predicts increased competition between AI labs and API developers. Chapters:00:00 - Introducing Dust: an enterprise AI platform06:07 - From Stripe to OpenAI: Stan's journey10:29 - Why research wasn't enough: building Dust15:10 - Best practices for building an AI product20:50 - Is prompt engineering here to stay23:40 - Understanding language models and their limitations32:56 - Predictions for AI in 202539:53 - Measuring progress toward AGI42:26 - The true value of AI technology--------------------------------------------------------------------------------------------------------------------------------------------------Humanloop is the LLM evals platform for enterprises. We give you the tools that top teams use to ship and scale AI with confidence. To find out more go to humanloop.com
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44:27
How Replicate is Democratizing AI with Open-Source Resources
In this episode, we explore how Replicate is breaking down barriers in AI development through its open-source platform. CEO Ben Firshman shares how Replicate enables developers without machine learning expertise to run AI models in the cloud.00:00 Introduction 00:29 Overview of Replicate 03:13 Replicate's user base 05:45 Enterprise use cases and lowering the AI barrier 07:45 The complexity of traditional AI deployment 10:24 Simplifying AI with Replicate's API 13:50 ControlNets and the challenges of image models 19:42 Fragmentation in AI models: images vs. language 25:05 Customization and multi-model pipelines in production 26:33 Learning by doing: skills for AI engineers 28:44 Applying AI in governments 31:12 Iterative development and co-evolution of AI specs 33:13 Final reflections on AI hype 35:18 Conclusion--------------------------------------------------------------------------------------------------------------------------------------------------Humanloop is an Integrated Development Environment for Large Language Models. It enables product teams to develop LLM-based applications that are reliable and scalable. To find out more go to humanloop.com
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36:15
The Principles for Building Excellent AI Features with Superhuman’s Lorilyn McCue
How do you build AI tools that actually meet users’ needs? In this episode of High Agency, Raza speaks with Lorilyn McCue, the driving force behind Superhuman’s AI-powered features. Lorilyn lays out the principles that guide her team’s work, from continuous learning to prioritizing user feedback. Learn how Superhuman’s "learning-first" approach allows them to fine-tune features like Ask AI and AI-driven summaries, creating practical solutions for today’s professionals. 00:00 - Introduction04:20 - Overview of the Superhuman06:50 - Instant Reply and Ask AI10:00 - Building On-Demand vs. Always-On AI Features13:45 - Prompt Engineering for Effective Summarization22:35 - The Importance of Seamless AI Integration in User Workflows25:10 - Developing Advanced Email Search with Contextual Reasoning29:45 - Leveraging User Feedback32:15 - Balancing Customization and Scalability in AI-Generated Emails36:05 - Approach to Prioritization39:30 - Real-World Use Cases: The Versatility of Current AI Capabilities43:15 - Learning and Staying Updated in the Rapidly Evolving AI Field46:00 - Is AI Overhyped or Underhyped?49:20 - Final Thoughts and Closing Remarks--------------------------------------------------------------------------------------------------------------------------------------------------Humanloop is an Integrated Development Environment for Large Language Models. It enables product teams to develop LLM-based applications that are reliable and scalable. To find out more go to humanloop.com
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42:35
Jeff Huber of Chroma: Building the open-source toolkit for AI Engineering
This week on High Agency, Raza Habib is joined by Chroma founder Jeff Huber. They cover the evolution of vector databases in AI engineering, challenge common assumptions about RAG and share insights from Chroma's journey. Jeff shares insights from Chroma's development, including their focus on developer experience and observations about real-world usage patterns. They also get into whether or not we can expect a super AI any time soon and what is over and under hyped in the industry today. 00:00 - Introduction02:30 - Why vector databases matter for AI06:00 - Understanding embeddings and similarity search12:00 - Chroma early days15:45 - Problems with existing vector database solutions19:30 - Workload patterns in AI applications23:40 - Real-world use cases and search applications27:15 - The problem with RAG terminology31:45 - Dynamic retrieval and model interactions35:30 - Email processing and instruction management39:15 - Context windows vs vector databases42:30 - Enterprise adoption and production systems45:45 - The journey from GPT-3 to production AI48:15 - Internal vs customer-facing applications51:00 - Advice for AI engineers--------------------------------------------------------------------------------------------------------------------------------------------------Humanloop is an Integrated Development Environment for Large Language Models. It enables product teams to develop LLM-based applications that are reliable and scalable. To find out more go to humanloop.com
High Agency is the podcast for AI builders. If you’re trying to understand how to successfully build AI products with Large Language Models and Generative AI then this podcast is made for you. Each week we interview leaders at companies building on the frontier who have already succeeded with AI in production. We share their stories, lessons and playbooks so you can build more quickly and with confidence.
AI is moving incredibly fast and no-one is truly an expert yet, High Agency is for people who are learning by doing and will share knowledge through the community.
Where to find us: https://hubs.ly/Q02z2HR40
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