In this episode, we chat with Ben, founder of Espresso AI, about his journey from building Excel Python integrations to optimizing data warehouse compute costs. We explore his experience at companies like Uber and Google, where he worked on everything from distributed systems to ML and storage infrastructure. We learn about the evolution of his latest venture, which started as a C++ compiler optimization project and transformed into a system for optimizing Snowflake workloads using ML. Ben shares insights about applying LLMs to SQL optimization, the challenges of verified code transformation, and the importance of formal verification in ML systems. Finally, we discuss his practical approach to choosing ML models and the critical lesson he learned about talking to users before building products.Chapters00:00 Ben's Journey: From Startups to Big Tech13:00 The Importance of Timing in Entrepreneurship19:22 Consulting Insights: Learning from Clients23:32 Transitioning to Big Tech: Experiences at Uber and Google30:58 The Future of AI: End-to-End Systems and Data Utilization35:53 Transitioning Between Domains: From ML to Distributed Systems44:24 Espresso's Mission: Optimizing SQL with ML51:26 The Future of Code Optimization and AIClick here to view the episode transcript.

David Mytton, founder and CEO of Arcjet, shares his journey from cloud monitoring to creating developer-first security tools. He dives into the challenges of bot detection and the failures of traditional security methods. Mytton discusses using WebAssembly for rapid security checks and highlights the balance between security and latency. He also touches on the importance of documentation in developer tools and his work on sustainability in cloud computing, revealing how environmental impacts shape modern tech solutions.

Delve into the evolution of developer environments with insights from a pioneer in browser-based IDEs. Discover the challenges of integrating complex systems and the distinction between user needs and buyer demands. Explore the impact of AI on coding practices and the unique approach of standardization and automation. Ivan shares his experience transitioning to open source and the future of integrated development environments. Plus, hear about the dynamics of organizing tech conferences and the importance of adapting to emerging trends.

In this episode, we chat with Roy Ben-Alta, co-founder of Oakminer AI and former director at Meta AI Research, about his fascinating journey through the evolution of data infrastructure and AI. We explore his early days at AWS when cloud adoption was still controversial, his experience building large language models at Meta, and the challenges of training and deploying AI systems at scale. Roy shares valuable insights about the future of data warehouses, the emergence of knowledge-centric systems, and the critical role of data engineering in AI. We'll also hear his practical advice on building AI companies today, including thoughts on model evaluation frameworks, vendor lock-in, and the eternal "build vs. buy" decision. Drawing from his extensive experience across Amazon, Meta, and now as a founder, Roy offers a unique perspective on how AI is transforming traditional data infrastructure and what it means for the future of enterprise software.Chapters00:00 Introduction to Roy Benalta and AI Background04:07 Warren Buffett Experience and MBA Insights06:45 Lessons from Amazon and Meta Leadership09:15 Early Days of AWS and Cloud Adoption12:12 Redshift vs. Snowflake: A Data Warehouse Perspective14:49 Navigating Complex Data Systems in Organizations31:21 The Future of Personalized Software Solutions32:19 Building Large Language Models at Meta39:27 Evolution of Data Platforms and Infrastructure50:50 Engineering Knowledge and LLMs58:27 Build vs. Buy: Strategic Decisions for Startups

In this episode, we chat with Nitzan Shapira, co-founder and former CEO of Epsagon, which was acquired by Cisco in 2021. We explore Nitzan's journey from working in cybersecurity to building an observability platform for cloud applications, particularly focused on serverless architectures. We learn about the early days of serverless adoption, the challenges in making observability tools developer-friendly, and why distributed tracing was a key differentiator for Epsagon. We discuss the evolution of observability tools, the future impact of AI on both observability and software development, and the changing landscape of serverless computing. Finally, we hear Nitzan's current perspective on enterprise AI adoption from his role at Cisco, where he helps evaluate and build new AI-focused business lines.03:17 Transition from Security to Observability09:52 Exploring Ideas and Choosing Serverless16:43 Adoption of Distributed Tracing20:54 The Future of Observability25:26 Building a Product that Developers Love31:03 Challenges in Observability and Data Costs32:47 The Excitement and Evolution of Serverless35:44 Serverless as a Horizontal Platform37:15 The Future of Serverless and No-Code/Low-Code Tools38:15 Technical Limits and the Future of Serverless40:38 Navigating Near-Death Moments and Go-to-Market Challenges48:36 Cisco's Gen .AI Ecosystem and New Business Lines50:25 The State of the AI Ecosystem and Enterprise Adoption53:54 Using AI to Enhance Engineering and Product Development55:02 Using AI in Go-to-Market Strategies

Brian Grant, the original lead architect of Kubernetes and a pioneer in GPU compiler technology, shares insights from his distinguished career in systems engineering. He recalls the unique challenges in early GPU computing and the transformative innovations in compiler architecture. The conversation delves into the architectural decisions of Kubernetes and Google's Borg, highlighting the importance of a rich abstraction model and the need for standardization in cloud infrastructure. Grant also envisions a future beyond mere infrastructure as code.

In this episode, we chat with JP, creator of FizzBee, about formal methods and their application in software engineering. We explore the differences between coding and engineering, discussing how formal methods can improve system design and reliability. JP shares insights from his time at Google and explains why tools like FizzBee are crucial for distributed systems. We delve into the challenges of adopting formal methods in industry, the potential of FizzBee to make these techniques more accessible, and how it compares to other tools like TLA+. Finally, we discuss the future of software development, including the role of LLMs in code generation and the ongoing importance of human engineers in system design.LinksFizzBeeFizzBee Github RepoFizzBee BlogChapters00:00 Introduction and Overview02:42 JP's Experience at Google and the Growth of the Company04:51 The Difference Between Engineers and Coders06:41 The Importance of Rigor and Quality in Engineering10:08 The Limitations of QA and the Need for Formal Methods14:00 The Role of Best Practices in Software Engineering14:56 Design Specification Languages for System Correctness21:43 The Applicability of Formal Methods in Distributed Systems31:20 Getting Started with FizzBee: A Practical Example36:06 Common Assumptions and Misconceptions in Distributed Systems43:23 The Role of FizzBee in the Design Phase48:04 The Future of FizzBee: LLMs and Code Generation58:20 Getting Started with FizzBee: Tutorials and Online PlaygroundClick here to view the episode transcript.

In this episode, we chat with Dean Pleban, CEO of DagsHub, about machine learning operations. We explore the differences between DevOps and MLOps, focusing on data management and experiment tracking. Dean shares insights on versioning various components in ML projects and discusses the importance of user experience in MLOps tools. We also touch on DagsHub's integration of AI in their product and Dean's vision for the future of AI and machine learning in industry.LinksDagsHubThe MLOps PodcastDean on LIChapters00:00 Introduction and Background03:03 Challenges of Managing Machine Learning Projects10:00 The Concept of Experiments in Machine Learning12:51 Data Curation and Validation for High-Quality Data27:07 Connecting the Components of Machine Learning Projects with DAGS Hub29:12 The Importance of Data and Clear Interfaces43:29 Incorporating Machine Learning into DAGsHub51:27 The Future of ML and AI

Amey Chaugule and Matt Green, co-founders of Denormalized, share their extensive engineering backgrounds from top tech firms. They discuss the creation of an embedded stream processing engine designed to simplify real-time data workloads. The duo tackles challenges in existing systems like Spark and Kafka, emphasizing developer experience and state management. They also compare DuckDB and SQLite in the context of streaming data, highlighting the future of user-friendly data tools and the importance of fault tolerance in modern applications.

Nikhil Simha and Varant Zanoyan, both seasoned engineers with rich backgrounds in data systems and ML infrastructure, discuss the intricate balance of structured and unstructured data in AI. They delve into the challenges of merging real-time data with machine learning, emphasizing the importance of user-friendly APIs. The conversation touches on failures in data transformation and effective strategies for startups to engage users. They also introduce Cronon, an open-source platform, highlighting its potential to improve data orchestration and user experience.