Insights from recent episode analysis
Audience Interest
Podcast Focus
Publishing Consistency
Platform Reach
Insights are generated by CastFox AI using publicly available data, episode content, and proprietary models.
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Total monthly reach
Estimated from 1 chart position in 1 market.
By chart position
- 🇹🇼TW · Technology#183500 to 3K
- Per-Episode Audience
Est. listeners per new episode within ~30 days
250 to 1.5K🎙 ~2x weekly·26 episodes·Last published 4w ago - Monthly Reach
Unique listeners across all episodes (30 days)
500 to 3K🇹🇼100% - Active Followers
Loyal subscribers who consistently listen
150 to 900
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Platform Distribution
Reach across major podcast platforms, updated hourly
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—
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* Data sourced directly from platform APIs and aggregated hourly across all major podcast directories.
On the show
From 14 epsHosts
Recent guests
Recent episodes
Feeding the Agents: Fast Structured Data Retrieval with Arrow — Ian Cook, Co-founder & CEO of Columnar
Aug 4, 2026
1h 07m 29s
Falling Into Databases: The DuckDB Story with Hannes Mühleisen
Jul 7, 2026
54m 04s
Rebuilding the Robot Stack: Why Robotics Needs a New Real-Time OS with Guillaume Binet (Copper Robotics)
Jun 19, 2026
55m 53s
Physical AI and the Future of Robotics with Sergey Arkhangelskiy of Positronic
Jun 5, 2026
51m 45s
Building the Open Lakehouse for the AI Era with Shubham Baldava from DataZip / OLake
May 21, 2026
58m 14s
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| Date | Episode | Topics | Guests | Brands | Places | Keywords | Sponsor | Length | |
|---|---|---|---|---|---|---|---|---|---|
| 8/4/26 | Feeding the Agents: Fast Structured Data Retrieval with Arrow — Ian Cook, Co-founder & CEO of Columnar | In this episode, we talk with Ian Cook, co-founder and CEO of Columnar and a member of the Apache Arrow Project Management Committee, about ADBC (Arrow Database Connectivity) and why the way applications connect to databases is overdue for a rethink.Ian traces the history from the row-oriented database APIs of the 1990s, ODBC and JDBC, to today's world where nearly every analytic database and destination tool is columnar under the hood. He explains why keeping data in a columnar format end to end, using Apache Arrow, can deliver 10x to 100x speedups by turning a CPU-bound conversion problem back into a fast, network-friendly one, and why ADBC's flexible driver model works even against row-oriented systems like Postgres.We then dig into the AI angle: how fast structured data retrieval matters more than ever when agents reason in milliseconds and bottleneck on tool calls, why today's models still lean on tool calls to understand tabular data, and what tabular foundation models might change. Along the way we cover Parquet, DuckDB, Snowflake, string view, the DeWitt clause and benchmarking culture, and Arrow's philosophy of growing an ecosystem by building consensus rather than enemies.Ian also shares how to try ADBC yourself with the dbc CLI, a UV-inspired installer that makes it easy to install drivers for 20+ databases (columnar.tech/dbc).Chapters00:00 Introduction to Ian Cook and Columner01:53 Understanding ADBC and Its Relationship with Arrow10:10 The Need for Columnar Paradigms in Database Connectivity14:37 Exploring Use Cases for ADBC in Modern Applications18:41 Performance Impacts of ADBC in Various Systems27:38 Integrating ADBC with AI and LLMs37:26 The Future of ADBC and Its Role in Data Infrastructure | 1h 07m 29s | ||||||
| 7/7/26 | databasesanalytics+3 | Hannes Mühleisen | DuckDBDuck Labs+1 | Amsterdam | DuckDBHannes Mühleisen+7 | — | 54m 04s | ||
| 6/19/26 | roboticsreal-time operating systems+4 | Guillaume Binet | Copper RoboticsTrilio+4 | — | roboticsreal-time OS+5 | — | 55m 53s | ||
| 6/5/26 | physical AIrobotics+4 | Sergey Arkhangelskiy | PositronicGoogle Search+5 | — | physical AIrobotics+8 | — | 51m 45s | ||
| 5/21/26 | open lakehousedata engineering+5 | Shubham Baldava | DataZipOLake+6 | — | lakehousedata engineering+7 | — | 58m 14s | ||
| 4/24/26 | AI Product EngineeringSession Replays+4 | Rohan KatyalRaghav Sethi | MilanaMeta+2 | — | AI Product Engineersession replays+5 | — | 1h 00m 01s | ||
| 4/9/26 | distributed systemsscaling storage solutions+3 | Jamie Turner | DropboxConvex | — | distributed systemsscaling+4 | — | 59m 13s | ||
| 3/17/26 | AI-powered debuggingproduction debugging+4 | Yasmin DunskyRoei+1 | Wild MooseChatGPT | California | debuggingAI+5 | — | 52m 40s | ||
| 1/29/26 | Machine LearningData Pipelines+4 | Hussain | xorqSnowflake+4 | — | ML pipelinesreproducibility+5 | — | 57m 26s | ||
| 12/1/25 | data infrastructureopen source sustainability+4 | Wes McKinney | Apache Arrowpandas+6 | — | pandasApache Arrow+6 | — | 1h 22m 05s | ||
Want analysis for the episodes below?Free for Pro Submit a request, we'll have your selected episodes analyzed within an hour. Free, at no cost to you, for Pro users. | |||||||||
| 9/8/25 | AIdata infrastructure+4 | JacopoCiro | BauplanAI+6 | — | AIdata infrastructure+7 | — | 58m 45s | ||
| 8/18/25 | email as a knowledge graphCRM+5 | Brett | MicroGoogle+4 | — | emailknowledge graph+7 | — | 1h 01m 28s | ||
| 7/28/25 | Rust programming languagedeveloper tooling+4 | Steve Klabnik | Ruby on RailsRust+4 | — | RustRuby on Rails+7 | — | 59m 02s | ||
| 6/5/25 | serverless computingCloudflare+4 | Josh Howard | Durable ObjectsWorkers+1 | — | Cloudflareserverless+5 | — | 52m 19s | ||
| 5/8/25 | business strategybranding+4 | Erik Swan | BestimerSplunk | — | business physicsgo-to-market dynamics+4 | — | 1h 01m 31s | ||
| 4/24/25 | Incremental Materialization: Reinventing Database Views with Gilad Kleinman of Epsio | SummaryIn this episode, Gilad Kleinman, co-founder of Epsio, shares his unique journey from PHP development to low-level kernel programming and how that evolution led him to build an innovative incremental views engine. Gilad explains that Epsio tackles a common challenge in databases: making heavy, complex queries faster and more efficient through incremental materialization. He describes how traditional materialized views fall short—often requiring full refreshes—and how Epsio seamlessly integrates with existing databases by consuming replication streams (CDC) and writing back to result tables without disrupting the core transactional system. The conversation dives into the technical trade-offs and optimizations involved, such as handling stateful versus stateless operators (like group-by and window functions), using Rust for performance, and the challenges of ensuring consistency. Gilad also contrasts Epsio’s approach with streaming systems like Flink, emphasizing that by maintaining tight integration with the native database, Epsio can offer immediate, up-to-date query results while minimizing disruption. Finally, he outlines his vision for the future of incremental stream processing and materialized views as a means to reduce compute costs and enhance overall system performance.Chapters00:00 From PHP to Kernel Development: A Journey07:30 Introducing Epsio: The Incremental Views Engine10:56 The Importance of Materialized Views15:07 Understanding Incremental Materialization19:21 Optimizing Query Performance with Epsio24:53 Integrating Epsio with Existing Databases27:02 The Shift from Theory to Practice in Data Processing29:42 Seamless Integration with Existing Databases32:02 Understanding Epsio Incremental Processing Mechanism34:46 Challenges and Limitations of Incremental Views36:49 The Complexity of Implementing Operators39:56 Trade-offs in Incremental Computation41:21 User Interaction with Epsio43:01 Comparing EPSIO with Streaming Systems45:09 Architectural Guarantees of Epsio50:33 The Future of Incremental Data Processing | 52m 19s | ||||||
| 3/21/25 | From Data Mesh to Lake House: Revolutionizing Metadata with Lakekeeper | SummaryIn this episode, Viktor Kessler shares his journey and insights from his extensive experience in data management—from building risk management systems and data warehouses to working as a solutions architect at MongoDB and Dremio, and now co-founding a startup.Initially exploring data mesh concepts, Viktor explains how real-world challenges—such as the disconnect between technical data models and business needs, inconsistent definitions across departments, and the difficulty in managing actionable metadata—led him and his co-founder to pivot toward building a lake house solution. His startup is developing Lakekeeper, an open source REST catalog for Apache Iceberg, which aims to bridge the gap between decentralized data production and centralized metadata management. The conversation also delves into the evolution of data catalogs, the necessity for self-service analytics, and how creating consumption-ready data products can transform data functions from cost centers into profit centers. Finally, Viktor outlines ways for interested listeners to get involved with the Lakekeeper community through GitHub, upcoming meetups, and a dedicated Discord channel.Chapters00:00 Introduction to Viktor Kessler and His Journey04:57 Transitioning from Data Mesh to Lake House09:15 Understanding Data Mesh: Pain Points and Solutions13:47 The Role of Metadata in Data Management18:16 The Evolution of Catalogs and Metadata Management28:14 Stabilizing the Consumption Pipeline31:18 Centralizing Metadata for Decentralized Organizations37:09 Bridging the Gap: Technical and Business Perspectives43:17 Rethinking Data Products and Consumption50:45 Finding Balance: Control and Flexibility in Data Management | 57m 25s | ||||||
| 3/6/25 | Reinventing Stream Processing: From LinkedIn to Responsive with Apurva Mehta | SummaryIn this episode, Apurva Mehta, co-founder and CEO of Responsive, recounts his extensive journey in stream processing—from his early work at LinkedIn and Confluent to his current venture at Responsive. He explains how stream processing evolved from simple event ingestion and graph indexing to powering complex, stateful applications such as search indexing, inventory management, and trade settlement. Apurva clarifies the often-misunderstood concept of “real time,” arguing that low latency (often in the one- to two-second range) is more accurate for many applications than the instantaneous response many assume. He delves into the challenges of state management, discussing the limitations of embedded state stores like RocksDB and traditional databases (e.g., Postgres) when faced with high update rates and complex transactional requirements. The conversation also covers the trade-offs between SQL-based streaming interfaces and more flexible APIs, and how Responsive is innovating by decoupling state from compute—leveraging remote state solutions built on object stores (like S3) with specialized systems such as SlateDB—to improve elasticity, cost efficiency, and operational simplicity in mission-critical applications.Chapters00:00 Introduction to Apurva Mehta and Streaming Background08:50 Defining Real-Time in Streaming Contexts14:18 Challenges of Stateful Stream Processing19:50 Comparing Streaming Processing with Traditional Databases26:38 Product Perspectives on Streaming vs Analytical Systems31:10 Operational Rigor and Business Opportunities38:31 Developers' Needs: Beyond SQL45:53 Simplifying Infrastructure: The Cost of Complexity51:03 The Future of Streaming ApplicationsClick here to view the episode transcript. | 58m 13s | ||||||
| 2/20/25 | Semantic Layers: The Missing Link Between AI and Data with David Jayatillake from Cube | In this episode, we chat with David Jayatillake, VP of AI at Cube, about semantic layers and their crucial role in making AI work reliably with data. We explore how semantic layers act as a bridge between raw data and business meaning, and why they're more practical than pure knowledge graphs. David shares insights from his experience at Delphi Labs, where they achieved 100% accuracy in natural language data queries by combining semantic layers with AI, compared to just 16% accuracy with direct text-to-SQL approaches. We discuss the challenges of building and maintaining semantic layers, the importance of proper naming and documentation, and how AI can help automate their creation. Finally, we explore the future of semantic layers in the context of AI agents and enterprise data systems, and learn about Cube's upcoming AI-powered features for 2025.00:00 Introduction to AI and Semantic Layers05:09 The Evolution of Semantic Layers Before and After AI09:48 Challenges in Implementing Semantic Layers14:11 The Role of Semantic Layers in Data Access18:59 The Future of Semantic Layers with AI23:25 Comparing Text to SQL and Semantic Layer Approaches27:40 Limitations and Constraints of Semantic Layers30:08 Understanding LLMs and Semantic Errors35:03 The Importance of Naming in Semantic Layers37:07 Debugging Semantic Issues in LLMs38:07 The Future of LLMs as Agents41:53 Discovering Services for LLM Agents50:34 What's Next for Cube and AI Integration | 59m 03s | ||||||
| 2/4/25 | From black holes to AI in mathematics: AI Innovation in Mathematics and Health with Yaron Hadad | In this episode, we chat with Yaron Hadad, a fascinating individual who transitioned from theoretical physics to entrepreneurship. We explore his groundbreaking work on black holes and gravitational waves, and learn about the Ramanujan Machine - an algorithmic system he helped develop that discovers new mathematical formulas and democratizes mathematical research. We'll hear about the scientific community's mixed reactions to this innovative approach. The conversation then shifts to his work with Neutrino, a company he founded that uses AI and continuous monitoring devices to understand how food affects individual health. We delve into the complexities of nutrition science, the challenges of processing multiple data streams, and the future of personalized health monitoring. Throughout the episode, Yaron shares insights on bridging theoretical research with practical applications, and the role of AI in advancing both pure mathematics and healthcare.00:00 Yaron Hadad's Journey: From Physics to AI in Healthcare04:50 The Complexity of Einstein's Equations and Their Solutions10:12 AI in Mathematics: The Ramanujan Machine and Conjectures15:41 Navigating Criticism: The Scientific Community's Response to Innovation29:24 The Impact of Algorithms in Mathematics35:30 The Planck Machine: A New Approach41:15 Neutrino: A Personal Journey in Nutrition50:11 Connecting Food Complexity to Health Metrics | 59m 24s | ||||||
| 1/16/25 | Building a Native Search Engine in PostgreSQL: ParadeDB's Journey to Replace Elasticsearch with Philippe Noël | In this episode, we chat with Philippe Noël, founder of ParadeDB, about building an Elasticsearch alternative natively on PostgreSQL. We explore the challenges and benefits of extending PostgreSQL versus building a separate system, diving into topics like full-text search, faceted analytics, and why organizations need these capabilities. We discuss the emerging bring-your-own-cloud deployment model, the state of the PostgreSQL extension ecosystem, and what makes a truly production-ready database extension. Philippe shares insights on the future of search technology and how recent AI developments are actually increasing the demand for traditional search capabilities. The conversation also covers the misconceptions around PostgreSQL's scalability and the trade-offs between multi-tenant and single-tenant architectures in modern data infrastructure.Chapters00:00 Introduction to ParadeDB and Its Mission06:35 User-Facing Search and Analytics11:45 The Role of Postgres in Modern Data Solutions17:30 Future of Multimodal Databases31:04 The Rise of Fintech and Data Integrity36:36 Deployment Models: BYOC and Control Plane43:41 The Evolution of Cloud Infrastructure and Serverless Databases49:38 The Future of Search and Community EngagementClick here to view the episode transcript. | 1h 00m 21s | ||||||
| 1/3/25 | Optimizing SQL with LLMs: Building Verified AI Systems at Espresso AI with Ben Lerner | 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. | 1h 06m 04s | ||||||
| 12/19/24 | Security as Code: Building Developer-First Security Tools with David Mytton | In this episode, we chat with David Mytton, founder and CEO of Arcjet and creator of console.dev. We explore his journey from building a cloud monitoring startup to founding a security-as-code company. David shares fascinating insights about bot detection, the challenges of securing modern applications, and why traditional security approaches often fail to meet developers' needs. We discuss the innovative use of WebAssembly for high-performance security checks, the importance of developer experience in security tools, and the delicate balance between security and latency. The conversation also covers his work on environmental technology and cloud computing sustainability, as well as his experience reviewing developer tools for console.dev, where he emphasizes the critical role of documentation in distinguishing great developer tools from mediocre ones.Chapters00:00 Introduction to David Mytton and Arcjet07:09 The Evolution of Observability12:37 The Future of Observability Tools18:19 Innovations in Data Storage for Observability23:57 Challenges in AI Implementation31:33 The Dichotomy of AI and Human Involvement36:17 Detecting Bots: Techniques and Challenges42:46 AI's Role in Enhancing Security47:52 Latency and Decision-Making in Security52:40 Managing Software Lifecycle and Observability58:58 The Role of Documentation in Developer ToolsClick here to view the episode transcript. | 1h 03m 51s | ||||||
| 12/4/24 | Dev Environments in the AI Era: Standardizing Development Infrastructure with Daytona's Ivan | In this episode, we chat with Ivan, co-founder and CEO of Daytona, about the evolution of developer environments and tooling. We explore his journey from founding CodeAnywhere in 2009, one of the first browser-based IDEs, to creating the popular Shift developer conference, and now building Daytona's dev environment automation platform. We discuss the changing landscape of development environments, from local-only setups to today's complex hybrid configurations, and why managing these environments has become increasingly challenging. Ivan shares insights about open source business models, the distinction between users and buyers in dev tools, and what the future holds for AI-assisted development. We also learn about Daytona's unique approach to solving dev environment complexity through standardization and automation, and get Ivan's perspective on the future of IDE companies in an AI-driven world.Chapters00:00 Introduction to Ivan and Daytona07:22 Understanding Development Environments13:59 The User vs. Buyer Dilemma22:20 Open Source Strategy and Community Building29:22 How Daytona Works and Its Value Proposition37:44 Emerging Trends in Collaborative Coding44:38 Latency Challenges in AI-Assisted Development50:41 The Future of Developer Tooling Companies01:02:29 Lessons from Organizing Conferences | 1h 09m 23s | ||||||
| 11/21/24 | Evolving Data Infrastructure for the AI Era: AWS, Meta, and Beyond with Roy Ben-Alta | 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 | 1h 03m 28s | ||||||
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Chart history for Tech on the Rocks
Peaked at #183 in TW, currently #183 in TW.
| Market | Genre | Peak | Current | Trend |
|---|---|---|---|---|
| TW | — | #183 | #183 | — |
Chart Positions
1 placement across 1 market.
Chart Positions
1 placement across 1 market.