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- 🇨🇦CA · Technology#1865K to 30K
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2.5K to 15K🎙 ~2x weekly·26 episodes·Last published 5d ago - Monthly Reach
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5K to 30K🇨🇦100% - Active Followers
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From 12 epsHosts
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Recent episodes
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
From Session Replays to Autonomous Improvement: Shipping the First AI Product Engineer with Milana
Apr 24, 2026
1h 00m 01s
From Exabyte Storage to Reactive Backends: Jamie Turner on Building Convex After Dropbox
Apr 9, 2026
59m 13s
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| Date | Episode | Topics | Guests | Brands | Places | Keywords | Sponsor | Length | |
|---|---|---|---|---|---|---|---|---|---|
| 6/19/26 | ![]() Rebuilding the Robot Stack: Why Robotics Needs a New Real-Time OS with Guillaume Binet (Copper Robotics) | In this episode, Nitay and Kostas sit down with Guillaume Binet, founder of Copper Robotics, to dig into one of the most overlooked problems in modern robotics: the software stack that runs the robots.Guillaume traces his journey from tinkering with old computers in the 80s and 90s, to telecommunications, to the dot-com era, and finally into more than a decade in robotics, including roles at Trilio, Google, Motional, Argo AI, and as CTO at Skyways building long-range carbon-fiber logistics drones. Along the way, he discovered a recurring gap: while cloud software has matured into a rich ecosystem of tools and frameworks, the software powering real-world robots is still surprisingly broken.That insight led him to start Copper Robotics, where he is building a new operating system and runtime designed specifically for real-time robots.We talk about:Why robots are fundamentally different from cloud services and laptops, and what "real-time" actually means when sensors, perception, and actuation all have to meet a fixed time budgetThe mismatch between microservice-style architectures (and ROS) and the constraints of an autonomous system that has to react in millisecondsHow Copper builds a statically described, deterministic operating system around your robot, with a scheduler tailored to the exact shape of the systemRunning the same Rust codebase across heterogeneous compute, from Linux hosts to bare-metal MCUs, with a single self-contained executableWhy TCP/IP is usually the wrong answer inside a robot, and how to think about latency, bandwidth, and dropped data in a real-time contextThe trade-off between compile-time and runtime flexibility, and where Copper draws the line versus ROSSafety certification for autonomous systems (ISO 26262, aerospace, medical) and why 100% deterministic replay is a game-changer for proving that what you tested in simulation is what runs on the robotThe community forming around Copper: students, new robotics startups, and teams who have already "hit the wall" with existing toolingA fun detour into restoring 80s and 90s computers, floppy disks, and the lost art of magnetic mediaIf you have ever wondered why we see so many incredible robot demo videos but so few robots actually deployed in the real world, this conversation is for you.Learn more about Copper Robotics and join the community via their open source project and Discord. | 55m 53s | ||||||
| 6/5/26 | ![]() Physical AI and the Future of Robotics with Sergey Arkhangelskiy of Positronic✨ | physical AIrobotics+4 | Sergey Arkhangelskiy | PositronicGoogle Search+5 | — | physical AIrobotics+8 | — | 51m 45s | |
| 5/21/26 | ![]() Building the Open Lakehouse for the AI Era with Shubham Baldava from DataZip / OLake✨ | open lakehousedata engineering+5 | Shubham Baldava | DataZipOLake+6 | — | lakehousedata engineering+7 | — | 58m 14s | |
| 4/24/26 | ![]() From Session Replays to Autonomous Improvement: Shipping the First AI Product Engineer with Milana✨ | AI Product EngineeringSession Replays+4 | Rohan KatyalRaghav Sethi | MilanaMeta+2 | — | AI Product Engineersession replays+5 | — | 1h 00m 01s | |
| 4/9/26 | ![]() From Exabyte Storage to Reactive Backends: Jamie Turner on Building Convex After Dropbox✨ | distributed systemsscaling storage solutions+3 | Jamie Turner | DropboxConvex | — | distributed systemsscaling+4 | — | 59m 13s | |
| 3/17/26 | ![]() From Art to Science: Wild Moose and the Future of AI-Powered Debugging✨ | AI-powered debuggingproduction debugging+4 | Yasmin DunskyRoei+1 | Wild MooseChatGPT | California | debuggingAI+5 | — | 52m 40s | |
| 1/29/26 | ![]() From Notebooks to Production: Xorq’s lockfile Approach for Reproducible, Portable ML Pipelines✨ | Machine LearningData Pipelines+4 | Hussain | xorqSnowflake+4 | — | ML pipelinesreproducibility+5 | — | 57m 26s | |
| 12/1/25 | ![]() From pandas to Arrow: Wes McKinney on the Future of Data Infrastructure✨ | data infrastructureopen source sustainability+4 | Wes McKinney | Apache Arrowpandas+6 | — | pandasApache Arrow+6 | — | 1h 22m 05s | |
| 9/8/25 | ![]() Navigating the Future of AI and Data Infrastructure with Bauplan✨ | AIdata infrastructure+4 | JacopoCiro | BauplanAI+6 | — | AIdata infrastructure+7 | — | 58m 45s | |
| 8/18/25 | ![]() Email as a Knowledge Graph: Micro CEO Brett on Rebuilding CRM at the Inbox✨ | email as a knowledge graphCRM+5 | Brett | MicroGoogle+4 | — | emailknowledge graph+7 | — | 1h 01m 28s | |
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| 7/28/25 | ![]() Community, Compilers & the Rust Story with Steve Klabnik✨ | Rust programming languagedeveloper tooling+4 | Steve Klabnik | Ruby on RailsRust+4 | — | RustRuby on Rails+7 | — | 59m 02s | |
| 6/5/25 | ![]() How Cloudflare Reinvents Serverless at Global Scale with Josh Howard✨ | serverless computingCloudflare+4 | Josh Howard | Durable ObjectsWorkers+1 | — | Cloudflareserverless+5 | — | 52m 19s | |
| 5/8/25 | ![]() Business Physics: How Brand, Pricing, and Product Design Define Success with Erik Swan✨ | 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 | ||||||
| 11/6/24 | ![]() From Functions to Full Applications: How Serverless Evolved Beyond AWS Lambda with Nitzan Shapira | 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 | 58m 18s | ||||||
| 10/22/24 | ![]() From GPU Compilers to architecting Kubernetes: A Conversation with Brian Grant | From GPU computing pioneer to Kubernetes architect, Brian Grant takes us on a fascinating journey through his career at the forefront of systems engineering. In this episode, we explore his early work on GPU compilers in the pre-CUDA era, where he tackled unique challenges in high-performance computing when graphics cards weren't yet designed for general computation. Brian then shares insights from his time at Google, where he helped develop Borg and later became the original lead architect of Kubernetes. He explains key architectural decisions that shaped Kubernetes, from its extensible resource model to its approach to service discovery, and why they chose to create a rich set of abstractions rather than a minimal interface. The conversation concludes with Brian's thoughts on standardization challenges in cloud infrastructure and his vision for moving beyond infrastructure as code, offering valuable perspective on both the history and future of distributed systems.Links:Brian Grant LIChapters00:00 Introduction and Background03:11 Early Work in High-Performance Computing06:21 Challenges of Building Compilers for GPUs13:14 Influential Innovations in Compilers31:46 The Future of Compilers33:11 The Rise of Niche Programming Languages34:01 The Evolution of Google's Borg and Kubernetes39:06 Challenges of Managing Applications in a Dynamically Scheduled Environment48:12 The Need for Standardization in Application Interfaces and Management Systems01:00:55 Driving Network Effects and Creating Cohesive EcosystemsClick here to view the episode transcript. | 1h 01m 45s | ||||||
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Chart Positions
1 placement across 1 market.
Chart Positions
1 placement across 1 market.
