Tech on the Rocks

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.

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

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

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.

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.

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

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.