Tech on the Rocks

Kostas, Nitay

Join Kostas and Nitay as they speak with amazingly smart people who are building the next generation of technology, from hardware to cloud compute. Tech on the Rocks is for people who are curious about the foundations of the tech industry. Recorded primarily from our offices and homes, but one day we hope to record in a bar somewhere. Cheers!

  1. 7h ago

    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.

    56 min
  2. Jun 5

    Physical AI and the Future of Robotics with Sergey Arkhangelskiy of Positronic

    In this episode, Nitay and Kostas sit down with Sergey Arkhangelskiy, founder of Positronic, to dig into the state of physical AI and what it will really take to bring general-purpose robotics into the real world. Sergey shares his journey from a decade at Google Search, where he worked on ranking and helped build the "Tetris" layer that unified web, image, and map results, to co-founding Wanna (a computer vision AR company acquired by Farfetch), and most recently launching Positronik to focus on robotics and physical AI. The conversation explores why robotics is approaching but has not yet hit its "GPT-3 moment," the data and hardware bottlenecks that make physical AI fundamentally harder than LLMs, and why measurement and evaluation matter so much. Sergey walks through Positronik's newly released Physical AI Leaderboard (fail.ai), which benchmarks open-source vision-language-action (VLA) models on real hardware using production-grade metrics like throughput (units per hour) and mean time between failures, rather than simple success rates. They also discuss why commercial and industrial applications (manufacturing, logistics, pick-and-place) are likely to lead before household robots, the economics of automation and the "cost of intelligence," the role of human-in-the-loop systems, latency and cloud-vs-edge tradeoffs for running VLA models, and the growing importance of open source in both robotic software (ROS) and hardware (OpenArm). Topics covered: Sergey's path from Google Search ranking to roboticsWhy physical AI is harder than LLMs: data, hardware, and the real worldThe Physical AI Leaderboard and how it evaluates VLA models on real robotsThroughput and mean time between failures as production metricsWhy commercial use cases will lead household roboticsThe economics of automation and the "cost of intelligence"Human-in-the-loop and the realistic path to full automationCloud vs. local inference, latency, and bandwidth constraints on the factory floorThe role of open source in robotics hardware and softwareWhat the ecosystem needs next to accelerate adoption

    52 min
  3. May 21

    Building the Open Lakehouse for the AI Era with Shubham Baldava from DataZip / OLake

    In this episode of Tech on the Rocks, Nitay and Kostas sit down with Shubham Baldava, co-founder of DataZip and creator of OLake, to trace the evolution of the modern open lakehouse — from the early days of Apache Hudi to today's Iceberg-centric world. Shubham shares stories from a decade of data engineering at scale, including building near real-time pipelines at Japanese fintech giant PayPay, scaling a TikTok-style social platform at ShareChat from 10M to 160M monthly active users, and the cost and complexity pressures that pushed teams to adopt lakehouse architectures in the first place. From there, the conversation digs into the table format wars: why Hudi was the early pick for truly open, vendor-neutral lakehouses, how Iceberg has caught up and pulled ahead on integrations, where Delta fits in, and what the Tabular acquisition means for the community. Shubham explains why he believes all the major formats are converging — single-file commits, deletion vectors, variant and geospatial types, Z-indexes — and why integration breadth, not features alone, is now the deciding factor. The discussion then turns practical: what the four real pillars of a lakehouse are (ingestion, optimization, query, governance), why Debezium is so hard to replace, what it takes to hit 10-minute CDC latency for fintech reconciliation, and how OLake is rethinking ingestion with Arrow-based writes, exactly-once semantics built on Iceberg metadata, multi-phase compaction, and watermark-based parallel backfills. Finally, Shubham looks ahead to a future where Iceberg becomes the single substrate for structured, semi-structured, and unstructured data — powering multi-engine analytics and AI workloads on top of formats like Lance and Vortex, now that Iceberg has decoupled from Parquet. Topics covered:• Lessons from PayPay, ShareChat, and indie app entrepreneurship• Hudi vs Iceberg vs Delta — history, trade-offs, and convergence• Why fintech reconciliation needs sub-10-minute CDC• The real cost of running BigQuery, Trino, and Spark side by side• Debezium's staying power and why Go (not Rust) for next-gen CDC• How OLake uses Arrow, equality and positional deletes, and multi-step compaction• The decoupling of Iceberg from Parquet and what Lance/Vortex unlock for AI• Where to build in-house vs adopt managed lakehouse tooling

    58 min
  4. Apr 24

    From Session Replays to Autonomous Improvement: Shipping the First AI Product Engineer with Milana

    In this episode, we sit down with Rohan Katyal and Raghav Sethi, co-founders of Milana, to discuss the shift from passive analytics to the world’s first AI Product Engineer. Rather than just providing another dashboard to monitor, Rohan and Raghav are building an agentic partner that you add to your product to bridge the gap between discovery and deployment. Drawing on their experience at Meta, Yelp, and Airtable, they explore how Milana enables autonomous improvement - turning deep user intelligence into shippable code and structural refinements that act as a tireless extension of your engineering team. The conversation dives into why session replays — a mature but historically underused technology — are now a powerful data asset thanks to vision LLMs. Raghav explains how session replays are really just high-granularity logging of DOM changes, not screen recordings, and why feeding them through AI unlocks insights that traditional event-based analytics simply can’t capture. The team breaks down how they use just-in-time structuring to extract meaning from dense, unstructured session data without requiring upfront instrumentation. Rohan shares hard-won lessons from building Yelp’s experimentation platform — including how teams that simply ran more experiments consistently outperformed those with better data resources. They discuss the tension between A/B testing rigor and iteration speed, why most experiments never ship, and how lowering the cost of generating and testing hypotheses changes everything about product development velocity. We also get into the technical details of semantic clustering across millions of sessions, why video is actually a more compact representation than raw DOM for LLM reasoning, and how Milana analyzes sessions from multiple perspectives — user researcher, PM, founder — to surface real pain points. Plus, a bold prediction: analytics dashboards are dying, and the future belongs to agentic systems that don’t just deliver insights but actually own and drive your OKRs. Topics covered: Why session replays are the ultimate untapped data asset for product teamsHow vision LLMs unlocked AI-powered analysis of user sessionsJust-in-time data structuring: querying unstructured sessions without upfront instrumentationLessons from building experimentation platforms at Yelp and AirtableWhy running more experiments beats having better dataSemantic clustering: separating signal from noise across millions of sessionsVideo vs. DOM vs. events — the best data representation for LLM reasoningAnalyzing agent behavior through session replaysThe death of dashboards and the rise of agentic growth systemsUser research horror stories and the surprising things users doChapters 00:00 Introduction to Rohan and Raghav's Journey04:47 The Importance of User Research08:03 Making Solutioning a Science11:09 Understanding Session Replays and Experimentation14:50 Defining Sessions and Experimentation Platforms18:54 The Need for Consistent Metrics22:11 The Role of Events vs. Session Replays29:46 Leveraging LLMs for Enhanced Insights35:04 Determinism vs. Non-Determinism in Data Analysis37:57 Understanding User vs. Agent Behavior39:47 The Art of Structuring Data45:25 Semantic Clustering and Its Importance47:09 Building Infrastructure for Complex Data51:24 The Future of User Simulation and Experimentation

    1 hr
  5. Mar 17

    From Art to Science: Wild Moose and the Future of AI-Powered Debugging

    In this episode, we sit down with the full founding team of Wild Moose — CEO Yasmin Dunsky, CTO Roei, and VP R&D Tom Tytunovich — to explore how they’re transforming production debugging from an art into a science using AI. The trio shares their unconventional founding story — from meeting across three different cities to living together for three months in a California Airbnb to stress-test both their idea and their relationship. They discuss how they identified production debugging as a massive unsolved problem before ChatGPT even launched, recognizing that while code generation is fundamentally a text problem, debugging is a search problem that demands a completely different approach. We dive deep into Wild Moose’s “microagents” architecture — fast, highly optimized AI agents that replicate the muscle memory of senior engineers to automatically investigate production incidents in under a minute. The team explains why accuracy trumps everything in their space (wrong answers are worse than no answers when you’re debugging at 3 AM), how they navigate the speed-cost-quality triangle, and why they built a test-driven approach to validate agents against past incidents. We also get into the multi-agent vs. single-agent debate, handling multimodal observability data (logs, metrics, traces, dashboards, code), and how the rapidly evolving LLM landscape creates both opportunities and challenges for production AI systems. Plus, the team shares their favorite outage war stories — including a “WatchCat” hack and a three-month hunt for a single rogue bit. Topics covered: The Wild Moose origin story and the California Airbnb experimentWhy production debugging is a search problem, not a text generation problemMicroagents: fast, specialized AI agents for incident investigationBuilding institutional knowledge into AI — capturing engineering muscle memoryThe speed-cost-quality triangle in real-time AI systemsMulti-agent vs. single-agent architectures: when to use whatHandling multimodal observability data with LLMsThe future of AI SRE and self-healing production environmentsFavorite outage war stories from the trenchesChapters 00:00 Introduction to the Wild Moose Team04:12 The Spark Behind Wild Moose08:41 Understanding the Debugging Landscape12:45 The Role of AI in Debugging17:31 Building Investigative Agents21:55 Optimizing Workflows and Feedback Loops29:12 Navigating Complexity in Software Systems33:42 Adapting to Rapid Changes in AI Technology40:02 Microagents: The Future of AI Architecture44:46 Outage Stories: Lessons from the Trenches50:49 Vision for the Future of AI in Production

    53 min
  6. 12/01/2025

    From pandas to Arrow: Wes McKinney on the Future of Data Infrastructure

    Summary In this episode of Tech on the Rocks, Kostas and Nitay sit down with Wes McKinney the creator of pandas and co-creator of Apache Arrow and Ibis, and long-time leader in the Python data ecosystem. Wes walks us through his journey from building pandas in 2008 to rethinking how we represent and move columnar data with Arrow, and why Arrow is fundamentally different from file formats like Parquet and ORC. We get into the future of data file formats, DataFusion and the new generation of query engines, the rise of open data lakes (Iceberg, Delta, Hudi), and why “big metadata” is becoming just as important as big data. Wes also shares candid thoughts on open source sustainability, how companies and infrastructure projects really survive, and how AI coding agents like Claude Code are changing the day-to-day work of software engineers, especially for complex systems work. If you care about the foundations of modern data infrastructure, or you’ve ever called import pandas as pd, this is an episode you won’t want to miss. Chapters 00:00 Intro — Wes McKinney & his journey in the Python data ecosystem 02:15 How pandas evolved & why UX first mattered for data science 06:14 Open source sustainability, funding & the Posit model 07:31 From pandas to Datapad, Cloudera & the origins of Apache Arrow and Ibis 13:38 What is Apache Arrow? In‑memory columnar data, batches & schemas 22:23 Inside Arrow IPC — zero‑copy, Flatbuffers & cross‑language interop 24:34 Arrow vs Parquet — columnar memory format vs columnar storage format 29:28 The next generation of columnar file formats & GPU‑friendly encodings 36:03 Big metadata, table formats & the rise of Iceberg/Delta/Hudi 43:05 Rethinking data systems: from big data to DuckDB, Rust & “no JVM” stacks 54:11 DataFusion as a modular Rust query engine for modern startups 57:58 Open source, the composable data stack & why infra is “AI‑resistant” 01:00:07 Vibe‑coding with AI agents — using Claude Code in real projects 01:09:49 AI, open source maintainers & the risks of AI‑generated contributions 01:18:57 Bridging LLMs and data: ADBC, data context & the future of infra + AI

    1h 22m

Ratings & Reviews

5
out of 5
5 Ratings

About

Join Kostas and Nitay as they speak with amazingly smart people who are building the next generation of technology, from hardware to cloud compute. Tech on the Rocks is for people who are curious about the foundations of the tech industry. Recorded primarily from our offices and homes, but one day we hope to record in a bar somewhere. Cheers!

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