Tech on the Rocks

Kostas, Nitay
  • 5.0 (5)
  • TECHNOLOGY
  • UPDATED BIWEEKLY

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. 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

  2. APR 9

    From Exabyte Storage to Reactive Backends: Jamie Turner on Building Convex After Dropbox

    Jamie, a seasoned startup founder and former Dropbox engineer, shares insights on building distributed systems, scaling storage solutions, and the impact of AI on infrastructure and application development. Discover practical lessons from scaling Dropbox, the evolution of data storage, and how Convex is shaping the future of app development.

    59 min

  3. 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

  4. JAN 29

    From Notebooks to Production: Xorq’s lockfile Approach for Reproducible, Portable ML Pipelines

    In this episode, Hussain shares the story behind xorq: a “lockfile for ML pipelines” that makes notebook work easier to reproduce, debug, and ship. We talk about why the research→production path is still so manual, how schemas (and Arrow) become the contract between systems, and what it takes to run the same pipeline across engines like Snowflake and Databricks. We also dig into escape hatches for imperative code, why feature stores didn’t become the default, and how xorq fits alongside other technologies like Iceberg. Chapters 00:00 Hussain's Journey in Data Science 06:00 The Need for xorq: Bridging Research and Production 10:38 Challenges in Machine Learning Deployment 17:40 The Role of Lock Files in Data Pipelines 29:51 Understanding Schema Management in Data Systems 34:40 Navigating Declarative and Imperative Transformations 36:39 The Developer's Journey with xorq 38:34 Feature Stores vs. xorq: A Comparative Analysis 43:43 The Future of Feature Stores and Machine Learning 51:41 Reproducibility in Data Pipelines: xorq vs. Git-like Operations 55:47 The Future of xorq and the Data Ecosystem

    57 min

  5. 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

  6. 09/08/2025

    Navigating the Future of AI and Data Infrastructure with Bauplan

    Summary In this conversation, the founders of Bauplan, Jacopo and Ciro, share their extensive backgrounds in AI and data infrastructure, discussing the evolution of NLP and the challenges faced in the industry. They highlight the importance of data pipelines in AI effectiveness and the complexities of building data infrastructure. The discussion also covers lessons learned from previous ventures, the shifting dynamics of the AI market, and the need for collaboration between data scientists and engineers. They emphasize the significance of simplicity in data tools and the future of data management focusing on standardization and accessibility. In this episode Bauplan was founded by experienced professionals in AI and data.Data challenges remain significant despite advancements in AI.Lessons from previous ventures inform current strategies.Building data infrastructure is complex and requires careful planning.Collaboration between data scientists and engineers is essential.Data engineering will resemble more and more software engineering.Simplicity in data tools can enhance user experience.The future of data management will focus on standardization and accessibility. If you care about making AI features shippable by regular software teams—not just data specialists—this conversation maps the terrain and the trade-offs. Chapters00:00 Introduction to Bauplan and Founders' Background02:27 The Evolution of NLP and AI Challenges05:05 Shifts in Data and AI Application07:56 Lessons from Previous Ventures10:20 The Search Market Landscape13:05 Behavioral Data's Role in Search15:52 Building Data Infrastructure vs. Applications18:22 The Complexity of Data Management21:03 Bridging the Gap Between Data Science and Engineering23:39 Challenges in Infrastructure Development29:52 Navigating the Infrastructure Landscape32:19 The Pendulum of Centralization and Decentralization34:00 The Need for Standardization in Data Infrastructure36:52 Simplifying Data Workflows40:29 Radical Simplicity in Data Management45:28 Overcoming Resistance to Change48:50 The Future of Data Abstractions and Git for Data

    59 min

  7. 08/18/2025

    Email as a Knowledge Graph: Micro CEO Brett on Rebuilding CRM at the Inbox

    Summary Brett — founder & CEO of Micro — joins Nitay and Kostas to share how he’s turning email into a knowledge graph and rebuilding CRM right inside the inbox. He traces a path from Google’s M&A and Allo product team to Clearbit and Launch House, then digs into why most “inbox zero” workflows fail, how interoperability and AI agents shift power to the interface, and what it takes to design an email experience people actually live in. What you’ll learn Why email is a system of record—and how Micro converts threads into people, companies, attachments, tasks, and “updates”The wedge: founders’ real workflows (fundraising, hiring, sales) and why CRM belongs in the inboxProduct & UX lessons: skeuomorphic first, flexible theming (consumer vs. enterprise), and copy-the-UI-before-evolving-itM&A realities from Google: talent vs. tech vs. business acquisitions, and why culture kills most dealsBurnout and agency: why founders report less burnout than big-company rolesThe next phase: cross-app “updates” (email, LinkedIn DMs, etc.), Salesforce/HubSpot read–write, and agentic automationChapters 00:00 Brett's Journey: From Consulting to Tech Innovator 02:41 The Role of Strategy in Tech Companies 05:16 Understanding M&A: Successes and Failures 07:55 The Evolution of AI in Corporate Strategy 10:26 Transitioning to Product Management 13:19 Lessons from Clearbit: Culture and Growth 15:50 The Impact of Burnout on Career Choices 18:15 Finding Fulfillment in Entrepreneurship 21:09 Navigating the B2B Landscape 23:34 The Necessity of Products in a Crisis 33:24 The Unexpected Layoff and New Beginnings 34:39 The Launch House Experience 37:16 Transforming Reality into an Accelerator 39:17 The Evolution of Founders and Content Creation 41:52 Introducing Micro: A New Email Experience 47:02 Extracting Information for Better Workflows 53:49 Integrating with Existing Ecosystems 01:01:16 The Future of Email and AI

    1h 1m

  8. 07/28/2025

    Community, Compilers & the Rust Story with Steve Klabnik

    Summary Steve Klabnik has spent the last 15 years shaping how developers write code—from teaching Ruby on Rails to stewarding Rust’s explosive growth. In this wide-ranging conversation, Steve joins Kostas and Nitay to unpack the forces behind Rust’s rise and the blueprint for developer-first tooling. From Rails to Rust: How a web-framework luminary fell for a brand-new systems language and helped turn it into today’s go-to for memory-safe, zero-cost abstractions.Community as UX: The inside story of Cargo, humane compiler errors, and why welcoming IRC channels can matter more than benchmarks.Standards vs. Shipping: What Rust borrowed from the web’s rapid-release model—and why six-week cadences beat three-year committee cycles.Three tribes, one language: How dynamic-language devs, functional programmers, and C/C++ veterans each found a home in Rust—and what they contributed in return.Looking ahead: Steve’s watch-list of next-gen languages (Hylo, Zig, Odin) and the lessons Rust’s journey holds for anyone building tools, communities, or startups today.Whether you’re chasing segfault-free code, dreaming up a new PL, or just curious how open-source movements gain momentum, this episode is packed with insight and practical takeaways. Chapters00:00 Introduction and Personal Connection00:59 Journey from Ruby on Rails to Rust02:21 Early Programming Experiences and Interests07:20 Community Dynamics in Programming Languages13:59 The Importance of Community in Open Source14:37 How Ruby on Rails and Rust Built Their Communities21:44 Standardization vs. Unified Development Models30:55 Community Debt in Programming Languages36:24 Release Cadence vs. Feature Development37:36 Rust's Unique Selling Proposition43:30 Attracting Diverse Programming Communities52:31 The Future of Systems Programming Languages

    59 min
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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!

Information

  • Creator
    Kostas, Nitay
  • Years Active
    2024 - 2026
  • Episodes
    27
  • Rating
    Explicit
  • Copyright
    © 2026 Kostas, Nitay

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