Tech on the Rocks

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 trenchesChapters00:00 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

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.Chapters00:00 Hussain's Journey in Data Science06:00 The Need for xorq: Bridging Research and Production10:38 Challenges in Machine Learning Deployment17:40 The Role of Lock Files in Data Pipelines29:51 Understanding Schema Management in Data Systems34:40 Navigating Declarative and Imperative Transformations36:39 The Developer's Journey with xorq38:34 Feature Stores vs. xorq: A Comparative Analysis43:43 The Future of Feature Stores and Machine Learning51:41 Reproducibility in Data Pipelines: xorq vs. Git-like Operations55:47 The Future of xorq and the Data Ecosystem

Wes McKinney, creator of pandas and co-creator of Apache Arrow and Ibis, is a long-time leader in the Python data ecosystem. He walks through pandas’ UX-driven origins and the move to columnar in-memory Arrow. Conversations cover Arrow vs Parquet, new GPU-friendly file encodings, big metadata and table formats, Rust query engines like DataFusion, and how AI agents are changing developer workflows.

SummaryIn 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 episodeBauplan 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

SummaryBrett — 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 learnWhy 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 automationChapters00:00 Brett's Journey: From Consulting to Tech Innovator02:41 The Role of Strategy in Tech Companies05:16 Understanding M&A: Successes and Failures07:55 The Evolution of AI in Corporate Strategy10:26 Transitioning to Product Management13:19 Lessons from Clearbit: Culture and Growth15:50 The Impact of Burnout on Career Choices18:15 Finding Fulfillment in Entrepreneurship21:09 Navigating the B2B Landscape23:34 The Necessity of Products in a Crisis33:24 The Unexpected Layoff and New Beginnings34:39 The Launch House Experience37:16 Transforming Reality into an Accelerator39:17 The Evolution of Founders and Content Creation41:52 Introducing Micro: A New Email Experience47:02 Extracting Information for Better Workflows53:49 Integrating with Existing Ecosystems01:01:16 The Future of Email and AI

SummarySteve 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

SummaryJosh Howard, Senior Engineering Manager at Cloudflare, joins Kostas and Nitay to discuss Cloudflare's innovative serverless platform, Durable Objects, and Workers. Learn how Cloudflare enables developers to build stateful applications with global scale, consistency, and simplicity at the network edge.Chapters00:00 Introduction and Background02:01 Journey into Storage Systems04:24 Cloudflare's Evolution and Developer Platform06:29 Understanding Durable Objects08:57 Durable Objects in Modern App Development11:18 Use Cases for Cloudflare's Developer Platform13:36 Building Agents and Real-Time Applications16:19 Developer Experience and Migration Strategies25:09 Exploring Workflow Systems: OLAP vs Applications26:47 Cloudflare's Development Platform: Future Offerings for Data Professionals28:42 Transitioning from Data Processing to Application Development31:37 The Impact of LLMs on System Design33:44 Serverless Platforms: Challenges and Limitations40:01 Future Directions: Cloudflare's Storage Relay Service and Global ExpansionClick here to view the episode transcript.

In an insightful discussion, Erik Swan, a serial entrepreneur and former Splunk executive, shares lessons from his extensive tech career. He introduces the concept of the 'physics of business,' emphasizing how brand strength, pricing strategies, and product design are pivotal for success. Erik highlights the importance of starting with simple tools before scaling, and the challenges of maintaining a long-term vision amidst short-term pressures. He also hints at his new venture, Bestimer, which aims to tackle data challenges using AI.

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

Viktor Kessler, co-founder of Lakekeeper and former solutions architect at MongoDB and Dremio, discusses his journey through data management. He explains the shift from data mesh concepts to lake house architecture, highlighting how it aligns technical solutions with business needs. Viktor dives into the role of actionable metadata and the evolution of cataloging systems. He emphasizes the importance of a centralized approach in decentralized environments and shares ways for listeners to engage with the Lakekeeper community.