Tech on the Rocks

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

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

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.

Chapters

00:00 Introduction to Bauplan and Founders' Background
02:27 The Evolution of NLP and AI Challenges
05:05 Shifts in Data and AI Application
07:56 Lessons from Previous Ventures
10:20 The Search Market Landscape
13:05 Behavioral Data's Role in Search
15:52 Building Data Infrastructure vs. Applications
18:22 The Complexity of Data Management
21:03 Bridging the Gap Between Data Science and Engineering
23:39 Challenges in Infrastructure Development
29:52 Navigating the Infrastructure Landscape
32:19 The Pendulum of Centralization and Decentralization
34:00 The Need for Standardization in Data Infrastructure
36:52 Simplifying Data Workflows
40:29 Radical Simplicity in Data Management
45:28 Overcoming Resistance to Change
48:50 The Future of Data Abstractions and Git for Data

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 inbox
Product & UX lessons: skeuomorphic first, flexible theming (consumer vs. enterprise), and copy-the-UI-before-evolving-it
M&A realities from Google: talent vs. tech vs. business acquisitions, and why culture kills most deals
Burnout and agency: why founders report less burnout than big-company roles
The next phase: cross-app “updates” (email, LinkedIn DMs, etc.), Salesforce/HubSpot read–write, and agentic automation
Chapters
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

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.

Chapters

00:00 Introduction and Personal Connection
00:59 Journey from Ruby on Rails to Rust
02:21 Early Programming Experiences and Interests
07:20 Community Dynamics in Programming Languages
13:59 The Importance of Community in Open Source
14:37 How Ruby on Rails and Rust Built Their Communities
21:44 Standardization vs. Unified Development Models
30:55 Community Debt in Programming Languages
36:24 Release Cadence vs. Feature Development
37:36 Rust's Unique Selling Proposition
43:30 Attracting Diverse Programming Communities
52:31 The Future of Systems Programming Languages