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

Summary

Josh 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.

Chapters

00:00 Introduction and Background
02:01 Journey into Storage Systems
04:24 Cloudflare's Evolution and Developer Platform
06:29 Understanding Durable Objects
08:57 Durable Objects in Modern App Development
11:18 Use Cases for Cloudflare's Developer Platform
13:36 Building Agents and Real-Time Applications
16:19 Developer Experience and Migration Strategies
25:09 Exploring Workflow Systems: OLAP vs Applications
26:47 Cloudflare's Development Platform: Future Offerings for Data Professionals
28:42 Transitioning from Data Processing to Application Development
31:37 The Impact of LLMs on System Design
33:44 Serverless Platforms: Challenges and Limitations
40:01 Future Directions: Cloudflare's Storage Relay Service and Global Expansion

Click here to view the episode transcript.

Summary
In this episode, Erik reflects on his long and storied tech career—from the days of punch cards to founding multiple startups, including a stint at Splunk.

At 61, he offers a unique perspective on how the industry has evolved and shares candid insights into what it takes to build a successful company. He discusses the evolution from building simple tools to creating comprehensive solutions and eventually platforms, emphasizing the importance of starting with a “hammer”—a focused, simple tool—before scaling to a broader offering.

Eril introduces his concept of the “physics of business,” a framework for understanding go-to-market dynamics, pricing, and the critical role of brand in differentiating a product in a crowded market.

He also touches on the challenges of product-led growth, the importance of achieving a strong “K value” (viral or network effects), and the pitfalls of allowing short-term quarterly pressures to derail long-term vision. Toward the end, he hints at his current project, Bestimer, which aims to apply lessons from his past ventures and leverage modern AI to tackle a massive, data-intensive problem.

Chapters

00:00 Erik's Journey Through Tech History
04:06 The Philosophy of Designing for Success
09:49 Understanding the Physics of Business
14:29 Timing and Luck in Startups
18:09 Lessons Learned from Splunk
23:30 The Power of Brand in Business
28:02 Leveraging AI for Brand Development
32:04 The Resilience of Splunk
36:45 Building a Competitive Edge
37:28 From Tool to Solution
40:59 The Importance of Onboarding
44:32 Navigating Growth and Market Fit
51:11 Innovating with AI: The Next Chapter

Summary

In 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.

Chapters

00:00 From PHP to Kernel Development: A Journey
07:30 Introducing Epsio: The Incremental Views Engine
10:56 The Importance of Materialized Views
15:07 Understanding Incremental Materialization
19:21 Optimizing Query Performance with Epsio
24:53 Integrating Epsio with Existing Databases
27:02 The Shift from Theory to Practice in Data Processing
29:42 Seamless Integration with Existing Databases
32:02 Understanding Epsio Incremental Processing Mechanism
34:46 Challenges and Limitations of Incremental Views
36:49 The Complexity of Implementing Operators
39:56 Trade-offs in Incremental Computation
41:21 User Interaction with Epsio
43:01 Comparing EPSIO with Streaming Systems
45:09 Architectural Guarantees of Epsio
50:33 The Future of Incremental Data Processing

Summary

In 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.

Chapters

00:00 Introduction to Viktor Kessler and His Journey
04:57 Transitioning from Data Mesh to Lake House
09:15 Understanding Data Mesh: Pain Points and Solutions
13:47 The Role of Metadata in Data Management
18:16 The Evolution of Catalogs and Metadata Management
28:14 Stabilizing the Consumption Pipeline
31:18 Centralizing Metadata for Decentralized Organizations
37:09 Bridging the Gap: Technical and Business Perspectives
43:17 Rethinking Data Products and Consumption
50:45 Finding Balance: Control and Flexibility in Data Management

Summary

In this episode, Apurva Mehta, co-founder and CEO of Responsive, recounts his extensive journey in stream processing—from his early work at LinkedIn and Confluent to his current venture at Responsive.

He explains how stream processing evolved from simple event ingestion and graph indexing to powering complex, stateful applications such as search indexing, inventory management, and trade settlement.

Apurva clarifies the often-misunderstood concept of “real time,” arguing that low latency (often in the one- to two-second range) is more accurate for many applications than the instantaneous response many assume. He delves into the challenges of state management, discussing the limitations of embedded state stores like RocksDB and traditional databases (e.g., Postgres) when faced with high update rates and complex transactional requirements.

The conversation also covers the trade-offs between SQL-based streaming interfaces and more flexible APIs, and how Responsive is innovating by decoupling state from compute—leveraging remote state solutions built on object stores (like S3) with specialized systems such as SlateDB—to improve elasticity, cost efficiency, and operational simplicity in mission-critical applications.

Chapters

00:00 Introduction to Apurva Mehta and Streaming Background
08:50 Defining Real-Time in Streaming Contexts
14:18 Challenges of Stateful Stream Processing
19:50 Comparing Streaming Processing with Traditional Databases
26:38 Product Perspectives on Streaming vs Analytical Systems
31:10 Operational Rigor and Business Opportunities
38:31 Developers' Needs: Beyond SQL
45:53 Simplifying Infrastructure: The Cost of Complexity
51:03 The Future of Streaming Applications

Click here to view the episode transcript.