Tech on the Rocks

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.

In this installment, Apurva Mehta, co-founder and CEO of Responsive, shares insights from his journey in stream processing at LinkedIn and Confluent. He breaks down the evolution of stream processing from simple tasks to powering complex applications. Apurva clarifies the concept of 'real time,' emphasizing low latency over instant responses. He discusses the pitfalls of traditional databases in handling high-update rates and explains how Responsive innovates by decoupling state from compute to enhance efficiency and operational simplicity.

David Jayatillake, VP of AI at Cube, shares his expertise on the critical role of semantic layers in bridging raw data and actionable insights. He reveals how Cube achieved 100% accuracy in natural language data queries using semantic layers, vastly outperforming traditional methods. The conversation dives into the challenges of building these layers, emphasizing the significance of clear naming and documentation. Jayatillake also discusses future trends, including AI-powered features set to launch in 2025, and the potential of LLMs as intelligent agents.

In this episode, we chat with Yaron Hadad, a fascinating individual who transitioned from theoretical physics to entrepreneurship. We explore his groundbreaking work on black holes and gravitational waves, and learn about the Ramanujan Machine - an algorithmic system he helped develop that discovers new mathematical formulas and democratizes mathematical research. We'll hear about the scientific community's mixed reactions to this innovative approach. The conversation then shifts to his work with Neutrino, a company he founded that uses AI and continuous monitoring devices to understand how food affects individual health. We delve into the complexities of nutrition science, the challenges of processing multiple data streams, and the future of personalized health monitoring. Throughout the episode, Yaron shares insights on bridging theoretical research with practical applications, and the role of AI in advancing both pure mathematics and healthcare.00:00 Yaron Hadad's Journey: From Physics to AI in Healthcare04:50 The Complexity of Einstein's Equations and Their Solutions10:12 AI in Mathematics: The Ramanujan Machine and Conjectures15:41 Navigating Criticism: The Scientific Community's Response to Innovation29:24 The Impact of Algorithms in Mathematics35:30 The Planck Machine: A New Approach41:15 Neutrino: A Personal Journey in Nutrition50:11 Connecting Food Complexity to Health Metrics

Philippe Noël, founder of ParadeDB and an expert in PostgreSQL, discusses his venture to create a native search engine alternative to Elasticsearch. He explores the challenges of integrating full-text search and analytics within PostgreSQL. The conversation touches on the benefits of the bring-your-own-cloud model and the evolution of database extensions. Philippe emphasizes the rising demand for traditional search capabilities amidst AI advancements and addresses misconceptions about PostgreSQL's scalability and the trade-offs in cloud architectures.

In this episode, we chat with Ben, founder of Espresso AI, about his journey from building Excel Python integrations to optimizing data warehouse compute costs. We explore his experience at companies like Uber and Google, where he worked on everything from distributed systems to ML and storage infrastructure. We learn about the evolution of his latest venture, which started as a C++ compiler optimization project and transformed into a system for optimizing Snowflake workloads using ML. Ben shares insights about applying LLMs to SQL optimization, the challenges of verified code transformation, and the importance of formal verification in ML systems. Finally, we discuss his practical approach to choosing ML models and the critical lesson he learned about talking to users before building products.Chapters00:00 Ben's Journey: From Startups to Big Tech13:00 The Importance of Timing in Entrepreneurship19:22 Consulting Insights: Learning from Clients23:32 Transitioning to Big Tech: Experiences at Uber and Google30:58 The Future of AI: End-to-End Systems and Data Utilization35:53 Transitioning Between Domains: From ML to Distributed Systems44:24 Espresso's Mission: Optimizing SQL with ML51:26 The Future of Code Optimization and AIClick here to view the episode transcript.