SQLite is embedded everywhere - phones, browsers, IoT devices. It's reliable, battle-tested, and feature-rich. But what if you want concurrent writes? Or CDC for streaming changes? Or vector indexes for AI workloads? The SQLite codebase isn't accepting new contributors, and the test suite that makes it so reliable is proprietary. So how do you evolve an embedded database that's effectively frozen?

Glauber Costa spent a decade contributing to the Linux kernel at Red Hat, then helped build Scylla, a high-performance rewrite of Cassandra. Now he's applying those lessons to SQLite. After initially forking SQLite (which produced a working business but failed to attract contributors), his team is taking the bolder path: a complete rewrite in Rust called Turso. The project already has features SQLite lacks - vector search, CDC, browser-native async operation - and is using deterministic simulation testing (inspired by TigerBeetle) to match SQLite's legendary reliability without access to its test suite.

The conversation covers why rewrites attract contributors where forks don't, how the Linux kernel maintains quality with thousands of contributors, why Pekka's "pet project" jumped from 32 to 64 contributors in a month, and what it takes to build concurrent writes into an embedded database from scratch.

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Turso: https://turso.tech/

Turso GitHub: https://github.com/tursodatabase/turso

libSQL (SQLite fork): https://github.com/tursodatabase/libsql

SQLite: https://www.sqlite.org/

Rust: https://rust-lang.org/

ScyllaDB (Cassandra rewrite): https://www.scylladb.com/

Apache Cassandra: https://cassandra.apache.org/

DuckDB (analytical embedded database): https://duckdb.org/

MotherDuck (DuckDB cloud): https://motherduck.com/

dqlite (Canonical distributed SQLite): https://canonical.com/dqlite

TigerBeetle (deterministic simulation testing): https://tigerbeetle.com/

Redpanda (Kafka alternative): https://www.redpanda.com/

Linux Kernel: https://kernel.org/

Datadog: https://www.datadoghq.com/

Glauber Costa on X: https://x.com/glcst

Glauber Costa on GitHub: https://github.com/glommer

Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.social

Kris on Mastodon: http://mastodon.social/@krisajenkins

Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

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0:00 Intro

3:16 Ten Years Contributing to the Linux Kernel

15:17 From Linux to Startups: OSv and Scylla

26:23 Lessons from Scylla: The Power of Ecosystem Compatibility

33:00 Why SQLite Needs More

37:41 Open Source But Not Open Contribution

48:04 Why a Rewrite Attracted Contributors When a Fork Didn't

57:22 How Deterministic Simulation Testing Works

1:06:17 70% of SQLite in Six Months

1:12:12 Features Beyond SQLite: Vector Search, CDC, and Browser Support

1:19:15 The Challenge of Adding Concurrent Writes

1:25:05 Building a Self-Sustaining Open Source Community

1:30:09 Where Does Turso Fit Against DuckDB?

1:41:00 Could Turso Compete with Postgres?

1:46:21 How Do You Avoid a Toxic Community Culture?

1:50:32 Outro

How do you build systems with AI? Not code-generating assistants, but production systems that use LLMs as part of their processing pipeline. When should you chain multiple agent calls together versus just making one LLM request? And how do you debug, test, and deploy these things? The industry is clearly in exploration mode—we're seeing good ideas implemented badly and expensive mistakes made at scale. But Google needs to get this right more than most companies, because AI is both their biggest opportunity and an existential threat to their search-based business model.

Christina Lin from Google joins us to discuss Agent Development Kit (ADK), Google's open-source Python framework for building agentic pipelines. We dig into the fundamental question of when agent pipelines make sense versus traditional code, exploring concepts like separation of concerns for agents, tool calling versus MCP servers, Google's grounding feature for citation-backed responses, and agent memory management. Christina explains A2A (Agent-to-Agent), Google's protocol for distributed agent communication that could replace both LangChain and MCP. We also cover practical concerns like debugging agent workflows, evaluation strategies, and how to think about deploying agents to production.

If you're trying to figure out when AI belongs in your processing pipeline, how to structure agent systems, or whether frameworks like ADK solve real problems versus creating new complexity, this episode breaks down Google's approach to making agentic systems practical for production use.

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Google Agent Development Kit Announcement: https://developers.googleblog.com/en/agent-development-kit-easy-to-build-multi-agent-applications/

ADK on GitHub: https://google.github.io/adk-docs/

Google Gemini: https://ai.google.dev/gemini-api

Google Vertex AI: https://cloud.google.com/vertex-ai

Google AI Studio: https://aistudio.google.com/

Google Grounding with Google Search: https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/overview

Model Context Protocol (MCP): https://modelcontextprotocol.io/

Anthropic MCP Servers: https://github.com/modelcontextprotocol/servers

LangChain: https://www.langchain.com/

Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.social

Kris on Mastodon: http://mastodon.social/@krisajenkins

Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/

How do you monitor distributed systems that span dozens of microservices, multiple languages, and different databases? The old approach of gathering logs from different machines and recompiling apps with profiling flags doesn't scale when you're running thousands of servers. You need a unified strategy that works everywhere, on every component, in every language—and that means tackling the problem from the kernel level up.

Mohammed Aboullaite is a backend engineer at Spotify, and he joins us to explore the latest in continuous profiling and observability using eBPF. We dive into how eBPF lets you programmatically peek into the Linux kernel without recompiling it, why companies like Google and Meta run profiling across their entire infrastructure, and how to manage the massive data volumes that continuous profiling generates. Mohammed walks through specific tools like Pyroscope, Pixie, and Parca, explains the security model of loading code into the kernel, and shares practical advice on overhead thresholds, storage strategies, and getting organizational buy-in for continuous profiling.

Whether you're debugging performance issues, optimizing for scale, or just want to see what your code is really doing in production, this episode covers everything from packet filters to cultural changes in service of getting a clear view of your software when it hits production.

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eBPF: https://ebpf.io/

Google-Wide Profiling Paper (2010): https://research.google.com/pubs/archive/36575.pdf

Google pprof: https://github.com/google/pprof

Continuous Profiling Tools:

Pyroscope (Grafana): https://grafana.com/oss/pyroscope/

Pixie (CNCF): https://px.dev/

Parca: https://www.parca.dev/

Datadog Continuous Profiler: https://www.datadoghq.com/product/code-profiling/

Supporting Technologies:

OpenTelemetry: https://opentelemetry.io/

Grafana: https://grafana.com/

New Relic: https://newrelic.com/

Envoy Proxy: https://www.envoyproxy.io/

Spring Cloud Sleuth: https://spring.io/projects/spring-cloud-sleuth

Mohammed Aboullaite:

LinkedIn: https://www.linkedin.com/in/aboullaite/

GitHub: https://github.com/aboullaite

Website: http://aboullaite.me

Twitter/X: https://twitter.com/laytoun

Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.social

Kris on Mastodon: http://mastodon.social/@krisajenkins

Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/