Developer Voices

Kris Jenkins

Deep-dive discussions with the smartest developers we know, explaining what they're working on, how they're trying to move the industry forward, and what we can learn from them.

You might find the solution to your next architectural headache, pick up a new programming language, or just hear some good war stories from the frontline of technology.

Join your host Kris Jenkins as we try to figure out what tomorrow's computing will look like the best way we know how - by listening directly to the developers' voices.

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22 episodes
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Avg 1 hr 23 min
English

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Wednesday · 1 hr 46 min
DuckDB And The Burden of Success (with Hannes Mühleisen)
Building something people actually want is supposed to be the happy ending. But it arrives with a bill attached: feature requests you didn't ask for, pull requests you'd rather not maintain forever, users demanding the one thing you swore you'd never build, and — if you're unlucky — a company where the sales team quietly starts deciding what engineering works on. DuckDB has spent the last two and a half years working through that list. So how do you stay a database engineering team when success keeps trying to turn you into something else? Hannes Mühleisen, co-creator of DuckDB, is back to talk through the answers they've landed on. Their fix for unwanted pull requests was an extension mechanism, which then forced them to make every part of the engine pluggable — including the parser, which meant ripping out 20,000 lines of Postgres' yacc grammar and rewriting SQL parsing on top of PEG. Their fix for the users demanding client-server was Quack, a protocol designed by people who'd already published a paper on why every existing database wire protocol is wrong. And their answer to Apache Iceberg, after three years of implementing it, was DuckLake: throw out the Avro-and-JSON metadata files and keep the metadata in a database, on the grounds that the Iceberg REST catalog has a Postgres in it anyway. Which brings us to the news Hannes breaks in this episode: DuckDB Labs is being acquired by AWS, while the DuckDB Foundation, the project and its licence stay where they are. There's the question of why a profitable, self-funded, 30-person company in Amsterdam would take that deal, what commitments you write into the contracts when you're worried today's promises might outlive today's management, and what it's actually like to have a boss again after five years without one. If you're curious how an open source project keeps its technical soul once the enterprise arrives — or you just want to know why parsing SQL is harder than parsing almost anything else — Hannes has some good answers. --- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join Our previous episode with Hannes: https://youtu.be/pZV9FvdKmLc DuckDB: https://duckdb.org/ DuckDB Foundation: https://duckdb.foundation/ DuckLabs (formerly DuckDB Labs): https://ducklabs.com/ DuckLake: https://ducklake.select/ Quack (DuckDB's client-server protocol): https://duckdb.org/quack/ DuckDB v2.0: Your Database Deserves a Better Parser: https://duckdb.org/2026/08/20/duckdb-20-peg-parser Runtime-Extensible Parsers (CIDR 2025 paper): https://duckdb.org/pdf/CIDR2025-muehleisen-raasveldt-extensible-parsers.pdf Don't Hold My Data Hostage (VLDB 2017 paper): https://www.vldb.org/pvldb/vol10/p1022-muehleisen.pdf cpp-peglib: https://github.com/yhirose/cpp-peglib GNU Bison: https://www.gnu.org/software/bison/ PEP 617 – New PEG parser for CPython: https://peps.python.org/pep-0617/ PRQL: https://prql-lang.org/ Apache Iceberg: https://iceberg.apache.org/ CWI (Centrum Wiskunde & Informatica): https://www.cwi.nl/en/ DuckCon #7, Amsterdam: https://duckdb.org/events/2026/06/24/duckcon7/ 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/
August 12 · 1 hr 1 min
The Enterprise AI Gap (with James Brown)
My guest this week is James Brown, an engineering lead at Schroders, and that's a useful vantage point — asset managers carry all the regulation and organisational weight of a bank, mixed with the first-to-market pressure of a startup. James starts with his own "Claude mania": months of agents running around the clock, and the wave of anxiety that hit him one morning walking to the shop without one running at home. From there, Clair — the plugin he's building to give coding agents proximal awareness of each other, using git orphan branches as a zero-infrastructure message bus; why AI behaves like oxygen in a room full of tiny fires; what "going well" actually measures inside a regulated firm; and why he thinks team sizes won't change, even when the number of teams does. There's a darker thread running under all of it. The collapse in junior hiring, the advice we no longer know how to give a 20-year-old, dark factories and evolutionary harnesses that might make the AI's ideas better than ours, and the burnout James expects to be our dominant topic for the next couple of years. If AI is working beautifully on your side projects but landing with a thud at work, James has some honest answers — including several about what he doesn't know yet. --- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join Clair (James's multi-agent proximity plugin): https://github.com/JBJamesBrownJB/clair Clair product docs: https://github.com/JBJamesBrownJB/clair/blob/main/docs/product.md James's blog: https://medium.com/@jameskinnahbrown "Milk, Eggs and Claude Mania": https://medium.com/@jameskinnahbrown/milk-eggs-and-claude-mania-49f445c5a77e Schroders: https://www.schroders.com/ Claude Code: https://www.claude.com/product/claude-code Agent Skills & progressive disclosure: https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview Git orphan branches (git checkout --orphan): https://git-scm.com/docs/git-checkout Moltbook: https://www.moltbook.com/ Team Topologies: https://teamtopologies.com/ "Expert Panel: How Far Can We Accelerate with AI?": https://youtu.be/Bg7L4vmmSKg XT26 (the conference the panel was part of): https://www.juxt.pro/xt26/ 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/
July 8 · 1 hr 5 min
What If Every SQL Query Could Update Incrementally? (with Lalith Suresh)
There's a problem that's bugged the database industry since the 1980s: you run an expensive query over millions of rows, cache the result, and then a single new row arrives. Logically that's one small update, but most engines throw the cached answer away and recompute everything from scratch. Some will handle changes incrementally, but only for "simple" queries - and the rules for what counts as simple are arbitrary and brittle. So can you incrementally maintain *any* SQL query, no matter how complex? For decades the answer was no. Then an award-winning paper called DBSP proved that the answer is yes - all queries are simple enough. Joining me to explain how that works is Lalith Suresh, CEO of Feldera, the company built on top of DBSP. We start with the problem itself, then trace how a group of VMware researchers arrived at it from the unlikely direction of Kubernetes and network control planes. Lalith walks through Z-sets, the weighted data structure that turns database changes into something you can add and subtract, and the four DBSP operators - including one borrowed straight from digital signal processing - that let you compile any SQL program into an incremental version deterministically. Along the way we get into which operations need state and which don't, how the delta join falls out for free, building a standalone query engine with its own storage layer and Calcite front-end, backfills as the real Achilles heel, and how this all differs from stream processors like Kafka Streams and Flink. If you've ever fought with materialized views that won't refresh, watched a nightly batch job recompute three years of data to capture last night's changes, or you're just curious how one elegant bit of maths unifies batch and stream processing, Lalith has some genuinely satisfying answers. There's an MIT-licensed open source edition and a sandbox at try.feldera.com if you want to play along. --- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join Feldera: https://www.feldera.com/ Feldera Sandbox (try it online): https://try.feldera.com/ Feldera on GitHub (open source): https://github.com/feldera/feldera DBSP Rust crate: https://crates.io/crates/dbsp DBSP Paper - "Automatic Incremental View Maintenance for Rich Query Languages" (VLDB 2023 Best Paper): https://arxiv.org/abs/2203.16684 Mihai Budiu - "Streaming Queries Without Compromise" (Current 2024): https://www.youtube.com/watch?v=cn1Yaxwl6x8 Mihai Budiu - DBSP talk at CMU Database Group: https://db.cs.cmu.edu/events/dbsp-incremental-computation-on-streams-and-its-applications-to-databases/ Differential Dataflow: https://github.com/TimelyDataflow/differential-dataflow Apache Calcite (Feldera's SQL front-end): https://calcite.apache.org/ Kafka Streams: https://kafka.apache.org/documentation/streams/ Apache Flink: https://flink.apache.org/ ksqlDB: https://ksqldb.io/ Apache Spark: https://spark.apache.org/ Snowflake: https://www.snowflake.com/ Databricks: https://www.databricks.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/
March 26 · 1 hr 40 min
What's Worth Knowing In AI Right Now? (with Henry Garner)
AI is changing the way we all build software — that much seems clear. But the landscape is moving so fast that even the people paid to keep up are struggling. MCP or skills? Fine-tune or just prompt? LangChain or let a thousand agents loose? With almost 70 competing technologies and a shelf life of maybe six months on any advice, how do you figure out what's actually worth your time? Henry Garner is CTO of JUXT, a consultancy with about 150 senior engineers working at the coalface of AI-assisted development, including building AI platforms for tier-one banks. JUXT publishes a quarterly AI Radar — 68 technologies rated and reviewed — and Henry's been watching his own team go through the full adoption arc, from "spicy autocomplete" skepticism through to building Byzantine-fault-tolerant distributed systems over a weekend with Claude. Along the way we cover MCP vs skills, Conway's Law for LLMs, neurosymbolic AI and the unexpected return of Prolog, the "Ralph Wiggum loop" for getting agents to converge on correct implementations, and Allium — a new behavioral specification language Henry's co-authored that sits between human prose and TLA+, aiming to give LLMs just enough structure to pin down what a system should do without falling into waterfall thinking. If you're trying to make sense of the AI tooling landscape, or you've hit that wall where your agents keep drifting away from what you actually wanted, Henry's thesis — velocity through clarity of intent — might well help out yours. -- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join JUXT: https://www.juxt.pro/ JUXT AI Radar: https://www.juxt.pro/ai-radar/ Allium on GitHub: https://github.com/juxt/allium Allium Documentation: https://juxt.github.io/allium/ Composition at a Distance (Henry's blog post): https://www.juxt.pro/blog/composition-at-a-distance/ A New Vocabulary for an Old Problem (Henry's blog post): https://www.juxt.pro/blog/new-vocabulary-for-an-old-problem/ Model Context Protocol (MCP): https://modelcontextprotocol.io/ LangChain: https://www.langchain.com/ LangGraph: https://www.langchain.com/langgraph Gas Town (Steve Yegge): https://github.com/steveyegge/gastown Kiro (spec-driven AI IDE): https://kiro.dev/ Phoenix (LLM observability): https://github.com/Arize-ai/phoenix Temporal: https://temporal.io/ Taalas (LLM-on-a-chip): https://taalas.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/
February 19 · 1 hr 26 min
Asciinema: Terminal Recording Done Right (with Marcin Kulik)
I have a theory that only bad projects get finished — good ones keep finding new things to do. Asciinema is a case in point. What started as a way to share terminal sessions with friends has, over 14 years, grown into a full suite of tools covering recording, hosting, playback, and live streaming — and been rebuilt multiple times along the way. So what does it actually take to record and replay a terminal session faithfully in a browser? Joining us for this conversation is Marcin Kulik, Asciinema's creator. The project's architecture has passed through almost every interesting corner of software engineering: a Python recorder built around pseudo-terminals (PTY), a ClojureScript terminal emulator for the browser that hit performance limits with immutable data structures and garbage collection pressure, a move to Rust compiled to WebAssembly, a Go experiment that didn't last, and a new Rust CLI for concurrent live streaming backed by an Elixir/Phoenix server that calls Rust code via NIFs. The same Rust terminal emulator library now powers all three components — the browser player, the server, and the CLI. If you've ever looked at those terminal animations embedded in a README and wondered what's underneath them, or if you're interested in how a passionate open-source developer navigates 14 years of language changes and rewrites, this conversation has plenty to offer. --- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join Asciinema: https://asciinema.org Asciinema Docs: https://docs.asciinema.org Asciinema CLI (GitHub): https://github.com/asciinema/asciinema Asciinema Player (GitHub): https://github.com/asciinema/asciinema-player Asciinema Server (GitHub): https://github.com/asciinema/asciinema-server AVT - Rust terminal emulator library: https://github.com/asciinema/avt vt-clj - the original ClojureScript terminal emulator: https://github.com/asciinema/vt-clj Paul Williams' ANSI/VT100 State Machine Parser: https://vt100.net/emu/dec_ansi_parser Rust: https://www.rust-lang.org WebAssembly: https://webassembly.org SolidJS: https://www.solidjs.com Elixir: https://elixir-lang.org Phoenix Framework: https://www.phoenixframework.org Rustler (Rust NIFs for Elixir/Erlang): https://github.com/rusterlium/rustler Clojure: https://clojure.org ClojureScript: https://clojurescript.org cmatrix: https://github.com/abishekvashok/cmatrix Marcin Kulik on GitHub: https://github.com/ku1ik Marcin Kulik on Mastodon: https://hachyderm.io/@ku1ik Marcin Kulik on asciinema.org: https://asciinema.org/~ku1ik "They're Made Out of Meat" demo: https://asciinema.org/a/746358 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/ --- 0:00 Intro 2:28 What Is Asciinema? 4:48 How Asciinema Started 9:51 The Problem of Parsing Terminal Output 14:07 Building a Cross-Platform Recorder 17:01 Rewriting the Parser in ClojureScript 22:19 The Hidden Complexity of Terminals 29:28 Rendering Terminals in the Browser 39:47 When ClojureScript Can't Keep Up 45:28 Moving to Rust and WebAssembly 52:01 The Go Experiment 57:43 Adding Live Terminal Streaming 1:07:12 Can You Scrub Back in a Live Stream? 1:14:40 Editing Recordings 1:25:27 Outro
February 4 · 1 hr 41 min
Building the SpacetimeDB Database, Game-First (with Tyler Cloutier)
Eighteen months ago, Tyler Cloutier appeared on the show with what sounded like an ambitious (some might say crazy) plan: build a new distributed database from scratch, then use it to power a massively multiplayer online game. That's two of the hardest problems in software, tackled simultaneously. But sometimes the best infrastructure comes from solving your own impossible problems. The game, Bitcraft, has now launched on Steam. SpacetimeDB has hit version 1.0. And Tyler returns to share what actually happened when theory met production reality. We cover the launch day performance disasters (including a cascading failure caused by logging while holding a lock), why single-threaded execution running entirely from L1 cache can outperform sophisticated multi-threaded approaches by two orders of magnitude, and how the database's reducer model - borrowed from functional programming - enables zero-downtime code deployments. We also get into how SpacetimeDB is expanding beyond games with TypeScript support and React hooks that make building real-time multiplayer web apps surprisingly simple. If you're building anything where multiple users need to see the same data update in real time - which, as Tyler points out, describes most successful applications from Figma to Facebook - SpacetimeDB's approach of treating every app as a multiplayer game might be worth understanding. -- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join SpacetimeDB: https://spacetimedb.com/ SpacetimeDB on GitHub: https://github.com/clockworklabs/SpacetimeDB Our previous episode with Tyler: https://youtu.be/roEsJcQYjd8 Clockwork Labs: https://clockworklabs.io/ Bitcraft Online: https://bitcraftonline.com/ Bitcraft on Steam: https://store.steampowered.com/app/3454650/BitCraft_Online WebAssembly: https://webassembly.org/ Flecs (ECS for C/C++): https://www.flecs.dev/flecs/ TigerBeetle: https://tigerbeetle.com/ CockroachDB: https://www.cockroachlabs.com/ Google Cloud Spanner: https://cloud.google.com/spanner Erlang: https://www.erlang.org/ Apache Kafka: https://kafka.apache.org/ Tyler Cloutier on X: https://x.com/TylerFCloutier Tyler Cloutier on LinkedIn: https://www.linkedin.com/in/tylercloutier/ -- 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/ 0:00 Intro 2:01 The Architecture of SpacetimeDB 5:01 Client-Side Prediction in Multiplayer Games 11:00 Reducers and Event Streaming 15:00 Launching Bitcraft on Steam 19:00 Debugging Launch Performance Problems 26:56 Hot-Swapping Server Code Without Downtime 30:01 In-Memory Tables and Query Optimization 42:00 Is SpacetimeDB Only For Games? 51:00 Performance Benchmarking For Web Workloads 55:00 Why Single-Threaded Beats Multi-Threaded 1:00:01 Multi-Version Concurrency Control Trade-offs 1:05:01 Sharding Data Across Multiple Nodes 1:10:56 Inter-Module Communication and Actor Models 1:17:00 Replication and the Write-Ahead Log 1:24:00 Supported Client Languages 1:29:00 Getting Started With SpacetimeDB 1:39:02 Outro
Dec 11, 2025 · 1 hr 51 min
Will Turso Be The Better SQLite? (with Glauber Costa)
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. -- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join 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/ -- 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
Nov 20, 2025 · 1 hr 5 min
Can Google's ADK Replace LangChain and MCP? (with Christina Lin)
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. -- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join 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/
Oct 31, 2025 · 1 hr 11 min
Building Observable Systems with eBPF and Linux (with Mohammed Aboullaite)
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. --- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join 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/
Oct 9, 2025 · 1 hr 11 min
Solving Git's Pain Points with Jujutsu (with Martin von Zweigbergk)
Git might be the most ubiquitous tool in software development, but that doesn't mean it's perfect. What if we could keep Git compatibility while fixing its most frustrating aspects—painful merges, scary rebases, being stuck in conflict states, and the confusing staging area? This week we're joined by Martin von Zweigbergk, creator of Jujutsu (JJ), a Git-compatible version control system that takes a fundamentally different approach. Starting from a simple idea—automatically snapshotting your working copy—Martin has built a tool that reimagines how we interact with version control. We explore the clever algebra behind Jujutsu's conflict handling that lets you store conflicts as commits and move freely through your repository even when things are broken. We discuss why there's no staging area, how the operation log gives you powerful undo/redo capabilities, and why rebasing becomes trivially easy when you can edit any commit in your history and have changes automatically propagate forward. Whether you're a Git power user frustrated by interactive rebases, someone who's lost work to a botched merge, or just curious about how version control could work differently, this conversation offers fresh perspectives on a tool we all take for granted. And if you're working with large monorepos or game development assets, Martin's vision for the future of Jujutsu might be exactly what you've been waiting for. --- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join Jujutsu (JJ): https://github.com/martinvonz/jj Jujutsu Documentation: https://martinvonz.github.io/jj/ Git: https://git-scm.com/ Mercurial: https://www.mercurial-scm.org/ Rust: https://www.rust-lang.org/ Watchman: https://facebook.github.io/watchman/ Google Piper: https://research.google/pubs/why-google-stores-billions-of-lines-of-code-in-a-single-repository/ 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/
Sep 24, 2025 · 1 hr 5 min
Getting New Technology Adopted (with Dov Katz)
Getting new technology adopted in a large organization can feel like pushing water uphill. The best tools in the world are useless if we're not allowed to use them, and as companies grow, their habits turn into inertia, then into "the way we've always done things." So how do you break through that resistance and get meaningful change to happen? This week's guest is Dov Katz from Morgan Stanley, who specializes in exactly this challenge - driving developer productivity and getting new practices adopted across thousands of developers. We explore the art of organizational change from every angle: How do you get management buy-in? How do you build grassroots developer enthusiasm? When should you use deterministic tools like OpenRewrite versus AI-powered solutions? And what role does open source play in breaking down the walls between competing financial institutions? Whether you're trying to modernize a legacy codebase, reduce technical debt, or just get your team to try that promising new tool you've discovered, this conversation offers practical strategies for navigating the complex dynamics of enterprise software development. Because sometimes the hardest part of our job isn't writing code - it's getting permission to write better code. --- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join Morgan Stanley: https://www.morganstanley.com/ OpenRewrite: https://docs.openrewrite.org/ Spring Framework: https://spring.io/ Spring Integration: https://spring.io/projects/spring-integration Apache Camel: https://camel.apache.org/ FINOS (FinTech Open Source Foundation): https://www.finos.org/ Linux Foundation: https://www.linuxfoundation.org/ Moderne (Code Remix conference organizers): https://www.moderne.io/ Code Remix Conference: https://www.moderne.io/events 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/
Sep 10, 2025 · 1 hr 12 min
From Unit Tests to Whole Universe Tests (with Will Wilson)
How confident are you when your test suite goes green? If you're honest, probably not 100% confident - because most bugs come from scenarios we never thought to test. Traditional testing only catches the problems we anticipate, but the 3am pager alerts? Those come from the unexpected interactions, timing issues, and edge cases we never imagined. In this episode, Will Wilson from Antithesis takes us deep into the world of autonomous testing. They've built a deterministic hypervisor that can simulate entire distributed systems - complete with fake AWS services - and intelligently explore millions of possible states to find bugs before production. Think property-based testing, but for your entire infrastructure stack. The approach is so thorough they've even used it to find glitches in Super Mario Brothers (seriously). We explore how deterministic simulation works at the hypervisor level, why traditional integration tests are fundamentally limited, and how you can write maintainable tests that actually find the bugs that matter. If you've ever wished you could test "what happens when everything that can go wrong does go wrong," this conversation shows you how that's finally becoming possible. --- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join Antithesis: https://antithesis.com/ Antithesis testing with Super Mario: https://antithesis.com/blog/sdtalk/ ...and with Metroid: https://antithesis.com/blog/2025/metroid/ MongoDB: https://www.mongodb.com/ etcd (Linux Foundation): https://etcd.io/ Facebook Hermit: https://github.com/facebookexperimental/hermit RR (Record-Replay Debugger): https://rr-project.org/ T-SAN (Thread Sanitizer): https://clang.llvm.org/docs/ThreadSanitizer.html Toby Bell's Strange Loop Talk on JPL Testing: https://www.youtube.com/results?search_query=toby+bell+strange+loop+jpl Andy Weir - Project Hail Mary: https://www.goodreads.com/book/show/54493401-project-hail-mary Andy Weir - The Martian: https://www.goodreads.com/book/show/18007564-the-martian Antithesis Blog (Nintendo Games Testing): https://antithesis.com/blog/ 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/
Aug 22, 2025 · 1 hr 24 min
Building Render: Inside a Modern Cloud Platform (with Anurag Goel)
How would you build a Heroku-like platform from scratch? This week we're diving deep into the world of cloud platforms and infrastructure with Anurag Goel, founder and CEO of Render. Starting from the seemingly simple task of hosting a web service, we quickly discover why building a production-ready platform is far more complex than it appears. Why is hosting a Postgres database so challenging? How do you handle millions of users asking for thousands of different features? And what's the secret to building infrastructure that developers actually want to use? We explore the technical challenges of building enterprise-grade services—from implementing reliable backups and high availability to managing private networking and service discovery. Anurag shares insights on choosing between infrastructure-as-code versus configuration, why they built on Go, and how they handle 100 billion requests per month. Plus, we discuss the impact of AI on platform adoption: Are LLMs already influencing which platforms developers choose? Will hosting platforms need to actively support agentic workflows? And what does the future hold for automated debugging? Whether you're curious about building your own platform, want to understand what really happens behind your cloud provider's dashboard, or just enjoy hearing war stories from the infrastructure trenches, this episode has something for you. – Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/join Render: https://render.com/ Render’s MCP Server (Early Access): https://render.com/docs/mcp-server Pulumi: https://www.pulumi.com/ Victoria Metrics: https://victoriametrics.com Loki: https://vector.dev/docs/reference/configuration/sinks/loki/ Vector: https://vector.dev/ 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/
Jul 30, 2025 · 1 hr 49 min
InfluxDB: The Evolution of a Time Series Database (with Paul Dix)
How hard is it to write a good database engine? Hard enough that sometimes it takes several versions to get it just right. Paul Dix joins us this week to talk about his journey building InfluxDB, and he's refreshingly frank about what went right, and what went wrong. Sometimes the real database is the knowledge you pick up along the way.... Paul walks us through InfluxDB's evolution from error logging system to time-series datasbase, and from Go to Rust, with unflinching honesty about the major lessons they learnt along the way. We cover technical details like Time-Structure Merge Trees, to business issues like what happens when your database works but your pricing model is broken. If you're interested in how databases work, this is full of interesting details, and if you're interested in how projects evolve from good idea to functioning business, it's a treat. -- Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@developervoices/join InfluxData: https://www.influxdata.com/ InfluxDB: https://www.influxdata.com/products/influxdb/ DataFusion: https://datafusion.apache.org/ DataFusion Episode: https://www.youtube.com/watch?v=8QNNCr8WfDM Apache Arrow: https://arrow.apache.org/ Apache Parquet: https://parquet.apache.org/ BoltDB: https://github.com/boltdb/bolt LevelDB: https://github.com/google/leveldb RocksDB: https://rocksdb.org/ Gorilla: A Fast, Scalable, In-Memory Time Series Database (Facebook paper): https://www.vldb.org/pvldb/vol8/p1816-teller.pdf Paul on LinkedIn: https://www.linkedin.com/in/pauldix/ 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/
Jul 17, 2025 · 1 hr 18 min
Beyond AI Hype, What Will Developers Actually Use? (with Zach Lloyd)
If AI coding tools are here to stay, what form will they take? How will we use them? Will they be just another window in our IDE, will they push their way to the centre of our development experience, displacing the editor? No one knows, but Zach Lloyd is making a very interesting bet with the latest version of Warp. In this deep dive, Zach walks us through the technical architecture behind agentic development, and how it's completely changed what he & his team have been building. Warp has gone from a terminal built from scratch, to what they're calling an "agentic development environment" - a tool that weaves AI agents, a development, a shell and a conversation into a single, unified experience. This may be the future or just one possible path; regardless it's a fascinating glimpse into how our tools might reshape not just how we code, but how we experience programming itself. Whether you're all-in on agentic coding, a skeptic, or somewhere in between, AI is here to stay. Now's the time to figure out what form it's going to take. # Support Developer Voices - Patreon: https://patreon.com/DeveloperVoices - YouTube: https://www.youtube.com/@DeveloperVoices/join -- Episode Links - Warp Homepage: https://warp.dev/ - Warp Pro Free Month (promo code WARPDEVS25): https://warp.dev/ - Previous Warp Episode: https://youtu.be/bLAJvxUpAcg - SWE-bench: https://www.swebench.com/ - TerminalBench: https://github.com/microsoft/TerminalBench - Model Context Protocol (MCP): https://modelcontextprotocol.io/ - Claude Code: https://claude.ai/code - Anthropic Claude: https://claude.ai/ - VS Code: https://code.visualstudio.com/ - Cursor: https://cursor.sh/ - Language Server Protocol (LSP): https://microsoft.github.io/language-server-protocol/ # Connect - Zach on LinkedIn: https://www.linkedin.com/in/zachlloyd/ - 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/
Jul 4, 2025 · 1 hr 31 min
The $500 Billion Integration Problem, And One Possible Solution (with Marty Pitt)
Ever wondered why data integration is still such a nightmare in 2025? Marty Pitt has built something that might finally solve it. TaxiQL isn't just another query language - it's a semantic layer that lets you query across any system without caring about field names, API differences, or where the data actually lives. Instead of writing endless mapping code between your microservices, databases, and APIs, you describe what your data *means* and let TaxiQL figure out how to get it. In this conversation, Marty walks through the “All Powerful Spreadsheet” moment that sparked TaxiQL, how semantic types work in practice, and why this approach might finally decouple producers from consumers in large organizations. We dive deep into query execution, data lineage, streaming integration, and the technical challenges of building a system that can connect anything to anything. If you've ever spent months mapping fields between systems or maintaining brittle integration code, this one's for you. – Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@developervoices/join – TaxiLang Homepage: https://taxilang.org/ TaxiLang Playground: https://playground.taxilang.org/examples/message-queue-and-database Taxi Lang GitHub repository: https://github.com/taxilang/taxilang OpenAPI Specification (formerly Swagger): https://swagger.io/specification/ YOW! Conference - Australian software conference series: https://yowconference.com/ Spring Framework Kotlin support: https://spring.io/guides/tutorials/spring-boot-kotlin/ Ubiquitous Language (DDD Concept): https://martinfowler.com/bliki/UbiquitousLanguage.html 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/ – 0:00 Intro
Jun 19, 2025 · 57 min
Making Software Crash Before It Breaks (with Isaac Van Doren)
At 23, Isaac is already jaded about software reliability - and frankly, he's got good reason to be. When your grandmother can't access her medical records because a username change broke the entire system, when bugs routinely make people's lives harder, you start to wonder: why do we just accept that software is broken most of the time? Isaac's answer isn't just better testing - it's a whole toolkit of techniques working together. He's advocating for scattering "little bombs" throughout your code via runtime assertions, adding in the right amount of static typing, building feedback loops that page you when invariants break, and running nightly SQL queries to catch the bugs that slip through everything else. All building what he sees as a pyramid of software reliability. Weaving into that, we also dive into the Roc programming language, its unique platform architecture that tailors development to specific domains. Software reliability isn’t just about the end user experience - Roc feeds in the idea we can make reliability easier by tailoring the language domain to the problem at hand. – Isaac’s Homepage: https://isaacvando.com/ Episode on Property Testing: https://youtu.be/wHJZ0icwSkc Property Testing Walkthrough: https://youtu.be/4bpc8NpNHRc Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@developervoices/join Isaac on LinkedIn: https://www.linkedin.com/in/isaacvando/ 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/
Jun 5, 2025 · 1 hr 29 min
Making Apache Kafka Diskless (with Filip Yonov & Josep Prat)
How do you retrofit a clustered data-processing system to use cheap commodity storage? That’s the big question in this episode as we look at one of the many attempts to build a version of Kafka that uses object storage services like S3 as its main disk, sacrificing a little latency for cheap, infinitely-scalable disks. There are several companies trying to walk down that road, and it’s clearly big business - one of them recently got bought out for a rumoured $250m. But one of them is actively trying to get those changes back into the community, as are pushing to make Apache Kafka speak object storage natively. Joining me to explain why and how are Josep Prat and Filip Yonov of Aiven. We break down what it takes to make Kafka’s storage layer optional on a per-topic basis, how they’re making sure it’s not a breaking change, and how they plan to get such a foundational feature merged. – Announcement Post: https://aiven.io/blog/guide-diskless-apache-kafka-kip-1150 Aiven’s (Temporary) Fork, Project Inkless: https://github.com/aiven/inkless/blob/main/docs/inkless/README.md Kafka Improvement Process (KIP) Articles: KIP-1150: https://cwiki.apache.org/confluence/display/KAFKA/KIP-1150%3A+Diskless+Topics KIP-1163: Diskless Core: https://cwiki.apache.org/confluence/display/KAFKA/KIP-1163%3A+Diskless+Core KIP-1164: Topic Based Batch Coordinator: https://cwiki.apache.org/confluence/display/KAFKA/KIP-1164%3A+Topic+Based+Batch+Coordinator KIP-1165: Object Compaction for Diskless: https://cwiki.apache.org/confluence/display/KAFKA/KIP-1165%3A+Object+Compaction+for+Diskless Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@developervoices/join Filip on LinkedIn: https://www.linkedin.com/in/filipyonov Josep on LinkedIn: https://www.linkedin.com/in/jlprat/ 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/
May 23, 2025 · 1 hr 24 min
Java's Cutting Edge Comeback (with Josh Long)
Java’s has been evolving faster than any 30 year old language has a right to do, and there’s probably no-one more pleased about it than my guest this week - Josh Long. He’s a Java & Kotlin programming, a JVM enthusiast in general, and an advocate for Spring, and he has chapters full of news about what’s been happening in Javaland over the past few years. Everything from new threading models to C interop changes, custom primitives to high performance computing and all the ways in which Java is modernising for age of AI workloads. If you’re out of touch with the latest in the JVM, or don’t know how much its changed, Josh’s brain is full of all the news you need to catch up. – Project Valhalla (Value Objects): https://openjdk.org/projects/valhalla/ Project Panama (JVM’s new native code support): https://openjdk.org/projects/panama/ Jextract: https://github.com/openjdk/jextract Spring Initializer: http://start.spring.io/ Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@developervoices/join 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/
May 8, 2025 · 1 hr 12 min
The State & Future of Apache Kafka (with Anatoly Zelenin)
I’m joined this week by one of the authors of Apache Kafka In Action, to take a look at the state of Kafka, event systems & stream-processing technology. It’s an approach (and a whole market) that’s had at least a decade to mature, so how has it done? What does Kafka offer to developers and businesses, and which parts do they actually care about? What have streaming data systems promised and what have they actually delivered? What’s still left to build? – Apache Kafka in Action: https://www.manning.com/books/apache-kafka-in-action Pat Helland, Data on the Inside vs Data on the Outside: https://queue.acm.org/detail.cfm?id=3415014 Out of the Tar Pit: https://curtclifton.net/papers/MoseleyMarks06a.pdf Martin Kleppmann, Turning the Database Inside-Out: https://martin.kleppmann.com/2015/11/05/database-inside-out-at-oredev.html Data Mesh by Zhamak Dehghani: https://www.amazon.co.uk/Data-Mesh-Delivering-Data-Driven-Value/dp/1492092398 Quix Streams: https://github.com/quixio/quix-streams XTDB: https://xtdb.com/ Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices Support Developer Voices on YouTube: https://www.youtube.com/@developervoices/join Anatoly’s Website: https://zelenin.de/ Kris on Mastodon: http://mastodon.social/@krisajenkins Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/ Kris on Twitter: https://twitter.com/krisajenkins

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