Developer Voices

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.
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Support Developer Voices on Patreon: https://patreon.com/DeveloperVoices
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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/

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.
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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/

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.
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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/

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.
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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/
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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

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.
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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/
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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