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