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.

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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/

Building Render: Inside a Modern Cloud Platform (with Anurag Goel)

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.

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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/

InfluxDB: The Evolution of a Time Series Database (with Paul Dix)

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/

Beyond AI Hype, What Will Developers Actually Use? (with Zach Lloyd)

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.

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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/

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0:00 Intro

The $500 Billion Integration Problem, And One Possible Solution (with Marty Pitt)

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.

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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

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Isaac on LinkedIn: https://www.linkedin.com/in/isaacvando/

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

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

Making Software Crash Before It Breaks (with Isaac Van Doren)

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.

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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

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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

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

Making Apache Kafka Diskless (with Filip Yonov & Josep Prat)

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.



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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/



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

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

Java’s Cutting Edge Comeback (with Josh Long)

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?

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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/

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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

The State & Future of Apache Kafka (with Anatoly Zelenin)

Building a database is a serious undertaking. There are just so many parts that you have to implement before you even get to a decent prototype, and so many hours of work before you could begin working on the ideas that would make your database unique. Apache DataFusion is a project that hopes to change all that, but building an extensible, composable toolkit of database pieces, which could let you build a viable database extremely quickly, and then innovate from that starting point. And even if you’re not building a database, it’s a fascinating project to explain how databases are built.

Joining me to explain it all is Andrew Lamb, one of DataFusion’s core contributors, and he’s going to take us through the whole stack, how it’s built and how you could use it. Along the way we cover everything from who’s building interesting new databases and how you manage a large, open-source Rust project.

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DataFusion Homepage: https://datafusion.apache.org/

DataFusion on Github: https://github.com/apache/datafusion

DataFusion Architecture (with diagrams!): https://youtu.be/NVKujPxwSBA?si=tw9ACxlbdpBuVsnv&t=1045

Datalog: https://docs.racket-lang.org/datalog/

Tokio: https://tokio.rs/

Andrew’s Homepage: http://andrew.nerdnetworks.org/

Andrew’s Blog Post about Tokio: https://thenewstack.io/using-rustlangs-async-tokio-runtime-for-cpu-bound-tasks/

Velox: https://velox-lib.io/

Arroyo: https://www.arroyo.dev/

Synnada: https://www.synnada.ai/

LanceDB: https://lancedb.com/

SDF+DBT: https://docs.sdf.com/integrations/dbt/integrating

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

DataFusion - The Database Building Toolkit (with Andrew Lamb)

Jupyter’s become an incredibly popular programming and data science tool, but how does it actually work? How have they built an interactive language execution engine? And if we understand the architecture, what else could it be used for?

Joining me to look inside the Jupyter toolbox are Afshin Darian and Sylvain Corlay, two of Jupyters long-standing contributors and project-steerers. They’ve going to take us on a journey that starts with today’s userbase, goes through the execution protocol and ends with a look at what Jupyter will be in the future - an ambitious framework for interactive, collaborative applications and more.

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

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Jupyter Homepage: https://jupyter.org/

Jupyter Xeus: https://github.com/jupyter-xeus/xeus

Jupyter AI: https://github.com/jupyterlab/jupyter-ai

Jupyter CAD: https://github.com/jupytercad/JupyterCAD

Jupyter GIS: https://github.com/geojupyter/jupytergis/

Jupyter GIS Announcement: https://blog.jupyter.org/real-time-collaboration-and-collaborative-editing-for-gis-workflows-with-jupyter-and-qgis-d25dbe2832a6

QGIS: https://qgis.org/

ZeroMQ: https://zeromq.org/

Sylvain on LinkedIn: https://www.linkedin.com/in/sylvaincorlay

Darian on LinkedIn: https://www.linkedin.com/in/afshindarian

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/

Jupyter’s Architecture Unpacked (with Afshin Darian & Sylvain Corlay)

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.

Kris Jenkins

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