The Dollar Bet that Fuzzed Figma: Exploding Laptops and UI Reliability with Jonathan Chan

Why simple workloads find the hardest bugs

You hit 100% test coverage, deploy to production, and the system still falls over. An old story, maybe aside from the 100% coverage percent part. In this episode, David sits down with Marco Primi and Sean Allen from the Antithesis Red Team to talk about a likely missing piece in your testing strategy: workloads. They break down exactly what workloads are, why they are so helpful for testing distributed systems, and the biggest pitfalls engineers hit when trying them out. They talk about why you have to let go of deterministic, "happy path" testing, and how even a workload as "dumb" as a basic `while true` loop can expose silent data loss that your unit tests would never catch.

Programming as an Act of Building Vocabulary

Why do LLMs struggle to build complex architecture?  According to Unmesh Joshi, Distinguished Engineer at ThoughtWorks, it often comes down to a lack of shared abstractions, of a shared vocabulary. Today he joins Will Wilson and David Wynn to unpack how we learn about, build, and test distributed systems. We cover the value of building miniature versions of systems like Kafka to grasp the basics, why Deterministic Simulation Testing (DST) is a highly effective tool for teaching as well as finding bugs , and why Guy Steele's 1998 keynote holds the secret to successfully collaborating with modern AI tools.

The Dollar Bet that Fuzzed Figma: Exploding Laptops and UI Reliability with Jonathan Chan

Can you stress-test a React UI completely hands-free? In this episode, Oskar Wickström and I are joined by Jonathan Chan to discuss the origins of Fuzzmap. From a janitor’s closet in Figma—where end-to-end tests once lived on a "spicy pillow" MacBook—Jonathan shares how he applied coverage-guided fuzzing (inspired by AFL) to the world of web front-ends. We dive into the gnarly hacks required to instrument React, the challenges of visualizing complex state machines, and the future of "full-stack" fuzzing in the age of LLMs and team agents.

Semmathesy and the Agentic Era: Learning Systems in 2026

With the "Agentic Era" coming online, teams are onboarding new AI "minions" into our codebases every thirty minutes. So a critical question emerges: Are we actually becoming more productive, or just more busy?Jessica Kerr (Jessitron) joins me to explore symmathesis—the theory of learning systems made of learning parts. We dive into viewing software as a teammate, how the definition of "legacy code" is shifting to anything an AI can't understand, and why our most important human contribution is moving from writing lines of code to orchestrating the flows of learning across the entire system.We dive into the shifting boundaries between the social and technical worlds, and why the "productivity unlock" of AI might actually make software engineering a more human endeavor than ever before.

From Scale to Rigor: An Engineering Journey at Meta and Oxide

Today we’re talking with Rain from Oxide Computing, tracing their journey from the massive, data-driven scale of Meta to the high-stakes, air-gapped world of shipping a 'cloud in a box'. We talk about moving from an environment of over 10,000 engineers—where a 10% tooling improvement is worth a thousand people—to a culture where you’re shipping hardware that simply cannot be patched once it leaves the building. Rain shares how they realized that technical writing is a senior engineer’s ultimate force multiplier for justifying complex designs. We also dive deep into the 'invisible superpower' of property-based testing and using Oracles to kill bugs in the developer’s inner loop long before they ever have a chance to reach production. Mentioned: https://rfd.shared.oxide.computer/rfd/0576 Further reading: https://oxide-and-friends.transistor.fm/episodes/engineering-rigor-in-the-llm-age