Observed Signal · Nov 5, 2026 · Technical Release · Source: The Pragmatic Engineer · Impact: 2/5 · Sentiment: Positive
Scaling Slack and GitHub: Maude Lemaire's Lessons
In this episode of The Pragmatic Engineer podcast, Maude Lemaire, a principal engineer with experience at Slack, GitHub, and Cursor, discusses the challenges of scaling large software products. She shares insights on load testing at Slack, including the development of the Koi Pond tool to simulate millions of WebSocket connections, and the thundering herd problem that led to a limit on custom emojis. At GitHub, she discusses the downsides of Active Record, including database load and sharding limitations. Lemaire also emphasizes the importance of human judgment when using AI, particularly for foundational work, and suggests rotation between platform and product engineers to improve alignment.
The article provides technical insights into scaling challenges at major tech companies, but it is a podcast summary without breaking news or immediate industry impact.
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Key Takeaways & Evidence Grounding
- Maude Lemaire worked for nearly 8 years at Slack leading the backend performance infrastructure team.
- Slack limits custom emojis to 23 to prevent thundering herds.
- Maude built the Koi Pond tool to load test 500,000 WebSocket connections cost-effectively.
- GitHub's use of Active Record led to scatter queries, limiting database sharding to 16 shards.
- Maude suggests rotation between platform and product engineers to improve alignment.
Connected Companies & Entities
3 Entities mapped“Maude Lemaire spent nearly eight years at Slack, where she led the backend performance infrastructure team.”
“She then joined GitHub to tackle database scaling challenges, including the Rails monolith and Active Record issues.”
“Most recently, she worked on improving performance at Cursor, including time to first token.”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Pragmatic Engineer Podcast: Design Engineering with Maggie Appleton
In this episode of the Pragmatic Engineer Podcast, host Gergely Orosz interviews Maggie Appleton, a staff research engineer at GitHub Next. Appleton discusses her unique career path from cultural anthropology to design engineering, her work illustrating React tutorials for Egghead, and her insights on how AI is transforming design processes. She shares practical advice on using notebooks and jigs for prototyping, the importance of understanding engineering constraints, and introduces the concept of 'capability gaslighting' where AI models overpromise and underdeliver. The conversation explores new artifacts for human-AI collaboration and encourages engineers to use AI agents as patient tutors for learning design principles.
Context Engineering: Interview with Dex Horthy
This Pragmatic Engineer podcast episode features Dex Horthy, CEO and cofounder of HumanLayer, discussing 'context engineering' — techniques for managing LLM context windows, loop and harness engineering, and approaches to AI-assisted software development. Horthy shares lessons from conversations with ~100 AI engineers and from experiments (including a failed attempt to deploy unread AI-written code), outlines practices like intentional compaction and slow nightly loops, and describes three software-factory approaches that balance agentic coding with human review. The episode links to Horthy’s '12-Factor Agents' and resources from HumanLayer, and includes timestamps and transcript.
GitHub COO on Agents, Copilot, and Scaling Challenges
GitHub COO Kyle Daigle discusses how the rapid rise of AI coding agents has driven dramatic increases in commits, builds and Actions usage, creating novel scaling, availability and security challenges for GitHub. He describes internal agent workflows (WorkIQ, MCP, FoundryIQ), the evolution of Copilot from completion to an agent SDK with CLI and desktop app, and GitHub’s approach to rolling AI into existing workflows via small “micro-skills.” Daigle outlines infrastructure bottlenecks (permissioning on an older MySQL layer, Actions CPU demand, monorepos), security moves (2FA, token invalidation), the npm acquisition and dependency-management tradeoffs, and the need for better trust signals for agent-generated contributions. He also notes his expanded role across GitHub and Microsoft and points to Build announcements (cloud agents, sandboxing, Azure Dev Compute) as part of GitHub/Microsoft’s response.
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