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Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Jun 28, 2026 · 14:57

Vaidas Razgaitis, Senior Research Engineer at Higharc, presents three tactical focus areas to accelerate the handoff of frontier ML research into production features. First, he advocates for a Research Prototype Taxonomy Document—a technical design document tailored for ML that maps domain context, business goals, type safety, persistence, and system architecture. Second, he details Higharc's monorepo of cleanly isolated microservices with a layered API/business logic/data structure, enabling researchers to own individual services. Third, he explains using Graphite for stack diffs to decompose monolithic prototypes into small, reviewable PRs that tap subject matter experts asynchronously. The talk offers diagnostic questions to assess team velocity across these three levers.

Self Coding Agents — Colin Flaherty, Augment Code

Apr 21, 2025 · 17:23

Colin Flaherty, founding researcher at Augment Code, describes how the company built an AI coding agent that wrote over 90% of its own 20,000-line codebase with human supervision. The agent autonomously added third-party integrations like Google Search and Linear by searching its own codebase and even used its own Google Search tool to look up Linear API docs. It profiled and optimized itself, replacing synchronous file hashing with a process pool after adding print statements and running sub-copies. Flaherty argues that agent capabilities improve with better context engines, code execution environments, and test harnesses—adding test-driven iteration boosted a bug-fixing benchmark by 20% versus 4% from just upgrading the foundation model. He cautions that agents are general-purpose, not category-specific, and have different strengths than humans, making knowledge bases crucial for onboarding. With code becoming cheap, the focus shifts to product insights and design, while better tests enable greater autonomy and accelerate agent self-improvement.