ESLint

Loop Engineering from First Principles — Kyle Mistele, HumanLayer

Jul 25, 2026 · 17:57

Kyle Mistele argues that the fix for AI-generated 40,000-line pull requests is not a better prompt but a better loop, borrowing from control theory: a sensor measures the gap between current and desired codebase state, a controller picks the smallest incremental change, and an actuator agent applies it using hand-written golden patterns. Mistele illustrates with HumanLayer's own loop that migrates their RPC API to Effect one procedure at a time, using AST grep as a deterministic sensor, a controller that selects the smallest unmigrated procedure, and an actuator agent gated by deterministic CI running a single iteration per day. The loop tracks its own PRs in version control, refuses to stack a new change while an earlier one is still open, and includes a feedback file and comment trigger for humans to re-steer it. Mistele concludes that this design makes the code incrementally better, readable, and verifiable, solving the problem of unreadable mass-generated code.

Harness Engineering: How to Build Software When Humans Steer, Agents Execute — Ryan Lopopolo, OpenAI

Apr 17, 2026 · 46:21

Ryan Lopopolo, a member of technical staff at OpenAI, argues that software engineers must shift to 'harness engineering' where agents produce all code and humans focus on delegation, system design, and guardrails. He describes how his team banned editors, using Codex with skills to manipulate apps, and reduced review friction by automating lint rules and review agents. Lopopolo explains that code is free but human time and model context are scarce, so teams should structure repositories for consistency and use 'garbage collection days' to eliminate recurring slop. He shares practical techniques like writing tests about source code (e.g., file length limits) and using review agents that inject prompts. His future vision involves agents autonomously executing long-horizon work based on token budgets and success metrics, freeing humans for higher-level activities.

Code Generation and Maintenance at Scale: Morgante Pell

Oct 17, 2024 · 18:54

Morgante Pell, founder of Grit, argues that AI agents for code generation must be built for modifying large existing codebases rather than generating new apps, and that combining static analysis with AI enables reliable migrations at scale. Grit has merged more PRs than any other company by focusing on supercharging top engineers, using GritQL to precisely find code via syntactic and semantic queries, and relying on compilers like TSC to catch errors that LLMs miss. They overcome slow enterprise builds (10 minutes for type checking) by precomputing in-memory indexes and using Firecracker to snapshot and fork environments for parallel agent execution, achieving PRs with only a few iterations. For editing, they developed a custom 'loose search and replace' format to avoid expensive full-file generation and LLM laziness. This approach allowed one customer to complete a multi-year OpenTelemetry migration in a week with under 100 developer hours. Morgante envisions future UIs that let engineers manage entire codebases like SimCity.