Neovim

Amp Code: Next Generation AI Coding – Beyang Liu, Amp Code

Dec 22, 2025 · 18:31

Beyang Liu, co-founder and CTO of Amp Code, presents Amp as an opinionated frontier coding agent that rejects MCP in favor of custom tools to avoid context confusion and optimize feedback loops. Amp's architecture relies on specialized subagents—the finder for code search, the oracle for deep reasoning, the librarian for external context, and the kraken for large-scale refactors—rather than a model selector. The agent offers two top-level modes: a slower 'smart' agent for complex tasks and a 'rush' agent for quick, in-loop edits, recently updated to leverage Gemini 3. To address inference costs, Amp ships subtle ads in the terminal to sponsor free usage for students and side projects. Liu also highlights a shared-threads feature for team learning and a community called the Weird Builder Cohort, emphasizing a culture of experimentation over hype.

AI Engineering at Jane Street - John Crepezzi

Mar 28, 2025 · 16:57

John Crepezzi, an engineer on Jane Street's AI Assistants team, explains how they built custom LLM-powered coding assistants for OCaml, a functional language with scarce public training data. To overcome off-the-shelf tool limitations, they trained models on data from workspace snapshots — automated captures of developer workstations every 20 seconds, tracking build status changes from red to green. They used a Code Evaluation Service (CES) for reinforcement learning and evaluation, pre-warming builds to quickly test if model-generated diffs compile and pass tests. Their sidecar architecture, AID, serves thin editor integrations for Emacs (used by 67% of the firm), VS Code, and Neovim, allowing easy model swapping and A/B testing (e.g., sending 50% of users to different models). The talk details the end-to-end process: data collection from features, commits, and snapshots; training with supervised data and RL; and building pluggable infrastructure for domain-specific tools.