barry@here:~$ about
Senior Staff AI Researcher @ Meta FAIR Coding ← OpenAI ← Augment Code ← DeepMind
I have been working on AI since 2015. Among all the AIs I have trained, model size has grown 10^6 times, from 1MB to 2TB; dataset size has grown 10^6 times, from 0.2GB to 200TB; and the number of GPUs I use has grown 10^5 times, from 1 GPU to 100K GPUs.
AIs I built after ChatGPT: Gemini, o3, o4-mini, Augment RLDB
AIs I built before ChatGPT: Lion Optimizer, DoReMi Pretraining Recipe, Automated Search for Architectures/Hyperparameters
type help to explore.
Selected work
- 2025 · Meta FAIR Coding — Push the boundary of single prompt optimization, agentic coding, and kernel generation.
- 2024 · OpenAI — Contributed to infra, ChatGPT o3 / o4-mini, and the pretraining data wall.
- 2023 · Augment Code — Led RL for coding models from real developer IDE behavior.
- 2020 · Google Brain/DeepMind — Research on LLMs[1,2] and AutoML[3,4,5] at Brain; later a founding member of Bard (now Gemini); contributed to Gemini 1.0 and 2.5.
Recent writing
Being an AGI Believer, Not Skeptic - Lessons from Augment Code
What it takes for AI research to compound—and what happens when it doesn't. Lessons from Augment Code on the gap between being right and winning.
Reinforcement Learning from Developer Behavior and Real IDE Environment
How we achieved a breakthrough in AI code completion by learning directly from natural developer IDE interactions, matching the gains of doubling model size and 10x more finetuning data.