
I am an applied AI researcher.
I am broadly interested in superhuman hardware co-design, and in recursive self-improvement through AI that designs the chips and energy systems that power it.
These days I am working on MHD modeling with AI agents and deep reinforcement learning.
Building scalable, self-improving AI systems is one of the most important goals of our time, and I am pursuing it across several domains. My personal aim is to contribute to abundance.
Here I share open, technical deep dives and worklogs. My open-source projects are on GitHub.
Projects
- Open sourcecu-Jev. CUDA-native System One decision engine with a Jev-compatible API.
Writing
- 2026 SepWorklog: a recurrent looped transformer in Lean
- 2026 SepIntroducing cu-Jev (CUDA-Jev)
- 2026 SepMulti-agent orchestration with Codex, borrowing Fable 5.1 and Fugu Ultra v1.1
- 2026 AprVerified Replay Distillation (VRD) recipe for continual learning in verifiable domains
- 2026 Marautoresearch-mamba: Karpathy-Style Autoresearch for Mamba-2, Mamba-3, and Hybrid Mamba–Transformer MoE
- 2026 MarA self-improving skill catalog for AI agents
- 2026 FebMem-RLM: Memory-Augmented Inference for Recursive Language Models
- 2026 FebClaude Code-Time Skill Acquisition with Agent Teams
- 2026 FebOn Compression, Computation and the Space Between
- 2025 NovDefeating Nondeterminism in LLM Inference: Reproducing Batch-Invariant Ops (RMSNorm & Tiled Matrix Multiplication) in JAX
- 2025 OctStreaming Deep Agents and Task Delegation with Real-Time Output