San Marcos, Texas · montoyaraul34@gmail.com
Making large models run on hardware you can actually own.
I research SAAQ (Spiking Adaptive Activity Quantization): ways to study and compress large MoE models with spiking / neuromorphic ideas, mostly on hardware I can run myself (e.g. an RTX 5080). I am still learning Julia, Rust, Python, and CUDA.
Repos: rmems · libraries: Limen Neural
| magere-brug | SAAQ lab (recipes, manifests, experiments) |
| corinth-canal | SAAQ reference pipeline (Rust) |
| xai-dissect | Inspect open-weight Grok checkpoints |
| grok-ozempic | Grok-scale SNN-style quantization experiments |
| Surrogate_Viz.jl | Symbolic regression on telemetry |
| XAIDissect_Viz.jl | Viz for xai-dissect reports |
| gaming-telemetry / Theseus-Quarry | Telemetry for neuromorphic work |
| operation-prometheus | Engineering-trajectory datasets for local agents |
More under rmems (training forge, benchmarks, experimental SNN-HFT research that is not live trading, worktree-hive early/experimental, etc.).
I normally go from issue → pull request, either from a local CLI or through Linear (cloud).
- Agents I use day to day: Grok Build, Codex, Claude, Cursor, Devin, and others — plus shared memory (Ogham / Chroma). Humans merge.
- PR review (rough order): Codex → CodeRabbit → Devin when I want a strong extra pass → Cursor Bugbot · Copilot · CodeAnt · Qodana (local + cloud).
- CI: GitHub Actions on pull requests and pushes to main research repos.
- SAAQ path: magere-brug + corinth-canal
- Grok-scale experiments: xai-dissect / grok-ozempic
- Pursuing AI Engineering @ WGU
README updated with Grok Build: Grok 4.5 (high)



