More Projects

These works are kept in the ledger even when they are not featured. Status and measurements reflect the recorded project state.

Trident-TS

Architecture complete · not trained

Motivation and problem. Physics-based numerical weather prediction is expensive; neural surrogates need uncertainty estimates and multimodal inputs.

Architecture and implementation. A Vision–Memory–Decoder design combines frozen DINOv2 spatial features and an Echo State Network for temporal dynamics, a Mamba selective state-space model for linear-time sequence processing, and a quantile head producing P25/P50/P75 forecasts. WeatherBench 2 data streams from Google Cloud Storage through Xarray, Zarr, Dask, and gcsfs.

Lessons and future work. The proposed 100x improvement over physics-based NWP is a design target, not a result. Training and an end-to-end benchmark are the next required work.

Alpaca

In progress

Motivation and problem. vLLM serves models efficiently but lacks the compact model-management workflow associated with Ollama; multi-node operation also requires manual Ray setup.

Architecture and implementation. A Python CLI wraps vLLM process management, PID tracking, port allocation, aliases, data types, and network configuration. Ray orchestration is being added for cluster start, monitoring, discovery, and shutdown.

Lessons and future work. Single-machine serving is functional. Multi-node Ray orchestration is not shipped and remains the explicit next milestone.

Hugging-Hat

Infrastructure complete · not trained

Motivation and problem. The project explores adding test-time computation to a frozen decoder-only language model without replacing Hugging Face's standard .generate() path.

Architecture. Three optional modules attach through forward hooks: a parameter-shared gated recurrent Thinker Hat, an early-layer Latent Router Hat selecting a fixed decode budget from {0, 2, 4, 8}, and a late-layer cross-attentive Critic Hat using cached prompt memory.

Implementation. Base weights remain frozen. Hat-only checkpoints use safetensors and version metadata. Semantic and explicit layer selectors, YAML configuration, JSONL completion masking, and a dummy-model harness test hooks and persistence. The true no-op path uses num_steps=0.

Lessons and future work. Wiring, persistence, and masking are verified, but no model-quality result exists. The staged plan is Thinker training, Router teacher sweeps, then an opt-in Critic.

Toxic Comment BERT PEFT

Completed

Motivation and problem. Full BERT fine-tuning is memory-intensive; the goal was a parameter-efficient classifier with a deployable serving path.

Architecture and implementation. BERT Base Uncased is adapted with LoRA on balanced Civil Comments samples. Accelerate handles distributed training. FastAPI exposes confidence-scored predictions; Docker, SageMaker, and CloudWatch provide deployment and monitoring. ONNX export is supported.

Results and lessons. F1 and accuracy were tracked, but no values are recorded in the source. The completed result is the end-to-end training and serving path, not an unreported quality claim. No future phase is recorded.

Blackhole

Completed

Motivation and problem. Common one-way remote copies should not require recalling a full set of rsync flags.

Architecture and implementation. A Python CLI wraps rsync and standard SSH syntax, preserving resumable transfers, key authentication, optional destination paths, and graceful interruption.

Lessons and future work. The tool remains intentionally thin: rsync owns transport semantics while the wrapper owns the common interface. No further version is recorded.

Telecommit

Completed

Motivation and problem. Writing commit messages from staged changes is repetitive.

Architecture and implementation. A Python CLI reads staged or optional unstaged diffs and sends them to the Anthropic Claude API. YAML at ~/.config/telecommit/config.yaml and an environment variable hold configuration. An optional flag executes the generated commit.

Lessons and future work. The tool fits into Git rather than replacing it. Since diffs leave the machine for generation, API use is an explicit system boundary. No next version is recorded.