Machine learning

Design and run reproducible LLM fine-tuning with TRL, PEFT, Hugging Face Jobs, validated datasets, experiment tracking, and durable Hub outputs.

Instrument, monitor, compare, and diagnose machine-learning experiments with Trackio metrics, alerts, dashboards, and structured exports.

Prepare, train, evaluate, and publish computer-vision and vision-language models with Hugging Face datasets, Transformers, PEFT, and Jobs.

Build and validate local browser or Bun inference with Hugging Face Transformers.js, WebGPU or WASM backends, quantization, caching, and cleanup.

Select and optimize embedding models for semantic search and RAG applications.

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.

Use when turning a transcribed source — a thesis, paper, standard, manual, or a folder of Markdown chapters under docs/references — into a Karpathy-style LLM…

Scan source files (Python, C/C++, TypeScript, Rust) and generate semantic logic index via LLM.

Use this skill when the user wants an AI governance, legal-risk, privacy, compliance, procurement, or vendor-risk review of an internal AI use case, an AI…

Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".

Guides migration, provisioning, search, log-analytics, trace-analytics, and Agentic AI Assistant workflows for Amazon OpenSearch Service and Serverless across…

Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors).

Team conventions for Python development — credentials, API clients, LLM response parsing, testing patterns, and data pipeline structure.

Build, integrate, migrate to, and answer how-to questions for Stream Chat, Video, and Feeds in Swift / SwiftUI / UIKit / iOS apps - including AI assistants…

Run reproducible local or remote model evaluations with inspect-ai or LightEval, bounded smoke tests, captured configurations, and comparable results.

Build, test, and publish accessible Gradio interfaces for models and data workflows using reproducible Python environments.

Prepare and publish reproducible Hugging Face paper pages with linked code, models, datasets, demos, metadata, and validated claims.

Discover and analyze Hugging Face papers with traceable sources, linked models and datasets, and clearly separated claims and evidence.

Write research prompts that come back decision-ready — zero-prior-knowledge framing, evidence hierarchy, fact/inference separation, and a mandatory gap round.

Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart".

Set up AI Runway on AKS — from bare cluster to running model.

Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence.

Debug live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation.

Search your own ADHX saves (posts kept from X, Instagram, TikTok, YouTube, Reddit and more) by meaning, including what is in pictures and videos, and fetch…