MLOps2017
Expresses data quality checks as reusable, declarative "expectations" and auto-generates human-readable validation reports and docs; integrates with Python data stacks to enforce and monitor data reliability in ML and analytics pipelines.
Provides 150+ executed Jupyter notebooks and code that reproduce the book 'Machine Learning for Algorithmic Trading (2nd ed.)' — covers feature engineering, alternative-data signal extraction, backtesting, NLP, deep learning and reinforcement learning for trading; best for quant researchers and practitioners.
Showed that fine-tuning a GPT model on public GitHub code yields a capable program synthesizer, and introduced HumanEval — the docstring-to-function benchmark that still anchors code-generation evaluation. A production variant powers GitHub Copilot.
AI Dataset2022
Provides 115M public GitHub source files (≈873GB of code, ~1TB uncompressed) with per-file metadata (repo, path, language, license). Supports streaming, language/license filtering and full download for training and evaluating code LLMs and code generation models.
AI Infra2022
Provides runtime support for reversible effects and reactive coeffects so components can be declared, composed, and hot-replaced safely; includes effect tracking, coeffect resolution, a declarative component loader and HMR — aimed at plugin-driven agent harnesses and dynamic systems.
AI Dataset2022
Contains 40,000 teacher-generated reasoning traces distilled from the Qwen3.8-27B model for supervised fine-tuning and analysis. Covers code, math, science and logic; each example pairs a <think> chain-of-thought with a final response and is distributed in JSONL/Parquet for SFT workflows.
Enables real-time (≥30 fps) 1080p novel-view synthesis by representing scenes as optimized anisotropic 3D Gaussians plus a visibility-aware splatting renderer; provides the paper's reference implementation, pretrained models and viewers — high-quality training requires CUDA GPU and significant VRAM.
AI Model2023
Provides code and pretrained models for WeatherNext 2 and WeatherNext Cyclones — ML-based global, medium-range atmospheric forecasting models from Google DeepMind/Google Research. Includes Colab demos, pretrained weights, and guides for cloud access and inference.
AI Client2023
Lets LLMs run code and control a user’s computer via natural language (Python, JavaScript, Shell, etc.) with interactive approval. Supports local or hosted models, terminal and Colab/Codespaces integrations, streaming output, and configurable safety/auto-run options.
AI Model2023
Estimates and tracks 6D poses of novel objects without per-object fine-tuning — supports both model-based (CAD) and model-free (few reference images) setups. Trained on large-scale synthetic data with a transformer-based architecture and contrastive learning; CVPR 2024 highlight with demos and pretrained weights.
A family of open code models (1.3B-33B) trained from scratch on 2T tokens of project-level code, using a 16K-window fill-in-the-blank objective. Beats Codex and GPT-3.5 on code benchmarks and ships under a license permitting commercial use.
AI Image2024
Produces real-time 3D reconstructions from multi-view images using Gaussian splatting, with on-device training and interactive viewing across native desktops, Android, and the browser. Uses WebGPU and the Burn ML framework to ship dependency-free binaries, a CLI, live training visualization, and streaming .ply support.