Made With ML is an educational data science project that implements an end-to-end ML pipeline for classifying GitHub repositories into categories (NLP, computer vision, MLOps, other) based on their titles and descriptions. The core data lineage flows from raw CSV datasets (dataset.csv, holdout.csv) through text cleaning, tokenization via SciBERT, and stratified splitting into train/val/test sets, followed by distributed training with PyTorch and Ray Train, hyperparameter tuning via HyperOpt, evaluation with slice-based metrics using Snorkel, and serving through a FastAPI/Ray Serve deployment. The project demonstrates MLOps patterns including MLflow experiment tracking, data validation with Great Expectations, behavioral model testing, and CI/CD via GitHub Actions with Anyscale for cloud execution. The codebase is well-structured with clear separation of concerns across data.py, train.py, tune.py, evaluate.py, predict.py, serve.py, models.py, and utils.py. It includes comprehensive tests at the code, data, and model levels. However, the project shows signs of staleness with pinned dependencies from 2023 (Ray 2.7.0, transformers 4.28.1, torch 2.0.0) that have known security vulnerabilities and compatibility issues with current Python versions. The data pipeline components—particularly the CustomPreprocessor, stratified splitting, and text cleaning—are genuinely reusable patterns. The evaluation framework with slice-based metrics and behavioral tests (invariance, directional expectations, minimum functionality) represents solid ML engineering practice. The deployment configuration is tightly coupled to Anyscale's proprietary platform, limiting portability.
BFO-2020 is the official release repository for the Basic Formal Ontology (BFO) 2020, an upper-level ontology for representing entities, continuants, occurrents, processes, and other fundamental categories. The repository contains OWL, Common Logic, and other serializations of the core ontology, plus temporal and atemporal profiles. It is not a software application; it is a knowledge-representation artifact with a software-engineering-style build pipeline. The Makefile drives ROBOT-based reasoning, SPARQL quality checks, and multi-format export, and GitHub Actions runs these checks on every push. This gives the repo a hybrid character: the primary content is formal ontology data, but it is maintained with application-grade CI/CD practices.
This repository is a comprehensive, curated list of anomaly detection (outlier detection) resources, maintained by the author of the PyOD library. It aggregates books, academic papers, tutorials, open-source toolkits, datasets, and relevant conferences/journals, covering tabular, time-series, graph, and text data modalities. The repository also includes two utility scripts: a downloader for paper PDFs and a robust URL checker for verifying links in the documentation. While the core value is the curated resource list itself, the utility scripts demonstrate clean engineering practices and are reusable for similar link-checking and batch download tasks. This is not a software application or data pipeline; it is a reference catalog for researchers and practitioners.
This repository provides the official PyTorch implementation of MAR (Masked Autoregressive with Diffusion Loss), a research method for class-conditional image generation on ImageNet 256x256. The core contribution is an autoregressive model that operates on continuous-valued latent tokens (via a pre-trained VAE) and uses a diffusion loss instead of vector quantization. The codebase includes model definitions (MAR encoder-decoder transformer, DiffLoss MLP), training and evaluation scripts with PyTorch DDP, a VAE latent caching pipeline, and pre-trained checkpoints for base/large/huge variants. While primarily a research prototype, it contains reusable components such as the DiffLoss module, diffusion utilities, and a clean data caching pattern. The Gradio app and Colab notebook make the model accessible for interactive use.
This repository is a research codebase for implementing and experimenting with dense retrieval techniques. It provides implementations of multiple advanced retrieval models including DPR, CITADEL, COIL, ColBERT, SPLADE, and their variants, along with data augmentation strategies like DRAGON and DRAMA. The codebase enables researchers to train, evaluate, and compare different retrieval approaches on standard benchmarks like Natural Questions, MS MARCO, and conversational datasets. It features a modular architecture with separate components for data processing, model definitions, training tasks, and evaluation, configured through Hydra for easy experimentation.
This repository is a curated, bilingual (English/Chinese) OPML feed collection focused on cybersecurity and AI, containing approximately 1000+ RSS/Atom feed URLs organized into 12 categories (e.g., Crypto, Pwn, WebSecurity, MobileSecurity, AI). It serves as the data backbone for a personal intelligence pipeline, designed to feed into AI-curated daily security digests. The repo includes automation scripts for feed health monitoring (opml_sync.py with HTTP retries, gzip/deflate decoding, and stateful failure tracking), interactive feed addition (add_feed_to_tiny.py), and optional LLM-assisted pruning (prune_opml.py). A GitHub Actions workflow runs sync operations on push and validates PRs. The actual value lies in the carefully vetted list of high-quality security sources, including many from the Chinese security community, with automated dead-link detection and deduplication. The scripts are well-structured and testable, but the repository's primary deliverable is the curated data, not the application logic.
This repository is the official implementation of the SANA family of diffusion models for high-resolution image and video generation, developed by NVIDIA Research. It is primarily a research codebase, containing training and inference pipelines for multiple model variants: SANA (text-to-image), SANA-1.5, SANA-Sprint (few-step distillation), SANA-Video, SANA-Video 2.0, SANA-Streaming (long video), LongSANA, and SANA-WM (world model for camera-controlled video). The codebase also includes post-training with reinforcement learning (Sol-RL), ControlNet support, and various schedulers (flow matching, DPM-Solver, LCM, self-forcing). While the core purpose is research, the repository also provides deployable Gradio applications, conversion scripts to Hugging Face diffusers format, and integration with ComfyUI and SGLang, giving it a hybrid application character. The code is heavily optimized with custom Triton kernels for efficient attention (LiteMLA, Gated Delta Net) and includes distributed training utilities (FSDP, context parallelism). The repository is actively maintained with frequent commits and supports a wide range of model sizes (600M to 14B parameters).
This repository is a curated collection of design system analyses for over 70 brands, each documented in a standardized DESIGN.md file. The analyses reverse-engineer public websites to extract design tokens (colors, typography, spacing, components) and present them in a machine-readable YAML front matter format alongside human-readable prose sections. The repo serves as a reference dataset for design system research, AI-assisted UI generation, and cross-brand comparison. It is not a software application or a traditional data pipeline; rather, it is a hybrid content repository that blends design research with structured data extraction. The project is maintained by VoltAgent and includes a commercial layer for requesting new analyses. While the data is well-structured and consistent, the analyses are static snapshots that may drift from live sites, and some files contain placeholder markers or typos.
This repository implements a font synthesis pipeline that merges BIZ UD Gothic (Japanese) with JetBrains Mono (Latin) to produce the UDEV Gothic family of programming fonts. The build process ingests source .ttf and .sfd files, uses FontForge for glyph preprocessing (size adjustment, duplicate handling, Nerd Font integration) and fonttools for hinting, merging, and table patching (OS/2, post, cmap with IVS). It generates 16+ variants with configurable width ratios, ligatures, and symbol sets via a config-driven, parallel build system (PowerShell on Windows, shell scripts on Linux). While primarily a font engineering pipeline, its well-structured Python scripts and configuration approach offer reusable patterns for similar font merging projects. The source fonts and dependencies are included, but cross-platform reproducibility is limited by hardcoded paths and lack of containerization.
This repository is a curated dataset of NFC tag dumps (`.nfc` files) from Toniebox audio figures, organized by language (English, French, German) and character series. The primary purpose is to enable emulation of Tonie figures on Flipper Zero devices using the official firmware's SLIX-L chip support. The dataset includes over 850 dumps, each containing the full memory image of an ISO15693 SLIX tag (UID, data content, passwords). Supporting scripts provide validation (`validate_files.sh`), automated directory listing generation (`build_directories.sh`), and a GUI tool (`figures_name_tool.pyw`) that uses upstream metadata from `tonies-json` to help contributors correctly place new dumps. The repository is part of the Toniebox reverse-engineering ecosystem and relies on community contributions. While the codebase is minimal, the data is well-structured and validated through CI pipelines, making it a reliable resource for Flipper Zero users.
This repository (OSGL) is a curated catalog of open‑source games, game engines, frameworks and tools. It ingests metadata from external APIs (GitHub, GitLab, SourceForge, LibreGameWiki) and transforms them into a collection of structured Markdown files (over 1800 entries). A static site generator (Python + Jinja2 + Bulma) produces a browsable website with alphabetical, genre, platform and inspiration indices. The project provides maintenance scripts (Qt UI, batch validation, screenshot management) and synchronisation modules for keeping the catalog up‑to‑date. It is primarily a data curation and publishing project rather than a runnable application, but its Python tooling demonstrates good engineering for a data pipeline.
This repository is a curated list of resources for embedded systems vulnerability research, not a software application or data pipeline. It aggregates books, blog posts, conference talks, papers, tools, and other references organized by category (e.g., Books, YouTube channels, Blogs, Papers, Tools). The primary content is a single README.md file containing over 200 links and brief descriptions. Additionally, the papers/ directory holds a few PDF files of research papers and a secondary README listing paper titles with links. The repo serves as a knowledge base for anyone interested in IoT/embedded security, providing a starting point for learning and staying current. There is no executable code, no data transformation logic, and no analytical models. The value lies in the curation and organization of external resources, not in any original software or data processing.
This repository provides a comprehensive pipeline for processing ECMWF's ERA5 reanalysis data into Analysis-Ready (AR) and Cloud-Optimized (CO) datasets stored in Zarr format on Google Cloud Storage. The workflow includes downloading GRIB data via the CDS API, splitting heterogeneous GRIB files (for soil and precipitation variables), converting to Zarr using Pangeo Forge Recipes, regridding to a uniform 0.25° latitude-longitude grid for AR datasets, and automating monthly updates via Apache Beam pipelines on Dataflow. The repository also includes tools for data sanity checks (comparing ERA5 with ERA5T) and utilities for updating configuration files that track data ingestion progress. While the code is tightly integrated with Google Cloud services (Secret Manager, Dataflow, Cloud Run) and relies on specific system dependencies (ecCodes via apt-get), it demonstrates solid data engineering practices for preparing large climate datasets for machine learning and research use. The included notebooks offer educational walkthroughs for working with the resulting model-level data, showcasing Metview integration for visualization and derived variable computation.
This repository curates and distributes Maxar Open Data satellite imagery for disaster response events. It provides a structured catalog of pre- and post-event high-resolution imagery (WorldView, GeoEye) in multiple formats: GeoJSON footprints, union geometries, MosaicJSON tile indices, and TSV metadata tables. The core pipeline (`maxar_data_catalog.py`) fetches the official Maxar STAC catalog, iterates over event collections, and generates these derived files, which are then committed to the repository. A Streamlit web app (`streamlit_app.py`) offers interactive browsing of footprints and mosaics. The repository is primarily a data asset with a supporting ETL pipeline and a thin frontend, making it a hybrid of data curation and a lightweight application. The data is organized by disaster event (e.g., hurricanes, earthquakes, floods) and includes rich acquisition metadata (platform, GSD, cloud cover, viewing angles) suitable for geospatial analysis and machine learning.
This repository is a curated, community-maintained catalog (awesome list) of high-quality macOS applications and tools, organized by categories such as developer tools, design, AI tools, utilities, and more. It is primarily a multilingual markdown document (English, Chinese, Korean, Japanese) with extensive metadata per entry (name, description, link, badges for open source, freeware, etc.). The repo also includes automated build scripts that parse the markdown into structured JSON (via `build/ast.mjs`) and generate RSS feeds tracking recent additions (via `build/feed.mjs`). The output is published as an npm package (`awesome-mac`), a Docker static site, and GitHub Pages. While not a traditional data research or software application, it functions as a living data catalog with programmatic consumption interfaces. The build pipeline and multilingual synchronization patterns are notable for reuse in other curation projects.
This repository contains the open-source book 'XQuant: 人人都是量化交易员' (XQuant: Everyone Can Be a Quantitative Trader), which serves as an educational resource for learning quantitative trading methodologies. Rather than being a functional trading application, it provides a structured curriculum teaching readers how to develop, test, execute, and monitor quantitative strategies using AI coding assistants. The book walks through a complete workflow: from selecting investment instruments (ETFs), determining position sizing, creating trading rules, validating strategy performance, avoiding overfitting, modeling execution costs, to monitoring and iterating on live strategies. Its unique value lies in teaching a spec-driven development approach where users write clear strategy specifications that AI coding assistants can translate into executable code, making quantitative research accessible to non-programmers.
This repository is a curated collection of Jupyter notebooks implementing core components of modern LLM architectures, intended for interview preparation. It covers attention mechanisms (MHA, GQA, MLA, KV-cache), normalization layers (LayerNorm, RMSNorm), functional components (RoPE, SwiGLU, LoRA, BPE, activation functions, cross-entropy, InfoNCE, sampling, quantization, SFT loss, tool calling parsing), and reinforcement learning losses (DPO, GRPO, DAPO, GSPO, KL divergence). Each notebook is a self-contained, minimal implementation of a specific algorithm, typically using PyTorch. The code is clean, concise, and well-commented, reflecting good engineering practices for educational purposes. While the repository is not a production application and lacks data pipelines, model training scripts, or continuous integration, it serves as a high-quality reference for understanding and reproducing the mathematical foundations of transformer-based language models. The hybrid nature lies in its focus on research-oriented ML components, but its structure is a pure code library rather than a data-driven research project.
This repository is a curated list of awesome downloadable SVG/PNG/Font icon projects, organized as an awesome list in README.md. It serves as a resource for developers seeking icon libraries, categorized into general purpose icons, logos, special icons, and other categories (CSS-only, paid, frameworks). The list is static content with no data processing or transformation. The project includes contribution guidelines and a code of conduct to manage community submissions. Quality is maintained via a GitHub Actions workflow that runs awesome-lint on pull requests and pushes, ensuring the list adheres to awesome list guidelines (proper formatting, no duplicates). This workflow is a reusable pattern for other awesome list projects. While the repository does not contain complex software or data pipelines, it demonstrates a lightweight, maintainable approach to curating technical resources. The workflow uses actions/checkout@v2 (slightly outdated) and awesome-lint via npm. The project shows active maintenance with recent commits and issue engagement, though the dependency management lacks exact version pinning.
Depth Anything V2 is a research codebase for monocular depth estimation, providing both relative and metric depth models. It builds on a DINOv2 encoder with a DPT decoder, offering four model scales (Small, Base, Large, Giant). The repository includes inference scripts for images and videos, a Gradio demo, and a subdirectory for fine-tuning on metric depth estimation using Hypersim (indoor) and Virtual KITTI 2 (outdoor) datasets. It also defines the DA-2K evaluation benchmark for relative depth. The code is well-structured with reusable transform utilities and evaluation metrics, but reproducibility is limited due to hardcoded dataset paths and unpinned dependencies. This is primarily a research project with a hybrid nature due to the included demo application.
The KhronosGroup/glTF-Sample-Assets repository contains a comprehensive collection of sample assets for the glTF (GL Transmission Format) specification. The repository is primarily used for testing and demonstrating the capabilities of glTF, including various features such as animations, physics, and materials.