thehardwarestore.ai

This repository is a meticulously curated knowledge base and toolkit for AI video prompt engineering. Its core is a structured data pipeline: raw knowledge from official vendor documentation and YouTube masterclasses is reverse-engineered into standardized methodologies (21+ SOPs) and a centralized prompt database (`prompts/data/all-prompts.json`). This data is then exposed through a cross-model benchmark (`prompts/data/cross-model-matrix.json`), zero-dependency web tools (Prompt Browser, Cross-Model Matrix), and executable Claude Code Skills (`skills/`). The architecture is data-centric. The primary value lies in the curated, structured data assets and the rigorous methodology used to create them. The application layer (web tools, skills) is a thin, well-crafted consumer of this data. The repository actively manages its primary risk—model version drift—through an automated monitoring system (`scripts/monitor_models.py`) that hashes external endpoints and files issues on change. While primarily a data_research project, it exhibits strong application characteristics through its interactive tools and AI skill integrations. It is a living knowledge base, actively maintained with a clear contribution workflow and a strong emphasis on data provenance and reproducibility. The `cross-model-matrix.json` is a standout asset, providing a controlled benchmark for comparing prompt styles across 11 major AI video models.

This repository is a curated collection of over 300 terminal color schemes (themes) and a comprehensive conversion toolchain that transforms a single source of truth (YAML + iTerm2 plist files) into configuration files for more than 20 terminal emulators, including Alacritty, Kitty, Windows Terminal, Xresources, Konsole, and many others. The core converter `tools/gen.py` reads source color data, applies WCAG contrast adjustments, and uses Jinja2 templates to generate output files. The repository also includes a terminal-based theme previewer, automated screenshot generation, and WCAG accessibility checks. It is a hybrid project: primarily a data collection with an ETL pipeline for multi-format export, but also contains application-level tools for preview and quality assurance. The codebase is well-organized, with reusable color science utilities (sRGB/P3 conversion, luminance, contrast ratio) and a template-driven generation architecture that can be adapted for other multi-format rendering tasks.

This repository is a curated, comprehensive geographic database of countries, states, cities, and postcodes, with a strong emphasis on ISO 3166 compliance and data quality. It is primarily a data_research project, though it includes supporting application code (export pipelines, validation tools, CI/CD) that enables multi-format distribution (JSON, CSV, SQL, Parquet, etc.). The raw data lives in the `contributions/` directory as JSON files, which are the source of truth. A rich ecosystem of Python, PHP, and Node.js scripts handles ingestion from community and official sources, idempotent correction, enrichment (timezones, translations), and export to 12+ formats. The repository is actively maintained by a community of contributors and automated bots, with rigorous PR validation (schema, coordinates, duplicates, cross-references) and extensive fix documentation. It answers questions like 'What are the administrative divisions of a country?' and 'What postcodes exist for a given locality?' and is used as a reference data source for applications worldwide.

This repository is a data pipeline that transforms crowdsourced survey responses about Chinese university quality of life into a structured MkDocs static site. The core ETL script (`questionnaires/main.py`) ingests a CSV of desensitized survey results, applies name normalization via alias mapping, blacklist/whitelist filtering, and splits data into current and archived sets based on a cutoff date. It generates per-university Markdown files with Q&A sections, a navigation structure, and a MkDocs configuration. The pipeline is triggered by GitHub Actions on pushes to master, pushing generated content to a separate branch and to the production website repository. The data is licensed under CC BY-NC-SA 4.0. While the code is functional and well-organized for its purpose, it is tightly coupled to the specific survey schema and data files, limiting direct reuse as a library. The most reusable component is the `FilenameMap` class for generating unique slugs from entity names.

This repository is the data pipeline used by Mullvad VPN to produce DNS blocklists for ads, trackers, adult content, gambling, social media, and malware. It ingests multiple upstream blocklists (from oisd, hagezi, AdGuard, Firebog, etc.) and custom lists via an Ansible playbook, cleans and normalizes them (removing localhost IPs, comments, special characters), combines them by category, and outputs plain-text domain lists and RFC-compliant zone files for both encrypted DNS (DoH) and VPN relay servers. The output files are GPG-signed for integrity. A CI workflow validates zone files using named-checkzone. The repository also includes scripts for generating social media blocklists by enumerating subdomains (via subfinder) and fetching IP ranges for major social platforms (Facebook, TikTok, Snapchat) using ASN lookups. While tightly coupled to Mullvad's infrastructure, the ETL pattern and zone file generation logic are well-structured and could be adapted for similar DNS blocking pipelines.

This repository provides the data pipeline for the Sekai dataset, a large-scale egocentric video dataset for world exploration and generation research. It contains two main toolchains: dataset_downloading (extracting YouTube URLs from CSV annotations and downloading videos via yt-dlp) and clip_extracting (a multi-step GPU-accelerated pipeline to split raw videos into annotated clips, transcode them to H.265, and remux with audio). The pipeline is designed for the Sekai-Real subset and requires NVIDIA GPUs with NVENC support, CUDA 11.8, and specific Python dependencies. The code is well-structured with clear separation of steps, logging, and multiprocessing support. While tightly coupled to the Sekai dataset and hardware requirements, the utility modules for GPU video decoding/encoding and frame sampling are reusable in other video processing projects.

This repository provides a collection of Python scripts for processing Pushshift Reddit dump files, which are zstandard-compressed ndjson files. The tools enable extraction, transformation, and loading of Reddit data, including splitting files by time or subreddit, merging data from multiple sources, backfilling missing data via APIs, and performing various analyses such as counting words, subreddits, and overlapping users. The codebase includes reusable utilities for reading compressed files and handling Reddit-specific ID formats, as well as sophisticated merge logic for combining data from different ingest sources.

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 the canonical source data for the ZenginCode ecosystem, providing a curated, machine-readable dataset of Japanese bank codes and branch codes. It contains structured branch information (code, name, kana, hira, roma) for thousands of financial institutions across Japan, stored in both JSON and YAML formats under data/branches/. The data is automatically updated daily via a GitHub Actions workflow that downloads the official Zengin bank branch list from an external source, converts it from Shift_JIS CSV to UTF-8, and generates per-bank branch files. The repository also includes a Ruby CLI tool (bin/zengin) that orchestrates the download, conversion, and commit process, making the pipeline reproducible. While primarily a data repository, it includes a well-structured ETL pipeline that can serve as a template for similar data curation projects. The dataset is consumed by language-specific packages (Ruby, JavaScript, Python) to provide bank branch lookup functionality in financial applications.

This repository implements an end-to-end predictive modeling pipeline for Premier League football match outcomes (home win, draw, away win). It ingests raw match statistics from the api-football.com REST API, processes JSON data into structured DataFrames, engineers features using rolling averages (5- and 10-game windows) and team-vs-opponent differentials, trains multiple classifiers (Random Forest, SVM, KNN, MLP) with hyperparameter tuning, and serves live predictions via a Flask web application. The dominant character is data research: the primary deliverable is a set of trained models and an analytical pipeline, with the web server being a secondary deployment artifact. The best-performing model is a Random Forest achieving ~50-51% test accuracy, demonstrating that football outcomes are modestly predictable from historical statistics. The codebase includes modular helper modules for feature engineering, data scaling, and evaluation plotting that are reusable for other sports or time-series classification tasks. However, the project suffers from hardcoded absolute paths, outdated dependencies (Python 3.7, sklearn 0.23.1), plain-text API keys in the repository, and a static dataset that will drift over time.