da-cli

DouK-Downloader is an open-source Python tool for downloading works and collecting data from Douyin and TikTok. It downloads videos, image posts, live photos, animated images, covers, livestreams, collections, and account posts, while collecting metadata, comments, search results, trending data, and engagement statistics. Downloads can be performed from individual or batch links, accounts, collections, or playlists, with support for incremental downloads, duplicate skipping, proxy requests, browser or manually supplied cookies, clipboard monitoring, resumable transfers, multithreading, folder-based archiving, and configurable filtering rules. The tool provides terminal interaction and a Web API, with automatically generated API documentation; its former Web UI mode is described as not currently updated. Collected data can be persisted as CSV, XLSX, or SQLite, and livestreams can be downloaded through FFmpeg. It runs as a Python application or packaged executable on Windows, macOS, and Linux, and can be deployed with Docker or to a private or public server. The repository states that it is licensed under GNU GPLv3 and that some functions require Node.js 18 or later.

AI Manager is a local-first desktop application for installing, updating, configuring, and repairing AI coding command-line tools on a user's computer. It provides a single interface for checking installed tools, managing versions, connecting tools to reviewed or custom HTTPS AI-service endpoints, and viewing the effective endpoint and environment-variable overrides without editing shell profiles or PATH files. Its sidebar includes software management, API endpoints, skills, MCP connections, global prompts, sessions, and settings. It installs skills from trusted GitHub catalogues or local ZIP files, supports stdio, HTTP, and SSE MCP servers, and searches local coding-tool sessions that can be resumed in a terminal. Tool behavior is controlled by a capability registry, and the application uses local signals for checks rather than claiming a process is healthy without evidence. The application is built with Tauri 2 and React 18 and is released under the GNU AGPL-3.0-or-later. It has no analytics or telemetry SDK, stores product data locally, and offers signed update verification and platform-specific installers. Provider credentials are currently stored in plaintext in the local database, backups, and SQL configuration exports. AI Manager began as a fork of CC Switch, with inherited portions retaining their original MIT licensing.

dif.sh is a developer tool for storing feature flags, A/B tests, holdouts, and staged rollouts as Markdown files in a Git repository. Each experiment uses frontmatter for its status, audience, variants, metrics, guardrails, and exclusion group, while Git provides versioning and pull requests provide review and audit history. Its CLI initializes the repository structure, drafts experiments from surface-specific learning logs, validates configurations, previews assignments, builds a typed client, and archives concluded tests. The build resolves exclusion groups and fails on conflicting live experiments, generates a client that evaluates variants in the application without a production service call, and refreshes dif/context.json for coding agents. Audience rules declare runtime attributes such as country, plan, or returning-visitor status without committing customer lists. The optional Dif Cloud integration can calculate lift through dif.track(); alternatively, custom event handlers can forward results to other analytics systems. The project provides an npm CLI package and an SDK, and its site describes a no-signup workflow.

drydock is a Rust terminal user-interface dashboard for monitoring the state of Git repositories across configured directories. It reports uncommitted changes, unpushed commits across all local branches, conflicts and unfinished merge or rebase operations, and release status based on tags, including commits or changes made after the latest tag. The dashboard uses color-coded states, supports filtering by repository activity and condition, fuzzy search, sorting, repository details, and actions such as fetching, opening an editor or terminal, and viewing the remote. It runs on macOS and Linux and can be installed with Homebrew, Cargo, or from source.

CDAF is an open sidecar format and toolkit for video that stores a timestamped plain-text description beside the corresponding video file. Its Python library and CLI can generate, parse, validate, read, and report the status of `.cdaf` files, while an agent skill teaches video agents to check for a matching sidecar before processing footage. Each sidecar contains a minimal versioned header with the video filename, SHA-256 hash, byte size, duration, generator, and creation time, followed by sections such as summary, timestamped segments, transcript, on-screen text, and tags. Conforming tools verify the video's freshness and refuse to use a stale sidecar after the video changes; the format is model-agnostic even though the included generator uses the Gemini Files API, with optional local-model support. The repository includes a normative specification, reproducible sidecar-versus-direct-video benchmarks, an agent skill installable with `npx cdaf-skill`, and CLI commands for generation, validation, reading, and status checks. The core validation functions require only the Python standard library; generation requires Python 3.10 or later and a user-supplied Gemini API key. The project is licensed under MIT.