By peter_lzh 4 Hot GitHub Projects Worth Bookmarking: A Website Downloader, Lightweight TTS, a Job-Hunt CLI, and a Fitness Dataset
Here are four trending GitHub open-source projects worth saving for later.
01 Website-downloader
A full-site offline downloader written in Node.js. Under the hood it uses wget for recursive mirroring plus archiver for compression, and reports progress in real time over a socket. Give it a URL and it grabs all dependent assets, HTML, CSS, JS, and images, automatically rewrites absolute links to relative paths for easy local browsing, and finally packages everything into a zip for you.
It is handy for studying others’ page structures, backing up your own site, or building offline demos, and supports one-click deployment to cloud platforms like Render and Railway. Just avoid scraping sites with access controls or anti-bot protection.
GitHub: https://github.com/AhmadIbrahiim/Website-downloader
02 pocket-tts
A lightweight TTS from Kyutai. The model is only about 100M parameters, runs on CPU without a GPU at a brisk roughly 6x real-time, and supports Python 3.10 to 3.14 and PyTorch 2.5+.
A single pip install or uvx gets you going, with a built-in CLI, a local HTTP server, and a Python library. It offers multiple preset voices and supports voice cloning from a few seconds of audio. English sounds best today, with larger-model versions available for other languages. Low latency and decent quality make it a painless choice for local speech synthesis.
GitHub: https://github.com/kyutai-labs/pocket-tts
03 career-ops
A framework that turns various AI coding CLIs, such as Claude Code, Gemini, and Codex, into a job-hunting command center. The author used it to evaluate 740+ roles and generate 100+ tailored resumes, landing a Head of Applied AI offer. The core idea is a “filter” rather than a mass-application tool: it scores roles A to F across 10 weighted dimensions and strongly recommends not applying to anything below 4.0/5.
Features include scanning 45+ company career pages, generating ATS-friendly PDFs matched to your resume, a terminal TUI for tracking progress, salary-negotiation scripts, and an interview STAR question bank. The first run is a conversational setup, after which you let the AI adjust the config on demand.
GitHub: https://github.com/santifer/career-ops
04 exercises-dataset
A structured dataset of 1,324 fitness movements. Each movement comes with an animated GIF, a 180×180 thumbnail, muscle group, equipment, target muscles, and step-by-step instructions in nine languages, English, Chinese, Spanish, Italian, Turkish, Russian, and more, all in ready-to-use JSON.
The repo also ships two pure-browser HTML tools: one to search, filter, and browse movements, and a setup.html that generates SQL, seven-language API client code, and even LLM prompts to help you stand up a fitness-app backend fast. About 25% of movements need no equipment, making it suitable for fitness apps or personal training. Watch the media-file licensing, of course.