@AYi_AInotes: Someone on GitHub turned 1,324 fitness exercises into an open-source database, not just text descriptions. Each exercise comes with a static JPG image, GIF animation, bilingual instructions, target muscles, and equipment type, all 2,648 media files packaged together. It also comes with a local webpage that supports...

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Summary

Someone on GitHub open-sourced a database of 1,324 fitness exercises, each with images, GIF animations, bilingual instructions, target muscles, and equipment type, along with a directly runnable local webpage and developer tools, suitable for fitness app prototypes, AI motion recognition, etc.

Someone on GitHub turned 1,324 fitness exercises into an open-source database, not just text descriptions. Each exercise comes with a static JPG image + GIF animation + bilingual instructions + target muscles + equipment type, all 2,648 media files packaged together. It also comes with a local webpage that supports filtering by body part and keyword search, ready to use out of the box. Three most practical things: index.html: Double-click to open and use, search + filter + grid display, click on an exercise to see the full image + play the GIF. Fully local. setup.html (developer's delight): One-click generation of SQL table creation statements (MySQL/PostgreSQL/SQLite), multi-language API example code (JS/Python/Go/Java), even built-in LLM prompts to let AI write backend interfaces for you. exercises.json + images/ + videos/ folders: Core data and media files directly readable, zero friction to integrate into your program. Very comprehensive data coverage: chest 163, back 203, legs 227, upper arms 292, shoulders 143, waist 169, bodyweight exercises 325. For building fitness app prototypes, short video materials, AI motion recognition training, personal knowledge bases — you can skip the step from zero to one. I think the most valuable part of this kind of project is not the data volume, but the four words "ready to use out of the box." The included local webpage directly demonstrates the value of the data without you needing to write a frontend first to see the effect. What it saves is not money, but that most grueling period of time from idea to something that actually runs.
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Cached at: 06/29/26, 04:29 PM

Someone on GitHub has turned 1,324 fitness exercises into an open-source database—and it’s not just text descriptions.

Each exercise comes with a static JPG image + animated GIF demonstration + bilingual description + target muscles + equipment type. All 2,648 media files are fully packaged.

It also includes a local web page with filtering by body part and keyword search, ready to use out of the box.

Three most practical components:

  • index.html: double-click to open – search + filter + grid view. Click an exercise to see a large image + play the GIF. Fully local.
  • setup.html (developer’s delight): one-click SQL table creation statements (MySQL/PostgreSQL/SQLite), multi-language API example code (JS/Python/Go/Java), and even built-in LLM prompts to let AI write backend endpoints for you.
  • exercises.json + images/ + videos/ folders: core data and media files ready to read – integrate with zero friction.

Coverage is comprehensive: Chest 163, Back 203, Legs 227, Upper Arms 292, Shoulders 143, Waist 169, Bodyweight exercises 325.
Building a fitness app prototype, short-video material, AI motion recognition training, a personal knowledge base – you can skip the “from zero to something” stage entirely.

I think the real value of a project like this isn’t the data volume – it’s the “out-of-the-box” four-letter word. The built-in local web page directly demonstrates the value of the data. You don’t need to write a frontend first to see results. What it saves isn’t just money – it’s that most grueling stretch of time from an idea to something that actually runs.

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