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AFFiNE
There can be more than Notion and Miro. AFFiNE(pronounced [Ιβfain]) is a next-gen knowledge base that brings planning, sorting and creating all together. Priβ¦
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View guide βOpen easy install for fit coach is an ai-powered fitness web app that deliver.
Virtual_Fitness_Trainer is an open-source easy install project by SuryaThejas-07 (SuryaThejas-07/Virtual_Fitness_Trainer on GitHub). It has 40 GitHub stars and 0 forks, is released under the Custom license, and was last updated on GitHub on 17 June 2026.
Fetch the source code onto your machine.
Pull in the Node packages the app needs.
Copy the sample config or env file and adjust it.
Start the development or production server.
Inspect, fork, and modify every line of the code.
Run it on your own machine β no account lock-in.
Issues and pull requests welcome on GitHub.
Hackable in a familiar, popular language.
No subscription or paywall for core features.
Audit exactly what the software does.
Get the source on your machine.
git clone https://github.com/SuryaThejas-07/Virtual_Fitness_Trainer.gitInstall Node dependencies.
cd Virtual_Fitness_Trainer && npm installRun the application.
cd Virtual_Fitness_Trainer && npm startIt fit Coach is an AI-powered fitness web app that delivers real-time form tracking via webcam, smart rep counting, posture scoring, workout and nutrition logging, body-metric monitoring, and progress analytics. It also includes a motivating coach chatbot for protein guidance and timed goal plans, giving you a working starting point you can run and adapt.
Yes β the code is openly licensed, so you can use and modify it without a license fee.
Clone the GitHub repository and follow the README; the typical flow is install dependencies, configure, and run.
It is primarily written in TypeScript, so you will need that toolchain to build or extend it.
The GitHub repository is the hub: open issues for bugs and check the README and discussions for guidance.
There can be more than Notion and Miro. AFFiNE(pronounced [Ιβfain]) is a next-gen knowledge base that brings planning, sorting and creating all together. Priβ¦
Use LLMs as plain Python functions: prompts become typed, testable code.
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