Как создать свое приложение с нуля? Урок по Cursor AI для новичков

Telegram channel: https://t.me/maya_pro Beget Cloud: http://beget.com/p1953357 Max channel: https://max.ru/maya_pro MCP Service "ALL INCLUSIVE": https://mcp-kv.ru/ Closed channel on Make/Vibecoding automation: https://kv-ai.ru/obuchenie-po-make Blueprint for Make: https://kv-ai.ru/blyuprinty-make-com-... In this video, I show how to use Cursor AI to create a real web application on your server: with user registration, a personal account (dashboard), API key issuance, and a working API for video stitching. We set up a server in the cloud, install Ubuntu, connect Cursor to the server, set up a domain and an SSL certificate, and create a full-fledged automation service. You'll see the full cycle: from concept to finished product. We'll set up a server on Beget, connect Cursor AI for wipe coding, deploy the backend, create a website with registration and login, implement the issuance and verification of API keys, connect the automation service (Make), and test the API using a real-world example—joining up to five video files in different formats. 00:00 Introduction — What is wipe coding and why use Cursor AI 00:00:55 Video objectives — Creating a real application with a server, API, and registration 00:01:49 Thanks, subscription, and invitation to the community 00:02:30 Visualization: service diagram, registration site, and issuing API keys 00:04:07 Cursor AI overview — setup, localization, and working with the neural network 00:06:31 Choosing a cloud server on Beget for the application 00:09:13 Installing Ubuntu and creating a virtual server 00:10:12 Connecting Cursor AI to the server: extension and SSH connection 00:13:11 Completing the connection setup and checking access to the server 00:13:40 Working with the server: file manager and new connection 00:14:31 Creating a project folder on the server for Applications 00:15:25 Domain registration and certificate issuance for the website 00:16:22 DNS setup: domain binding to the server by IP 00:18:06 Creating a service using a neural network: promt and project structure 00:23:10 Installing dependencies, Python extension, and server launch 00:26:15 Project check: server operation, registration, and login 00:28:33 Improving the design of the application's main page 00:28:47 Video splicing service overview: formats, quality, and video limits 00:29:47 First website tests: registration, login, and the idea of ​​a dashboard for tokens 00:30:45 Minimalistic design, redirection after login, and custom page style 00:32:35 Working with API keys: creating, copying, and restarting the server 00:34:22 Setting up a domain and SSL certificate, verification HTTPS 00:38:16 Security enhancements: complex passwords and email verification upon registration 00:39:11 Dashboard: video merging tab and functionality check 00:41:10 Logging and debugging video merging errors 00:44:12 Final service setup and API documentation tab 00:45:15 Service overview: video merging automation and possible features 00:46:08 API documentation: authorization, OAuth, token limits and expiration dates 00:47:49 Connecting the service to Make: working with the API key and server 00:48:35 Request example: merging two videos via API 00:49:28 Result: link verification and the final merged video 00:50:40 Conclusions: the finished application on your server and security tips 00:51:37 Conclusion — Motivation to create your own services and an invitation to training #CursorAI #neuralnetworks #createapp #server #API #videomerging #automation #SaaS #cloudserver #Ubuntu #Make #webdevelopment #nocode