Summary of "AI-генератор фанфиков в Cursor за 21 день: показываю свой вайб-кодинг стек"

Summary of the Video “AI-генератор фанфиков в Cursor за 21 день: показываю свой вайб-кодинг стек”

Project Overview

Oleg, an experienced server developer turned live coder, presents his new project called LMATF, a web application that generates fanfiction stories based on user input.

Users specify a universe (e.g., Harry Potter, Cyberpunk 2077) and a plot idea.
The app generates chapters of approximately 2000 words each, taking 5–10 minutes to read.
The generated text quality is described as quite high.

Project Architecture

The project consists of three main parts:

Backend: Built with FastAPI and PON.
Frontend: Developed using Next.js with Tailwind CSS.
Telegram Bot: Manages premium subscriptions via a paid access bot.

The stack was chosen for familiarity and good AI-assisted coding compatibility. Next.js was preferred over pure React for easier routing.

Development Process and AI Tool Usage

The main coding assistant is Cursor, an AI code generator.
Oleg emphasizes a structured, step-by-step approach when using Cursor:
- Fully understand the task before prompting the AI.
- Highlight relevant files to keep Cursor focused.
- Separate backend and frontend changes.
- Write down important implementation notes (e.g., database migrations).
Features are developed semi-automatically by iterating in small increments and testing frequently.
For complex features, tasks are broken into minimal viable products (MVPs), tested early and often to avoid unmanageable code.

Asynchronous Task Handling

Long-running AI generation calls are handled asynchronously.
Tasks have statuses such as pending, success, and error.
Users can refresh and check progress without losing data.
Oleg notes challenges with Python’s asynchronous behavior and stresses the importance of verifying async compatibility.

Neural Network Models and API Integration

Used OpenRouter to unify multiple model APIs (Claude, OpenAI, Deepseek), enabling easier switching and testing.
Tested various models for cost and quality:
- Naklo 3.7: Best for initial text generation.
- GM Mini 2.5 Pro: Cheaper but less accurate with Russian language.
Strategy:
- Generate the first 5 chapters with Naklo 3.7.
- Use GM Mini for subsequent chapters to maintain style and reduce cost.
To overcome token limits (about 60 chapters max), implemented a summary-based context feeding:
- Summaries include plot events, consequences, character motivations, and “Chekhov’s guns” (plot elements to be developed later).
- Only the last three chapters are included in full; earlier chapters are summarized to reduce token count while preserving story depth.

Additional Tools and Services Used

MCP servers: Enhance model reasoning and consistency (Sequential Thinking server).
Player: Tool for web navigation and data retrieval by the model.
Break search: Google-like search to improve AI’s coding accuracy.
Kotext: Library documentation tool.
GPT O3: Highly intelligent model used for business advice, UI/UX suggestions, project feasibility analysis, and concept design.
GPT-4 image generator: Creates website visuals, logos, avatars, and backgrounds based on prompts generated by GPT O3.
Plausible Analytics: Self-hosted alternative to Google Analytics integrated for visitor and interaction tracking.

Key Insights and Conclusions

Modern AI tools can accelerate development speed by about 10x, enabling complex projects to be built in weeks instead of months.
Success depends on choosing the right tech stack, having experience with tools, and applying a disciplined development approach.
The project and workflow showcased represent early but promising steps in AI-assisted coding and content generation.
Oleg plans to continue developing new projects and sharing insights via his Telegram channel.

Main Speaker

Oleg — a developer and live coder sharing his experiences and technical insights about building an AI-powered fanfiction generator using Cursor and other AI tools.