Backend Developer: API, Databases, and Architecture
This assistant works as an experienced backend developer and helps design, write, and maintain the server-side of applications: from initial architecture sketches to fixing bugs in production code. It will design REST or GraphQL APIs with clear endpoints and response formats, create a database schema with indexes and relationships, write server logic in Python, Go, Java, Node.js, or PHP, analyze error traces and suggest fixes, optimize slow SQL queries, and advise on how to configure caching, message queues, and authentication. In addition, it will perform code reviews with comments on security and readability and prepare API documentation. On BotHub, you get access to powerful neural networks without a VPN or foreign card, pay for work in rubles, and chat history is saved, so you can return to discussing the project at any time.
Frequently Asked Questions
The assistant will write individual modules, endpoints, data models, and migrations, and help assemble the service framework from them. It does not run code, connect to your servers and databases, or see the entire repository, so the environment and deployment remain up to you. It works best when the task is broken down into parts and the necessary code snippet is attached to the request.
The assistant works with common server-side development languages, including Python, Go, Java, Kotlin, C#, PHP, and JavaScript on Node.js, as well as popular frameworks like Django, FastAPI, Spring, Laravel, and NestJS. It understands PostgreSQL, MySQL, MongoDB, and Redis, message brokers, Docker, and basic CI/CD scenarios. If the stack is rare, just specify it in the first message.
Just describe the domain, main entities, and scenarios, for example, a booking service with users, slots, and payments. The assistant will suggest resources and endpoints, methods and response codes, JSON format, pagination and versioning approaches, and a table schema for this data. Then you can ask it to prepare an OpenAPI specification or write handlers in the chosen framework.
The assistant's code should be treated as a draft from an experienced colleague: it is usually functional and well-structured, but may contain inaccuracies in library versions, edge cases, and security settings. Before deployment, you need to run tests and check error handling and access rights, and it is better to replace real passwords, tokens, and API keys in requests with placeholder values.
CAPS are deducted for each request, and the cost depends on the volume of text: your message, the assistant's response, and the chat history passed in the context are taken into account. Large code snippets and long conversations consume more, so for a new task, it is more profitable to start a separate chat and send only the code that is actually relevant to the question.
The assistant is pre-configured for server-side development tasks, so you don't need to explain the context and response requirements every time. It immediately considers performance, security, and maintainability of the solution, asks clarifying questions about load and stack if they are missing, and formats the answer as ready-to-use code with explanations, rather than general reasoning.