Task

Thinklens Bot addresses a practical task: a Telegram assistant with OpenAI for reflective prompts and summarization. Python and dialogue memory control.

This is a personal learning project (in active development) where specific patterns and tools were practiced without unnecessary overhead.

The case demonstrates an approach to tasks in the area of "Chatbots and process automation": from problem statement to delivery with a clear module structure.

Before starting, readiness criteria were established: a clear first screen, predictable form errors, and the possibility of enhancements without rewriting half of the codebase.

Solution

A learning yet fully functional implementation has been created, emphasizing code readability and reproducible builds.

The scenarios are designed so that the main actions fit within one or two screens: fewer switches, fewer input errors. Navigation and field labels align with familiar patterns — users do not need to "learn the interface".

Special attention is given to loading states and empty lists: skeleton/spinner where data arrives asynchronously, and clear empty-state texts instead of an empty table.

Architecture and stack

Server and data: Python.

Infrastructure and utilities: Telegram, OpenAI.

Language distribution in the repository: TypeScript (92.4%), Shell (6.1%), Dockerfile (1%), HTML (0.3%), CSS (0.2%).

Layers are separated so that the UI is unaware of transport details: API/storage adapters, domain types, presentation components. This simplifies backend replacement and targeted UX adjustments.

Key features

• Python — used in product logic: reduces the amount of custom code and speeds up the addition of new screens.
• Telegram — used in product logic: reduces the amount of custom code and speeds up the addition of new screens.
• OpenAI — used in product logic: reduces the amount of custom code and speeds up the addition of new screens.

Additionally:

• Integration and use of Python in product logic and interface.
• Integration and use of Telegram in product logic and interface.
• Integration and use of OpenAI in product logic and interface.

Result

The MVP already covers the key scenario; next steps include expanding reporting, testing, and monitoring.

The source code is open: https://github.com/astropsy999/thinklens-bot — you can evaluate the module structure and commit style.

What I would improve

The next step is observability (logs/metrics), regression tests, and API documentation for integrators. For high-load scenarios — profiling bottlenecks and caching reference data.