Telegram

The Problem with Waiting Large language models are slow. A typical response takes seconds — sometimes tens of seconds — to generate. During that time, the user stares at an empty chat window, wondering if anything is happening at all. Every major LLM chat interface solved this the same way: stream tokens as they arrive. ChatGPT, Claude, Gemini — they all render partial text while the model is still thinking. The experience feels responsive even when the full response takes 15 seconds. ...

From Design to Implementation In the previous article, I outlined the design goals and high-level architecture of a reactive Telegram bot framework — why non-blocking I/O matters, how separating polling from processing enables horizontal scaling, and why sealed interfaces make response dispatch type-safe at compile time. That article showed simplified code to explain the concepts. This one goes deeper. We will walk through the actual implementation: how Flux.create and expand drive the polling loop with back-pressure, how atomic variables coordinate concurrent state without locks, how the channel abstraction separates read and write concerns, and how Apache Pulsar’s reactive client integrates natively with Project Reactor. ...

Why another Telegram bot framework? Building Telegram bots is straightforward — the API is well-documented, and there are dozens of libraries in every language. However, most existing Java frameworks share the same fundamental limitation: they process updates sequentially on blocking threads. This works fine for hobby projects handling a few messages per minute, but it becomes a bottleneck the moment your bot needs to serve thousands of concurrent users, handle complex multi-step workflows, or integrate with other microservices in a distributed system. ...