This talk focuses only on profiling: how Python developers should measure performance, why traditional tracing profilers are not always enough, and how Tachyon changes the debugging workflow for production-scale systems. We'll go deep on: sampling vs tracing — when each model works and where each one lies; Tachyon Profiler internals — how low-overhead sampling works conceptually; production debugging — how to profile services without destroying the signal; async, threads, and free-threading — why modern Python profiling is harder than "find the slow function"; and profiler interpretation — how to read results without chasing fake bottlenecks.
This talk will be interactive: we'll walk through several real profiling outputs live, and invite the audience to spot the likely bottlenecks and guess which code paths are the actual "usual suspects" eating up runtime. Beyond just showing how to use Tachyon Profiler, this talk aims to help developers build a performance engineer's mindset — learning to locate performance issues efficiently and accurately in complex Python systems.