Thanks! I started with cljfx and then moved to pure javafx, first because it wasn't straight forward for me to understand the performance overhead of cljfx under different scenarios (when using subscriptions and contexts), and second because I want to drag as few dependencies as possible so they don't conflict with debuggee one's.
So recording in FlowStorm doesn't use Clojure metadata capabilities in any way, it is basically about storing function calls, function returns and the pointers to all expressions intermediate immutable values together with their code coordinates.
Not sure I follow, can expand on that? I gave a presentation on it recently https://www.youtube.com/watch?v=BuSpMvVU7j4 which goes over demos and implementation details if you are interested in those topics.
For instrumentation, instead of an instrumenting agent it uses a fork of the official Clojure compiler (in Clojure you can easily swap compilers at dev) that adds extra bytecode. What is interesting about recording Clojure programs execution is that most values are immutable, so you can snapshot them by just retaining the pointers.
Edit: Since the OP demo is about exploring a web app for people interested in this topics I'm leaving a demo of FlowStorm debugging a web app also https://www.youtube.com/watch?v=h8AFpZkAwPo
I'm working on http://www.flow-storm.org/ A time travel debugger for Clojure with some unique features, aiming to enhance the already awesome interactive development of Clojure by enabling you to record and explore executions on demand.
Yeah I don't know how stickers trace code, but FlowStorm tracing debugger takes advantage of the immutable data structures used everywhere and by default in Clojure. You can then instrument entire code bases, run them, and tracing is just a matter of retaining(leaking) pointers of every intermediate expression so the GC doesn't collect them. I don't think this is possible when working with mutable objects, since then only way would be to somehow serialize the objects involved at every step so you can analyze the data later, which is prohibitively expensive in most situations.