Ignoring the snark for a second. It's not because you are unfamiliar with the go toolchain that it's inherently bad, nor does it put you in a good position to give accurate criticism.
- "tea" is an explicit alias that was added to the import statement in the tutorial examples, which you did not reflect in your snippet:
import tea "charm.land/bubbletea/v2"
- The following also just works as you expected, but directly assumed wouldn't work:
import github.com/charmbracelet/bubbletea
The only surprise here is that the repository authors decided to change the name of the module between v1 and v2 of the package. The git branch tagged v2 contains a module named 'charm.land/bubbletea', earlier v1 branches are named 'github.com/charmbracelet/bubbletea'. That's on them for breaking convention and surprising users, the go toolchain does not factor into, this beyond supporting versioning by naming convention.
I would be fine with a chaotic bubbly mess of an outside presentation, if the libraries were more robust and foundational. At the moment the underlying code, when you scratch the surface, have the feel of things thrown together to be replaced at later date.
I bounced off of bubble tea not because of the aesthetics and the unhelpful naming, but because of the programming model: a MVC-architecture cribbed from the Elm language. Why? It completely takes over and rips apart my CLI structure. A CLI is not a DOM or System.Windows.Forms, MVC is scattering around logic and adding indirection layers needlessly.
I am still using huh? and vhs, but their libraries have the feel of looking really good in demo and in the provided examples, but break down quickly when coloring just outside those intended lines.
The problem is that there is no baseline for measuring GC overhead. You cannot turn it off, you can only replace and compare with different strategies. For example sbrk is technically a noop GC, but that also has overhead and impact because it will not compact objects and give you bad cache behavior. (It illustrates the OP's point that it is not enough to measure pauses, sbrk has no pauses but gets outperformed easily.)
You could stop collecting performance counters around GC phases, but you even if you are not measuring the CPU still runs through its instructions, causing the second order effects. And as you mentioned too-short-to-measure barriers and other bookkeeping overheads (updating ref counters etc) or simply the fact that some tag bits or object slots are reserved all impact performance.
There is a good write-up of the problem and a way to estimate the cost based on different GC strategies, as you suggested, here: https://arxiv.org/abs/2112.07880
The way I found to measure a no-GC baseline is to compare them in an accurate workload performance simulator.
Mark all GC and allocator related code regions and have the simulator skip all those instructions. Critically that needs to be a simulator that does not deal with the functional simulation, but gets it's instructions from a functional simulator, emulator or PIN tool that does execute everything. It's laborious, not very fast and impractical for production work. But, it's the only way I found to answer a question like "What is the absolite overhead of memory management in Python?". (Answer: lower bound walltime sits around +25% avg, heavily depending on the pyperformance benchmark)
The last company I worked at created TLAs for everything, or at least engineering always did. New company doesn't seem to have caught that bug yet though, thankfully.
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