(In case you wonder, the answers I saw are: (1) semantics-aware code search/replace with no IDE requirement; or maybe better "grep" with no false positives (2) write queries on command line or in file, and pass them to tool; (3) very low: install the binary, there no need to "import" or "setup project" or any things like those).
Compared to that into, Glamorous Toolkit presets itself much worse. Here were my thoughts when I opened the website: It seems to be some sort of data explorer tool, but it's also kinda weird. For example, the API explorer clearly shows post-processed data, so it's not actually exploring Github API, but rather some sort of binding to it (GhRepo according to the title).. so what's the use of API explorer which requires bindings to be written first? DevOps explorer seems interesting, but I don't care about Jenkins, so what I really want to see is how hard is it to teach it about new system. Maybe it's in the videos, but I am not going to watch long videos unless I am already interested in the tool. Maybe if I click around? Nope, and the blog is not very helpful too... In fact, the comments for the post were much more informative than the website, I love HN!
So, apparently the answers are (1) it can visualize the data, if I am willing to learn smalltalk (2) it's smalltalk, so you create smalltalk classes, and they become stuck in the "image" that you cannot easily share with others nor use with any existing workflows and (3) it's probably a few hours of youtube (the first video alone is 45 minutes) + experimenting before I can get any useful output.
I don't see it ever catching at all, sorry.
Granted, it seems like a general problem with Smalltalk: the collaboration story is bad. It seems every Smalltalk user lives in their own little world, and sharing stuff with other people is an afterthought, at best. Just compare gritql's and GT's homepage: one starts with 3 copy-pasteable commands which would immediately show something cool, another starts with mysterious "Download" button followed by 45 minute video.
Because it's really not any one thing other than an environment that is built from the ground up for building highly explainable systems, including itself. Think about it like a "meta-tool", or a tool for building tools. Similar to how an operating system is a piece of software used for writing other pieces of software easier.
So naturally this type of workflow lends itself to data analysis. However it's no less applicable in building p2p networks, or working with codebases in other languages.
Regarding sharing code, it's actually really straight forward. Your classes aren't stuck in images, but are normally stored as normal plaintext files and committed into git. The library story is arguable better than in most other languages, because of how flexible smalltalk is.
Because if it wasn't for the fact the graphical stack was implemented as smalltalk objects, you couldn't build tools like the driller or debugger since they would have to be implemented as a secondary piece of software that loses the original context.
Like for example, I built a custom tool for myself when I was working on this p2p network and had a section of the codebase with some non obvious control flow, since it was handling multiple different p2p networks at the same time. Normally this is where you include a diagram in the docs, but in about an hour I built a custom code editor for the class, that visualized all the control flow and explained the cases in a flow diagram by simply introspecting on the methods defined in the class. And this tool never fell out of sync like a static diagram, since it wasn't hardcoded by me. And from that point on, I worked within this tool whenever hanlding anything related to this.
And fwiw, the python story is pretty seamless from my usage of it a few months ago. I was able to integrate and use python libraries into this project without much hassle.