I see many tools out there claiming that they monitor ai search results and helping you grow. Honestly I think it's too much noise.
Can you recommend one or two tools that are actually legit?
In the old days people would study computer science for 4-5 years and then start coding, probably with c++ or java.
Then the not so old days we have these bootcamps where people went for a few months, learnt the basics while practicing.
How does one start today? How 'formal' or structured should the training be?
if so, how do you usually explain the trade offs to users who are concerned about the memory footprint, size etc.
and most of the times we are like yeah, of course its there. The broader footprint is real. Chromium is not free. Electron has overhead. Pretending otherwise would be foolish. So we are constantly optimizing what we can, and we will keep doing so…
At the same time, I do feel that a lot of these comparisons feel weirdly flattened. For example people often compare: the full Electron process footprint VS the smallest possible Tauri/native mental model.
…without always accounting for development speed, cross-platform consistency, ecosystem maturity, plugin/runtime complexity, UI flexibility, and the fact that some apps are doing much more than others. Which is by the way the reason that we went with Electron in the first place.
So all this context to get to my real question, which is:
What do you say when people reduce the whole discussion to “Electron bad, Tauri good”?
Have you found a good way to explain footprint in practical terms?
Where do you think optimization actually matters, vs where people are mostly reacting to the idea of Electron?
any ideas welcome :)
this is the tool btw in case you want to stress test it (it's an API IDE) : github.com/voidenhq/voiden