OpenKnowledge seems to maybe fill that niche somewhat (it also supports annotating Markdown with comments to feed into an agent), and has agent integration within the GUI, but one killer feature that seems to be missing is Remote SSH support (short of hosting its web server on the remote and having the IDE live in the browser).

For instance, in Claude Code you can have it bring up this TUI overlay after every edit it makes (it literally is an overlay, rather than a separate tab in your terminal) and you can annotate line-by-line anything you want changed or rejected. Then once you exit the overlay those suggestions are automatically piped back into the agent.

I tried a few of these TUI-based code annotators, and this one is by far the best. It also works for files not under version control.

I'm currently not using it as I also prefer something more akin to "text editor with agent built in", but I wanted to mention here in case you were curious.

I mainly use Cursor for another reason though: all of my work is remote over SSH and I don't like keystroke latency, so I prefer code editing / agent conversations to feel local. (Everything else is done in the terminal.) But I would also prefer to use something less propriety. I may go back to VSCode with plugins to achieve a similar experience.

Comments URL: https://news.ycombinator.com/item?id=41760069

Points: 6

# Comments: 1

You're curious about whether there is gain in parameterising activation functions and learning them instead, or rather, why it's not used much in practice. That's an interesting and curious academic question, and it seems like you're already experimenting with trying out your own kinds of activation functions. However, people in this thread (including myself) wanted to clarify some perceived misunderstandings you had about nonlinearities and "why" they are used in DNNs. Or how "squashing functions" is a misnomer because `g(x) = x/1000` doesn't introduce any nonlinearities. Yet you continue to fixate and double down on your knowledge of "what" a tanh is, and even that is incorrect.

> Sure there's a squashing function on the output to keep it in a range from 0 to 1 but that's done BECAUSE we're just adding up stuff.

It's not because you're "adding up stuff", there is specific mathematical or statistical reason why it is used. For neural networks it's there to stop your multi layer network collapsing to a single layer one (i.e. a linear algebra reason). You can choose whatever function you want, for hidden layers tanh generally isn't used anymore, it's usually some variant of a ReLU. In fact Leaky ReLUs are very commonly used so OP isn't changing the subject.

If you define a "perceptron" (`g(Wx+b)` and `W` is a `Px1` matrix) and train it as a logistic regression model then you want `g` to be sigmoid. Its purpose is to ensure that the output can be interpreted as a probability (given that use the correct statistical loss), which means squashing the number. The inverse isn't true, if I take random numbers from the internet and squash them to `[0,1]` I don't go call them probabilities.

> and not only is it's PRIMARY function to squash a number, that's it's ONLY function.

Squashing the number isn't the reason, it's the side effect. And even then, I just said that not all activation functions squash numbers.

> All the training does is adjust linear weights tho, like I said.

Not sure what your point is. What is a "linear weight"?

We call layers of the form `g(Wx+b)` "linear" layers but that's an abused term, if g() is non-linear then the output is not linear. Who cares if the inner term `Wx + b` is linear? With enough of these layers you can approximate fairly complicated functions. If you're arguing as to whether there is a better fundamental building block then that is another discussion.

If I can plug a Youtuber who really 'gets it' (mental health and depression), it's Dr Scott Eilers. Everything else is just clichéd garbage.

(Disclaimer: I am not 'gifted' but did very academically well during high school and uni)

Mostly theoretical-ish deep learning stuff as of late (I'm a PhD candidate in that field). But I want to expand it into really anything: psychology, dating, video game reviews, etc.

Comments URL: https://news.ycombinator.com/item?id=35307751

Points: 1

# Comments: 0

Huh, that's funny, I guess which one came first?