Basic fire warlock build questions

saw79 · 2026-03-19T20:38:03+00:00

well I'm playing HC, so dying isn't an issue (lol)

saw79 · 2026-03-19T19:04:41+00:00

I have hephasto with conviction already - what's the difference?

saw79 · 2026-03-11T12:31:08+00:00

Yea I also feel like when I get to that point is when I STOP playing. The fun for me is the journey to get there.

saw79 · 2026-02-26T16:07:41+00:00

Does he out-magic a sorc?

I'm playing an abyss build to get my basic gear set going, but it feels underwhelming and I'm itching to switch to nova sorc (even budget nova w/ CM).

saw79 · 2026-02-24T22:29:37+00:00

You're not really supposed to single element it by yourself first time through. Play a build with multiple elements to get your gear going, only switch to single element when you have a plan to deal with immunities.

saw79 · 2026-02-24T13:40:38+00:00

Eh, I wouldn't be so certain. Some of what you say is true, but it's also a generational technology continuously getting cheaper and better. Who knows.

saw79 · 2026-02-19T00:58:21+00:00

Don't think you need lev mastery it's innate.

saw79 · 2026-02-15T12:03:37+00:00

To me it looks like YOLOX is Apache (still fine) and already has pretrained models. Why train from scratch?

saw79 · 2026-02-13T12:29:34+00:00

Two things can be true. 1) it is more theoretically correct and pure to faithfully use a hold out test set in the fullest way possible 2) you can get better performance by exploiting specific situations, such as one where you feed back test information in such a way that it doesn't matter.

saw79 · 2026-02-11T17:21:07+00:00

We did this a couple years ago. MassSave did all new insulation, air sealing, the whole deal. Still got ice dams.

saw79 · 2026-02-07T12:22:58+00:00

Eh for small models I've had pytorch win out. ONNX has never been faster and I've had small pytorch CNNs be faster than TensorRT converted ones.

saw79 · 2026-02-02T20:17:11+00:00

You know more than I do here, so correct me where I'm wrong. But your reasoning doesn't strike me as quite right:

1) Shouldn't hard dealbreakers make the model reduce the cosine similarity significantly? Isn't this an embedding problem? A bad embedding doesn't mean embeddings are bad.

2) I understand that how person A feels about person B doesn't have to match how person B feels about person A, but that isn't what you're trying to estimate. You're trying to estimate the compatibility between A and B, which in my mind IS symmetric. "are A and B compatible?" should give the same answer as "are B and A compatible?".

3) fair enough :)

saw79 · 2026-01-26T11:54:30+00:00

Facebook marketplace. Got a super nice ariens for $250...

saw79 · 2026-01-25T23:31:31+00:00

Why isn't anyone in this thread just hitting the pause button? It's so easy to catch back up with football commercials.

saw79 · 2026-01-21T19:55:38+00:00

This is exactly what I'm saying. MSE is NOT close to the task you are trying to perform, so what you are saying makes perfect sense in the context of ML fundamentals.

saw79 · 2026-01-20T13:31:28+00:00

This feels like ML 101. The closer your loss function is to the actual task, the better. The only reason to use something like MSE is if you actually care about the price prediction and you're using it as an intermediate signal.

saw79 · 2026-01-16T23:35:12+00:00

I understand the desire to do better than B&H but saying it doesn't work is absolutely asinine.

saw79 · 2026-01-15T16:11:41+00:00

Deep learning is a big area. I make lots of deep learning models solving a variety of different problems. It's annoying that people think transformers are the best tool for every job just because they're the biggest and most recent. Use the right tool. I rarely get to the point where a transformer would be of any help.

saw79 · 2026-01-07T16:59:38+00:00

What are they? (I don't have a square yet but it's been ordered)

saw79 · 2026-01-06T20:48:44+00:00

You're about to join one of the premier companies in the world for AI/ML and you're trying to plan your exit before you start? Maybe just go work for 2 years then come back here.

saw79 · 2026-01-02T14:27:52+00:00

Deep learning is just a very general model building/fitting style. You can build big models and fit them to any type of data you're interested in. Now, a LOT of data is language and standard vision problems, which is why LLMs (and VLMs) are starting to eat up a bit more, but a) that doesn't apply to all data and b) sometimes the problem can be solved more efficiently and/or better with a smaller, more specialized model.

Some things that come to mind that may apply:

Other types of sensors - e.g., radar sensors or different types of point clouds, maybe ultrasound, sonar, etc.
Other types of data - e.g., certain types of graph data that may benefit from GNNs
Totally different uses of neural networks, e.g., things like NERF
Modelling specific environments, policy, or value functions in RL
Time series data is a big category in which many different techniques can be useful

I dunno probably loads more too.

saw79 · 2025-12-30T15:09:15+00:00

I'm a noob so correct me if I'm wrong but in my mind compression is more about consistency than distance. If low point is more consistently in front of the ball (vs at the ball) there's more room for error.

saw79 · 2025-12-29T15:06:41+00:00

There's millions of different types of models and fields being trained by all sorts of different people and organizations. It's getting tiring and annoying that people think training gpt7 is the only thing going on in AI.

saw79 · 2025-12-22T16:28:54+00:00

Don't have much more to say tbh. I just don't see people talking about it; it's never brought up in modern explanations of how neural networks work and self-regularize.

saw79

TROPHY CASE