What I do have an issue with is new users that try and have problems and immediately start whinging that “FreeCAD isn’t like what I know. And it needs to be like my favorite” Those are the lazy people that can’t be bothered to learn something new. And they should either expend the effort to learn or go back to whatever they were using

I think that’s fair, but most criticisms of FreeCAD from people coming from other CAD packages rather fall into your latter category that you mention here:

But if you have given FreeCAD, (or ANYTHING new in life), an honest try and you can’t get the hang of it or simply don’t like it.

I don’t think we’re actually disagreeing in principle, just on what we perceive as the common criticisms of FreeCAD. Normally, I’ve seen people from other CAD programs get frustrated at limitations within FreeCAD or needing to work around bugs in ways that slow them down. For example, FreeCAD previously was unable to cope with multiple geometries being contained in a single sketch (I believe 1.0 now supports multiple extrudes from different sketch regions, but previously FreeCAD would throw an error), which made modeling less efficient for those coming from programs like Solidworks where this feature exists. Throw other issues like toponaming into the mix and it’s no surprise people from other CAD programs tried learning it, got frustrated (since their baseline was better than what FreeCAD could offer) and moved on.

I agree that criticizing FreeCAD for having different workflows than other CAD programs is a bit silly, though. I don’t really care what the exact workflow is as long as it 1) works and 2) is fast, and for me FreeCAD 1.0 (and previously Realthunder’s branch) ticks all the boxes there.

I appreciate the respectful discussion!

I do think the point about all CAD packages having failure paths is a little overblown. Yes, you can definitely get proprietary CAD to break but in my experience (at least with Solidworks and Fusion), it usually requires much more complex parts than FreeCAD parts. Post 1.0 the situation is definitely better though.

You’re right that users should try following best practices from day one, but realistically most users are not going to learn everything correctly automatically. They might use an out of date tutorial, or might have just learned by tinkering themselves.

The point I was trying to make was that because FreeCAD operates differently than other CAD programs do to one another and because it’s generally a bit more brittle and demanding of the user, I can’t say I blame anyone for not wanting to switch to it if they already have a CAD program they’re proficient with. You could call it being lazy, but from a practical standpoint there isn’t necessarily a ton to gain for a relatively large amount of time investment required to be capable of using it.

I really hope FreeCAD improves enough one day in the new user experience department. I love the software and have been using it as my tool of choice for years now, but evidently not everyone thinks it’s worth the time investment.

The main benefit I think is massive scalability. For instance, DOE scientists at Argonne National Laboratory are working on training a language model for scientific uses. This isn’t something you can do on even 10s of GPUs for a few hours, like is common for jobs run in university clusters and similar. They’re doing this by scaling up to use a large portion of ALCF Aurora, which is an Exascale supercomputer.

Basically, for certain problems you either need both the ability to run jobs on lots of hardware and the ability to run them for long (but not too long to limit other labs’ work) periods of time. Big clusters like Aurora are helpful for that.

I’ll mention this fix is aimed at mitigating toponaming primarily for sketch attachment. Some features still struggle with toponaming, namely chamfers and fillets. But in any case, it’s a massive step forward and makes FreeCAD much easier to recommend! Until now I’ve been using Realthunder’s fork since toponaming was such a headache to resolve manually.

I think that’s a little unfair. The bigger issue IMO is that FreeCAD doesn’t quite share the same workflow as other (proprierary) CAD packages, so someone coming from proprietary CAD also needs to unlearn habits that were previously fine but now potentially harmful. For example, adding chamfers and fillets in FreeCAD pretty much should only be done at the end to avoid toponaming issues, which is less of an issue in other packages.

I’m a researcher in ML and that’s not the definition that I’ve heard. Normally the way I’ve seen AI defined is any computational method with the ability to complete tasks that are thought to require intelligence.

This definition admittedly sucks. It’s very vague, and it comes with the problem that the bar for requiring intelligence shifts every time the field solves something new. We sort of go “well, given these relatively simple methods could solve it, I guess it couldn’t have really required intelligence.”

The definition you listed is generally more in line with AGI, which is what people likely think of when they hear the term AI.

I believe this is the referenced article:

https://arxiv.org/abs/2311.03348

I’ve been using FreeTube since Piped was very inconsistent for me, but I guess that’s just the nature of these services. I’ll have to check out Invidious again, last time I tried it was several years ago and I stopped using it after the main instance shut down. Is it still under active development? I remember its development status being unclear, partially because the language it uses is not super mainstream, but it’s probably changed since then.

Fortunately, Invidious, Piped, Libretube and Newpipe all exist and work flawlessly so there’s no excuse to use proprietary trash like that.

Isn’t the very point of this post that Invidious and Piped don’t work flawlessly?

Can’t you still modify and distribute Grayjay, just not commercially? I understand that still prevents the app from being considered open source, but their reasoning is valid IMO (to prevent people from making ad-infested clones on the play store, which has happened with NewPipe before).

I think what they mean is that ML models generally don’t directly store their training data, but that they instead use it to form a compressed latent space. Some elements of the training data may be perfectly recoverable from the latent space, but most won’t be. It’s not very surprising as a result that you can get it to reproduce copyrighted material word for word.

Not sure what other people were claiming, but normally the point being made is that it’s not possible for a network to memorize a significant portion of its training data. It can definitely memorize significant portions of individual copyrighted works (like shown here), but the whole dataset is far too large compared to the model’s weights to be memorized.

The big thing you get with frameworks is super simple repairability. This means service manuals, parts availability, easy access to components like the battery, RAM, ssd, etc. Customizable ports are also a nice feature. You can even upgrade the motherboard later down the line instead of buying a whole new laptop.

I haven’t read the article myself, but it’s worth noting that in CS as a whole and especially ML/CV/NLP, selective conferences are generally seen as the gold standard for publications compared to journals. The top conferences include NeurIPS, ICLR, ICML, CVPR for CV and EMNLP for NLP.

It looks like the journal in question is a physical sciences journal as well, though I haven’t looked much into it.

I’m curious what field you’re in. I’m in computer vision and ML and most conferences have clauses saying not to use ChatGPT or other LLM tools. However, most of the folks I work with see no issue with using LLMs to assist in sentence structure, wording, etc, but they generally don’t approve of using LLMs to write accuracy critical sections (such as background, or results) outside of things like rewording.

I suspect part of the reason conferences are hesitant to allow LLM usage has to do with copyright, since that’s still somewhat of a gray area in the US AFAIK.

Also one very important aspect of this is that it must be possible to backpropagate the discriminator. If you just have access to inference on a detector of some kind but not the model weights and architecture itself, you won’t be able to perform backpropagation and therefore can’t generate gradients to update your generator’s weights.

That said, yes, GANs have somewhat fallen out of favor due to their relatively poor sample diversity compared to diffusion models.

For reference, ICML is one of the most prestigious machine learning conferences alongside ICLR and NeurIPS.