commandar

Sure, but the reason I'm confused by the pricing is that the pricing doesn't exist in a vacuum.

Pro barely performs better than Thinking in OpenAI's published numbers, but comes at ~10x the price with an explicit disclaimer that it's slow on the order of minutes.

If the published performance numbers are accurate, it seems like it'd be incredibly difficult to justify the premium.

At least on the surface level, it looks like it exists mostly to juice benchmark claims.

Spinning rust still typically holds advantages for archival storage, as well.

There was literally a headline on the front page here a few days ago re: data degradation of SSDs during cold storage, as one example.

>I think people who do this think that people who use Windows perceive that the Mac experience is smoother, and may have some sort of Mac envy.

There's an irony in this due to this:

>b) WSL-based, VSCode-using devs who are one step away from just using Linux. These are the folk who fifteen years ago would have been using what was then still OSX. But these folk don't use Windows as Windows: they use it as a semi-Unix.

The people still doing the "hurr durr wind0ze suxx" routine are the ones stuck 15 years in the past. Modern Windows is an entirely different and vastly more capable beast and it still runs huge swathes of the enterprise world.

The best technologists I know don't really care all that much which desktop platform you stick them on anymore since most of what they really need is either available everywhere or running on a backend that isn't their desktop anyway.

It was a built-in expander. The bed was 4x6 in standard configuration and could pull out another 2 feet to get 4x8 when you needed it.

I'm firmly of the opinion that, as a general rule, if you're directly embedding the output of a model into a workflow and you're not one of a handful of very big players, you're probably doing it wrong.[1]

If we overlook that non-determinism isn't really compatible with a lot of business processes and assume you can make the model spit out exactly what you need, you can't get around the fact that an LLM is going to be a slower and more expensive way of getting the data you need in most cases.

LLMs are fantastic for building things. Use them to build quickly and pivot where needed and then deploy traditional architecture for actually running the workloads. If your production pipeline includes an LLM somewhere in the flow, you need to really, seriously slow down and consider whether that's actually the move that makes sense.

[1] - There are exceptions. There are always exceptions. It's a general rule not a law of physics.

>(Healthcare in the US is nearly entirely Windows based).

This wasn't my experience in over a decade in the industry.

It's Windows dominant, but our environment was typically around a 70/30 split of Windows/Linux servers.

Cerner shops in particular are going to have a larger Linux footprint. Radiology, biomed, interface engines, and med records also tended to have quite a bit of nix infrastructure.

One thing that can be said is that containerization has basically zero penetration with any vendors in the space. Pretty much everyone is still doing a pets over cattle model in the industry.

>Anthropic feels like a one trick pony as most users dont need or want anthropic products.

I don't see what the basis for this is that wouldn't be equally true for OpenAI.

Anthropic's edge is that they very arguably have some of the best technology available right now, despite operating at a fraction of the scale of their direct competitors. They have to start building mind and marketshare if they're going to hold that position, though, which is the point of advertising.

Anthropic, frankly, needs to in ways the other big names don't.

It gets lost on people in techcentric fields because Claude's at the forefront of things we care about, but Anthropic is basically unknown among the wider populace.

Last I'd looked a few months ago, Anthropic's brand awareness was in the middle single digits; OpenAI/ChatGPT was somewhere around 80% for comparison. MS/Copilot and Gemini were somewhere between the two but closer to Open AI than Anthropic.

tl;dr - Anthropic has a lot more to gain from awareness campaigns than the other major model providers do.

I don't entirely agree there.

In a vacuum for a standalone object, a 3D mesh app like Blender can be useful for brainstorming.

Most of my CAD usage is designing parts that have to fit together with other things. The fixed elements drive the rest of the design. A lot of the work is figuring out "how do I make these two things fit together and be able to move in the ways they need to."

There is still a lot of room for creativity. My workflow is basically "get the basic functionality down as big square blocks, then keep cutting away and refining until you have something that looks like a real product." My designs very rarely end up looking like what they started out as. But the process of getting them down in CAD is exactly what lets me figure out what's actually going to work.

It's a very different workflow, and it's definitely not freeform in the same way as a traditional mesh modeling app, but CAD is for when you have to have those constraints. You can always (and it's not an uncommon pattern) go back and use a mesh modeler to build the industrial design side of things on top once the mechanical modeling is done.

ETA:

I'd also add: I'm not sure "thinking in CAD" comes naturally to anyone; it's a skillset that has to be built.

>Changing dimensions or previous sketches is usually fine, but anything more complicated often results in everything in your stack breaking with strange errors, leading it to just be easier to re-create the model.

This is usually the result of design workflows and how you avoid it is going to vary based on the CAD package. It definitely requires being pretty deliberate in design, which can make it harder to draft out an initial design. And the path of least resistance is often one that's more likely to break.

One example would be in Fusion, using projected faces in sketches is far more fragile than projecting a body -- but Fusion will happily project faces by default.

Which constraint types you use where are another common cause of breakage.

The thing that makes it frustrating is that none of this is really well documented anywhere and largely ends up being best practice wisdom passed from one person to another, since a lot of this stuff is really non-obvious. And it's confounded yet again by people cargo culting best practices from one CAD package to another that then gets repeated third and fourth hand.

All that said, as you work with it more and delve into more complex designs, you'll end up settling into workflows that result in more resilient models if you're deliberate about it. The "scrap it and start over" cycle is part of the learning experience, IME, as frustrating as it is at the time.