twelfthnight

Wrote this elsewhere, but this printer prints a full volume in 2 hours. In many scenarios it's 10x faster than FDM with better resolution and compatibility with advanced materials, like food grade / skin contact, ultra strength and rubber-like consistency.

You actually would probably want the wash/cure and VOC extractor upgrades so it's more like 1200$ before it's useful, but if you have the clean room space and ability to handle the toxic resin, functionally resin printing is typically better than fdm for single color prints.

My first printer was resin without a dedicated wash/cure set up and it was difficult enough that I sold the printer and got an FDM. Currently I'm using an Snapmaker U1 and it's really awesome and much easier. It also prints in multicolor seamlessly.

That said, if you do spend the extra for this along with the wash/cure and the voc extractor, the demos are really impressive. It seems like this printer is blazing fast, for single color prints it will print basically anything at better resolution than an FDM in <2 hours, whereas FDM could take anywhere from 4-12+ for a similar part with bigger layer lines.

Why not use a text embedder for the unstructured data and concatenate with the structured data?

For example you could freeze most of the layers of the embedder but let the final ones learn. Then you wouldn’t need to do either feature or prompt engineering?

I wonder if most people like/don’t care about recommendations and don’t want to spend time giving companies more feedback.

I work in streaming recommendations and basically no one voluntarily gives extra feedback, and most initiatives that aren't machine learning based on true behavior fail on A/B tests.

I’ve been in so many meetings where the outcome is to plan another meeting, and include even more folks. Whichever team brings the most folks steers the decision in their favor, and thus manages hire more unnecessary employees for political will (which then increases the need for even more meetings).

The way out is creating a singular vision (eg leadership) and assigning teams goals they can work independently on towards that vision. It is to remove dependences between teams (and thus the need for them to communicate as much), not to increase communication or Jira tickets or Gantt charts or RACI matrices.

Seems equally valid to come out of this with the takeaway that code quality _does_ matter, because poor coding practices are what led to the leak.

Sure, the weights are where the real value lives, but if the quality is so lax they leak their whole codebase, maybe they are just lucky they didn’t leak customer data or the model weights? If that did happen, the entire business might evaporate overnight.

I agree AI could probably do a decent job on Kaggle problems. Of course, almost no DS job is building models with well-defined objectives and perfect data. The DS and MLE folks I work with mostly spend their time reframing ill-posed product requests into ML systems that can be maintained and improved with feedback loops.

A _huge_ part of a DS is saying "No" to bad ideas posed by non-experts. The issue with LLMs is all they ever say is "Yes" and "Wow, that's such a great idea!"

Classic 2024 company. Started out with a few genuinely cool products and a quirky, fun attitude. Got popular, decided to exploit that good will and now everything they do seems greedy and inauthentic (regardless of whether any specific product is or not). Maybe this is a great product? But after the OP Field pricing shenanigans and OP-Z quality issues... hard to give them a second chance.

That sounds right to me. I don't work in physics, but have many colleagues who have left it for that reason.

~The said, I think Dune still does seem useful. Hard to say whether the cost overruns are a lack of good management or if the experiment is just a quagmire, though. Guess I lean towards the scientists here that it's the former~

EDIT:

Doing some research it seems the Hyper K experiment might just be better, and funding might be better directed there than Fermilab.

I don't know all the details, but I think they are starting to do novel work with the accelerator to produce neutrinos in an experiment called Dune. So there actually is important stuff being done that isn't just superceded by Cern.

The larger scale models aren't necessarily built on the smaller models, they are independent and generally less specific. They are probably even inputs to the more specific models. So, like rising sea levels and increased temps (on average) is basically guaranteed, the _exact_ change for a particular place is more uncertain.

Neil deGrasse Tyson in his Cosmos show described it like a person walking a dog. We don't know where the dog will be exactly, but we can see the person with the leash is moving in one direction, and the dog isn't going to ever get too far from him (because of the leash).

Huh. I actually can understand devs not wanting to need permission to install libraries/versions, but with a pull-through cache there's no restrictions save for security vulnerabilities.

I think it actually winds up speeding up ci/cd docker builds, too.

Because of containers, my company now can roll out deployments using well defined CI/CD scripts, where we can control installations to force usage of pass-through caches (GCP artifact registry). So it actually has that data you're talking about, but instead of living in one person's head it's stored in a database and accessable to everyone via an API.