Assuming a US startup is considering engineering hires outside the United States, how does one currently assess the likelihood of getting them a visa to work in the USA? And what timeline and cost would be involved?
Unfortunately, a case by case analysis would be required. However, if they are from a country with its own visa (that is, Australia, Canada, Chile, Mexico, and Chile), it's relatively easy to get engineers visas.
Agreed, to me they are very different takes on what is a punk attitude.
Palahniuk: Underneath the veneer of the banal, you will discover everything is rotten and sycophantic but somehow tender and relatable.
Bourdain: Underneath the veneer of the banal, you will discover an honest struggle for something far more respectable than what is typically venerated. Eat their food, dance to their music, and you will enjoy.
Isn't the challenge that introspecting graphql will lead to either a) a very long set of definitions consuming many tokens or b) many calls to drill into the introspection?
Well either that or stuff the tool usage examples into the prompt for every single request. If you have only 2-3 tools GraphQL is certainly not necessary - but it wont blow up the context either. If you have 50+ tools, I don't see any other way to be honest, unless you create your own tool discovery solution - which is what GraphQL does really well with the caveat that whatever you decide to do is certainly not natural to these LLMs.
Keep in mind that all LLMs are trained on many GraphQL examples because the technology has been in existence since 2015. While anything custom might just work it is certainly not part of the model training set unless you fine-tune.
So yes, if I need to decide on formats I will go for GraphQL, SQL and Markdown.
What were the problems? I've been trying it out and haven't hit issues yet, but not using it at scale yet so I'm curious what to watch out for. I figure it's open source (MIT) so I can make changes as needed if there was anything particulary annoying.
Learning the size of objects using pure text analysis requires significant gymnastics.
Vision demonstrates physical size more easily.
Multimodal learning is important. Full stop.
Purely textual learning is not sample efficient for world modeling and the optimization can get stuck in local optima that are easily escaped through multimodal evidence.
("How large are lions? inducing distributions over quantitative attributes", Elazar et al 2019)
Ask a blind person that question - they can answer it.
Too many people think you need to "see" as in human sight to understand things like this. You obviously don't. The massive training data these models ingest is more than sufficient to answer this question - and not just by looking up "dimensions of a lion" in the high-dimensional space.
The patterns in that space are what generates the concept of what a lion is. You don't need to physically see a lion to know those things.
The text-based software that would eat work management is one that embraces the incumbents rather than avoid them.
I want a bidirectional SaaS <=> YAML/JSON adapter. So that I can push and pull our CRM (and other SaaS utilities like project management) into a common (schematized) YAML format.
The YAML then can be analyzed and modified using LLMs and/or stored in git.
And then use the bidirectional sync to reconcile conflicts and push.
So I can do work processes on the console, and still collaborate with people who want the native web UI.
Thinking of Terraform, you have data blocks that can grab data from an external source. Still trying to grok what would be a convenient way of doing something like this - whether that gets generated to DSL, or if data pulled in dynamically as you build the org graph...
Having your plain-text workspace as a unified structural source where you pull in data from external systems would be potentially powerful.
I’d be happy with SQL access, which I think gets to roughly the same place.
I’ve done something like what you’re talking about before for a CMDB, though it was one way YAML -> DB sync. Many to many relationships were a pain to view, there’s not a great way to put them in YAML that makes them easy to read. Can’t embed them because then you have multiple copies and which one is the real one. References suck because you can’t see the relationship and the related objects at once.
The real killer is permissions, though. Your sync tool basically has to have admin privileges, which means permissions have to be checked at merge time, and then you’re rebuilding the entire permissions flow as a git hook.
SQL with RLS is capable of implementing permissions in a way that works for both API access and direct SQL access. I get the feeling few companies do it, but they could.
Assuming a US startup is considering engineering hires outside the United States, how does one currently assess the likelihood of getting them a visa to work in the USA? And what timeline and cost would be involved?
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