stevekaram

Ha! Even a racklette might be a smidge too big for the homelab. But you know, given these Voxel 'sleds' are ~6GB RAM each, we could probably get a solid mini-PC farm going now....

I don't want to speak for swherdman, but I don't know that you'd want to nuke anything just to use this. If you want to learn about the Oxide stack, go for it and it'll be a great teacher. But if you're just looking for a better way to run your homelab, I don't know that this would be it.

Of course, I say this as someone running a Nucleo STM32H753ZI board with Hubris as an SP for my home NAS....

Hey that's really interesting, thanks for the share! It looks like this is using the standalone Omicron build, but on a Proxmox VM, right? Great stuff for running the CP in easy mode!

Enterprise software and hardware, built from the ground up. Definitely not just a VMware alternative, though the end product is virtual instances of course.

We're building racks over here! And all our software is open source.

Not saying we are or aren't looking for that kind of candidate, but I _will_ say that we're very keen on the motivation to join Oxide at all, and the role you're applying for specifically.

Sans AI is definitely the way to go if you plan to submit!

As for the materials, they play a part in the conversation stage, and even if you get hired and interview others hoping to join. Every interviewer reads the materials of the candidate, and we send the interviewer's original materials to the candidate. It's not nearly as tedious as some of these comments are making it out to be (and believe me, I've been through the "ten elevator pitches" gauntlet in my career).

tbh, I really enjoyed filling out my Oxide materials. They got me thinking and taking stock of my wins and motivations, not just the crap we throw on resumes to attract the eye.

I'd also add that a huge use for LLMs and vectors in the enterprise is to build queries against production data. Keeping the vector DB external to your RDBMS or other production data store is a unique chance to amplify performance without excess latching and other performance hits against the same database you count on for day to day business. Like external super smart indexes.

I think one of the biggest struggles small startups and practitioners are facing is lack of a good option between "I wonder if this works" and "ready for prime time." Running locally is an option with consumer hardware but is cost prohibitive for a team. Cloud providers are full of complications and hidden costs. Tools like Friendli and Bento are good but ambiguous on costs and get difficult to price end-to-end once you need the full stack of options. Hugging Face inference endpoints and other tools still seem like the best option around along with cloud DBs like Zilliz.

That said, it's no wonder people just pay extra for the simplicity of a slightly smarter endpoint like OpenAI. Sure, over time the costs are insane and you lack any flexibility to create a truly targeted solution, but it feels like an all-in-one easy fix.