If you do end up getting a used Apple device I recommend making sure that it has the newer keyboard style that the current models have (I think it switched in 2018). The old keyboard was not fun to use.
Working with various data, ML, and researcher-type engineers there's a bit benefit to using Python. Especially if you want to be at the forefront of what's being built.
I've been building with Python FastAPI and a React/Nextjs app. I've gone back and forth on whether we should have used Django, but we've been full steam ahead with what we have.
If I wasn't building in the AI space I think I probably would have used Node <-> React with tRPC to make the typed full stack monorepo experience seamless.
Oh and Postgres - I'm irrationally loyal to Postgres
Based on nothing but a guess, my bet is that it's probably roughly breakeven
Though I'm curious how they're not charging any subscription given that the device will have ongoing costs to run the models. That feels like it could hemorrhage money – though if users are actively using it I guess it'd be a good problem to have
A bit of a different take, but given the amount of experience you have, I wouldn't put as much weight on using a CV at all.
With an updated LinkedIn you can reach out to past bosses, coworkers, and friends for introductions to other people in industry (whether they're hiring or not) – and follow that chain until you find something you're interested in.
I would agree. Applying to every public job posting out there is a waste of time.
Either the postings are fake (there for legal reasons, or to make the company look like they are healthy and growing) or they will be spammed by so many applicants that even if your resume is a perfect match it will just get lost in the noise.
It feels like it's sort of it's own thing. LLMs are really good at morphing or fuzzy finding.
An interesting example – I had a project where I needed to parse out addresses and dates in a document. However, the address and date formats were not standardized across documents. Utilizing LLMs was way easier then trying to regex or pattern match across the text.
But if you're trying to take a text document and break it down into some sort of a structured output, the outcome using LLMs will be much more variable.
