if you apply to roles within finance you'll have a better shot of landing something. Also, do a quick project using k3s and / or docker to get the basics, so it sounds like you know what you're doing. You can be creative with the truth.
Get a few RPis and get https://k3s.io/ running on them. Get familiar with kubectl. Deploy a few apps to your cluster, tail some logs, restart containers, scale them, etc. Install Prometheus, get monitoring working, make some dashboards with Grafana. This point is to gain familiarity with "modern" cloud infrastructure, but in a low stakes environment where nothing can go wrong. Then, you can talk about your experience, and it will technically be true!
You'll need to send much more than 12, try 2-3 a day for a month. Of those, maybe 5% will reply, but it's not bad that bad once you get into the swing of it.
Yeah if only there were actually openings, thats the problem. The market is crap atm. Its not like ive not applied to a job before, or dont have experience.
Yeah I've noticed we've done an about-face on degrees in industry. It used to be super legit to drop out. As things get more specialized MS and PhD are seen more favorably now.
Referrals are also not a golden ticket right now. Got a few tossed in the bin, we've got a lot of FAANG engineers on the market right now, companies are scooping them up at bargain rates.
> Referrals are also not a golden ticket right now.
They were never a "golden ticket." Results are never guaranteed, but some approaches (blindly applying online to job ads) are inferior.
My point stands that the parent's approach is flawed. Current supply-demand doesn't explain why parent has only gotten 1-2% responses (not even interviews?) in the course of 5+ months (since March 2023).
The parent's comment implies they applied to at least 50 jobs (maybe 100+?). My assumption is that the bulk (all?) of these were non-referred applications, yet, parent has 10+ years of experience and should be exhausting their network for referrals.
Yes, things are worked out pretty well, in the end it didn't take long to find _something_ as a dev, but I think early next year should be a good time to apply to "good" companies. In-network people are telling me things are frozen where they're at, and to wait for Q1 next year.
I was in this boat a few months ago, doing a lot of exercises on leetcode legitimately helps, because you can quickly type an ~80-90% solution immediately from memory, and you have a lot of time left over to finish the remaining portion. It also looks super impressive from the hiring side.
RE point 1: in the actual job these days, we're coding through so many layers of distributed madness that you end up doing YAML/JSON engineering and gluing systems together more than anything, for years on end. A lot of "software engineers" are not writing software, or engineering. Candidates will have to brush up on the basics, because it's been so long. An anecdote: explicit for loops were banned from a codebase I worked on, only interior iteration was allowed. I too, forgot how to use for loops. I spent quite some time writing in embedded YAML DSLs as well (complete with static analysis and compiler).
RE point 2: after sending out a lot of applications, you tend to forget the specifics of where you applied, sometimes companies reach out after weeks or months, and everyone wants 3-5 rounds of multi-hour interviews, so it all becomes a blur. Maybe spreadsheets or a CRM would help candidates here. There is no excuse on the candidate's part for not brushing up before the interview though.
RE point 3: very fair, candidates should jot down a few good questions to ask beforehand.
It's crazy on both sides, not sure how to fix tech hiring. Somehow the industry soldiers on.
The above project somewhat supports GPUs if you pass the correct GGML compile flags to it. `GGML_CUBLAS` for example is supported when compiling. You get a decent speedup relative to pure C/C++.
For extremely low latency, Clojure can be an awkward fit. By default there's a lot of sugar in the syntax, so if you need to be precise about the underlying types of your data and the exact datastructure, you need to toss out most of the standard datatypes and datastructures. It's small details like: "I definitely have an array of unboxed integers, and I need to be sure this one operation won't accidentally allocate a new list of boxed integers, and that this function I'm calling doesn't dispatch dynamically" or things like that. You start using `deftype` everywhere, sometimes dipping into Java, etc. You may as well just write java at that point. However from a pure Clojure standpoint, the author of Neanderthal has done great work on making Clojure viable for high performance numerical computing.
