It's been a long time since I worked at Geoworks and my memory is a bit fuzzy. The tool chain was highly proprietary object oriented assembly. The builds were all distributed for speed. The tools were all developed on sun workstations. There was a strong pivot to tablets (inspired by apple newton) and smartphones by the time I joined. Eventually a toolchain around an object oriented extension to C was developed and I don't believe that required a sun workstation, but I don't remember the details.
I believe G offers a sort of interview workshop at some of their sites. Not sure what they are doing during the pandemic. A recruiter could tell you if they offer some sort of prep workshop.
The title implies that it's going to replace ML models. But it seems that it still uses ML models, but provides a different interface. It also seems that it's using some AutoML training system, so that in theory little ML expertise is required to use the system.
I didn't finish ready the article because it didn't give a succinct summary of what predictive databases are. But at a glance this seems to be a SQL interface to an AUTOML system. Is that a correct summary? I don't get the distinction between ML and predictive databases. It seems predictive databases use ML.
I started working during the build up to the dot com boom at a small company. It was exciting and I threw myself into the work. Unfortunately, the company ended up going under and I scrambled to find the next gig. I kept chugging along making decent money (for the time) but was extremely bored. It started to feel like I was forced to sit in an office for 8 hours a day and do nothing (of value). I felt part of the problem was that I had transitioned away from coding to managing and missed the technical challenge of work.
I decided the best way to stop managing and go back to the hard technical challenges was to do a PhD. I was fortunate enough to financially have the option to quit working. I was 36 at the time.
Going back to do a PhD was great. I especially enjoyed the first 4 years where I was learning a ton of new stuff, meeting new people and going to conferences. I picked the subject, AI, based on what I had always been interested in. It was great to work fulltime on a subject that I found very interesting. It helped that I didn't care about the money or career prospects. The last two years of the PhD were a bit more difficult and I felt pressure to get the dissertation done, but it wasn't too bad.
After finishing I decided that the lifestyle required to pursue an academic career wasn't for me and ended up moving to a city my wife and I enjoyed and taking a job at a FAANG company. I've been there ever since making good money. I've been able to stay engaged with difficult technical problems. Unfortunately, although I find the problems technically interesting to solve, they aren't really addressing issues that I feel passionate about.
Now I'm starting to reach the point of thinking about quitting again. I'm currently more inclined to switch projects at the same company or retire rather than go to a startup or another FAANG company. A large non software focused company isn't that appealing because it seems like I would become part of a cost center. Working for another FAANG company feels like a sideways move and a startup seems like it would be a giant hit to the work life balance. I'm mainly planning on saving enough to not _have_ to work.
I see a lot of colleagues who started working around the same time I did and who ended up at a FAANG company and are now quitting to retire | startup | passion job. I feel very fortunate to be working in a field that I enjoy, for a company that pays me enough that I'll be able to retire relatively early.
I was stuck in middle mgmt after just 7 years in the industry. Although I had been at 3 different companies it still felt like a rut. I ended up going back to get a PhD in a topic I was really interested in. It worked out well, in that now I can focus on things I like. Also with the PhD employers seem to focus less less on whether you now a particular tech. Though financially it's probably a net negative.