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Lots of doomers on here, yet somehow I keep meeting highly technical people still working on interesting things that are in their 40s, 50s, 60s, and older. I think HN probably skews really young and tech tends to fetishize youth, so this is the mentality you get.


I am 46. I am more or less describing my present situation, except I haven't yet been fired. I say "yet" because my employer's products are government-facing and the incoming administration has priorities incompatible with the usage of our products.

I am told I work on interesting things, but the interesting things are my hobbies and not anything related to my employment. My day job is largely a slowly losing battle against technical debt.


I'm 48 and the only age bias i've encountered so far has been me against myself. Usually it's like I think i'm too old to learn something new, or don't have the energy to keep up, but if I give myself the chance then it turns out I'm wrong. You'll surprise yourself with what you're capable of if you're actually willing to make the effort.


I’m 64 and just landed a great new job … it’s always my long history of C/C++ and mobile skills that get me in the door.


What do you by "Mobile skills"? Like writing Android or iOS apps using Java/Kotlin/Swift, or do you mostly write C++ for those devices?

I mostly write C++ services/libraries for Android, but I've wondered if learning the app dev side of things is worth it. I'm sort of concerned that mobile app dev pays less than system level C++ work.


I needed to hear something like this. I'm 61, got laid off last April and haven't had an interview since June. I have a long history in PHP and Perl, half a decade on Golang, Vue, React and devops. I was beginning to think that ageism was playing against me. Now I know I need to dig deeper.. thanks.


At 50, I pivoted from enterprise-style Java server development to AI (computer vision, now LLMs), and I am having fun at work every day.


What kind of AI projects are you working on?


Adapting ML models to run inference on devices: phones, smart cameras, edge accelerators. Computer vision models first, now LLMs.




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