Nothing, because they don’t understand RecSys which is half of ML at social media companies. Probably could help with prediction for ads but the domains are very different.
??? The tweeter you linked printed out her code as a joke, her tweet right below this is quoting Elon, "Comedy is now legal", you taking a joke as confirmation that something happened is a great example of why twitter is a net negative on humanity.
IME for engineering at these companies is that the social or political side of things is mostly irrelevant for Eng. Some folks want to be involved in internal activism but it’s easy to ignore and basically never comes up in Eng work. Ppl just avoid politics like normies and talk about average stuff like “how’s your kid?” not “let’s go to that protest”. Lots of good smart technical peeps more likely to nerd out on cool tech stuff than be political.
This is assuming that political power comes from influencing all Americans rather than a small fraction who hold true power (politicians, donors, journalists, industry groups, etc.). If the small fraction is all on Twitter then viola the rhetoric is meaningful.
if it's "long" running, you want the full JIT experience, because you essentially lose the entire "But how does my code actually run" optimisations that the JIT can do, and only have stuff that can be done ahead of time.
I think this is an unhelpful comparison. Simply put, when users exchange messages on WhatsApp it causes minimal traffic to a WhatsApp data enter as it’s mostly p2p. No ads, no persistence.
Snapchat requires semi persistence for stories (viewable for 24hr) and millions of users can view a single story. When a celebrity posts a story and 5mil users view it that requires more engineering effort that p2p what’s app messages. Additionally, serving ads requires a large amount of compute (ad ranking/serving), storage (impressions), offline processing (Hadoop clusters are hard), engineering, salepeople, economists, and auxiliary systems to support advertisers buying ads, etc.
what are the most common types of queues for large data center applications (eg. Stateless app that handles rpcs, running on N machines). Are they mostly M/M/N? For example, is memcached different than nginx, or Postgres?
While an M/M/n is a good starting point, there are several extra important features to capture.
First, many of these applications have a load-balancing step, where arriving jobs are dispatched to queues at each of the machines. The performance will depend on how this load-balancing is done.
Second, some applications will parallelize across many cores or machine, while others will run each job on a single core or machine. This obviously has major implications for performance.
Third, your application may have variance in interarrival times or in job completion lengths. This is also important to measure and incorporate into a model. Something like Kingman's formula can be useful: https://en.wikipedia.org/wiki/Kingman%27s_formula
Interesting, anecdotally at a faang adjacent company, I do not know of any Seniors who do not have code as their primary output. Even a large majority of staff actively code IME.
