I agree. Tenures may be short but careers are long and tech is (surprisingly) small. Credibility builds trust and trust between people is ultimately what business run on. "Do right be people" is a good strategy.
This is a pretty cool idea but I'm trying to think of the advantage of WASM vs other execution engines.
It seems to me one of the main use-cases for WASM is to execute lambdas, which are often short-lived (like 500ms timeout limits). Maybe this could have a place in embedded systems?
I never really thought about this perspective but in some ways it makes sense. I think the ironic part is that LinkedIn now provides built-in AI tools that make you sound more like a bot.
Maybe they could fingerprint slop generated with they tools and allow it through to incentivize upgrading
One area where this absolutely makes a difference is when embedding python. Like it or not Python is extreme popular in data/AI/ML so if you want to build an experience where users can deploy custom functions, removing the GIL allows you to more efficiently scale these workloads.
One thing that's helped me is to every-so-often oil my wood utensils the same way I oil my wood cutting board. It's helps protect the wood and retain moisture so it doesn't crack. Also, at least some (if not all) woods have anti-microbial properties.
I've always wondered about this too. It should be pretty easy to recognize statistical outliers. I'm sure cheaters would start to adapt but that adaptation might start to look more in-line with normal skill levels so at least the game wouldn't be utterly ruined
"streaming data" can generally be thought of as a sequence of events in time. Think measurements from sensors, mouse click events from uses on a webpage, or access logs from a server. These events are often fed into a message bus type system like Apache Kafka or AWS Kinesis.
Stream processing is a programming paradigm for working with these types of timestamped events and a "stream processing engine" like Denormalized seeks to actually execute stream processing compute jobs against streaming data.
The goal of the engine is to abstract away as much of the low level complexities needed to effectively work with this data at scale and provide an ergonomic way for developers to write and operate streaming applications.
Could definitely be wrong but I always thought waxing was about reducing the friction between the ski and the snow by both creating a uniform surface and because the wax has a lower coefficient of friction than the plastic of the skis.