I don’t buy it. He’s says it doesn’t work but can’t explain why it didn’t work. Could be his method was faulty, the type of bean, the age of the bean, etc.
I rather take the experience of an entire country.
7,576 karma · joined January 28, 2012
I don’t buy it. He’s says it doesn’t work but can’t explain why it didn’t work. Could be his method was faulty, the type of bean, the age of the bean, etc.
I rather take the experience of an entire country.
We mostly hired people with no previous Clojure experience. Majority of hires could pick up and get productive quickly. People fresh out of college picked it up faster. I even had a case of employee transitioning careers to S.E., with no previous programming experience, and the language was a non issue.
I can't remember an instance where the language was a barrier to ship something. Due to reduced syntax surface and lack of exotic features, the very large codebase followed the same basic idioms. It was often easy to dive into any part of the codebase and contribute. Due to the focus on data structures and REPL, understanding the codebase was simply a process of running parts of a program, inspecting its state, making a change, and repeat. Following this process naturally lead to having a good test suite, and we would rely on that.
Running on the JVM is the opposite of a problem. Being able to leverage the extensive JVM ecosystem is an enormous advantage for any real business, and the runtime performance itself is top tier and always improving.
The only hurdle I could say I observed in practice was not having a lot of compile time guarantees, but since it was a large codebase anyway, static guarantees would only matter in a local context, and we had our own solution to check types against service boundaries, so in the end it would've been a small gain regardless.
Even if you ignore this disastrous quarter, SPX is up mostly in nominal terms only.
Is this an instance of weaponization of the LEO? No statement from SpaceX?
Due to this, these lenses developed a cult following, and even more now that some prominent cinematographers used in some high caliber productions (The Batman (2022), Dune (2021)).
This what Rich Hickey (Clojure author) has termed “place oriented programming”, when the focus is mutating memory addresses and having to synchronize everything, but failing to model time as a first class concept.
I’m not aware of any general purpose programming language that successfully models time explicitly, Verilog might be the closest to that.
https://en.wikipedia.org/wiki/CIE_1931_color_space#Color_vis...
You'll see it is based on the physics and human biology, and is the basis for everything else.
The thing is, this color space isn't very useful for color calculations in the perceptual/subjective sense (e.g. if I just want to change one characteristic, like luminosity, without affecting the chromaticity), so we have transformations to more useful spaces like XYZ, Lab, etc.
There's also the fact human vision is a subjective/psychological phenomenon, so only frequency response is not enough: our vision can map different frequency responses to the same perceptual color (metamerism), our vision adapts color perception based on light source, etc.
In this case it was the producers (not the users) that were wrong in wanting to throw away something that already worked.
I believe his point isn't exactly about users not knowing what they want, but instead the tension between evolutionary design vs. "keep piling features".
But please take a step back and check what % of the population can be considered mentally fit, and the potential damage amplification this new technology can have in more subtle, dangerous and undetectable ways.
- Software engineering is a cost center, they are middlemen between the C-level ideas and a finished product.
- Software engineering is about figuring out how to automate a problem, exploring the domain, defining context, tradeoffs, and unlocking new capabilities in the process
AI is turning the entire web into LinkedIn itisnotaboutism.
We finally made the computer able to speak “our” language - but we still see computers as just automation. There’s a lot of untapped potential in the other direction, in encoding and compressing knowledge IMO.