Comment by jmarlow47
1 day ago
It's really refreshing to see the pace of Java development pick up so much over the past few years.
The last few years of server-side has been on Kotlin for me, however, modern Java is looking more and more like anything I'd need in a language.
I never fully switched to Kotlin. It’s good, but not good enough. I also like the direction Java has been evolving in over the last 10 years, to the point that a JVM server is actually lighter than a Node.js server :)
What wasn't "good enough" for you in Kotlin?
Sure, Java is slowly getting most of the features and sometimes even in a better form because they control the runtime (virtual threads arguably just work). But IMO Java is still incredibly verbose, Kotlin's type system is just better and I've genuinely never come across anything that would be easier to do in Java. Even virtual threads can be used from Kotlin.
For me, Java 8 was tolerable. Also used IJ a lot and it hides Java verbosity and reduced a lot of typing. That's why I didn't really see the need to switch. For better or worse, these days I don't even open IJ and just use VSCode + AI Badger mostly to review my changes. Language matters even less.
I don't think Kotlin has fully fledged pattern matching with destructuring yet (like in Java's records) does it?
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I see no reason other than Android, where Android engineers live in the past and don't bother to support modern Java, other than when Kotlin starts to lose the amount of Java libraries able to run on Android.
Many years ago I learnt that mastering the language that goes alongside the platform is quite valuable versus the languages that come and go.
Hence why despite my rants, I keep my C knowledge up to date, as it is the language of UNIX, same with JavaScript on the Web, Erlang on BEAM, and naturally Java on the JVM, even though Scala and Clojure are much cooler.
Elixir developers outnumber Erlang 2 to 1, I think you'd do well with Elixir.
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I’m so glad they made that change. So much better than the old days.
I love Java and I’m so glad it’s kept improving so much despite all the turmoil in its ownership.
> see the pace of Java development pick up so much over the past few years.
I don't think that is a fair assessment. Java / JVM has been steadily progressing in the past 10 - 15 years. From languages to different part of JVM. It is only getting the sense development being picked up because HN and other social media are finally getting the clicks and interest again. The pendulum swing back because of all of different reasons.
And in reality a lot of these recent landed improvement were 5 - 10 years in the making.
I still don't like its syntax though. But I am not even sure if it is even an issue in AI driven development era.
Java 8 (early 2014) is where Java got interesting again. Lambdas and streams fundamentally changed the language. java.time was a very big deal too.
Yah but why? The things it was good at doesn’t really matter nearly as much post AI.
> Yah but why? The things it was good at doesn’t really matter nearly as much post AI.
Pretty much the opposite. Those who disliked it usually cited having to type too much due to class verbosity and variable verbosity (the former has merit, the latter was purely a choice).
If you no longer need to type in any of the code, what better than a language with a blazing fast and scalable runtime, with excellent debugging and monitoring support.
Most importantly IMO the JVM ecosystem has lots of solid, battle-tested, enterprise-grade frameworks like Spring and Quarkus, which do all the heavy lifting for those kinds of applications. The code can still be verbose, but that is solved by LLMs, as you said.
Rust has all the individual bits you need as libraries (web server, database access, etc.) but you have to wire it all together yourself. Sure LLMs can do that too, but how do you know the LLM did it correctly? There could be complex interactions between the individual libraries.
Using a complete framework solves that, you know its bits will work together correctly. It also makes the code structure of different applications look more similar, which probably helps LLMs understand it better too.
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But if you want "blazing fast" performance then there are other languages like Rust that the AI can write, and Rust runs basically everywhere Java does these days (and more) except perhaps Android, for which there are still ways around it.
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Quite the opposite. Java is almost all about an optimising runtime with deep telemetry, and it helps with exactly the things AI needs the most help: https://youtu.be/fXBGBovpH88
I now manage a team writing languages on the jvm. I have found the jvm ecosystem terrible for observability. Examples;
- junit parallelisation - how are shards handled? how do you balance the tasks across forked processes? What is the bottleneck? Options are 1) pay someone for better tooling or 2) write your own
- long pauses in garbage collection - how do you know which objects are causing the pauses? the options; 1) profiling 2) analysing heap dumps that you periodically capture 3) add metrics or gc logging
- what is the performance bottleneck? options 1) profiling
Sure AI can do this stuff, but it's painful and time consuming
Contrast it to Elixir on the BEAM;
- exunit parallelization - basically free and because you don't have coupling due to OOP. mocks are basically an antipattern
- long pauses due to gc? They just don't happen - everything is isolated and can be cleaned up in parallel.
- performance bottleneck? Open inspector and see what is doing all the reductions, and if any of the inbox queues are backlogged. You don't have to profile, you can just look in production - memory? Open inspector and sort processes by size descending. Again no profiling needed
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