It's more or less a perfect replacement for Python for "one-off programs" and "quick scripts". Many bonus points for not having to fight shell quotation rules and trying to remember differences between sh, bash and zsh.
If you don't know Go, it's more efficient to learn it than to waste the hardware resources of thousands to stay within JavaScript.
If it doesn't matter, and for most applications it doesn't, then TypeScript is far more readable than Go - so use that.
Then again, Golang has one as well, though it does manage to start it up faster it seems.
1. Type checking built in 2. More concise and readable than most languages 3. Trivial to inspect while running, ability to change a running program 4. There seems to be a massive amount of lisp that it is inhaling from somewhere 5. Large amount of libraries.
This has the added benefit that even if you publish the code, nobody will be stealing it.
Edit -- I find it very useful to write tests for critical functions. This catches situations where the agent decides some interesting functionality is no longer interesting.
Golang or just shell scripts.
Never saw an instantly starting JVM in my life though.
That adds up, fast. No idea how is it nowadays, admittedly. Maybe a ton of optimization work was done.
Yes, between Java 8 and modern java there were changes to the GC, startup time, JIT and probably more.
If you want, it java should now start pretty quickly.
Isn't readability what matters here? Conciseness isn't the same thing.
Type safety is great, but you can't just quietly disregard the benefits some dynamically typed languages provide; that would be completely ignoring that different tasks weight the two axes differently.
Systems code, performance-critical code, code where correctness across all cases matters more than exploration: parsers, compilers, network protocols, data structures - statically typed languages (like Rust) give you an edge here. The compiler's depth pays for the verbosity, and exploration is less of the work because the problem shape is known up front.
For stuff like building a web scraper, or rapidly prototyping, or exploratory scripts, something like Rust would be actively bad. You cannot poke at a live browser (you can with Clojure). Async Rust adds another layer of type complexity. The signal-to-noise for "figure out what is on the page" collapses entirely.
If I were picking a single language for general LLM-assisted work, weighted across task types, it would be Clojure (or Elixir), with OCaml as the most interesting alternative if the ecosystem were stronger.
Most developers evaluate programming languages by comparing features in isolation, never stepping back to consider the overall experience of using one.
Features are easy to talk about. They're discrete, nameable, and comparable. "Does it have Foo?" is a question you can actually answer. "What's it like to build and maintain a real system in language X for two or three years?" isn't. So people default to what's measurable.
Most devs haven't spent serious time in more than two or three languages in production. Without that contrast, the holistic experience is invisible - you don't know what you're missing, and you don't notice the pain you've learned to live with.
Language communities form around features because features make good rallying points. "We have algebraic types." "We have macros." These become identity markers. The holistic experience doesn't tribalize as cleanly - it's harder to put on a t-shirt.
There's also a sunk-cost angle: devs who've spent years in a language have every incentive to believe its features justify the investment. Honestly evaluating the overall experience might undermine that.
The irony is that the languages with the most devoted communities tend to be loved for exactly these holistic reasons - the ones that are nearly impossible to convey through a feature list. You can rave about Clojure or Elixir all day, but a curious newcomer will land on the homepage, scan the features, and walk away unimpressed: "Meh, it doesn't even have Foo. People say this is great? They clearly don't know what they're talking about."
that's a nice breakdown
I think there's something key you get at in terms of the combo of dynamic environment + type safety maximising both. With a dynamic environment, the LLM can do a lot of interrogation to understand the problem space on the fly. I've witnessed agents sort out pretty complex issues through `python -c "..."`, `groovy -e "..."`, executing snippets of code with Node etc which is much less accessible if they have to compile it first. They can also inject logging code that interrogates the runtime as well (what type do we really have at line 1003?) etc which works better with runtimes that have deep introspection capabilities.
The type-safety-plus-dynamism point you make is real and interesting (basically Clojure with Spec/Malli), but it's orthogonal to whether you're using a REPL or just shelling out snippets.