- the tooling is decades behind, say, Rust or Go
- finding the right library in looks very different in Haskell--you frequently start with the signature on Hoogle. Agents can learn this but it's not the same as "web search"
- creating the right solution also looks different. It's usually borne out of thinking about the types and coming up with the correct algebra. Again models can probably learn to create the right types and orient the solution around that, but it's not automatic
- same today as yesterday, laziness is a blessing and a curse. The runtime can do unpredictable things when you suddenly evaluate a deep thunk
- GHC directives effectively mean there are multiple "Haskells"
Some of those are a result of the "avoid success at all costs" mantra. You can't shake that off in a day. It will take a concerted effort to make it more amenable for seamless adoption.
Haskell continues to be my favorite language to write and read, but Rust is the more practical language with a rich type system. If you're looking for something approaching Haskell's expressiveness but with fewer of these issues, check out PureScript.
Laziness is hard to observe, maybe Strict and StrictData would become more popular in use within this context.
I haven't checked in a while now if effects have become the norm in the ecosystem, or if some solution exists for "string" types, but for me all of Haskell's expressivity is lost in the noise of endless conversion function, wrapper types when stacking monads, and import fiddling.
The problem you describe was solved more than a decade ago.
You use a Stackage snapshot (https://www.stackage.org/lts) which is a curation of packages that work together, similar to a Linux distribution like Debian, carrying one version per package.
Our company using Haskell has not spent 1 minute doing "dependency resolution" in the last 10 years, not has anybody we know.
If only Rust had something like GHCi.
Stack and Cabal have longer history than cargo, and Stackage for puzzle dependencies.
> - GHC directives effectively mean there are multiple "Haskells"
A bit like macro libraries and what features are enabled where in Rust
That's definitely not true, even if that was true maybe 6 years ago. As someone who's uses Haskell daily and also many other languages, I can see Haskell's tooling as more advanced than many others.
Also there's no support for Android and iOS, atleast not without spending months in recompiling GHC with haskell.nix or other third party projects.
Eventually I switched to Rust. It's tooling is so mature that I can focus on developing than compiling GHC.
Despite these demerits, I love Haskell language and lazy eval. I wish I can use it one day.
Having to enable them in the code is just a hassle. Just make it official and be done with it, just roll it into the language.
No way. Where vibe-coded Rust contains tons of "unsafe", you can have your vibe-coded Haskell sprinkled with "unsafePerformIO" and "unsafeCoerce" ;)
Can AI not help speed this up?
As someone who DOESN'T use Haskell... What specifically is it missing?
Are you conflating ecosystem with tooling?
> If you're looking for something approaching Haskell's expressiveness but with fewer of these issues, check out PureScript
Rust is quite expressive. Is Haskell really substantially much more?
I do think Rust is a great language for LLMs because I think expressiveness is key.
Even without AI most of my Haskell time is spent thinking.
Also, I hand writing Haskell is one of my small after work pleasures.
* https://github.com/digitallyinduced/ihp/ is mainly written with Claude now
* https://jappie.me/haskell-vibes.html is gushing over agent-written Haskell
* https://discourse.haskell.org/t/anti-llm-sentiment-considere... huge thread about the divisiveness of LLM's from some person who loves using them with Haskell
I can see how agent coding is a nice fit for Haskell, since LLM's tend to be best at tasks where you can easily verify the output – and GHC lets you easily verify much more of your domain than say Python (sure you can test some of what dynamic typing doesn't catch, but LLM-written tests do not always do what you think they should[0]). At the same time, the LLMs tend to not code golf unless asked to, and they don't care if they have to wait for dependencies to compile, so that takes away the two major Haskell time sinks.
[0] https://haskellforall.com/2026/05/type-out-the-code#:~:text=...
The terseness of Haskell just hides the inherent complexity of the problem. The AI (or human) still needs to uncover that complexity in order to do non-trivial things.
[1] https://github.com/augustss/djinn
[2] http://lambda-the-ultimate.org/node/1178
As far as benchmarks go, I'd also like to see benchmarks that try to find what LLMs are good at. Most of the benchmarks seem designed to give LLMs hard problems and see if they can succeed. In that sense a "good" benchmark is one with a low pass rate.
But if we're going to do agentic coding we also need to know the opposite. We need to know which types of tasks given in which format LLMs will succeed at with like 95%+ accuracy. Then we can more easily build multi prompt pipelines with high confidence in each step.
Compact syntax is generally only a good thing for LLMs because it saves context windows and tokens.