Michael Chavinda

I gotta starting taking cold showers before a job interview for a job that I want - wash off the smell of desperation.

Where would Haskell sit here? There's a way to write it such that you can balance human and machine readability.

Small experiment: treat feature engineering as program synthesis, then use an LLM as a lightweight prior over which derived quantities are “nameable.” The learner stays classical; the artifact gets way more readable.

mchav.github.io/learning-bet...

Symbolic AI is built on the premise that models should be presented in terms that are understandable to us. When you interact with a symbolic system you learn something about the reality that it tries to model. That alone makes symbolic approaches worth betting on in the long term.

All is data science

www.datahaskell.org/blog/2025/12...

On the flip side some Haskell can get extremely dense and people can do crazy with types. Enough so that they become a distraction from the actual logic. Same with inheritance in Java. Line by line go is typically very readable. More broadly would be a matter of experience and taste.

Okay. I guess we agree that it’s readable by some definition. I think the broader definition of readability (blocks of code) depends on style guide, domain knowledge, and team context more than programming language.

The trade off is that you get:
- very readable code
- good, predictable performance
- fast compile times
- a lot of built in tooling (go profile + bench are great)

I admit that it’s easy to write bad code but we invested in linters a style guide and tests so we don’t deal with the ugly parts.

I write go at work and I think it’s a great language in general. What do you dislike about it?

I find that working with Haskell developers often involves trying to make them think more like engineers - conversely working with Go and Python developers often involves trying to make them think like scientists.

In software it’s often important to distinguish between solving the scientific problem (how do we make this generalize for all instances of the problem) versus the engineering problem (how do we make this work for the environment we anticipate it’ll be used in).

Great article! The fix also really outlines that contributions don’t have to be hundreds of lines of code to be impactful.

Just updated the dataframe SQL library to auto generate expression bindings from the table types.

The read input surface is looking pretty great now: CSV, JSON lines, Parquet and now various SQL DBs.

hackage.haskell.org/package/data...

State of Haskell 2025 Hello everyone! The Haskell Foundation is reviving @taylorfausak’s State of Haskell Survey. It’s been a few years and so we’re doing it a bit differently, but the plan is to start doing this yearly a...

The State of #Haskell 2025 survey is out! Please take ~10 minutes to fill this out and share it with friends/colleagues/coworkers, whether or not they are users of Haskell.

That said I’ve been procrastinating writing this Parquet writer for months cause hand rolling thrift is so painful. Also implementing RLE/bit packing + the whole dremel level stuff is tiring. But it is the boring thing that must be done.

Learning how to do the necessary but boring skill is underrated. It pays off so much in the long run.

Pivoting a lazy frame is such a crazy feat. Congratulations.

Layoutz – A tiny zero-dep lib for beautiful #Elm-style TUI's in #Haskell 🪶

flora.pm/packages/@ha...

www.reddit.com/r/haskell/co...

discourse.haskell.org/t/layoutz-0-...

Haskell IS a Great Language for Data Science I’ve been learning Haskell for a few years now and I am really liking a lot of the features, not least the strong typing and functional approach. I thought it was lacking some of the things I missed from R until I found the dataHaskell project. In this post I’ll demonstrate some of the features and explain why I think it makes for a good (great?) data science language.

Perhaps you saw the post series "Python is not a great language for data science"... well, here's

Haskell IS a Great Language for Data Science

https://jcarroll.com.au/2025/12/05/haskell-is-a-great-language-for-data-science/

#haskell :haskell:
#rstats :rstats:

Hasktorch: LibTorch Haskell bindings for deep learning using FFI This blog post will introduce Hasktorch, a Haskell binding for deep learning. We'll explore how Hasktorch leverages Foreign Function Interface (FFI) to integrate with Libtorch. The post will demonstra...

Great article that gives a quick run through of Hasktorch.

www.stackbuilders.com/insights/has...

Article to checkpoint where we are with the Haskell Jupyter kernels.

www.datahaskell.org/blog/2025/11...

These are all really great improvements! Do you have a sense of which change proportionally drove this number down the most?

You just reminded me to call my mother.

I usually type in variations of “here”, “now I’m here” or “bet you thought you wouldn’t find me here”

Great to see Ingo still looking into the project.

There’s something beautiful at the end of all this. I just know it.

ulwazi-exh9dbh2exbzgbc9.westus-01.azurewebsites.net/lab/tree/Iri...

Bro you speak like 13 languages.

Does Marimo support languages outside of Python? Also what’s the integration process?

Continuing the series on program synthesis:

mchav.github.io/an-introduct...

As cultural beings, we are echoes of the past: even our most personal thoughts, emotions, and experiences are mediated by words, images, and ideas we inherited from previous generations. Your mind was born and started growing many thousands of years before your body.” Francois Chollet