Mistral isn’t specialising in French language LLMs either.

The point was that across different European countries and languages there are collaborations and M&A happening.

German and French speaking together at last.

https://news.ycombinator.com/item?id=48197995

Something like TLA+[1] and Quint[2] specifications can be verified for correctness using Apalache[3]. Then test the Rust code against the specifications using quint_connect.[4]

[1] https://www.learntla.com/

[2] https://quint.sh/

[3] https://apalache-mc.org/

[4] https://docs.rs/quint-connect/latest/quint_connect/

I upgraded my plan last night to Mistral Le Chat Teams. This now costs me €60 per month for two users. Limits have been reset, but I have no idea now if my per seat limit is higher than the Pro plan, or if the limit is shared between the seats, it’s really not clear. I guess I will find out next month. The limits reset on the first of the month and I really hope I don’t hit them in the next seven days.

I use Mistral Vibe CLI and I’ve written and implemented a couple of new skills[1]. Caveman, based on an idea I found online somewhere, this skill removes all extraneous response text, including articles. Makes for some fun reading, but supposedly reduces output tokens significantly. Hash-anchors, this one is based on a concept from Dirac[2], reduces search failures and also includes multi-file dispatch. It will be hard to measure, but Vibe tells me these two should result in roughly a 40% reduction in token burn.

[1] https://codeberg.org/MimosaDev/skills

[2] https://dirac.run/

Co-op is correct here, but not in the title (Coop). Probably personal taste, but I'd also like to see hyphentation for "co-ordinating", "co-operate" and "co-ordinator" as well.

Then I noticed the em-dashes, so perhaps I'm reading the machine's work anyway.

https://www.fourfourtwo.com/

[1] https://mistral.ai/news/voxtral

> Europe's leading artificial intelligence firm, France's Mistral AI, said on Tuesday it has acquired Linz-based Emmi

Of course Maltese citizens can still choose not to use ChatGPT (until it becomes mandatory), but if the State supported education is bound to one particular tool, storing user data outside the EU’s jurisdiction, I think that’s something to discuss.

[1] https://www.moonbitlang.com/

[2] https://veralang.dev/

[1] https://goose-docs.ai/

- Haskell

- OCaml

- F#

- Scala

- Gleam

- Purescript

- Grain

- Idris

Then I asked if there were any Schemes or Lisps that met the initial requirements, which added a bunch more options (Typed Racket, Typol, Elm, ReScript etc).

Then I asked about Julia specifically, as it's a language I'm already reasonably familiar with and knew that it's possible to write it with static annotations.

Next I started filtering the list based on additional criteria; didn't want to target a JS compilation target, performance, size of package ecosystem, tooling, community, learning curve (I do want to review and understand the output).

There were a bunch of follow-up questions over a few hours of prompting, reading and a couple of beers. All this resulted in the shortlist of OCaml, Typed Racket and Julia.

Julia pretty much remains in there, even though it doesn't really meet the strongly typed initial criteria, based on my familiarity, the ecosystem especially for AI/ML tasks and performance factors.

I know zero about OCaml and find the thought of learning it a bit daunting. Typed Racket seems more approachable anyway.

Just want to be sure I'm reading the results correctly... When I compare GPT-5.5 with Mistral Medium 3.5, I see in the tables:

a) Mistral beats GPT in Java and C++

b) It's close for Rust

c) GPT-5.5 easily wins for Go, Javascript, Python and Typescript

Model choice really does appear to be language dependent (assuming I'm reading the results correctly).

a) Typed Racket

b) OCaml

c) Julia

I would love to see those three added to your benchmarks. And Mistral Medium 3.5 added to the LLM list, please.

The meeting agent automates the process of capturing, transcribing, and generating structured meeting notes. It records audio from both microphone and speaker outputs, transcribes the audio using Mistral's speech-to-text API, and generates structured meeting notes in Markdown format.

Years ago I used to write a lot of R code for data analysis. Then life changed and I wasn't doing that anymore. However, I still had small product ideas that required coding, but no time to do it. Dikaletus is my first vibe coded app, scratching one of those itches (I'm terrible at taking meeting notes in general).

Written in R, because that's what I know. Tried to follow good practices with linting and testing.

Comments URL: https://news.ycombinator.com/item?id=48073470

Points: 2

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Comments URL: https://news.ycombinator.com/item?id=48063539

Points: 3

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