It took me way too long to see this. Am I the only one that was slow on seeing this? 😄

Hi guys I'm waiting for Mistral Large 4 Le Chonk in my Mistral web app -> when? is there anybody who knows that date?

It looks like Mistral is testing voice live conversations on the Vibe app 👏👏👏

It’s looking good. It’s still a bit slow to answer. And the English voice is the same voice they were using for the dictation feature, which speaks really slow and makes it feel unnatural.

Hi everyone! When Timestral came out I wanted to try it on something personal, so I built Daycast: a small Windows app that records which apps and websites you use and forecasts your day hour by hour, like a weather forecast for your screen time.

How Timestral is used

• Timestral-5M runs fully offline on CPU. The 21 MB weights are bundled in the app, so nothing is downloaded and nothing leaves your PC.
• Once a day a separate low-priority process loads the model and forecasts the rest of the week. It covers total PC usage and every app or site used for at least 10 minutes, plus daily totals, with the p10–p90 range from Timestral's quantiles.
• A 24-hour forecast takes ~20 ms, and the whole daily run a few seconds. The background logger never loads PyTorch and stays at ~50 MB of RAM.

The part I found most interesting: it has to earn its place

Every day Daycast re-forecasts the last 7 days with both Timestral and a simple baseline (average of the same weekdays), each using only the days before it. It then shows whichever had the lower error on your data.

On my synthetic test data (a very regular week repeated) the baseline actually wins: about 3.6 vs 4.5 minutes of error per hour. That makes sense, since a perfectly repeating week is the best case for a plain average. I'm curious to see what happens on real, messier usage over a few weeks.

Other details

• Websites are recognised from the address bar domain (only the domain is stored, never full URLs or page titles).
• Categories are yours to define. The interface is in English and Italian.
• Open source (MIT); Timestral weights under Mistral's Modified MIT license, with the attribution included.

Honest caveats

• The zip is ~185 MB, almost all PyTorch.
• The exe isn't signed, so Windows SmartScreen will warn you.
• Windows only, for now.

Next: mobile

My real goal is to bring Daycast to phones, with the forecast running on the device so usage data never leaves it. On Windows I'm happy with PyTorch, but on a phone that's not an option, so I'm looking at ExecuTorch, ONNX Runtime Mobile or Core ML.

Questions for people who know Timestral better than me

1. Has anyone run Timestral on-device on Android or iOS? Did export (ONNX / ExecuTorch / Core ML) work out of the box, or did parts like the attention masks or RoPE need changes?
2. How well does Timestral-5M hold up when quantized to int8 or fp16? At 21 MB in fp32 it would be tiny on a phone.
3. Is Mistral planning official ONNX or mobile-friendly exports of the Timestral checkpoints?
4. On a phone you start with only a few days of history. Any advice on getting good zero-shot forecasts from very short hourly contexts?

GitHub: https://github.com/Fede046/Daycast
Download: https://github.com/Fede046/Daycast/releases/latest

Feedback very welcome!

Hi everyone!

I've been experimenting with Timestral-28M in a real-world application: predicting laptop battery discharge and charging times.

The result is Battery Forecast, a Windows port of Matteo Bonfanti's Till Dusk.

The app collects battery readings locally, uses historical observations as context for Timestral, and generates a 24-hour forecast.

It estimates when the battery will reach a reserve threshold or a charging target like 80%.

Everything runs offline on the CPU, without retraining the model or uploading data.

The application also includes a Windows tray interface, battery history, configurable alerts and forecast charts.

Current limitations: Accuracy hasn't been systematically validated yet, and charging forecasts remain experimental.

GitHub: https://github.com/Mattboxx/battery-forecast

Website: https://mattboxx.github.io/battery-forecast/

I'd love feedback from anyone experimenting with Timestral, particularly on forecasting from irregular real-world sensor data!

Im wondering which one of these is better for agentic tasks, running the base agent with (and calling CLIs with more powerful models when needed for complex planning and coding etc), doing more basic but multi step tasks like gathering info from web, processing it and creating report etc. and also for basic chatting with the agent.

Im sure some of you have tested these head to head, and i wonder if there is clear all around winner between them, or perhaps some specific stuff done better on one and better on other?

(tried using search, but apparently its too complex query for reddit search to find anything meaningful, as i would assume someone has asked this before)

Sur les derniers modèles Pixel, iPhone ou Samsung, on a déjà assez de puissance pour faire tourner des modèles locaux entre 0.8-3B voir du 7B de manière fluide et avec des outils de base. C'est ce que propose notamment Off Grid AI à la fois sur Android et iOS mais il y en a d'autres, y compris open source et gratuit.

Ce serait bien que Mistral, qui a déjà ouvert ses services à des modèles tierces, nous offre la possibilité d'utiliser son application y compris hors-ligne, via le téléchargement de modèles locaux et compatibles.

Je pense que ça ferait venir tout un tas de monde axés sur le local-first, et sensibles aux données personnels tout en profitant de l'interface mobile de Mistral qui est plutot classe.

Des avis là dessus ?

Been requested to do a small presentation on interesting use cases with some examples. Nothing too crazy but more of a showcase to people who aren't technical and want to make the most out of AI beyond email writing.

We use mistral at work for our in-house chat bot as we're not allowed to use the publicly available ones. It's not advertised widely for our workflows just yet but slowly being rolled in. We're an engineering company, so we work with a lot of technical documents and our bot allows us to upload them. My team also consists of software devs, engineers and project managers. That's all the context I can share. Cheers!

Hi!

I own a Pixel Watch 5, most smartwatches that run Android will give you Gemini as the default and only AI out of the box. I wanted to make my own native app because I don't want to use Gemini + by making my own app I could make something fun and good that goes beyond what's currently there.

Wearstral is open source and its code can be found on GitHub (https://github.com/Hiburger/Wearstral), it works well with my API. Hopefully you'll find it useful too. It's of course easy to use and privacy friendly.

Where it gets complicated is the installation, because I haven't published it on Play store yet, you'll have to be a bit techy and use adb. Mistral Vibe CLI helped a lot the whole time with the code :)

You can find screenshots on the GitHub repo mentioned above, and I obviously take feedbacks/questions ! There's also a roadmap there, when omong other things I plan to add support for other providers aswell.

Thank you ! 🧡

Hi all, I have an interview coming soon for the Applied AI Engineer, Fullstack Software Engineer. Did anybody go through it recently?

From what I’ve found online, it seems to be either:

• Mix of code review, an LLM/RAG quiz, PyTorch/algorithm coding and a project discussion
• Frontend live coding, agentic programming, system design

Any general tips, experiences to share, how to prep?

Thanks in advance!

It basically shows what might go wrong with the product you are looking for, quotes everything and weights it. It is open source and supports Mistral Vibe CLI.

I'd like to hear your opinions about this.

Kriko Website

Github Repo

Edit: I forgot to add links.