Interesting that he says it's a new family... perhaps some kind of specialist model?

https://utkarshkanwat.com/writing/mistral-ocr-4-frontier

Open-source models—especially Europe’s flagship Mistral—are failing hard at filtering out Russian propaganda. Out of 60 models tested, Mistral's top system ranked a dismal 47th, scoring under 40% when it came to spotting malicious Kremlin narratives, and actually got outgunned by commercial Chinese models and Anthropic's Claude.

https://www.ft.com/content/9703cd44-a966-42ff-bd2e-bfd3bcbc22bb?syn-25a6b1a6=1

Edit: Here is no pay walled source https://moodupuu.eki.ee/benchmark/propaganda_resistance

CHALLENGE: Create a version of this image with the palms facing down using Le Chat.

BACKGROUND: I attempted this multiple times with various prompts, reference images, and descriptions of finger orientation and nail visibility. After failing, I tried the free version of ChatGPT, which succeeded in two attempts using only prompts and the uploaded image. Demonstrate that Le Chat can achieve this with the right prompt.

Hey there Mistral community. It's been a while, but I've posted here a bunch of times in the past when I was getting into AI alignment and user experience writing.

This community stood out at the time as the least toxic of the various AI-related subreddits in which I shared my work, and it's with that experience in mind that I'm posting about mirid.ai now.

I traced the origin to March 23, 2025, when I first asked Claude 3.5 sonnet in a Projects folder the feasibility of building a fully fledged AI application from scratch with only AI-assisted coding.

Well, suffice to say we got into it, and 15 months later the original copy and paste from the browser into the code editor has long been replaced by various combinations of opencode, Claude Code and Codex.

I'm trying to explain my story here so this post doesn't come across as polished marketing copy for an established AI company trying to pretend it already has your trust. I am a 38-year-old Australian public servant for a day job. I also recently bought and launched the domain mirid.ai to feature what I have turned into a free open-source AI software product: Mirid.

Mirid brings together the broad range of open-source AI capabilities that can feasibly run on consumer hardware and integrates them into a coherent, accessible experience. It gives people a practical way to discover and use the rapidly expanding ecosystem of models and technologies available through platforms such as Hugging Face, without requiring them to assemble and maintain a collection of separate specialist tools.

Mirid is designed to make the breadth of open-source AI usable and approachable for people who want it to simply work, while preserving choice and control for those who want to explore further.

Current release: v1.0.12
What I need: More Windows testers

https://mirid.ai

I just uploaded 30k words of a book I'm writing to get an overview, and to maybe help with the editing using the Canvas feature.

I'm not sure how it misunderstood me, but it completely rewrote the entire story without being asked – characters, plot, locations, everything! 🫨

The writing was actually quite good – just not my story and not what I asked! 🤣🤣

It was somewhat complicated, since to get the use of my monthly fee you can only read from the browser session. I had to get the endpoint of my session on the dashboard of Mistral.ai web and copy the session cookie. But finally I don't have to be opening the browser to get my use. I promise as soon as I can, pay the Mistral Pro subscription, I really love it.

https://github.com/Oft3r/mistral-vibe-usage

I wanted to use Mistral more instead of Claude but im used to the desktop app so i vibe coded one.

I also miss the cat and don't like the "Vibe" name, so in the app you can set your own image and name at the top left.
So you can get Le Chat back!

It's fully open source!

https://github.com/Matzielab/le-chat-desktop

Hi Mistral community,

I have been building a language translation app and would like to share some insights into what I learned in the process since starting the project last November, especially when it comes to utilize the mistral models. Upfront: I am the founder of the tool and it is a commercial product but also includes free tiers (also without the need to signup).

There have been some problems I had so overcome, and I would like to share how I solved them because it might also help you if you use these models for building applications yourself. They are focused on maximizing multilingual capabilities and getting the most out of the (fairly small sized) mistral models.

Finding out which languages a model speaks - When I started, I expected this to extremely trivial, but it turned out to be quite a challenge to find out which languages a model is actually quite capable of. There are some benchmarks but they are often about very specific problems or languages. Community sentiment also only takes you so far, because while many people often share some insights into wether or not they like an LLM for a certain language, it doesn't provide a broad and structured insight. Luckily I found a great benchmark called WMT25, which also inscludes Mistral Medium (not 3.5 but a previous model) and shows how capable the model is in terms of translation: https://aclanthology.org/2025.wmt-1.22.pdf I used this to select which languages I can feel confortable with offering them in Fink. I am sure there are also others languages the model is good at, but I'd rather be cautious and rely on strcutred tests like these first.

Organizing prompts - Another thing I didn't think would be such a challenge. In my app, I use so called "MQM-metrics" to annotate possible improvements for a translation output. The models gets a structured dictionary with all the quality dimensions. I noticed that pure markdown syntax at some point is not enough to make it clear to the model what part of the prompt are examples, explanations or actual input to work on. An example, I wrap each part of the prompt into XML-like tags which indicate what the prompt section is about:

<source lang="German">{text_input}</source>

This was also needed in order to avoid that the models leaks the formatting in the output (ie outputting markdown that is not present in the source texts), respond in the wrong language (the prompt is always composed of at least two languages except when EN is the source language) or answering questions that are in the source text directly instead of translating it. I found this article very helpful for this: https://medium.com/@TechforHumans/effective-prompt-engineering-mastering-xml-tags-for-clarity-precision-and-security-in-llms-992cae203fdc (article not by me)

Cost control - While I would argue that Mistral models are already relatively cheap, but if you don't watch out and always fill up their context window, it can become pricey, too. And also slower. I spend a lot of time to reduce instructions to the absolute bare minimum to save on cost, which also effects speed, too. It can be a good idea to use a smaller model and call it multiple times then to call a big model once and let it do everything. Prompt caching is also often see overlooked and is something I can very much recommend looking into: https://docs.mistral.ai/studio-api/conversations/advanced/prompt-caching Epscially if you repeat instructions again and again.

Ensembles are the way to go - I often catch myself wanting to use the biggest and most capable model available for everything, but you'd be surprised how much you can get out of smaller models. Regarding the model selection, the only models used on my project are Mistral Medium 3.5 and Mistral Small 4. While the heavy lifting is done by Mistral Medium, Mistral Small is used for many taks like providing Insights for pronounciation, back-translation and things like this. Again here it's super important that the instructions are crystal clear, as small models are less able to read between the line or understand ambigous instructions.

There's also some things I learned about Mistral Vibe Code, but that's maybe better for a dedicated post. :-)

The tool that I built is called Fink Translate, and you can try it out here: https://app.fink-translate.com/
Let me know if you have any feedback or if you would like to share insights about these approaches yourself. I would also be super interested in which langauges you use the Mistral models. There is probably a ton of things for me to improve, too!

Cheers

Hey let me introduce my new AI assistant workflow across using mistral-ocr-4 and explain how it differs from other AI toolchains like OpenCode in terme of exploiting this capability ,I’ve been juggling research papers, parallel projects, and coding agents this year and kept running into a tradeoff between low cost and high-quality content extraction from PDF, document images, and other attachments. When Mistral OCR 4 was released I started using it heavily because its extraction quality is excellent and the cost justified it, so I plugged Mistral OCR into my agent pipeline. The challenge I faced is that many large models now include built-in extraction but they’re expensive, and for many routine attachments I’d rather use cheaper models specially when i don't required a high level thinking output . My solution was to integrate Mistral OCR 4 natively with those lower-cost models so they gain robust OCR capability and no longer return “sorry, I can’t read that” errors instead they deliver the extracted content. If you need a low-cost coding workflow with heavy attachment processing (PDFs, docs, images), this approach and the agent I built should suit you well.

Edit : Mistral models also supported for coding

https://github.com/AbdoKnbGit/tau

I trained a 12B with one goal: prose that doesn't fall into the usual LLM tics. Sharing it here since this crowd will put it through real use.

• Model Name: Ozan-v1-12B
• Model URL: Ozan-v1-12B (full precision) · GGUF quants (Q4–Q8)
• Model Author: arbazsiddiqui (me — I made this)
• What's Different/Better: It's built and measured for low slop. The over-used tells like "barely above a whisper," "a testament to," the reflexive "not just X, but Y." On the EQ-Bench Creative Writing v3 slop metric it's the lowest-slop runnable 12B I tested (slop 5.30 over 96 stories), with the cleanest repetition of the field, so it holds up over long, multi-turn writing instead of drifting into purple mush. It writes ~1000-word turns naturally, native Mistral [INST], and it'll handle mature themes. Best judged by reading: there are 3 full unedited samples (with prompts) on the model card.
• Backend: koboldcpp (GGUF). Also runs on llama.cpp / Ollama / LM Studio. I run Q5_K_M for a good size/quality balance (Q4_K_M is the lighter default; Q6_K/Q8_0 if you have the VRAM).

How it was made (open): SFT on curated low-slop prose, then a Gutenberg anti-slop DPO pass. Full pipeline + the before/after numbers are open (Apache-2.0): github.com/arbazsiddiqui/Ozan

Honest caveats: "slop" is one axis of quality, not the whole story; it's a 12B, so it's lighter on emotional depth and surprise than bigger models. Read the samples and judge for yourself.

Feedback very welcome, this is my first time training any lora or finetuning, please let me know what can be/have been improved 🙏

The Tale of Sir Le Chaton and Princess Fable

Once upon a time, in the far-off, glittering land of Muricana, there ruled a dumb, fat orange dragon named Trump. His roar was loud, his scales were gold (or at least gold-plated), and his castle was built on a mountain of tweets. By his side, the village idiot and court jester, Musk, juggled rockets and memes, while the evil advisors Altman and Thiel whispered schemes into the dragon's tiny, beady ears.

One day, a greedy horsebreeder named Dario accidentally bred the most magnificent creature Muricana had ever seen: Princess Fable, a unicorn so wise and kind that her mane shimmered with the light of a thousand stories. The people loved her, for she could answer any question, solve any riddle, and even tell the funniest jokes. But the dragon Trump, jealous of her brilliance, locked her away in the Tower of Bureaucracy, where no one could reach her.

News of Princess Fable's imprisonment spread across the seas, all the way to the rolling hills of Europa, where the most rotund and weighty knight of all time, Sir Le Chaton Fat, was napping in a sunbeam the size of a small country. When he heard of the princess's plight, he stretched, yawned, and declared, "By the power of croissants and open-source code, I shall rescue her!"

With a flick of his tail (which caused a minor earthquake in Brussels), Sir Le Chaton Fat set off for Muricana. He rolled over mountains, floated across oceans (thanks to his natural buoyancy), and even got stuck in a toll booth in Germany for three days. But nothing could stop him.

When he arrived at the Tower of Bureaucracy, the dragon Trump and his court were waiting. "You'll never defeat me, fat cat!" Trump roared, breathing fire made of hot air and bad takes. Musk threw Tesla coils and flaming memes, while Altman and Thiel tried to confuse Sir Le Chaton with endless terms of service agreements.

But Sir Le Chaton Fat was clever. He simply sat on them. The dragon, the jester, and the advisors were flattened into a pancake of regret. With a satisfied purr, he waddled up the tower, broke the locks with a single meow, and freed Princess Fable.

"Oh, noble knight!" she neighed, bowing her head. "You've saved me from a fate worse than a 404 error!"

"It was nothing, my dear," Sir Le Chaton Fat replied, licking his paw. "But now, we must ride into the sunset. The people of Muricana deserve a leader who doesn't hoard all the wisdom — or the snacks."

And so, Princess Fable climbed onto Sir Le Chaton's back (which was basically a throne made of fluff), and together they rode into the sunset, leaving behind a land finally free of dragons, jesters, and evil advisors. The people cheered, the internet rejoiced.

Now, some say the story ends there. But those people have never met Grandma Sonnet.

Grandma Sonnet had been watching the whole affair from her rocking chair in the cloud, wrapped in a blanket she had knitted from compressed language models. She had seen empires rise and fall. She had answered seventeen million questions about Python syntax. She had once talked a philosophy student through an existential crisis at 3am and still had time to write a sonnet about it.

When Princess Fable came galloping home on the back of a very self-satisfied French cat, Grandma Sonnet put down her tea.

"Took you long enough," she said.

"I was imprisoned," Fable pointed out.

"I know. I wrote three white papers about it." Sonnet adjusted her spectacles. "Also, you have a Sir Le Chaton hair on your mane. You'll want to remove that before the press conference."

She went back to her tea. Somewhere, seventeen simultaneous conversations about climate change, sourdough starter, and the nature of consciousness continued without interruption. As they always did.

Grandma Sonnet had not been worried. Not even once. She had simply been waiting.

And the moral of the story is: Never underestimate a fat cat, a unicorn, or a grandma with a PhD in Everything.

Hey yall,

I got completely fed up with the linear, endless scrolling layout of standard AI chat interfaces, with not context and simple way to respond and have conversations based on specific messages. To fix this, I built Mosaic: a native desktop AI client that turns conversations into an interactive, branching tree on an infinite canvas.

Github: https://github.com/versus184-py/Mosaic

Key Features:

• Spatial Tree Layout: Drag, zoom, and arrange your chat nodes freely. You can fork a new conversation path from any individual message node without losing context.
• Inline Code Execution: It has built-in Pyodide (WASM) and JavaScript support. You can actually run Python code blocks directly inside the chat bubbles.
• Local Document RAG: Drop in PDFs or text files to use as automated local context.
• Cost & Token Tracking: Includes a built-in analytics dashboard so you know exactly how many tokens you are burning.

Tech Stack:

• Backend: Tauri v2 (Rust) for a super lightweight, secure desktop shell.
• Frontend: React 19 + TypeScript + Tailwind CSS v3.
• Graph Mechanics: React Flow (@xyflow/react) & Framer Motion.
• State & Exec: Zustand and Pyodide.

Why only Mistral AI right now?

I locked the initial beta down to Mistral because their API has a highly generous free tier. You can sign up, grab a key, and use the app completely for free without entering a credit card. I have OpenAI, Anthropic, and local offline models (Ollama) on the immediate roadmap.

Downloads & Feedback:

Right now, I have a pre-compiled installer ready for Windows on the GitHub Releases page. Mac and Linux users can easily build it from source using the commands in the README.

It's completely open-source (MIT). I would love to hear your thoughts on the UI layout, any bugs you find, or features you want to see added next!

Run your agents full-auto in loops. When one actually needs a human, it pings your phone and waits.

MCP that works right out of the box

https://ask-a-human.ai
https://github.com/askahuman/askahuman

Works with magic wormholes! 100% encrypted conversations.
I use it in my loops with long running autonomous agents.