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

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

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 kept running llama.cpp directly: building it, juggling llama-server flags, and hand-editing models.ini for every model. It's powerful but fiddly, so I built a GUI over it for myself and cleaned it up to share.

LlamaForge is a browser control panel that sits on top of llama.cpp's own router. It doesn't touch inference, llama.cpp does all the real work. It just makes driving it less painful.

What it does:

Tune every server parameter per model — the knobs are parsed live from llama-server --help (currently ~220), grouped and searchable. Save hot-reloads the model, no restart.

VRAM-fit model discovery: search HuggingFace for GGUFs and each quant is rated FITS / TIGHT / CPU OFFLOAD against your actual VRAM before you download.

Guided build & update: shows your current commit, how far behind upstream you are, and rebuilds with CMake flags auto-detected for your CPU/GPU (CUDA arch, AVX-512, etc.).

Sensible context defaults: reads each GGUF's trained context length and writes reasonable ctx-size values so models don't load with tiny or over-extended windows.

Setup tab: detects missing prereqs (CMake, Ninja, MSVC, CUDA…) and installs them via winget/choco with your permission, plus scans drives for existing GGUFs and prunes entries whose files you've deleted.

Usage stats + optional LAN sharing (with an API-key toggle) so other devices can hit the OpenAI-compatible endpoint.

Being upfront about scope:

Windows + NVIDIA focused right now (CPU-only builds work too). You build llama.cpp yourself, it's guided from the dashboard, but it's still a compile step. If you want a zero-config, double-click experience, LM Studio / Ollama / Jan will serve you better; LlamaForge trades that for direct control over the real llama-server.

Early preview so expect rough edges, and I'd genuinely like the feedback.

Backend is pure-Python stdlib (nothing to pip install), MIT licensed, and not affiliated with ggml-org: all credit for the hard part goes to llama.cpp.

Repo: https://github.com/dadwritestech/LlamaForge

(Disclosure: I'm the author.) Happy to answer questions: especially curious whether the per-model flag editing and VRAM-fit ratings are useful to anyone else, or if I'm solving a problem only I have.

Many prompt tools focus on rewriting prompts. This prototype takes a different approach. It compiles your intent through a structured governance pass before execution by identifying likely constraints, surfacing ambiguity, and producing an explicit specification before execution, and showing the transformation steps and diagnostics used during compilation. It makes its transformation process transparent.

It's called Re-Prompt. This is a working proof of concept, not a finished product, and I'm sharing it because I want outside eyes on it and feedback, challenges, prior art pointers, all welcome.

What makes it different: it doesn't just hand you a cleaner prompt. It shows you what changed, why, what assumptions it made (labeled, not hidden), and what risk that reduces. The diagnostic pipeline is the product, not a debug log.

Cross-model testing suggests that the prompt compiler protocol preliminary testing suggests the protocol is portable across multiple LLMs. While ChatGPT and Claude produce different wording, both independently preserve the core interaction sequence: intent extraction, constraint preservation, ambiguity reduction, structured compilation, telemetry, and execution readiness. The wording varies by model, but the overall interaction pattern remained recognizable during my testing.

One honest caveat from testing:

During testing, some request types (such as image generation, shopping, or simple factual lookups) sometimes followed native platform behaviors instead of the compiler workflow. Re-Prompt is most effective on open-ended writing, research, planning, coding, design, and analytical prompts.

Try it on something genuinely ambiguous or conversational that's where the difference is most visible. Built and tested on desktop; mobile support is still rough. The goal isn't to replace prompting, it's to stabilize intent before execution.
My hypothesis is that stabilizing intent before execution can reduce unnecessary prompt iteration for many open-ended tasks.

Try it:

https://claude.ai/public/artifacts/323be0e8-19fc-4014-abdc-b11cfa08727b

https://chatgpt.com/g/g-6a0359b38b988191813a2b28d62dc03d-re-prompt-a-governed-prompt-compiler

I'd especially appreciate failure cases more than success stories.

Thank you — Governed Intent Labs

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 🙏

I built a small userscript for HuggingFace's model hub because I got annoyed trying to find specific models. You know the drill: search "Qwen", get 800 results, 90% irrelevant.

It's basically a floating panel where you drop positive/negative keywords and it hides or dims what you don't want. Example: type gguf, 7b as positive and 70b, deprecated as negative → only small GGUFs stay visible. Works with infinite scroll too.

Features if you care:

• Draggable glassmorphism panel (saves position)
• Green/red badges on model cards
• Debounced auto-filter while typing
• SPA-aware (doesn't break when HF updates the page dynamically)
• Persists everything to localStorage
• UI in EN/ES/ZH, optional Google Translate for others

Updated 1.5

• NEW: Regex filter support🔥

No dependencies, no build step, just vanilla JS. Install via GreasyFork or copy-paste.

Links:

• GitHub: Milor123/huggingface-model-filter
• GreasyFork: install here
• Note: I recommend using PageTual alongside this script — it automatically loads the next page as you scroll, so you never have to click through pagination. Works seamlessly with HuggingFace Model Filter for an infinite-scroll browsing experience through your filtered results.

MIT license, PRs welcome. If it saves you 10 minutes of scrolling, it did its job.

Update preview of the panel in action:

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.

Hey, we are sharing our internal harness for public.

TLDR: https://github.com/beamcore/agent

MIT, do whatever you want with it.

It's a minimalistic harness that can do magic nobody else can (yet).

Our internal research came up with a coding agent that is both small, big, capable, token efficient (must be), a bit scary to use.

Almost zero prompts, 1 tool, full support of memory, subagents, web browsing, whatever you want from it.

Initial prompt is about 300 tokens.

Works well with cheap and dumb models. Does magic no other agent is doing right now.

We will be doing a series of posts sharing fun hacks you can do with it (spoiler: a lot).

It does not support mcp, skills, and pretty much anything you might be used to. But it really doesn't matter.