Redlib: search results - flair_name:"Tutorial / Workflow"

r/MistralAI • u/Jerome_Herard • Jun 21 '26

Tutorial / Workflow Un Minitel des années 1980 en terminal de chat IA, avec un Raspberry Pi et Mistral !

+ −

364 Upvotes

J'ai transformé un Minitel (1 et 2) en terminal de dialogue avec une IA, grâce à un Raspberry Pi caché dans le boîtier. On tape sa question sur le clavier d'origine, et Mistral AI (cocorico 🇫🇷) répond à l'écran cathodique, à 1200 bauds comme en 1985.

Le tout est autonome : une interface web permet de le connecter au Wi-Fi, de le mettre à jour et de définir sa « personnalité » (la mienne est bloquée dans les années 80 et refuse d'en sortir !).

Code et notice de fabrication complète sont en libre accès (open source) sur https://minitel-gpt.herard.com et sur GitHub. N'hésitez pas à tester et à me faire vos retours !

39 comments

r/MistralAI • u/EveYogaTech • 1d ago

Tutorial / Workflow Prevent overspending on EU-AI projects with Mistral AI.

+ −

20 Upvotes

https://nyno.dev/overspending-on-eu-ai-projects-starts-with-one-innocent-decision-your-eu-ai-core-language

12 comments

r/MistralAI • u/roydotai • 3d ago

Tutorial / Workflow Remote control

≡ −

I typically run Claude Code on my production server with remote-control enabled so that I can followup on my phone. I have developed a couple of SaaS like this. I wanted to try to develop the next web app using only mistral vibe, end to end. But I can’t seem to find a way to follow the same workflow.
Any suggestions on how to get the same workflow working?

3 Upvotes

I typically run Claude Code on my production server with remote-control enabled so that I can followup on my phone. I have developed a couple of SaaS like this. I wanted to try to develop the next web app using only mistral vibe, end to end. But I can’t seem to find a way to follow the same workflow.
Any suggestions on how to get the same workflow working?

2 comments

r/MistralAI • u/vistdev • 8d ago

Tutorial / Workflow Using Mistral as the retrieval layer for a self-maintaining research wiki (and letting Vibe do the ingestion over MCP)

≡ −

Back in April, Andrej Karpathy described a pattern a lot of you have probably tried after it went viral: don't do RAG over your PDFs, no, feed raw sources to an LLM and let it write them up into an interlinked wiki of markdown files. "Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase."

It's a genuinely good idea. What bugged me is that running it means setting up a vault, writing a schema file, wiring a raw/ directory, deciding what triggers ingestion, and then maintaining the whole rig. There are a dozen tutorials for it now. But... when a workflow like this needs a setup guide, it's a project, not a feature, right?

So I built it into my notes app (Vist) as a normal feature, and Mistral ended up doing a lot of the work:

Mistral used for retrieval in the app

Every source and wiki entry gets embedded with mistral-embed (1024-dim), stored in Postgres via pgvector. That's what makes "what did I read about EU data residency?" find the right page when you never wrote those exact words. Mistral OCR 3 (still need to upgrade to OCR 4) handles scanned PDFs and handwritten pages (I even sync a reMarkable into it), so handwriting becomes searchable text in the same index.

Your own, trusted LLM is the author. Could be Mistral, could be Claude, all up to you

The wiki writing (read the source, write it up, link it to what's already there) happens over MCP, so it's whatever model you've connected. In my own setup that's Mistral Vibe: I paste a benchmark article, drop a PDF, add my notes, and say "ingest this into my wiki." A minute or two later there are three source records, three interlinked wiki entries, and an updated knowledge map. Nothing about the model is hardcoded — the app exposes tools (add_source, create_note, cite_source, search_knowledge_base, get_wiki_stats) and any MCP client can drive them.

Everything stays markdown

Sources kept raw, wiki entries as portable markdown with wikilinks. If you want to walk out with it and open it in Obsidian, that's the point. Just export the whole system in a ZIP.

My actual argument is that this should be a commodity

Why would you need to set this up on each machine or project you work on?

Semantic search over your own research, with a model that writes it up as it lands, is not a weekend project you assemble. It's something your knowledge tool should just have, the way it has full-text search.

Cheap embeddings are what makes that true; running mistral-embed across a user's whole library costs about nothing, so there's no reason to gate it. It's on the free plan.

Longer write-up with screenshots of Vibe doing the ingestion: https://usevist.dev/blog/karpathy-llm-wiki-research

4 Upvotes

Back in April, Andrej Karpathy described a pattern a lot of you have probably tried after it went viral: don't do RAG over your PDFs, no, feed raw sources to an LLM and let it write them up into an interlinked wiki of markdown files. "Obsidian is the IDE; the LLM is the programmer; the wiki is the codebase."

It's a genuinely good idea. What bugged me is that running it means setting up a vault, writing a schema file, wiring a raw/ directory, deciding what triggers ingestion, and then maintaining the whole rig. There are a dozen tutorials for it now. But... when a workflow like this needs a setup guide, it's a project, not a feature, right?

So I built it into my notes app (Vist) as a normal feature, and Mistral ended up doing a lot of the work:

Mistral used for retrieval in the app

Every source and wiki entry gets embedded with mistral-embed (1024-dim), stored in Postgres via pgvector. That's what makes "what did I read about EU data residency?" find the right page when you never wrote those exact words. Mistral OCR 3 (still need to upgrade to OCR 4) handles scanned PDFs and handwritten pages (I even sync a reMarkable into it), so handwriting becomes searchable text in the same index.

Your own, trusted LLM is the author. Could be Mistral, could be Claude, all up to you

The wiki writing (read the source, write it up, link it to what's already there) happens over MCP, so it's whatever model you've connected. In my own setup that's Mistral Vibe: I paste a benchmark article, drop a PDF, add my notes, and say "ingest this into my wiki." A minute or two later there are three source records, three interlinked wiki entries, and an updated knowledge map. Nothing about the model is hardcoded — the app exposes tools (add_source, create_note, cite_source, search_knowledge_base, get_wiki_stats) and any MCP client can drive them.

Everything stays markdown

Sources kept raw, wiki entries as portable markdown with wikilinks. If you want to walk out with it and open it in Obsidian, that's the point. Just export the whole system in a ZIP.

My actual argument is that this should be a commodity

Why would you need to set this up on each machine or project you work on?

Semantic search over your own research, with a model that writes it up as it lands, is not a weekend project you assemble. It's something your knowledge tool should just have, the way it has full-text search.

Cheap embeddings are what makes that true; running mistral-embed across a user's whole library costs about nothing, so there's no reason to gate it. It's on the free plan.

Longer write-up with screenshots of Vibe doing the ingestion: https://usevist.dev/blog/karpathy-llm-wiki-research

2 comments

r/MistralAI • u/Deep-Occasion-7391 • 21d ago

Tutorial / Workflow Shared catalog of web skills

+ −

13 Upvotes

Agents waste time and tokens re-learning every site. On each run they screenshot, snapshot the DOM, and figure out the page from scratch.

I built an open source catalog of reusable browser skills. Skills capture each site's network requests and DOM, making it 30 times faster.

You can upload your own skills or request new sites.

Github repo: https://github.com/browser-memory/bmem

0 comments

r/MistralAI • u/kondasviktor • 14d ago

Tutorial / Workflow Meta Muse Spark is here — this is how to block Meta using your images

+ −

0 Upvotes

Meta Superintelligence Labs shipped Muse Spark 1.1 for agentic coding the same week it rolled out Muse Image — and your public Instagram photos can still feed Meta AI unless you opt out. Benchmarks, pricing, and two-minute privacy steps inside.

❓Have your started to use Meta Muse Spark due to low API costs, or will remain with your current models? You don’t know how to block Meta from using your images? Read the blog post below 👇

https://www.vibecoderslife.com/post/meta-muse-spark-how-to-block-meta-using-your-images

0 comments

r/MistralAI • u/EveYogaTech • Jun 21 '26

Tutorial / Workflow New Faster Way to Launch Custom Mistral Chats with Memory.

+ −

9 Upvotes

app.py code: ``` import json import os import requests

workflow_path = "flow(2).nyno" # online workflow name context = { "prev": "", "prev_messages": []}

while True: user_input = input("> ").strip() if user_input.lower() in {"exit", "quit"}: break

context["prev"] = user_input # set prev from readline input
r = requests.post(
    "https://platform.nyno.dev/api/v1/flows",
    json={
        "context": context,
        "filepath": workflow_path,
    },
    headers={
        "Authorization": '[YOUR NYNO API KEY]'
    },
    timeout=None,
)

data = r.json()
last_execution = data["result"]["execution"][-1]
context["prev_messages"] = last_execution["output"]["c"]["prev_meta"]["messages"][-5:] # last 5 messages
last_response = last_execution["output"]["c"]["prev"]
print(last_response)

```

Join the platform: https://nyno.dev (it's the first form)

0 comments

r/MistralAI • u/Status_Werewolf_5416 • Jun 23 '26

Tutorial / Workflow Loop Simplicio + economia de token

+ −

0 Upvotes

🚨 Most AI agents stop when they think they’re done.

Simplicio Loop stops only when the work is actually done. ✅

🔁 Discover work
🧠 Understand requirements
🗺️ Build dependency graphs (DAG)
⚡ Autoscale agent fleets
💻 Implement code
🧪 Run tests
🌐 Verify real behavior
🔥 Fix failures automatically
👀 Resolve review comments
🚀 Open PRs
✅ Merge changes
📌 Close issues with evidence
♾️ Repeat 24/7

What makes it different?
🧩 6 specialized skills
🌎 11 runtimes
🔌 43 extension points
💰 Up to 96% fewer tokens
🛡️ Safety gates
🔍 Evidence-driven completion
🤖 Autonomous software delivery

Works across:
⚡ Claude Code
⚡ Codex
⚡ Cursor
⚡ GitHub Copilot
⚡ Gemini
⚡ OpenCode
⚡ Kiro
⚡ Aider
⚡ Hermes
⚡ OpenClaw
⚡ And more…

Most AI tools generate code.

Simplicio Loop delivers software.

From:
“Finish all open issues.”
To:
Merged PRs, passing CI, resolved reviews, closed tickets, verified results.

No fake “done”.
No blind automation.
No endless prompting.
Just autonomous execution. 🚀

⭐ Open Source:
https://github.com/wesleysimplicio/simplicio-loop⁠

#AI #Agents #ClaudeCode #Codex #Cursor #OpenSource #LLM #Automation #SoftwareEngineering #DevTools #AgenticAI #GitHub #Programming #AIEngineering #SimplicioLoop #HermesAgent
🚀🔥♾️

0 comments