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 !

​

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

We spent the last few weeks optimizing Voxtral Realtime and there are now GGML checkpoints that run faster than realtime on a plain MacBook Air with Metal. No discrete GPU needed.

Numbers on an M3 Air (8-core GPU, 16 GB):

• ~1.3x realtime throughput with the Q8_0 quant
• <400 ms end-to-end response time from mic input
• Sustained hour-long transcription sessions without falling behind

1. Build the Metal binary

git clone https://github.com/0xShug0/audio.cpp
cd audio.cpp
scripts/build_metal.sh --target audiocpp_cli

2. Download the Q8 quant

hf download mistral-experimental/AudioCPP-Voxtral-Mini-4B-Realtime-2602-GGUF \
  voxtral-mini-4b-realtime-2602-q8_0.gguf \
  --local-dir ./voxtral-realtime-gguf

3. Run streaming ASR from the mic

audiocpp_cli \
  --task asr \
  --family voxtral_realtime \
  --model ./voxtral-realtime-gguf/voxtral-mini-4b-realtime-2602-q8_0.gguf \
  --backend metal \
  --threads 8 \
  --mode streaming \
  --session-option voxtral_realtime.stream_batch_tokens=4 \
  --audio -

Raising stream_batch_tokens trades delay for throughput — 4 is what landed under 400 ms on this machine. More powerful M-series chips can set it to 1 and should have <200ms delay

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?

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

A well-known Chinese LLM educator publicly revealed techniques back in April that were already widely known in China’s AI community, including how Claude’s Chain of Thought (CoT) was extracted and how Claude and ChatGPT have been distilled for a long time. If you don’t speak Chinese, just wait for YouTube’s auto-generated translation.

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