Lean Deep Tech blog

Créer une VM qui démarre depuis un serveur PXE

Fri, 07 Aug 2026 22:45:00 +0000

Voici un script de création de VM sur OSX pour booter sur un serveur PXE. J’avais besoin de tester le bon fonctionnement d’un serveur PXE que j’avais créé de tout pièce en Rust. Ce script m’a permis de créer la VM de test.

Proxy socks avec IPv6

Fri, 07 Aug 2026 22:45:00 +0000

Voici la commande pour créer un proxy socks avec un serveur dédié qui n’a qu’une interface en ipv6

ssh -N \
  -D 127.0.0.1:1080 \
  -o ExitOnForwardFailure=yes \
  -o ServerAliveInterval=30 \
  user@IP_V6::1

Pour l’utiliser dans un navigateur il suffit de spécifier socks proxy ::1 sur port 1080.

Installer Docker sur Ubuntu 26

Wed, 05 Aug 2026 07:00:00 +0200

Effacer l’installation précédente

Désinstaller les packages précédemment installés

for pkg in docker.io docker-doc docker-compose docker-compose-v2 podman-docker containerd runc;
  do sudo apt-get remove -y $pkg
done

rm -rf /var/lib/docker && rm -rf /var/lib/containerd

Ajouter la clé Docker GPG

apt-get install -y ca-certificates curl gnupg lsb-release

sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
chmod a+r /etc/apt/keyrings/docker.gpg

Ajouter le dépot officiel Docker

echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

Installer Docker

apt-get install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

Activer Docker

systemctl enable docker
systemctl start docker
systemctl status docker

Vérification

docker ps
docker compose ps

Utiliser Docker sans sudo

sudo usermod -aG docker $USER
newgrp docker

Zed + vLLM on DGX Spark

Wed, 01 Jul 2026 05:23:00 +0000

Dans cet article, je partage mes notes sur l’utilisation de Zed sur OSX avec comme AI provider vLLM, Qwen 3.6 sur un Nvidia DGX spark distant privé. Mon objectif est d’avoir l’équivalent d’un petit Cursor (remplacé par Zed) avec un LLM local. Je suis en train de mettre en place un Agent qui travaille pour moi tout le temps et qui répond à toutes mes questions (via texte ou voix). Je veux pouvoir cramer autant de tokens que je veux et ne rien avoir à payer car étant très très geek, j’ai une consommation en tokens stratosphérique (malgré le fait que je code depuis depuis que j’ai l’âge de 13 ou 14 ans).

Installer tailscale sur raspbian armv7l

Wed, 20 May 2026 21:15:00 +0000

Dans cet article, nous allons voir comment installer Tailscale sur un Raspberry pi qui tourne sous Raspbian Bullseye (version 32 bits armv7l) afin qu’il serve d’exit node et permette d’accéder aux appareils de votre réseau local.

Installation

sudo apt update && sudo apt upgrade -y

curl -fsSL https://pkgs.tailscale.com/stable/raspbian/bullseye.noarmor.gpg | sudo tee /usr/share/keyrings/tailscale-archive-keyring.gpg > /dev/null

curl -fsSL https://pkgs.tailscale.com/stable/raspbian/bullseye.tailscale-keyring.list | sudo tee /etc/apt/sources.list.d/tailscale.list

sudo apt update
sudo apt install tailscale

Editer le fichier /etc/sysctl.d/99-tailscale.conf et ajouter le contenu:

Install Hermes on Ubuntu 26.04

Sun, 17 May 2026 22:15:00 +0000

Installation on Ubuntu 26.04

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

# or update
# hermes update

For the Inference Provider I used lm studio.

Enter your LM_API_KEY
Enter your server IP (example: http://192.168.1.234:1234/v1)
Select the LM server model you want to use

Once fully installed execute:

source ~/.bashrc
hermes

Ask the available skills installed by default.

● Which skills are available ?

Answer:

I have access to a wide range of skills (abilities) designed to handle various tasks. Here's a list of the skill categories and some examples within each:

1. Autonomous AI Agents
    - claude-code: Delegate coding to Claude Code CLI.
    - codex: Delegate coding to OpenAI Codex CLI.
    - hermes-agent: Configure, extend, or contribute to Hermes Agent.
    - opencode: Delegate coding to OpenCode CLI.

2. Creative
    - architecture-diagram: Dark-themed SVG architecture/cloud/infra diagrams as HTML.
    - ascii-art: ASCII art: pyfiglet, cowsay, boxes, image-to-ascii.
    - ascii-video: ASCII video: convert video/audio to colored ASCII MP4/GIF.
    - baoyu-comic: Knowledge comics (educational, biography, tutorial).
    - baoyu-infographic: Infographics with 21 layouts x 21 styles.
    - claude-design: Design one-off HTML artifacts (landing, deck, prototype).
    - comfyui: Generate images, video, and audio with ComfyUI.
    - design-md: Author/validate/export Google's DESIGN.md token spec files.
    - excalidraw: Hand-drawn Excalidraw JSON diagrams (arch, flow, seq).
    - humanizer: Humanize text: strip AI-isms and add real voice.
    - ideation: Generate project ideas via creative constraints.
    - manim-video: Manim CE animations: 3Blue1Brown math/algo videos.
    - p5js: p5.js sketches: gen art, shaders, interactive, 3D.
    - pixel-art: Pixel art w/ era palettes (NES, Game Boy, PICO-8).
    - popular-web-designs: 54 real design systems (Stripe, Linear, Vercel) as HTML/CSS.
    - pretext: Create browser demos with @chenglou/pretext — DOM-free text layout.
    - sketch: Throwaway HTML mockups: 2-3 design variants to compare.
    - songwriting-and-ai-music: Songwriting craft and Suno AI music prompts.
    - touchdesigner-mcp: Control a running TouchDesigner instance via twozero MCP.

3. Data Science
    - jupyter-live-kernel: Iterative Python via live Jupyter kernel (hamelnb).

4. DevOps
    - kanban-orchestrator: Decomposition playbook + anti-temptation rules for orchestrating work through Kanban.
    - kanban-worker: Pitfalls, examples, and edge cases for Hermes Kanban workers.
    - webhook-subscriptions: Webhook subscriptions: event-driven agent runs.

5. Email
    - himalaya: Himalaya CLI: IMAP/SMTP email from terminal.

6. Gaming
    - minecraft-modpack-server: Host modded Minecraft servers (CurseForge, Modrinth).
    - pokemon-player: Play Pokemon via headless emulator + RAM reads.

7. GitHub
    - codebase-inspection: Inspect codebases w/ pygount: LOC, languages, ratios.
    - github-auth: GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.
    - github-code-review: Review PRs: diffs, inline comments via gh or REST.
    - github-issues: Create, triage, label, assign GitHub issues via gh or REST.
    - github-pr-workflow: GitHub PR lifecycle: branch, commit, open, CI, merge.
    - github-repo-management: Clone/create/fork repos; manage remotes, releases.

8. MCP
    - native-mcp: MCP client: connect servers, register tools (stdio/HTTP).

9. Media
    - gif-search: Search/download GIFs from Tenor via curl + jq.
    - heartmula: HeartMuLa: Suno-like song generation from lyrics + tags.
    - songsee: Audio spectrograms/features (mel, chroma, MFCC) via CLI.
    - spotify: Spotify: play, search, queue, manage playlists and devices.
    - youtube-content: YouTube transcripts to summaries, threads, blogs.

10. MLOps
    - audiocraft-audio-generation: AudioCraft: MusicGen text-to-music, AudioGen text-to-sound.
    - dspy: DSPy: declarative LM programs, auto-optimize prompts, RAG.
    - evaluating-llms-harness: lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.).
    - huggingface-hub: HuggingFace hf CLI: search/download/upload models, datasets.
    - llama-cpp: llama.cpp local GGUF inference + HF Hub model discovery.
    - obliteratus: OBLITERATUS: abliterate LLM refusals (diff-in-means).
    - segment-anything-model: SAM: zero-shot image segmentation via points, boxes, masks.
    - serving-llms-vllm: vLLM: high-throughput LLM serving, OpenAI API, quantization.
    - weights-and-biases: W&B: log ML experiments, sweeps, model registry, dashboards.

11. Note-taking
    - obsidian: Read, search, create, and edit notes in the Obsidian vault.

12. Productivity
    - airtable: Airtable REST API via curl. Records CRUD, filters, upserts.
    - google-workspace: Gmail, Calendar, Drive, Docs, Sheets via gws CLI or Python.
    - linear: Linear: manage issues, projects, teams via GraphQL + curl.
    - maps: Geocode, POIs, routes, timezones via OpenStreetMap/OSRM.
    - nano-pdf: Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
    - notion: Notion API + ntn CLI: pages, databases, markdown, Workers.
    - ocr-and-documents: Extract text from PDFs/scans (pymupdf, marker-pdf).
    - powerpoint: Create, read, edit .pptx decks, slides, notes, templates.
    - teams-meeting-pipeline: Operate the Teams meeting summary pipeline via Hermes CLI.

13. Red-teaming
    - godmode: Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN.

14. Research
    - arxiv: Search arXiv papers by keyword, author, category, or ID.
    - blogwatcher: Monitor blogs and RSS/Atom feeds via blogwatcher-cli tool.
    - llm-wiki: Karpathy's LLM Wiki: build/query interlinked markdown KB.
    - polymarket: Query Polymarket: markets, prices, orderbooks, history.
    - research-paper-writing: Write ML papers for NeurIPS/ICML/ICLR: design→submit.

15. Smart Home
    - openhue: Control Philips Hue lights, scenes, rooms via OpenHue CLI.

16. Social Media
    - xurl: X/Twitter via xurl CLI: post, search, DM, media, v2 API.

17. Software Development
    - debugging-hermes-tui-commands: Debug Hermes TUI slash commands: Python, gateway, Ink UI.
    - hermes-agent-skill-authoring: Author in-repo SKILL.md: frontmatter, validator, structure.
    - node-inspect-debugger: Debug Node.js via --inspect + Chrome DevTools Protocol CLI.
    - plan: Plan mode: write markdown plan to .hermes/plans/, no exec.
    - python-debugpy: Debug Python: pdb REPL + debugpy remote (DAP).
    - requesting-code-review: Pre-commit review: security scan, quality gates, auto-fix.
    - spike: Throwaway experiments to validate an idea before build.
    - subagent-driven-development: Execute plans via delegate_task subagents (2-stage review).
    - systematic-debugging: 4-phase root cause debugging: understand bugs before fixing.
    - test-driven-development: TDD: enforce RED-GREEN-REFACTOR, tests before code.
    - writing-plans: Write implementation plans: bite-sized tasks, paths, code.

Install Composio

Open a chat hermes and just enter

Présentation du RAG open source SurfSense

Thu, 07 May 2026 19:15:00 +0000

flowchart TD A[SurfSense : Assistant IA gratuit et open source] --> B[1. SurfSense vs Google NotebookLM] A --> C[2. Fonctionnalités clés et mises à jour de SurfSense] A --> D[3. Accès et installation de SurfSense] B --> B1[Contrôle et confidentialité] B --> B2[Flexibilité des modèles] C --> C1[Intégration des sources de connaissance] C --> C2[Système d'agents dynamiques] D --> D1[Options d'installation] D --> D2[Prérequis essentiels]

SurfSense vs Google NotebookLM

Le besoin d’une alternative open source

Bien que Google NotebookLM soit populaire pour l’interaction avec les documents, l’outil open source SurfSense offre un plus grand contrôle, une personnalisation accrue et une confidentialité des données supérieure.

Claude code with Private AI

Tue, 05 May 2026 07:15:00 +0000

In this article we are going to see how to use Claude code with our own private models Gemma4, Qwen3, GPT OSS 120 or uncensored ones.

Prerequisites

LM Studio installed wherever you want

Installation

Install Claude Code

curl -fsSL https://claude.ai/install.sh | bash

Configuration

Create a new file under ~/.claude/lmstudio.private-ai-server.json and add the following content:

{
  "env": {
    "ANTHROPIC_BASE_URL": "http://127.0.0.1:1234/",
    "ANTHROPIC_AUTH_TOKEN": "dummy",
    "API_TIMEOUT _MS": "3000000",
    "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": 1,
    "ANTHROPIC_MODEL": "default_model",
    "ANTHROPIC_SMALL_FAST_MODEL": "default_model",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "default_model",
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "default_model",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "default_model"
  }
}

Test

Load a huge thinking Open Source model in LM Studio and set the context to the maximum limit. Then run the following command in your app repository claude --settings ~/.claude/lmstudio.private-ai-server.json. And finally select default_model using /model command after claude has started.

Private AI with Gemma 4

Sun, 26 Apr 2026 12:15:00 +0000

Option 1

Prerequisites

Pyenv
Cuda 13

Installation

pyenv install 3.12.2
pyenv global 3.12.2
python --version

python -m venv ./vllm-gemma
source ./vllm-gemma/bin/activate

pip install --upgrade pip

pip install torch --index-url https://download.pytorch.org/whl/cu130
python -c "import torch; print(torch.cuda.is_available())"

pip install vllm

pip install huggingface_hub
hf download google/gemma-4-26B-A4B-it --local-dir ~/hf_models/gemma-4-31B-it

vllm serve google/gemma-4-26B-A4B-it --max-model-len 32768 --gpu-memory-utilization 0.90 --limit-mm-per-prompt '{"image": 150, "video": 1, "audio": 0}--enable-prefix-cache --host 0.0.0.0 --port 8000

Option 2 - Docker

# Base CUDA 13 (ARM sbsa)
FROM nvidia/cuda:13.0.0-devel-ubuntu22.04

ENV DEBIAN_FRONTEND=noninteractive

ENV USE_CUDA=1
ENV USE_NCCL=1
ENV USE_DISTRIBUTED=1

ENV VLLM_USE_FLASH_ATTENTION=0

# --------------------------------------------------
# System deps
# --------------------------------------------------
RUN apt-get update && apt-get install -y \
    build-essential \
    git \
    cmake \
    ninja-build \
    curl \
    wget \
    vim \
    python3 \
    python3-dev \
    python3-pip \
    python3-venv \
    libopenblas-dev \
    libssl-dev \
    zlib1g-dev \
    libffi-dev \
    libbz2-dev \
    libreadline-dev \
    libsqlite3-dev \
    libncursesw5-dev \
    xz-utils \
    tk-dev \
    libxml2-dev \
    libxmlsec1-dev \
    liblzma-dev \
    gcc-12 \
    g++-12 \
    && rm -rf /var/lib/apt/lists/*

# Set python
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3 1

RUN python -m pip install --upgrade pip setuptools wheel

# --------------------------------------------------
# CUDA env
# --------------------------------------------------
ENV CUDA_HOME=/usr/local/cuda
ENV LD_LIBRARY_PATH=/usr/local/cuda/targets/sbsa-linux/lib:$LD_LIBRARY_PATH
ENV PATH=/usr/local/cuda/bin:$PATH

# --------------------------------------------------
# Compilers
# --------------------------------------------------
ENV CC=gcc-12
ENV CXX=g++-12

# --------------------------------------------------
# Build PyTorch from source
# --------------------------------------------------
WORKDIR /opt

RUN git clone --recursive https://github.com/pytorch/pytorch

WORKDIR /opt/pytorch

# CUDA arch (à ajuster selon GPU réel)
ENV TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9;9.0"

# Fix ARM SVE issue
ENV BUILD_IGNORE_SVE_UNAVAILABLE=1

# Optionnel mais recommandé (évite d'autres warnings CPU)
ENV USE_NATIVE_ARCH=0

# Install Python deps
RUN python -m pip install -r requirements.txt

# Build PyTorch
RUN python setup.py develop

# --------------------------------------------------
# Install Triton
# --------------------------------------------------
RUN python -m pip install ninja cmake

# Try wheel first
RUN python -m pip install triton || true

# Fallback build Triton from source
RUN if ! python -c "import triton" ; then \
      git clone https://github.com/openai/triton /opt/triton && \
      cd /opt/triton/python && \
      python -m pip install -e . ; \
    fi

# --------------------------------------------------
# Build vLLM
# --------------------------------------------------
WORKDIR /opt

RUN git clone https://github.com/vllm-project/vllm

WORKDIR /opt/vllm

ENV VLLM_BUILD_WITH_CUDA=1
ENV MAX_JOBS=8

RUN python -m pip install -e .

# --------------------------------------------------
# Runtime
# --------------------------------------------------
WORKDIR /workspace

EXPOSE 8000

CMD ["python", "-m", "vllm.entrypoints.openai.api_server", \
     "--model /models/gemma", \
     "--trust-remote-code", \
     "--max-model-len", "8192",
     "--max-num-batched-tokens", "4096", \
     "--gpu-memory-utilization", "0.90", \
     "--disable-flash-attn",
     "--enforce-eager",
     "--host", "0.0.0.0", \
     "--port", "8000"]

docker build -t vllm-cuda13 .
  docker run --gpus all -p 8000:8000 \
    -v ~/hf_models/gemma-4-31B-it:/models/gemma \
    vllm-cuda13

If you see something like this it means it is all good:

LLM studio on Spark

Sun, 26 Apr 2026 07:15:00 +0000

Installation

curl -fsSL https://lmstudio.ai/install.sh | bash

curl -L -O https://raw.githubusercontent.com/lmstudio-ai/docs/main/_assets/nvidia-spark-playbook/bash/run.sh
# Or with Python
curl -L -O https://raw.githubusercontent.com/lmstudio-ai/docs/main/_assets/nvidia-spark-playbook/py/run.py

Download model

lms get openai/gpt-oss-120b
lms get qwen/qwen3.6-27b

Test

lms server start --bind 0.0.0.0 --port 1234

hostname -I
curl http://server_ip:1234/api/v1/models 

curl -LsSf https://astral.sh/uv/install.sh | sh
uv run --script run.py

Connect lms headless to LM Link network

On the server run:

lms login
lms link enable

Create a service

Create a file /etc/systemd/system/lms.service and add the content: