What's new in the guide
Every guide carries the date it was last checked. Here they are all listed from the freshest to the oldest, month by month, with their summary, so you can see at a glance what changed since your last visit.
I rerun every command before changing the date. If it says so, it runs.
October 2026
33 guides-
The machines we tested: which mini-PC for AI?
In short If the model stays in the cloud, a small, quiet 16 or 32 GB mini-PC is enough. To run AI at home, memory decides: 32 to 64 GB for small models, 128 GB of unified memory (Ryzen AI Max+ 395, like the GMKtec EVO-X2) for the big ones, where the Frandroid Lab measured gpt-oss 120B at 35.7 tokens per second. The Mac mini M6 is the simplest route but tops out at 32 GB, and Nvidia's DGX Spark is mostly for people who develop with CUDA. Prices have climbed with the memory crisis: check them before you buy.
· Guide 5 · 14 min
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Frontier models on a subscription: Claude, ChatGPT, Grok, Gemini
In short The four big vendors each sell a subscription that unlocks their terminal coding agent: Claude Pro ($20 a month) or Max ($100 or $200) for Claude Code, ChatGPT Plus ($20) or Pro ($100 to $500) for Codex, SuperGrok ($30) for Grok Build, Google AI Pro (€21.99) or Ultra (€99.99 or €219.99) for Antigravity CLI, prices checked on October 1, 2026. The right way to work is hybrid: the mini-PC runs the agents, one or two frontier plans do the bulk of the reasoning, and Ollama's local models take the confidential, the repetitive and the offline. The rules differ: OpenAI and xAI let you plug their subscription into OpenCode, Hermes or OpenClaw, Anthropic keeps it for Claude Code (which those tools can drive), and Google forbids it outside its own tools.
· Guide 7 · 16 min
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Chinese LLMs: MiMo, Qwen, DeepSeek, GLM, Kimi
In short Chinese models (Xiaomi's MiMo, Moonshot's Kimi, Z.ai's GLM, DeepSeek, Alibaba's Qwen, MiniMax) fill the top sixteen rows of the Quelle IA open-weights ranking (28 September 2026 edition), about ten points below the best American models, often for much less: MiMo-V2.6-Pro costs nearly fifteen times less than Claude Opus 5.5 per million tokens. There are three ways to use them: a coding plan from the vendor ($6 to $199 a month), the pay-as-you-go API or an aggregator such as OpenCode Go, or locally with Ollama for the versions that fit in memory, such as Qwen 3.8 27B. Claude Code connects through ANTHROPIC_BASE_URL, OpenCode through /connect, Codex through a provider in ~/.codex/config.toml. Depending on the offer, your requests go through China, Singapore, Europe or the United States: never send them secrets or client code.
· Guide 8 · 16 min
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Vignette: a complete project, from brief to signed binary
In short Vignette, a sticky-notes app docked to the edge of the screen, was designed, built, hosted and shipped by agents on this guide's mini PC, in one night and a morning at the end of August 2026. It runs online (web, macOS, Linux, Android), with a self-hosted Supabase backend, unit tests, 24 end-to-end scenarios and backups. The story shows what worked (a brief with mockups, human decisions taken early, several agent sessions checking each other) and what got stuck: never validate on an exit code, and a v0.1 is still a v0.1.
· 16 min
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Running several agents together
In short Run one agent session per project, each inside tmux and within its own scope, and add, if needed, a review session that writes no code: an agent that didn't write the code spots what the author no longer sees. The rule that holds it all together is the right to refuse: a session that disagrees says so with numbers, even to the supervisor, and a permission denied to one session is never carried out from another. Claude Code lets sessions on the same machine message each other; with Codex or OpenCode, a shared folder does the same job. The setup costs more and is only worth it with measurements.
· Guide 23 · 12 min
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Spotting your needs: what to automate, what to hand to an agent
In short Before building tools, list your recurring tasks and weigh each one as time multiplied by frequency. Anything that follows clear rules goes to a script; anything that requires reading, judging or writing goes to an AI agent; judgment, relationships and creativity stay with you. Start with a single task that is frequent, tedious and easy to automate, measure the time saved over a week, then move on to the next one.
· Guide 2 · 16 min
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Why a mini-machine
In short A mini-machine is a small, silent PC that stays on around the clock and hosts a coding agent, your projects and, if it has enough memory, local AI models. It is a sandbox where the agent can work on its own without touching your main computer. For an agent wired to a cloud model, an entry-level 16 GB mini-PC is enough; to run real models at home, the memory shortage has pushed prices up: count on roughly €1,400 for 32 GB and €2,200 for 64 GB of unified memory (prices checked on 30 September 2026).
· Guide 1 · 6 min
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Project ideas
In short A mini-machine that stays on around the clock can run your home automation (Home Assistant), stream your films (Jellyfin), host your sites, apps and APIs, display dashboards fed by your own data, and run automations handed to an agent. You control it from your phone, and it does the heavy computing. These projects fit together: start with the idea you like, add the others later.
· Guide 3 · 9 min
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Choosing the hardware
In short For local AI, memory decides everything: 32 GB for a compressed 30-billion-parameter model, 64 GB to be comfortable, 128 GB for the biggest ones. Unified-memory machines (Ryzen AI Max, Mac, DGX Spark) lend most of it to the graphics processor, which no consumer card can do. The memory shortage has pushed prices up: about €1,400 for 32 GB and €2,200 for 64 GB with a Ryzen AI Max (Framework Desktop without SSD, 30 September 2026). If the model stays in the cloud, a 16 GB mini-PC or an old PC is enough.
· Guide 4 · 12 min
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What about a Mac mini?
In short Yes, a Mac mini makes a very good workshop: coding agents, Ollama, Tailscale, Cloudflare Tunnel, Git and Docker run on it as on Linux, and only the system install and a few commands change (Homebrew, launchd, the macOS firewall). Its unified memory is an asset for local AI, but you pay for it: €1,049 for the 16 GB Mac mini M6, €2,039 for 32 GB and €3,649 for the 64 GB M5 Pro (Apple France configurations with a 1 TB SSD for the last two, prices checked on 1 October 2026). Look at memory and memory bandwidth together: bandwidth is what sets the model's speed.
· Guide 6 · 8 min
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Installing Linux
In short For a mini-machine dedicated to AI and code, install Ubuntu 26.04.1 LTS Desktop, supported until April 2031: download the ISO from ubuntu.com, write it to a USB stick of 12 GB or more with Rufus or balenaEtcher, boot from it and choose 'Erase disk and install Ubuntu'. On first boot, run sudo apt update && sudo apt upgrade -y, then install openssh-server so you can drive the machine remotely without a screen.
· Guide 9 · 25 min
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Essential system settings
In short After installing Ubuntu, four settings are enough for a clean, stable machine: install the basic toolkit (build-essential, git, curl, htop, tmux), give the machine a short name with hostnamectl, enable zram for compressed swap in RAM (zram-tools package, PERCENT=50) and check that automatic security updates (unattended-upgrades) are on. A quick look at timedatectl confirms the time zone. From then on, the coding agent can repeat these steps for you.
· Guide 10 · 15 min
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Installing the agent (very early)
In short Install the coding agent right after Linux, so it can set up the rest of the machine with you. Three agents are followed at parity, all installed by a script with no Node: Claude Code (curl -fsSL https://claude.ai/install.sh | bash, a Claude Pro or Max subscription or a Console account), Codex (curl -fsSL https://chatgpt.com/codex/install.sh | sh, a ChatGPT subscription or an API key) and OpenCode, the open-source agent that accepts any provider or a local model (curl -fsSL https://opencode.ai/install | bash). Grok Build from xAI and Antigravity CLI from Google round out the picture for subscribers of those two vendors. Then launch the agent, describe the goal to it in plain language, and review every action before approving it.
· Guide 11 · 12 min
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What is an AI agent?
In short An AI agent is a language model given a goal, tools to act (read and write files, run commands, search the web) and a loop that makes it try again until the job is done. Where a chatbot advises, an agent executes, which is why you set its level of autonomy and review what it does. Claude Code and OpenCode are agents of this kind, ready to use for software work.
· Guide 12 · 12 min
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Git, GitHub & backups
In short Before letting an agent write code, version everything with Git and push a copy to GitHub: every commit becomes a restore point, and the code survives the loss of the machine. The simplest way to link the mini-PC to GitHub is the GitHub CLI (gh auth login, SSH protocol), with a .gitignore written before the first commit so no secret ever gets pushed. For everything that isn't in Git (.env files, databases, configs), apply the 3-2-1 rule with restic, automate the backup and test a restore at least once.
· Guide 13 · 16 min
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Docker: isolate your projects
In short Docker puts each project in a container, a lightweight box that carries its own version of Node or Python and its dependencies: projects stop stepping on each other, deploy identically and contain the damage when an agent experiments. Install it with the official script (curl -fsSL https://get.docker.com | sudo sh), run ready-made images with docker run, describe a multi-service project in a compose.yml, then package your own code with a Dockerfile. For an exposed service, harden it: non-root user, capped resources, only the ports you really need published.
· Guide 14 · 16 min
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Tailscale: your private network
In short Tailscale links your devices (mini-PC, laptop, phone) into a private network encrypted with WireGuard, without opening a single port on your router. One command installs it, you log in with a Google, GitHub or Microsoft account, and every machine becomes reachable by name, for example ssh ulrich@mini. The free Personal plan is plenty for personal use: up to 6 users and an unlimited number of personal devices.
· Guide 15 · 10 min
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Securing access
In short To secure a mini-PC that is reachable from anywhere and driven by an AI agent: SSH with keys only (passwords off, root login refused), a ufw firewall closed by default, no raw service exposed on the Internet (Tailscale for private, Cloudflare Tunnel for public) and secrets kept out of git, in a .env file set to chmod 600. The agent runs as your user, without automatic sudo, inside the project folder, with its permission rules or its sandbox turned on. Add automatic security updates and backups whose restore you have actually tested.
· Guide 16 · 20 min
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Working remotely (terminal + VNC)
In short To drive the mini-PC from a Mac, a Windows PC or a phone, an SSH terminal over Tailscale covers almost everything: you run your agent there, inside a tmux session that survives network drops. A VNC graphical desktop helps for the rare visual needs, as long as it goes through an SSH tunnel and never straight onto the Internet. With a claude.ai subscription, Remote Control also lets you follow and steer a Claude Code session from the Claude app on your phone.
· Guide 17 · 12 min
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Cloudflare Tunnel: expose things cleanly
In short Cloudflare Tunnel puts an app from your mini-PC online on your own domain, over HTTPS, without opening a port on your router or revealing your IP address: the cloudflared program opens an outbound connection to Cloudflare, and public traffic comes back down that tunnel. You need a domain managed by Cloudflare, and the free plan is enough. Keep it for web apps meant for the public, protect the rest with Cloudflare Access, and leave SSH, Ollama or a database on Tailscale.
· Guide 18 · 15 min
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Setting up your agents
In short To tailor a coding agent to your needs, you work four levers: MCP servers that add tools (GitHub, databases, a browser), sub-agents that take on searches and parallel tasks, permissions that set what it may do without asking, and memory files that remind it of your rules every session. Start read-only, with as few tools as possible, and test on a small task before any serious use.
· Guide 19 · 15 min
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Agents in the service of a project
In short In a real project, a coding agent plays four roles: it implements a feature, reviews a diff (Claude Code ships /review and /security-review out of the box), digs through a codebase to bring back the answer, and applies repetitive changes everywhere. On a big job, an orchestrator splits the work across parallel sub-agents, at the cost of a heavier bill. In every case: a scoped task, context written in a memory file, autonomy set to the stakes, and a systematic review of the result.
· Guide 20 · 14 min
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Agents running on the mini-PC
In short An always-on mini-PC can run agents without you: a cron or systemd timer launches the agent in non-interactive mode (claude -p, codex exec or opencode run) on a fixed task, the agent loops until it's done, writes a log and notifies you. For frequent or private tasks, a local model served by Ollama costs nothing to run; a top-tier online model (Claude, GPT) stays for the hard decisions. An unattended agent needs tight permissions, a confined folder, never sudo, logs and frequent commits.
· Guide 21 · 15 min
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Hermes & OpenClaw: your in-house agents
In short To drive an agent from your phone, start with what Claude Code does on its own: Remote Control opens your local session in the Claude app or at claude.ai/code, and Channels (in preview) connect the session to a Telegram, Discord or iMessage bot. Hermes Agent (Nous Research) and OpenClaw (OpenClaw Foundation), two open-source gateways, add what the native route doesn't: any model, including your local models via Ollama, some twenty messaging apps, memory and scheduled tasks. Either way, lock access down to your own account and keep permissions tight.
· Guide 22 · 14 min
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Choosing and sizing your model
In short Your machine's memory decides: count about 0.6 GB per billion parameters at 4 bits, plus the context. According to Quelle IA (28 September 2026 edition), Qwen3.5-9B is the best pick up to 16 GB, and Qwen 3.8 27B the best from a 24 GB graphics card up to a 128 GB mini-PC, with Gemma 4 26B A4B as the faster option and gpt-oss-120b for agents. The largest open models, such as GLM-5.3-Flash or DeepSeek V4.1 Flash, need more than 200 GB. For code, the best local model still sits well below Claude: local for bounded, private tasks, the cloud for long jobs.
· Guide 24 · 16 min
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Ollama & local models
In short Ollama runs open AI models on your own machine: one command to install it, one to start a model, and a local API on port 11434 that speaks both the OpenAI and Anthropic formats. To start, qwen3.5:9b fits in 16 GB and qwen3.8:27b needs a 24 GB graphics card or 32 GB of unified memory. OpenCode plugs into it natively, so does Claude Code since Ollama 0.15 with ollama launch claude, and Codex with ollama launch codex or codex --oss. Set the context size yourself, because the default depends on memory and climbs to 256,000 tokens on big machines.
· Guide 25 · 12 min
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Going hybrid: cloud + local
In short The hybrid setup gives hard reasoning, architecture and review to a frontier model on a subscription (Claude with Claude Code, GPT with Codex, Grok with Grok Build, Gemini with Antigravity CLI), and repetitive, sensitive or offline work to a local model served by Ollama. Claude Code orchestrates: it writes the scripts that call the local API on port 11434. Since Ollama 0.15, Claude Code can also run on the local model itself with ollama launch claude, for code that must not leave the machine or when the connection drops. The golden rule: only delegate well-bounded tasks to local, and have the result reviewed.
· Guide 26 · 14 min
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Framing a project with an LLM
In short Framing means turning a fuzzy idea into a spec before writing a single line of code, by asking an LLM to interview you instead of answering: a one-sentence goal, users, prioritized features, tech stack, explicit out-of-scope and risks, then a split into 3 to 5 shippable milestones. Use the best model within reach for this conversation, for example Claude Opus 5.5 or Claude Sonnet 5.5 at Anthropic. The spec then becomes the foundation of the project's memory file (CLAUDE.md or AGENTS.md).
· Guide 27 · 18 min
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Memory files (CLAUDE.md & co)
In short A code agent starts from scratch every session, except for what it reads at startup in its memory file: CLAUDE.md for Claude Code, AGENTS.md for Codex, OpenCode and most other tools (Claude Code also reads AGENTS.md when the project has no CLAUDE.md). Put in it the project's goal, the rules, the commands that matter and the lessons learned, never a secret or what the code already says, and keep it short. The /init command generates a first draft to prune, and a global file (~/.claude/CLAUDE.md for Claude Code, ~/.codex/AGENTS.md for Codex) carries the preferences you want everywhere.
· Guide 28 · 14 min
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Skills: automating your workflows
In short A skill is a reusable bundle of instructions that the agent runs from a slash command, or loads on its own when the task fits: a folder holding a SKILL.md file (a name, a description, then the instructions). It is now the main mechanism of both Claude Code and Codex, which have put it in place of their old custom commands, and an open format, Agent Skills, that OpenCode, Cursor and Gemini CLI read too. Create one as soon as you type the same instruction twice, and keep the ones that deploy or delete on manual trigger only.
· Guide 29 · 12 min
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Review, audit, secure
In short With a code agent, your job becomes checking: review every diff before accepting it, run a regular quality audit and a security audit before each release. Claude Code ships /code-review (alias /review) to hunt bugs in the diff, /simplify for code quality and /security-review for vulnerabilities; Codex has /review in the session and codex review on the command line; with OpenCode, you turn these into reusable commands. Have the code reviewed by a different model from the one that wrote it, and keep a human review on authentication, payments and personal data.
· Guide 30 · 13 min
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Maintain and update
In short A machine that is always on stays healthy with two habits: security fixes apply themselves (unattended-upgrades), and once a month you back up, run sudo apt update && sudo apt upgrade, update Ollama, the agents and the dependencies one at a time, then clean up the disk. Moving from Ubuntu 24.04 to 26.04 is done with sudo do-release-upgrade, after a backup, keeping an eye on the new Rust base tools (rust-coreutils, sudo-rs) and the switch to Python 3.14. To know when something breaks, propagate exit codes, hook OnFailure onto your services and actually test the alert.
· Guide 31 · 12 min
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Resources, help & troubleshooting
In short For a technical question, the official docs are the authority: code.claude.com/docs for Claude Code, opencode.ai, docs.ollama.com, tailscale.com/docs, the Cloudflare Tunnel docs and docs.ubuntu.com. To pick a local model for your machine, Quelle IA (quelleia.com, in French) ranks open models machine by machine. To ask for help: r/LocalLLaMA, the Ollama Discord, Ask Ubuntu and Ubuntu Discourse, Stack Overflow. When something breaks: read the error, hand it to your agent, check the logs, search the exact phrase, roll back with git, reboot.
· 8 min
