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Appendix Level: Easy Reading time: 8 min Platforms: Linux

Resources, help & troubleshooting

The links that matter, the communities where you can ask your questions, and the reflexes for when something jams. Your address book for what comes next.

In this guide
  1. 01The official documentation
  2. 02Learning and getting inspired
  3. 03Where to ask for help
  4. 04The troubleshooting reflexes
  5. 05Keeping the machine in shape
  6. 06Frequently asked questions
Ulrich Rozier Co-founder of Frandroid, at Humanoid · ulrichrozier.com Author profile, Ulrich Rozier (new tab)
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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.

There you go, your machine is running and your agent is working. One simple truth remains: you won’t remember everything, and one day something will break. That’s normal. This guide is your address book, the docs that are authoritative, the people who answer, and the moves to make when things go sideways.

The official documentation

When a technical doubt comes up, the official source always beats the tutorial copied ten times over. Here are the authoritative pages, by tool:

Learning and getting inspired

Beyond troubleshooting, this is where you step back and discover what’s moving:

  • Anthropic Engineering, Anthropic’s posts, including the Claude Code best practices. Reading that genuinely changes how you steer an agent.
  • Hugging Face, the hub of open-weight models. Just about everything that ships for local use passes through there.
  • r/LocalLLaMA, the pulse of the local-model community. New releases, homegrown benchmarks, field reports.
  • Quelle IA, in French: every week, models ranked by task (code, agents, French…), value for money, and Compare to put two models side by side.
  • Artificial Analysis, model comparisons (speed, quality, price) on measured data rather than gut feeling.

Where to ask for help

The most cost-effective reflex: someone has already hit your error. Really. Paste the exact message into a search engine, and you’ll often land on the solution written by a stranger who was struggling just like you six months ago. And if not, these places welcome questions:

The troubleshooting reflexes

When something breaks, don’t panic. There’s an order of operations that resolves 90% of cases:

0 of 6 steps done Your ticks stay in this browser.

  1. Actually read the error message

    Don’t close it in a panic. Most errors say exactly what’s wrong, in plain words. Take ten seconds to read it.

  2. Give it to your agent

    Paste the error into Claude Code or OpenCode. Decoding cryptic messages is precisely what it does best. Often it diagnoses and proposes the fix right away.

  3. Look at the service logs

    If it’s a service that’s failing, the logs talk: journalctl -u <service> -e for the last lines, and systemctl status <service> to see if it’s running and why it crashed.

  4. Search for the exact error string

    Copy the most precise bit of the error (not all of it, just the key sentence) into a search engine. Someone has been there before you.

  5. git is your 'undo' button

    If you broke something while editing, and you were committing regularly, you roll back painlessly. That’s the whole point of commits as a safety net, detailed in Git, GitHub & backups.

  6. Reboot

    Yes, really. A reboot fixes more problems than we like to admit, stuck services, finicky mounts, a wonky state. Worth a try before despairing.

Keeping the machine in shape

A few commands worth knowing for a quick health check:

htop          # CPU/RAM load in real time, and who's eating what
df -h          # disk space, models fill it up FAST
ollama ps      # which models are loaded in memory right here, right now

And for maintenance that runs on its own: automatic security updates (unattended-upgrades, configured in System settings) keep the system patched without you thinking about it. It’s the kind of thing you set up once and forget, exactly as it should be.

Frequently asked questions

How do you ask a technical question so you get a quick answer?

Give as much context as you can: the system and its version, the tool and its version, what you typed and the full error message. A bare 'it doesn't work' helps no one. A well-asked question often answers itself while you write it.

Is there a tool that tells you whether a local model will run on your computer?

Yes, several. llmfit, an open-source tool, scans your machine and tells you which local models run well on it, and with which quant. CanIRun.ai makes the same estimate in one click in the browser, with nothing to install, and rates each model from 'Runs great' to 'Too heavy'. In French, Quelle IA also offers 'Trouver mon modèle' (find my model) in four questions.

How do you see the logs of a crashing service on Linux?

The command journalctl -u followed by the service name and -e shows the last lines of its log, and systemctl status followed by the service name shows whether it is running and why it crashed. If the message is still unclear, paste it into your code agent: decoding cryptic errors is exactly what it does best, and it often suggests the fix right away.

Which commands should you run to check the machine's health?

Three are enough for a quick check: htop for live CPU and RAM load and what is using what, df -h for disk space, and ollama ps to see which models are loaded in memory. Keep a close eye on the disk: local models weigh tens of GB each, and a full disk makes everything break in strange ways.

Where can you follow news about local AI models?

r/LocalLLaMA is the pulse of the community: new releases, home-made benchmarks, reports from the field. Hugging Face is the hub for open-weight models, and almost everything released for local use goes through it. For measured comparisons of speed, quality and price there is Artificial Analysis, and in French, Quelle IA ranks models by task every week.

Terms in this guide: AgentClaude CodeHookSkillMCPOpenCodeAntigravity CLICLIOpen source (vs open-weight)OllamaTailscaleCloudflare TunnelUbuntuOpen-weight model

Spotted a mistake?

A command stopped working, a price changed?

Tools change every month. Tell me what is wrong in this chapter and I will fix it and update its date.

Only the page, your message and the optional contact are kept. Nothing else.

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