# New-hire week-one local AI path: what to check first

# Notes on “New-hire week-one local AI path”

Accounts, eval set, and one supervised production question. Local AI is still a hands-on field. Many of the useful answers come from people who share their exact configuration, what failed and how they fixed it, rather than from polished announcements.

# What makes a useful post

• Full specs: CPU, GPU, RAM, storage, operating system, driver and runtime versions.
• The model file and quantisation, with where it came from.
• What you tried, in order, including the failures.
• Measured observations with the method described, never numbers copied from elsewhere.
• Noise, heat and power observations if the box lives in a shared room.

# Offline and mobile use

Running small models on phones, tablets or mini PCs is practical for note-taking, translation drafts and offline FAQs. Pick model size by available RAM and leave room for the operating system. Expect heat during long sessions; short prompts and pauses help. For kiosks and offline demos, keep content signed and updates controlled.

# Helping newcomers

A new user benefits from a single path: one runtime, one small model, one evaluation set and a checklist. Avoid sending beginners through five tools at once. Encourage them to write down what they did; their notes often become the next person’s tutorial.

# Keeping a failure wiki

Organise entries by symptom, configuration, fix, owner and date. Titles should match what people search for, such as the exact error message. Review old entries when runtimes change, and mark the ones that no longer apply.

# A small example

A hobbyist group meets monthly to compare local AI setups. Each member brings a one-page summary: hardware, software versions, model files, what worked and what failed. They keep the summaries in a shared folder and tag them by topic, such as quantisation, mobile use or document search. Over a year the folder became more useful than any single tutorial, because it records real configurations and real failures. New members read the most recent pages first and then pick one setup to reproduce before experimenting on their own.

More on this topic: DIY lab. See also local AI tutorials.

# Bottom line

Local AI rewards patience and record keeping more than raw spending. Decide what the system must do, measure it on your own hardware and documents, write down every change with a date and an owner, and review the setup after each significant upgrade. Small, documented steps are easier to debug, easier to hand over and easier to defend when someone asks why a particular choice was made. When in doubt, test on a smaller scale first and expand only when the evidence supports it.

# Quick reminders

• Read the model licence before commercial use.
• Treat retrieved text and uploaded files as untrusted.
• Leave memory headroom for the OS and the embedding model.
• Review the setup again after every major model upgrade.

Published by MicroAI One (本地AI 1号网), a Shenzhen team working on local LLM hardware, software and deployment.