Contribute

LocalFTW is a community-driven site covering local AI, on-device inference, and everything that keeps your data and your models under your own roof. We welcome contributions from anyone in the community.

How to Submit

1. Head to the Submit a Post form on GitHub
2. Fill in the title, summary, tags, and your post content in Markdown
3. We'll review it and publish it if it's a good fit

You'll need a GitHub account. All submissions are reviewed before publishing.

What We're Looking For

• Practical how-tos: Build guides, deployment walkthroughs, performance tuning, hardware setups
• News and analysis: Developments in local AI, new model releases, framework updates
• Community projects: Open-source tools, interesting experiments, benchmarks
• Lessons learned: What worked, what didn't, and why

If it's genuinely useful to someone running AI locally, we're interested.

Guidelines

Do

• Write for the local AI community first — assume readers care about running models on their own hardware
• Include specifics: model names, hardware specs, benchmark numbers, config snippets
• Link to your sources — original repos, papers, announcements
• Tag your company or project if it's relevant to the post, that's fine

Don't

• Pitch consulting services, paid products, or "contact us for more" CTAs
• Write thinly-veiled marketing dressed up as a tutorial
• Submit AI-generated slop without genuine analysis or original insight
• Copy-paste press releases — add context about why it matters for local deployment

The short version: if the reader walks away having learned something useful, it belongs here. If they walk away feeling sold to, it doesn't.

Editing and Attribution

We may lightly edit submissions for clarity, formatting, or to match the site's style. Your name and any relevant links will be credited in the published post. If we make significant changes, we'll check with you first.

Questions?

Open an issue on the GitHub repository or reach out via the discussions there.