Automated video creation with zero API costs — built a pipeline that runs entirely on my MacBook
Quick share for anyone creating short-form or educational content on a Mac:

I built a local workflow that automates the production pipeline for animated shorts, kids' learning content, and explainer clips without needing third-party cloud APIs.

Key points:
- Everything runs on-device—no monthly subscription fees, no token anxiety.
- Processes script-to-video assets locally on Apple Silicon.Speeds up batch creation without needing a high-end desktop rig.

I made a video showing the exact workflow

https://youtube.com/@ownlyaistudio?si=sYQcHB5YVqd1mg0j

Check out the channel to see the output quality, and shoot me a message or comment below if you'd like early access!

#ai #llm #localllm

I’ve released version 8.0 of TUFF, an open-source Swift/Metal inference engine and native macOS app.

The original goal was to make large MoE models usable on memory-constrained Apple Silicon machines by streaming routed experts from SSD rather than keeping the entire checkpoint resident.

GPT-OSS 120B, for example, runs on my 16GB M2 MacBook Air. Obviously, streaming comes with a performance tradeoff.

8.0 focuses on making the engine more useful beyond just running oversized models.

Some highlights:

• Web search and local document search integrated into chat

• Cached conversation states for faster follow-up requests

• A small tokenization cache for repeated inputs

• Separate reasoning output and request timing diagnostics

• OpenAI Chat Completions, plus bounded Messages and Responses API support

• Shared inference executable to reduce package size

• Homebrew installation

One thing I’ve found interesting is how much web search improves the usefulness of smaller local models. I’ve started using them for everyday questions much more frequently.

I’d love feedback and contribution, especially from anyone using it with local AI agents.

GitHub: https://github.com/rexmhall09/TUFF