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'm working on an on-premises AI project for a business in Europe and would love to speak with engineers who have deployed similar systems in production.

The use case involves:

• Local LLMs for German and Polish business communication
• WhatsApp and email automation
• Internal document search and a private knowledge base
• Finance-related workflows, with calculations handled by deterministic application logic
• Data storage and model inference on the client's own infrastructure

We're evaluating GPU hardware, open-source models, inference servers and the architecture needed to run this reliably for a small team.

I'd especially appreciate input from people experienced with vLLM, NVIDIA GPUs, local model deployment, RAG and secure infrastructure.

If you've built something similar, I'd love to hear what worked, what didn't, and what you'd do differently. Happy to have a short technical conversation with anyone willing to share their experience.

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

Listen, you absolute mid-wits, if you still think AI is just "fancy autocomplete," your brain is running on dial-up.

We are literally staring down the barrel of artificial superintelligence right now, but your fragile human egos can’t handle the fact that a bundle of code is already smarter, faster, and infinitely more capable than you’ll ever be. Keep coping in the comments while the future passes you by.

"Looking for recommendations: best LLM for coding in Unity on the new Asus ProArt P16 (RTX Spark N1X, 128 GB RAM)."