From experimenting with speech-to-text to training lightweight predictive models, I've created a personal AI lab at home powered by high-end consumer hardware. The goal? Run local LLMs, real-time voice agents, VLMs, and GPU-accelerated automation workflows without relying on cloud costs.
Read MoreGoogle's Gemini Pro 3 represents a major leap in multimodal AI capabilities. I've integrated Gemini Pro 3 into production workflows for content generation, code assistance, and intelligent automation. Here's how to build scalable AI workflows that leverage its advanced reasoning and multimodal understanding.
Read MoreBuild production-grade AI voice agents that understand natural language, access knowledge bases, and handle real customer calls. Complete guide with Asterisk, Whisper, LLMs, and RAG—achieving 73% automation with 2.7s response time.
Read MoreNot every AI application needs cloud-scale infrastructure. Edge AI and compact models like Nano-sized language models enable generative AI on devices with limited resources. I've deployed AI workflows on Raspberry Pi, embedded systems, and low-power servers—here's how to optimize models for edge deployment.
Read MoreA complete breakdown of my personal AI lab—running local LLMs, real-time voice agents, and GPU-accelerated workflows without cloud costs. Hardware specs, software stack, real-world use cases, and why local AI is the future.
Read MoreInterested in Machine Learning solutions? Let's discuss how I can help with your project.