Nvidia used CES 2025 to showcase its "physical AI" paradigm—training models in synthetic environments and deploying them in real-world systems. From Cosmos for physics simulation to the Vera Rubin AI superchip, this is the future of AI.
Read MoreI needed transcription for my app. I tried Google Speech-to-Text, AWS Transcribe, and Whisper. Whisper won. Here's why it's still the best choice.
Read MoreI wanted to build a RAG system for my documentation. Three attempts, three failures. Here's what went wrong each time, why it failed, and what finally worked.
Read MoreI wanted to run multiple LLMs simultaneously on my GPU. Simple goal, right? Wrong. GPU memory management became a nightmare. Here's what I learned the hard way.
Read MoreI spent a week testing Gemini 2.0 and GPT-4o side-by-side on real work tasks. Not benchmarks or demos—actual coding, writing, and analysis. Here's what I found, when to use which, and the real costs.
Read MoreI spent $2,400 fine-tuning a language model, thinking it would solve my problem. Three months later, I realized I could have achieved 90% of the results with prompt engineering for $0. Here's the expensive truth about fine-tuning that nobody tells you.
Read MoreAfter trying three different approaches to build a local LLM chat interface, I finally found what works. Here's what failed, what succeeded, and the real performance numbers you won't find in tutorials.
Read MoreBuilding real-time voice interfaces requires low-latency speech recognition and seamless audio streaming. I've integrated OpenAI Whisper with WebRTC to create production-ready voice transcription systems that work in browsers without plugins.
Read MoreRetrieval-Augmented Generation (RAG) has become the go-to approach for building AI applications that need accurate, contextual responses. I've built several RAG systems in production, and here's what I learned about making them reliable, fast, and maintainable.
Read MoreFrom 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 MoreStep-by-step tutorial for building an intelligent IVR system with natural language understanding. Replace frustrating phone menus with Whisper speech recognition and GPT-powered intent classification—achieving 92% routing accuracy.
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 MoreInterested in AI solutions? Let's discuss how I can help with your project.