Building Transcription: Why Whisper Is Still the Best
I 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.
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Local AI labs, LLMs, GPU computing, and machine learning experiments
Monitoring AI Models: The Metrics That Actually Matter
I monitor AI models in production. Most metrics are noise. Here are the metrics that actually matter, what I track, and what I ignore.
Read MoreBuilding RAG: Why My First Three Attempts Failed
I 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 MoreRunning Multiple LLMs: My GPU Memory Management Nightmare
I 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 MoreGemini 2.0 vs GPT-4o: I Tested Both for Real Work
I 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 MoreBuilding Voice Control That Actually Works (Without Cloud APIs)
I wanted voice control for my home automation, but I didn't want to send my voice data to Google or Amazon. So I built a local solution using Whisper. Here's why I chose local, the challenges I faced, and what actually works.
Read MoreFine-Tuning LLMs: The Expensive Truth Nobody Talks About
I 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 MoreI Built a Local LLM Chat App—Here's What Actually Works
After 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 MoreWhy 24GB VRAM Is the New Minimum for Serious AI Work
Modern AI models are breaking on 12GB cards. After running local LLMs, training models, and deploying AI systems, I've learned that 24GB VRAM is now the practical minimum for serious AI work. Here's why, and what it means for your hardware choices—comparing RTX 3090, 4090, and A6000.
Machine Learning
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LLM
CUDA
Hardware
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AI Hardware
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RTX 3090
RTX 4090
A6000
Deep Learning
GPU Computing
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Model Inference
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Real-Time Speech-to-Text with Whisper and WebRTC: Building Voice Interfaces
Building 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 MoreBuilding Production-Ready RAG Systems with Python and Vector Databases
Retrieval-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 MoreMy Home AI Lab Setup — GPU Computing for Local LLMs
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.
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