AI Model Compression: Making Large Models Fit on Small Devices
Running large AI models on edge devices requires compression. Quantization, pruning, distillation—here's how to make big models small without losing too much capability.
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Laravel Performance: How I Cut Response Time by 60%
My Laravel API was slow. Average response time was 800ms. After optimization, it's down to 320ms. Here's what I did and the optimizations that actually worked.
Read MorePostgreSQL 17: The Query That Finally Works Fast
I had a query that took 45 seconds in PostgreSQL 16. After upgrading to PostgreSQL 17, it runs in 2 seconds. Here's the query, what changed, and why PostgreSQL 17 made such a difference.
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 MoreEdge AI and Compact Models: Running Generative AI on Resource-Constrained Devices
Not 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 MoreDive Deeper into Optimization
Interested in Optimization solutions? Let's discuss how I can help with your project.