Ai

Data scientists should have domain-driven thinking and why understanding the problem domain still matters more than the model even in the rise of LLMs.

An overview of the current landscape of GPUs and AI compute for local inference as of October 2025 from Nvidia and AMD to Intel, Apple, and the cloud.

Notes from testing DeepSeek-OCR as a local vision-language model for OCR and text compression on a large archive of screenshots. Includes observations on model performance, visual-token compression, and multilingual results.

NVIDIA’s DGX Spark arrives late as an AI inference system whose performance is lagging behind. With low speed unified VRAM, immature software optimizations, and heavy competition from Apple, AMD, and Intel, the Spark exposes how little remains of NVIDIA’s CUDA moat.

How AI vision models can help street photographers create detailed captions and discover details they missed in their first pass through images.

There is a lot of synthetic data that is being generated by LLMs, These include false metrics.

Creating a practical, no nonsense guide for using ChatGPT-5 efficiently by manual model switching and prompt style to each task while avoiding wasted tokens and bad outputs.

Lets explore the data of the AI infrastructure investments from NVIDIA, AMD, and hyperscalers like GCP, AWS, and Azure.

How I rebuilt my search strategy in 2025 after declining quality in search results

I built an AI powered digital twin of an investment committee where different financial experts debate and analyze investment opportunities.