AI-ML

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.

A brief look at Gemini Pro 2.5 compared with ChatGPT 5 and Claude Opus/Sonnet, plus notes on Gemini 2.5 variants, NotebookLM, and mobile privacy concerns.

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.

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.

OpenAI’s removal of GPT-4o, o3, and other models after GPT-5’s launch breaks fundamental MLOps principles. Without model versioning and control, data science workflows become unreliable. Local LLMs offer a better alternative for maintaining consistency.

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

2025 brought us Claude 4, MCP, Agents, GPT-5, o3 reasoning. A timeline of artificial intelligence developments, breakthroughs, and milestones throughout 2025

I sent a picture to ChatGPT for OCR and it gave me a response that leaked from somewhere.