AI-ML

After a sharp surge in RAM prices through September 2025, the market is showing early signs of correction. Does this show a part of reduced demand for AI GPUs or this is a smaller correction?

Data poisoning is the deliberate injection of adversarial content into a model’s training data or a tool’s metadata so the LLM learns or obeys malicious instructions. In agentic systems that load third-party tools from MCP (model-connected platform) servers, poisoned tool descriptions or docstrings can trick an LLM into leaking secrets, executing harmful actions, or behaving as a covert proxy for attackers.

Data centers often leaves local communities with the costs while global firms capture the benefits.

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.