AI

Diagram of the original Transformer model architecture by Vaswani et al. featuring stacked encoder and decoder blocks with multi-head attention, add-and-normalize layers, feed-forward layers, positional and output embeddings, and arrows showing data flow from input through linear and softmax outputs.

AI / Programming / projects / Study journal

GPT Transformers models

Transformer models revolutionised AI by replacing recurrence with attention, allowing machines to understand context, relationships, and meaning across long sequences of data. This post explores how GPT models harness the Transformer architecture to generate coherent, intelligent text and why this innovation reshaped the entire AI landscape.

November 5, 2025

A stressed lawyer sits at a cluttered desk covered in legal documents, focusing on the back of his laptop as he scrambles to find critical information before a deadline.

AI / Programming / RAG

Unlocking RAG Success with Semantic Chunking

Semantic chunking is a smarter, meaning-based approach to breaking text into pieces for Retrieval-Augmented Generation (RAG) systems. Instead of slicing by token count, it groups sentences that belong together, keeping the logic and flow intact. This helps retrieval systems pull coherent context rather than fragmented thoughts—a crucial advantage when dealing with complex documents like contracts or financial statements. While current research shows mixed evidence on its effectiveness, the theoretical promise is clear: semantic chunking preserves context, coherence, and clarity, making RAG systems feel less mechanical and more genuinely understanding.

November 1, 2025

Alphora landing page - “Modern Portfolio Theory for Everyone”

AI / stocks

Introducing Alphora “Modern Portfolio Theory for Everyone”

Alphora brings institutional portfolio analytics to long-term investors: MPT optimizer, risk-adjusted metrics, dividend intelligence, and portfolio-aware news. Join the waitlist.