Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation
Enterprise Document Intelligence [Vol.1 #6quater] - Question parsing takes one messy string and writes four typed pieces, each read by a different downstream call
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Why Your Betas Explode: The Hidden Geometry of Multicollinearity
Why your regression coefficients keep changing, and what geometry has to do with it.
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Cross-provider PR review with Codex in GitHub Actions, and why a second opinion from a different lab beats any self-review
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How I Mastered Data Structures and Algorithms for ML (In 6 Weeks)
The strategies, questions, and process I used to ace coding interviews.
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A Gentle Introduction to Autoencoders & Latent Space
Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of the principal approaches to mitigate this problem is to compress input data into a lower-dimensional representation whi...
Context Rot: Why Claude Code Sessions Decay, and How to Govern Them
Long sessions rot quietly, well before any token limit is reached. Here’s why, and how to govern your context in Claude Code.
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RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each
Two techniques, two different problems, and why the question is not really "which one wins"
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That Is Embarrassing: Why Frontier AI Still Makes Things Up, and What to Do About It
The best AI models still hallucinate. These hallucinations are sometimes funny, and sometimes cause actual damage. In this post we will consider recent tales of AI hallucinations, and then look under the hood to understand why they happen.
The post That Is Embarrassing: Why Frontier AI Still Makes T...
RAG Was Always a Temporary Workaround. What is Next?
Vector databases are a temporary bridge. Discover why the next AI infrastructure revolution relies on persistent neural state and strict latency budgets, not on vector databases.
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Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy
A measured look at distributed training, from DDP and FSDP to the ZeRO stages in between, and why the wiring between your GPUs matters as much as the strategy you choose
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Find the optimal way to interact with your coding agents
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Loop Engineering for Hierarchical Retrieval: Reading a Long Document by Its Table of Contents
Enterprise Document Intelligence [Vol.1 #7quater] - A 492-page document has a 358-entry table of contents. You can’t read it all, and top-k over every page mixes the answer with its neighbours. Route through the TOC instead: a bounded loop inside retrieval that saves tokens and lifts precision
The p...
Where Does an AI’s Personality Actually Come From?
They aren’t designed, you can’t help perceiving one anyway, and that makes them an engineering problem almost no one is solving.
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Inside the Subspace Where Spurious Correlations Are Born
Why small samples can produce large correlations by chance, and why large does not always mean meaningful
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A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers
Enterprise Document Intelligence [Vol.1 #9A] - Same paper, same question as Article 1. One upgraded contract per brick: document parsing, question parsing, retrieval, generation
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Increase the effectiveness of your coding agents through end-to-end testing.
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Validating the RAG Answer Before the User Sees It: Spans, Quotes, and the Feedback Loop
Enterprise Document Intelligence [Vol.1 #8C] - Structured output is the start of validation, not the end: check the evidence, accept not-found, loop the feedback
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AI Agents Explained: What Is a ReAct Loop and How Does It Work?
How agents reason, act, and observe their way to a final answer, one step at a time
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Long Context vs. Short Context Model: When Does a Long Context Model Win?
Balancing context capability against cost, speed, and data
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The Untaught Lessons of RAG Retrieval: Cosine Is Not the Foundation
Enterprise Document Intelligence [Vol.1 #7ter] - Six positions on the retrieval brick that contradict the cosine-first reflex of mainstream RAG
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