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...
LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does
In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why...
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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How to Measure Video Similarity: 6 Techniques I Tested (and the One I Shipped)
Two short clips. One question: how alike do they look? Sounds trivial, it isn’t, and I learned that the slow way. My setup: one reference clip, eight others to rank against it, all waterfalls (more on why in a second). I figured this was an afternoon job, grab a model, compute a number, move on. […...
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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RAG Evaluation Frameworks Compared: RAGAS vs TruLens vs DeepEval
LLMs are getting stronger every day, and building a RAG pipeline has never been easier. Knowing whether it actually works is not. Most teams ship a RAG system, see decent-looking answers, and call it done, until users hit hallucination, missing context, or irrelevant chunks. That’s where evaluation ...
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...
7 Steps to Automating Descriptive Statistics with Python
Stop writing mean() and std() for every column. Learn how to automate descriptive statistics in Python and generate publication-ready summary tables in just a few steps.
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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How to Clean Messy CSV Files with Python: A Beginner’s Guide
Learn how to clean CSV files with pandas by handling missing values, duplicate rows, messy text, wrong data types, mixed date formats, invalid emails, and currency values.
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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Zero-Shot Local Document Parsing with Gemma 4: Treating PDFs as Images
Treating PDFs as images and feeding those images to Gemma 4 dissolves the scanned-versus-digital distinction that makes every text-extraction pipeline fragile. Fix that.
Increase the effectiveness of your coding agents through end-to-end testing.
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