RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work
Most RAG tutorials focus on retrieval or prompting. The real problem starts when context grows. This article shows a full context engineering system built in pure Python that controls memory, compression, re-ranking, and token budgets — so LLMs stay stable under real constraints.
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Data Modeling for Analytics Engineers: The Complete Primer
The best data models make it hard to ask bad questions and easy to answer good ones.
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Your Model Isn’t Done: Understanding and Fixing Model Drift
How production models fail over time, and how to catch and fix it before it breaks trust.
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By compiling a simple program directly into transformer weights.
The post I Built a Tiny Computer Inside a Transformer appeared first on Towards Data Science.
Why storing and retrieving data isn’t enough to build reliable AI memory systems
The post Stop Treating AI Memory Like a Search Problem appeared first on Towards Data Science.
A deep-dive and practical guide to cross-encoders, advanced techniques, and why your retrieval pipeline deserves a second pass.
The post Advanced RAG Retrieval: Cross-Encoders & Reranking appeared first on Towards Data Science.
Introduction to Reinforcement Learning Agents with the Unity Game Engine
A step-by-step interactive guide to one of the most vexing areas of machine learning.
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When Things Get Weird with Custom Calendars in Tabular Models
Since September 2025, we have had Calendar-based Time Intelligence in Power BI and Fabric Tabular models. While this feature offers great possibilities, we must be aware of its pitfalls. Here are some of them.
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A long-form article featuring over 100 visualizations, covering a range of topics from how to build linear regression model, measure the quality and how to improve the model
The post A Visual Explanation of Linear Regression appeared first on Towards Data Science.
The mathematical foundations of Vision-Language-Action (VLA) models for humanoid robots and more
The post How Visual-Language-Action (VLA) Models Work appeared first on Towards Data Science.
The Future of AI for Sales Is Diverse and Distributed
True creativity and innovation will come from human-agent collaboration. One human, millions of agents.
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Democratizing Marketing Mix Models (MMM) with Open Source and Gen AI
A practical system design combining open-source Bayesian MMM and GenAI for transparent, vendor independent marketing analytics insights.
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From 4 Weeks to 45 Minutes: Designing a Document Extraction System for 4,700+ PDFs
How a hybrid PyMuPDF + GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and why the latest models weren’t the answer
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How to optimize context, a precious finite resource for AI agents
The post Context Engineering for AI Agents: A Deep Dive appeared first on Towards Data Science.
The Geometry Behind the Dot Product: Unit Vectors, Projections, and Intuition
The geometric foundations you need to understand the dot product
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Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost
A new way to build vector RAG—structure-aware and reasoning-capable
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Building a Python Workflow That Catches Bugs Before Production
Using modern tooling to identify defects earlier in the software lifecycle.
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A Practical Guide to Measuring Relationships between Variables for Feature Selection in a Credit Scoring.
The post Building Robust Credit Scoring Models with Python appeared first on Towards Data Science.
When we try to train a very deep neural network model, one issue that we might encounter is the vanishing gradient problem. This is essentially a problem where the weight update of a model during training slows down or even stops, hence causing the model not to improve. When a network is very deep, ...
I Replaced Vector DBs with Google’s Memory Agent Pattern for my notes in Obsidian
Persistent AI memory without embeddings, Pinecone, or a PhD in similarity search.
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Why thinking longer can matter more than being bigger
The post How Can A Model 10,000× Smaller Outsmart ChatGPT? appeared first on Towards Data Science.
The Map of Meaning: How Embedding Models “Understand” Human Language
Learn why embedding models are like a GPS for meaning. Instead of searching for exact words, it navigates a "Map of Ideas" to find concepts that share the same vibe. From battery types to soda flavors, learn how to fine-tune these digital fingerprints for pinpoint accuracy in your next AI project.
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I’ve been so surprised by how fast individual builders can now ship real and useful prototypes. Tools like Claude Code, Google AntiGravity, and the growing ecosystem around them have crossed a threshold: you can inspect what others are building online and realize just how fast you can build today. O...
Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection
SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after the decision, and requires a background dataset you have to maintain at inference time. This article benchmarks a neuro-symbolic model that produces a deterministic, human-readable explanation in 0.9 ms — as a...