ContentsTL;DRThe Breaking Point of RAG SystemsWhat Context Engineering Actually IsWho This Is ForFull Pipeline ArchitectureComponent 1: The RetrieverComponent 2: The Re-rankerComponent 3: Memory with Exponential DecayDeduplicationToken Budget Under PressureComponent 4: Context CompressionComponent 5: The Token Budget EnforcerWhat Happens Under Real Token PressureMeasuring What It Actually Buys YouMemory Decay by Importance ScorePerformance CharacteristicsHonest Design DecisionsTrade-offs and …

RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work Read More »

Image by Author   Contents# Introduction# Understanding the .claude Folder# Establishing Why the Folder Is Created# Analyzing Common Files and Structure# Explaining How the Folder Operates# Evaluating the Deletion of the .claude Folder# Implementing Best Practices for Management# Identifying Common Developer Mistakes# Concluding Thoughts # Introduction  You open a project, run a Claude-powered tool, and suddenly, there is a new folder sitting in your directory …

Breaking Down the .claude Folder Read More »

, my AI assistant stored a memory with an importance score of 8/10. Content: “Investigating Bun.js as a potential runtime swap.” I never actually switched to Bun. To be fair, it was a two-day curiosity that went nowhere. But this memory persisted for six months, popping up each time I asked about my build process …

Stop Treating AI Memory Like a Search Problem Read More »

Image by Editor   Contents# Introduction# 1. Surgical Precision with Prompt-Based Slide Revisions# 2. Bridging the Gap with PPTX Export# 3. High-Fidelity Synthesis via Cinematic Video Overviews# 4. Frictionless Artifact Creation Directly from Chat# 5. Ingesting Scale: EPUB and Long-Form Source Support# The End-to-End Power Workflow # Introduction  Google NotebookLM has evolved far beyond a simple study aid. With the addition of the …

Advanced NotebookLM Tips & Tricks for Power Users Read More »

ContentsIntroductionSetup of the CalendarsPrevious Months and different month lengthsWhat happens with the previous year?Weekly calculations – Head scratchingMixing weekly with monthly logicWeekly calculations – That’s interesting!ConclusionReferences Introduction After the initial euphoria with the new calendar-based time intelligence, I started looking deeper into the new feature to see what these new possibilities mean in the real …

When Things Get Weird with Custom Calendars in Tabular Models Read More »

Image by Editor   Contents# Introduction# Understanding Method Chaining# Entering Pyjanitor: Application Example# Wrapping Up # Introduction  Working intensively with data in Python teaches all of us an important lesson: data cleaning usually doesn’t feel much like performing data science, but rather like acting as a digital janitor. Here’s what it takes in most use cases: loading a dataset, discovering …

All About Pyjanitor’s Method Chaining Functionality, And Why Its Useful Read More »

in AI for a while, you are probably an LLM/Agent/Chat user, but have you ever asked yourself how these tools will be trained in the near future, and what if we have already used up the data we need to train models? Many theories say that we are running out of high-quality, human-generated data to …

Why AI Is Training on Its Own Garbage (and How to Fix It) Read More »

Image by Author   Contents# Introduction# Firebase at a Glance# Supabase at a Glance# Comparing Core Features# Choosing Between Firebase and Supabase# Wrapping Up # Introduction  Choosing a backend is one of the most important decisions you will make when building a modern web or mobile app. For years, Firebase has been the go-to choice for developers who want to launch quickly …

Supabase vs Firebase: Which Backend Is Right for Your Next App? Read More »

This article is the first of three parts. Each part stands on its own, so you don’t need to read the others to understand it. The dot product is one of the most important operations in machine learning – but it’s hard to understand without the right geometric foundations. In this first part, we build …

The Geometry Behind the Dot Product: Unit Vectors, Projections, and Intuition Read More »

Image by Author   Contents# Introduction# Understanding Simpson’s Paradox// Demonstrating With A/B Test Data# Identifying Selection Bias// Simulating Survey Non-Response# Preventing p-Hacking// Watching False Positives Accumulate# Managing Multiple Testing# Addressing Confounding Variables// Simulating A Confounded Relationship# Wrapping Up # Introduction  When applying for a job at Meta (formerly Facebook), Apple, Amazon, Netflix, or Alphabet (Google) — collectively known as FAANG — interviews rarely test whether you can …

The Most Common Statistical Traps in FAANG Interviews Read More »