Contents# Introduction# 1. Harvard’s CS50: Introduction to AI with Python# 2. Google’s Machine Learning Crash Course# 3. fast.ai’s Practical Deep Learning for Coders# 4. The Hugging Face NLP Course# 5. ndrej Karpathy’s Neural Networks: Zero to Hero# Wrapping Up # Introduction  The barrier to entry for learning artificial intelligence has never been lower, but the signal-to-noise ratio has never been worse. With …

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ContentsThe ScenarioThe data sourceDo it in Power QueryThis is not practicalDo it in DAXConclusionReferences The Scenario to report on seat ownership. Each seat in the office building must be assigned to an organisational unit (OU). There is a list of all seats, and the owning OU should be set for each seat. But some seats …

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    The European Commission’s latest draft guidelines provide much-needed clarity on how organizations should classify high-risk AI systems under Article 6 of the EU AI Act. However, they also raise an important question for enterprises: could your existing AI systems already be considered high-risk without you realizing it? The answer may depend on more …

Could Your AI Systems Already Be High-Risk Under the EU AI Act? Read More »

, I will give my first impressions of the newest OpenAI model, GPT-5.6. The model was released a few days ago, and I’ve gotten the chance to test it extensively since the release and compare it against other models such as GPT-5.5, Opus 4.8, and Fable 5. I’ll give my judgment on the model, share …

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  Contents# Introduction# AI Agents for Beginners (Microsoft)# Hugging Face AI Agents Course# Building Effective Agents (Anthropic)# Multiagent Systems (Shoham & Leyton-Brown)# Google & Kaggle Agents Whitepaper Series# Where to Go Next # Introduction  Everyone is building agents. Far fewer people can explain, precisely, why their agent loops forever, ignores a tool it was given, or confidently reports success on a task it …

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a marketing mix model to a senior director. The slide showed two beta coefficients side by side: Linear TV at +2.4, Digital TV at +1.8. He nodded, satisfied. Then he asked the question I was dreading. “Let’s assume we run this with last week’s refreshed data. Same channels, same model, same everything, but one extra …

Why Your Betas Explode: The Hidden Geometry of Multicollinearity Read More »

  Contents# Introduction# 1. Ollama# 2. Smolagents# 3. PydanticAI# 4. CrewAI# 5. AgentScope# 6. LangGraph# 7. Microsoft Agent Framework# Wrapping Up # Introduction  An agent that calls a cloud API for every decision is renting its intelligence. It needs a key, it racks up a bill per token, and every request leaves your machine before you get an answer back. An agent built to run …

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In my latest post on structured outputs, the three main approaches for getting machine-readable responses from an LLM. Those are JSON Mode, Function Calling, and OpenAI’s Structured Outputs. If you haven’t read that post yet, it’s worth a quick read before this one, since we’ll be building directly on top of it. So, today, we’ll …

Pydantic + OpenAI: The Cleanest Way to Get Structured Outputs from LLMs Read More »

  Contents# Introduction# 1. E-commerce Customer Churn Analysis Using SQL# 2. SQL Data Warehouse Project# 3. Sales Data Analysis Using SQL# 4. Bank Customer Segmentation Analysis# 5. Healthcare Data Analysis Using SQL# Final Thoughts # Introduction  SQL is still one of the most important skills for data analysts, data scientists, business intelligence analysts, and analytics engineers. But learning SQL syntax is only the …

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I’ve worked on, conversation state tends to grow quickly over time. It’s common to resend large portions of the history on each turn—including older tool outputs, repeated RAG retrievals, and context that’s no longer relevant. As this accumulates, prompts can become significantly larger, which may increase inference cost and latency, and in some cases affect …

Long Context Isn’t Free — I Built a Safe Prompt-Pruning Layer That Makes LLM Systems Work Read More »