I Rewrote a Real Data Workflow in Polars. Pandas Didn’t Stand a Chance.
From 61 seconds to 0.20 seconds — and the mental model shift I didn't expect
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When the Uncertainty Is Bigger Than the Shock: Scenario Modelling for English Local Elections
A scenario analysis case study on calibrated uncertainty, historical error, and why some models are most useful when they refuse to forecast.
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Anthropic’s 10 AI Agents are Redefining Finance Work
The headline may sound extreme here. Of course, Claude is not replacing CFOs tomorrow morning. But with the debut of Claude’s new Financial Services Solution by Anthropic, it has clearly moved to a new direction in the world of finance, one where AI does way more than crunch numbers or explain stuff...
Timer-XL: A Long-Context Foundation Model for Time-Series Forecasting
Exploring the inner workings of a decoder-only Transformer foundation model
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Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and im...
Discrete Time-To-Event Modeling – Predicting When Something Will Happen
Part 1: The basics — discretization of time, censoring and the life table
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RAG Hallucinates — I Built a Self-Healing Layer That Fixes It in Real Time
Your RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations before they reach users.
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Part 2. Building scale-invariant agents that seamlessly change contexts
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How AI Tools Generate Technical Debt in IoT Systems — and What to Do About It
AI tools speed up IoT development — but closer to the hardware, the same code that looks correct can silently break thousands of devices at once.
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ML Intern in Practice: From Prompt to a Shipped Hugging Face Model
Most ML projects do not fail because of model choice. They fail in the messy middle: finding the right dataset, checking usability, writing training code, fixing errors, reading logs, debugging weak results, evaluating outputs, and packaging the model for others. This is where ML Intern fits. It is ...
Projects are the bridge between understanding AI and actually building with it. While the last couple of years were dominated by generative models, the shift now is toward systems that can think in steps, use tools, and act with a clear objective. This guide brings together over 15 solved agentic AI...
Which Regularizer Should You Actually Use? Lessons from 134,400 Simulations
A practitioner's decision framework for Ridge, Lasso, and ElasticNet based on three quantities you can compute before fitting a model
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AI chatbots are the new norm. What earlier was “ask Google” has now largely become “ask Claude”. And that is not just a change of platforms. The new form of conversational guidance goes a whole lot deeper than trying to find the best car for you or looking for an upskilling course. It now spills […]...
How a 2021 Quantization Algorithm Quietly Outperforms Its 2026 Successor
One scale parameter determines accuracy in rotation-based vector quantization.
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The “Robust” Data Scientist: Winning with Messy Data and Pingouin
This article uncovers the craftsmanship of using robust statistics in data science processes: illustrating what to do when data fail tests due to not meeting standard assumptions.
MemPalace Explained: Building Long-Term Memory for AI Agents Beyond RAG
Modern AI systems struggle with memory. They often forget past interactions or rely on Retrieval-Augmented Generation (RAG), which depends on constant access to external data. This becomes a limitation when building assistants that need both historical context and a deeper understanding of users. Me...
Learn how the Voxtral TTS model works, what makes its voice cloning and low‑latency performance special, and how to start generating speech with just a few lines of Python code.