MCP is a key enabler into turning your LLM into an agent by providing it with tools to retrieve real-time information or perform actions.
In this deep dive we cover how MCP works, when to use it, and what to watch out for.
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Agentic AI Swarm Optimization using Artificial Bee Colonization (ABC)
Using Agentic AI prompts with the Artificial Bee Colony algorithm to enhance unsupervised clustering and optimization workflows.
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Six Lessons Learned Building RAG Systems in Production
Best practices for data quality, retrieval design, and evaluation in production RAG systems
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4 Ways to Supercharge Your Data Science Workflow with Google AI Studio
With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicate clearer, and automate quicker.
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A Practical Toolkit for Time Series Anomaly Detection, Using Python
Here's how to detect point anomalies within each series, and identify anomalous signals across the whole bank
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The Machine Learning “Advent Calendar” Day 17: Neural Network Regressor in Excel
Neural networks often feel like black boxes. In this article, we build a neural network regressor from scratch using only Excel formulas. By making every step explicit, from forward propagation to backpropagation, we show how a neural network learns to approximate non-linear functions with just a ha...
Production-Grade Observability for AI Agents: A Minimal-Code, Configuration-First Approach
LLM-as-a-Judge, regression testing, and end-to-end traceability of multi-agent LLM systems
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3 Techniques to Effectively Utilize AI Agents for Coding
Learn how to be an effective engineer with coding agents
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The Machine Learning “Advent Calendar” Day 13: LASSO and Ridge Regression in Excel
Ridge and Lasso regression are often perceived as more complex versions of linear regression. In reality, the prediction model remains exactly the same. What changes is the training objective. By adding a penalty on the coefficients, regularization forces the model to choose more stable solutions, e...