📌 The Death of the “Everything Prompt”: Google’s Move Toward Structured AI

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-09 | ⏱️ Read time: 16 min read

How the new Interactions API enables deep-reasoning, stateful, agentic workflows.

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📌 The Machine Learning Lessons I’ve Learned Last Month

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-02-09 | ⏱️ Read time: 5 min read

Delayed January: deadlines, downtimes, and flow times

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🚀 Loss Functions in Machine Learning
Choosing the right loss function is not a minor detail. It directly shapes how a model learns, converges, and performs in production.

Regression and classification problems require very different optimization signals.

👉 Regression intuition
- MSE and RMSE strongly penalize large errors, which helps when large deviations are costly, such as demand forecasting.
- MAE and Huber Loss handle noise better, which works well for sensor data or real world measurements with outliers.
- Log-Cosh offers smooth gradients and stable training when optimization becomes sensitive.

👉 Classification intuition
- Binary Cross-Entropy is the default for yes or no problems like fraud detection.
- Categorical Cross-Entropy fits multi-class problems such as image or document classification.
- Sparse variants reduce memory usage when labels are integers.
- Hinge Loss focuses on decision margins and is common in SVMs.
- Focal Loss shines in imbalanced datasets like rare disease detection by focusing on hard examples.

Example:
For a credit card fraud model with extreme class imbalance, Binary Cross-Entropy often underperforms. Focal Loss shifts learning toward rare fraud cases and improves recall without sacrificing stability.

Loss functions are not interchangeable. They encode assumptions about data, noise, and business cost.

Choosing the correct one is a modeling decision, not a framework default.

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Effective Pandas 2: Opinionated Patterns for Data Manipulation

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📌 Implementing the Snake Game in Python

🗂 Category: PROGRAMMING

🕒 Date: 2026-02-10 | ⏱️ Read time: 17 min read

An easy step-by-step guide to building the snake game from scratch

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📌 How to Personalize Claude Code

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-02-10 | ⏱️ Read time: 8 min read

Learn how to get more out of Claude code by giving it access to more…

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📌 How to Model The Expected Value of Marketing Campaigns

🗂 Category: DATA SCIENCE

🕒 Date: 2026-02-10 | ⏱️ Read time: 9 min read

The approach that takes companies to the next level of data maturity

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📌 Not All RecSys Problems Are Created Equal

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-02-11 | ⏱️ Read time: 9 min read

How baseline strength, churn, and subjectivity determine complexity

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📌 Building an AI Agent to Detect and Handle Anomalies in Time-Series Data

🗂 Category: AGENTIC AI

🕒 Date: 2026-02-11 | ⏱️ Read time: 13 min read

Combining statistical detection with agentic decision-making

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📌 AI in Multiple GPUs: Understanding the Host and Device Paradigm

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-12 | ⏱️ Read time: 7 min read

Learn how CPU and GPUs interact in the host-device paradigm

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📌 How to Leverage Explainable AI for Better Business Decisions

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-02-12 | ⏱️ Read time: 10 min read

Moving beyond the black box to turn complex model outputs into actionable organizational strategies.

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📌 The Evolving Role of the ML Engineer

🗂 Category: AUTHOR SPOTLIGHTS

🕒 Date: 2026-02-13 | ⏱️ Read time: 5 min read

Stephanie Kirmer on the $200 billion investment bubble, how AI companies can rebuild trust, and…

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