📌 Building Robust Credit Scoring Models with Python

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-04 | ⏱️ Read time: 24 min read

A Practical Guide to Measuring Relationships between Variables for Feature Selection in a Credit Scoring.

#DataScience #AI #Python

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📌 A Data Scientist’s Take on the $599 MacBook Neo

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-05 | ⏱️ Read time: 7 min read

Why it doesn’t fit my workflow but still makes sense for beginners

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📌 Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost

🗂 Category: LARGE LANGUAGE MODEL

🕒 Date: 2026-04-05 | ⏱️ Read time: 23 min read

A new way to build vector RAG—structure-aware and reasoning-capable

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📌 The Geometry Behind the Dot Product: Unit Vectors, Projections, and Intuition

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-04-06 | ⏱️ Read time: 12 min read

The geometric foundations you need to understand the dot product

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📌 How to Run Claude Code Agents in Parallel

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-04-06 | ⏱️ Read time: 11 min read

Learn how to apply coding agents in parallel to work more efficiently

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📌 Behavior is the New Credential

🗂 Category: CYBERSECURITY

🕒 Date: 2026-04-06 | ⏱️ Read time: 7 min read

We are living through a paradigm shift in how we prove we are who we…

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📌 From 4 Weeks to 45 Minutes: Designing a Document Extraction System for 4,700+ PDFs

🗂 Category: DATA ENGINEERING

🕒 Date: 2026-04-07 | ⏱️ Read time: 8 min read

How a hybrid PyMuPDF + GPT-4 Vision pipeline replaced £8,000 in manual engineering effort, and…

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📌 Context Engineering for AI Agents: A Deep Dive

🗂 Category: AGENTIC AI

🕒 Date: 2026-04-07 | ⏱️ Read time: 8 min read

How to optimize context, a precious finite resource for AI agents

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📌 The Arithmetic of Productivity Boosts: Why Does a “40% Increase in Productivity” Never Actually Work?

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-07 | ⏱️ Read time: 5 min read

Why does grand productivity promises never actually deliver? Is every product just bad, or is…

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🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning

Both code and weights are available under the MIT license on HuggingFace.

👉 Key details:

• Trained from scratch (not a finetune) on proprietary data and infrastructure
• Mixture-of-Experts (MoE) architecture

Models:

🧠 GigaChat-3.1 Ultra
• 702B MoE model for high-performance environments
• Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
• Supports FP8 training and MTP

⚡️ GigaChat-3.1 Lightning
• 10B model (1.8B active parameters)
• Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
• Efficient local inference
• Up to 256k context

Engineering highlights:

• Custom metric to detect and reduce generation loops
• DPO training moved to native FP8
• Improvements in post-training pipeline
• Identified and fixed a critical issue affecting evaluation quality

🌍 Trained on 14 languages (optimized for English and Russian)

Use cases:

• chatbots
• AI assistants
• copilots
• internal ML systems

Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.

📌 Why AI Is Training on Its Own Garbage (and How to Fix It)

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-04-08 | ⏱️ Read time: 7 min read

Deep Web Data Is the Gold We Can’t Touch, Yet

#DataScience #AI #Python