📌 Building a Python Workflow That Catches Bugs Before Production

🗂 Category: PROGRAMMING

🕒 Date: 2026-04-04 | ⏱️ Read time: 17 min read

Using modern tooling to identify defects earlier in the software lifecycle.

#DataScience #AI #Python

📌 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.

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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

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📌 Detecting Translation Hallucinations with Attention Misalignment

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-04-08 | ⏱️ Read time: 15 min read

A low-budget way to get token-level uncertainty estimation for neural machine translations

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📌 How to Use Claude Code to Build a Minimum Viable Product

🗂 Category: AGENTIC AI

🕒 Date: 2026-04-08 | ⏱️ Read time: 8 min read

Learn how to effectively present product ideas by building MVPs with coding agents

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✔️ 10 Books to Understand How Large Language Models Function (2026)

1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.

2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.

3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.

4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.

5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.

6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.

7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.

8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.

9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.

10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. 📚🤖

📌 Grounding Your LLM: A Practical Guide to RAG for Enterprise Knowledge Bases

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-04-08 | ⏱️ Read time: 17 min read

A clear mental model and a practical foundation you can build on

#DataScience #AI #Python