📌 System Design Series: Apache Flink from 10,000 Feet, and Building a Flink-powered Recommendation Engine

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-29 | ⏱️ Read time: 17 min read

A deep dive into how Apache Flink works, why it exists, and learning it while…

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📌 4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers

🗂 Category: DATA ENGINEERING

🕒 Date: 2026-04-29 | ⏱️ Read time: 10 min read

How we replaced Python pipelines with dlt, dbt, and Trino — and cut delivery time…

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📌 A Gentle Introduction to Stochastic Programming

🗂 Category: MATHEMATICS

🕒 Date: 2026-04-30 | ⏱️ Read time: 15 min read

How to make decisions when your spreadsheet is lying about the future

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📌 Proxy-Pointer RAG: Multimodal Answers Without Multimodal Embeddings

🗂 Category: LARGE LANGUAGE MODEL

🕒 Date: 2026-04-30 | ⏱️ Read time: 15 min read

Structure is all you need

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📌 How to Study the Monotonicity and Stability of Variables in a Scoring Model using Python

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-30 | ⏱️ Read time: 10 min read

How can you validate that your variables tell a consistent risk?

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📌 Why AI Engineers Are Moving Beyond LangChain to Native Agent Architectures

🗂 Category: AGENTIC AI

🕒 Date: 2026-04-30 | ⏱️ Read time: 8 min read

Frameworks accelerated the first wave of LLM apps, but production demands a different architecture.

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🗂 Category: CAREER ADVICE

🕒 Date: 2026-05-01 | ⏱️ Read time: 7 min read

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📌 Churn Without Fragmentation: How a Party-Label Bug Reversed My Headline Finding

🗂 Category: DATA SCIENCE

🕒 Date: 2026-05-01 | ⏱️ Read time: 11 min read

A data quality case study from English local elections on categorical normalisation, metric validation, and…

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📌 Ghost: A Database for Our Times?

🗂 Category: AGENTIC AI

🕒 Date: 2026-05-01 | ⏱️ Read time: 12 min read

The first database built for AI Agents

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🚀 Master Binary Classification with Neural Networks! 🧠✨

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Pandas vs Polars vs DuckDB: Which Library Should You Choose? 🤔📊

pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows 📝📈. Polars focus on fast, memory-efficient DataFrame processing ⚡💾, while DuckDB brings a SQL-first approach for querying local files and embedded analytics 🗄️🔍.

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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼

The chain looks neat, but breaks on real data and easily gives unexpected results:

s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]

The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯

It's more reliable to gather everything into one expression:

s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]

One mask, one point of truth. ✅

It's easier to debug. Fewer surprises when the code grows. 🚀

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A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy 🚀. In many cases, it's enough to replace a single line:

import cupy as cp

The same code can run on CUDA up to 100 times faster ⚡️.

What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.

The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy

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Don't learn ML by randomly jumping through tutorials. 🚫📚

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