📌 What It Actually Takes to Run Code on 200M€ Supercomputer

🗂 Category: DISTRIBUTED COMPUTING

🕒 Date: 2026-04-16 | ⏱️ Read time: 11 min read

Inside MareNostrum V: SLURM schedulers, fat-tree topologies, and scaling pipelines across 8,000 nodes in a…

#DataScience #AI #Python

📌 Your RAG System Retrieves the Right Data — But Still Produces Wrong Answers. Here’s Why (and How to Fix It).

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-04-18 | ⏱️ Read time: 17 min read

Your RAG system is retrieving the right documents with perfect scores — yet it still…

#DataScience #AI #Python

📌 Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval

🗂 Category: LARGE LANGUAGE MODEL

🕒 Date: 2026-04-19 | ⏱️ Read time: 14 min read

Open source. 5-minute setup. Vector RAG done right—try it yourself.

#DataScience #AI #Python

📌 Dreaming in Cubes

🗂 Category: DEEP LEARNING

🕒 Date: 2026-04-19 | ⏱️ Read time: 10 min read

Generating Minecraft Worlds with Vector Quantized Variational Autoencoders (VQ-VAE) and Transformers

#DataScience #AI #Python

📌 KV Cache Is Eating Your VRAM. Here’s How Google Fixed It With TurboQuant.

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-04-19 | ⏱️ Read time: 11 min read

Explore the end-to-end pipeline of TurboQuant, a novel KV cache quantization framework. This overview breaks…

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📌 What Does the p-value Even Mean?

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-20 | ⏱️ Read time: 7 min read

And what does it tell us?

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📌 Context Payload Optimization for ICL-Based Tabular Foundation Models

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-04-20 | ⏱️ Read time: 16 min read

Conceptual overview and practical guidance

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📌 The LLM Gamble

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2026-04-20 | ⏱️ Read time: 8 min read

Why it tickles your brain to use an LLM, and what that means for the…

#DataScience #AI #Python

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📌 From Risk to Asset: Designing a Practical Data Strategy That Actually Works

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-20 | ⏱️ Read time: 11 min read

How to turn data into a strategic asset that enables faster decisions, reduces uncertainty, and…

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📌 DIY AI & ML: Solving The Multi-Armed Bandit Problem with Thompson Sampling

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-04-21 | ⏱️ Read time: 17 min read

How you can build your own Thompson Sampling Algorithm object in Python and apply it…

#DataScience #AI #Python

📌 Git UNDO : How to Rewrite Git History with Confidence

🗂 Category: PROGRAMMING

🕒 Date: 2026-04-21 | ⏱️ Read time: 24 min read

For any data scientist who works in a team, being able to undo Git actions…

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📌 How to Call Rust from Python

🗂 Category: PROGRAMMING

🕒 Date: 2026-04-21 | ⏱️ Read time: 10 min read

A guide to bridging the gap between ease of use and raw performance.

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🔥 Google Colab has added the option of retraining 500+ open-source neural networks

Unsloth has released a convenient notebook for configuring models.

Instructions:

1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb

2. Run the blocks and the Unsloth Studio itself.

3. Select a model and a dataset.

4. Click "Start Training" and monitor the progress in real time.

5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.

📌 I Replaced GPT-4 with a Local SLM and My CI/CD Pipeline Stopped Failing

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-04-21 | ⏱️ Read time: 13 min read

The hidden cost of probabilistic outputs in systems that demand reliability

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📌 Your RAG Gets Confidently Wrong as Memory Grows – I Built the Memory Layer That Stops It

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-04-21 | ⏱️ Read time: 15 min read

As memory grows in RAG systems, accuracy quietly drops while confidence rises — creating a…

#DataScience #AI #Python

📌 Using Causal Inference to Estimate the Impact of Tube Strikes on Cycling Usage in London

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-22 | ⏱️ Read time: 19 min read

Turning free-to-use data into a hypothesis-ready dataset

#DataScience #AI #Python

📌 Correlation vs. Causation: Measuring True Impact with Propensity Score Matching

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-22 | ⏱️ Read time: 12 min read

Learn how Propensity Score Matching uncovers true causality in observational data. By finding “statistical twins,”…

#DataScience #AI #Python

11 Plots Data Scientists Use 90% of the Time 📊🚀

Here’s the secret → Data scientists don’t actually use 100+ types of charts. 🤫

When real decisions are on the line, it always comes back to the same 11.

https://t.iss.one/DataScienceM

📌 From Ad Hoc Prompting to Repeatable AI Workflows with Claude Code Skills

🗂 Category: AGENTIC AI

🕒 Date: 2026-04-22 | ⏱️ Read time: 8 min read

How I turned LLM persona interviews into a repeatable customer research workflow

#DataScience #AI #Python

📌 Ivory Tower Notes: The Methodology

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-22 | ⏱️ Read time: 6 min read

A short intro to scientific methodology to combat “prompt in, slop out”

#DataScience #AI #Python