📌 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

Today, the public mint for Lobsters on TON goes live on Getgems 🦞

This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.

Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.

So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.

What makes this especially interesting is the timing.

In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:

Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility

Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.

Public mint: today, 16:00
Price: 50 TON

👉 Mint your Lobster on Getgems 🦞🦞🦞

📌 How to Run OpenClaw with Open-Source Models

🗂 Category: LARGE LANGUAGE MODELS

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

Run OpenClaw assistant through alternative LLMs

#DataScience #AI #Python

📌 Using a Local LLM as a Zero-Shot Classifier

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-04-23 | ⏱️ Read time: 8 min read

A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted…

#DataScience #AI #Python

A trusted platform for cryptocurrency enthusiasts and reliable trading.

📌 I Simulated an International Supply Chain and Let OpenClaw Monitor It

🗂 Category: AGENTIC AI

🕒 Date: 2026-04-23 | ⏱️ Read time: 9 min read

Mario asked me why 18% of his shipments were late when every team hit their…

#DataScience #AI #Python

📌 Your Synthetic Data Passed Every Test and Still Broke Your Model

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-23 | ⏱️ Read time: 11 min read

The silent gaps in synthetic data that only show up when your model is already…

#DataScience #AI #Python

📌 Lasso Regression: Why the Solution Lives on a Diamond

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-04-23 | ⏱️ Read time: 24 min read

It’s simpler than you think.

#DataScience #AI #Python

📌 Introduction to Approximate Solution Methods for Reinforcement Learning

🗂 Category: MACHINE LEARNING

🕒 Date: 2026-04-24 | ⏱️ Read time: 9 min read

Learn about function approximation and the different choices for approximation functions

#DataScience #AI #Python

📌 I Built an AI Pipeline for Kindle Highlights

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2026-04-24 | ⏱️ Read time: 12 min read

A local, zero-cost project that cleans, structures, and summarizes your reading automatically

#DataScience #AI #Python

📌 How to Improve Claude Code Performance with Automated Testing

🗂 Category: AGENTIC AI

🕒 Date: 2026-04-24 | ⏱️ Read time: 10 min read

Learn how to get the most out of Claude Code

#DataScience #AI #Python

📌 How to Select Variables Robustly in a Scoring Model

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-24 | ⏱️ Read time: 7 min read

More variables don’t make a better scoring model. Stable variables do. Here’s how to find them.

#DataScience #AI #Python

📌 Causal Inference Is Different in Business

🗂 Category: DATA SCIENCE

🕒 Date: 2026-04-25 | ⏱️ Read time: 12 min read

How does decision-gravity dictate this gap?

#DataScience #AI #Python

📌 The Essential Guide to Effectively Summarizing Massive Documents, Part 2

🗂 Category: LLM APPLICATIONS

🕒 Date: 2026-04-25 | ⏱️ Read time: 18 min read

We have the document clusters, and it’s time to unlock their true potential! Let’s explore…

#DataScience #AI #Python

13+ attention mechanisms worth knowing:

▪️ self-attention
▪️ cross-attention
▪️ causal attention
▪️ linear attention
▪️ attention with softmax
▪️ sliding window (local attention)
▪️ global attention
▪️ FlashAttention
▪️ multi-headed attention (MHA)
▪️ multi-query attention (MQA)
▪️ grouped attention by queries (GQA)
▪️ multi-headed latent attention (MLA)
▪️ interleaved heads attention (IHA)

Slim Attention, KArAt, XAttention, Mixture-of-Depths Attention (MoDA)
https://t.iss.one/DataScienceM

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