Demystifying the concept of a parameter in machine learning: what they are, how many parameters a model has, and what could possibly go wrong when learning them.
A Beginner’s Reading List for Large Language Models for 2026
The large language models (LLMs) hype wave shows no sign of fading anytime soon: after all, LLMs keep reinventing themselves at a rapid pace and transforming the industry as a whole.
In late 2025, an open-source project called Clawdbot was released. Built by Peter Steinberger, it was designed to be a practical personal AI assistant: not a chatbot, but a system that could actually do things. As it evolved, Clawdbot was renamed twice. First to Moltbot, and then to OpenClaw. You ca...
End-to-End Machine Learning Project on Amazon Sales Data Using Python
Machine learning projects work best when they connect theory to real business outcomes. In e-commerce, that means better revenue, smoother operations, and happier customers, all driven by data. By working with realistic datasets, practitioners learn how models turn patterns into decisions that actua...
LLMs aren’t limited to AI and related fields! They’re powering almost every tech, and thereby is one of the most asked about topics in interviews. This makes it essential to have a surface level familiarity of the technology. This article is designed to mirror how LLMs show up in real interviews. We...
TDS Newsletter: January Must-Reads on Data Platforms, Infinite Context, and More
Don't miss our most-read and -shared stories of the past month
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Make PPTs, PDFs, and Excel Sheets in Seconds With Kimi K2.5
Just earlier this month, Moonshot AI dropped a bomb in the AI world with the Kimi K2.5. With a 1 trillion-parameter MoE model with 32 billion active parameters, Kimi K2.5 roared onto the scene, shaking the likes of GPT 5 and Gemini 3 Pro. At the time, we had covered how it is one of […]
The post Mak...
Federated Learning, Part 2: Implementation with the Flower Framework 🌼
Implementing cross-silo federated learning step by step
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This Is How Successful Data Teams Are Using AI (Sponsored)
Successful data teams aren’t using more AI; they’re using AI differently. They embed it into workflows and decisions, employing ownership models that many SMBs haven’t adopted.
I Ditched My Mouse: How I Control My Computer With Hand Gestures (In 60 Lines of Python)
A step-by-step guide to building a “Minority Report”-style interface using OpenCV and MediaPipe
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Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning
Estimating neighborhood-level pedestrian risk from real-world incident data
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Most AI assistants still stop at conversation. They answer questions, forget everything afterward, and never actually do anything for you. Clawdbot changes that. Instead of living inside a chat window, Clawdbot runs on your own machine, stays online, remembers past interactions, and executes real ta...
Going Beyond the Context Window: Recursive Language Models in Action
Explore a practical approach to analysing massive datasets with LLMs
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Layered Architecture for Building Readable, Robust, and Extensible Apps
If adding a feature feels like open-heart surgery on your codebase, the problem isn’t bugs, it’s structure. This article shows how better architecture reduces risk, speeds up change, and keeps teams moving.
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This crash course will take you from a complete beginner to a confident ComfyUI user, walking you through every essential concept, feature, and practical example you need to master this powerful tool.
Exploring the RAG pipeline in Cursor that powers code indexing and retrieval for coding agents
The post How Cursor Actually Indexes Your Codebase appeared first on Towards Data Science.
Building Intelligent iOS Apps with Apple’s Foundation Models Framework
The iOS development world has undergone a radical change. Only a few years back, implementing AI functionalities required costly cloud APIs or, at best, on-device processing with limited capabilities. The introduction of Apple’s Foundation Models framework heralds the availability of a 3 billion par...