How to diversify a budget without losing the shadow prices that explain the result
The post The Budget Split That Explains Itself appeared first on Towards Data Science.
First, pick the line that applies to you. Since August 2nd, 2026, Claude marks all content during generation. For instance, text receives a hidden watermark, while files receive a signature. Anthropic committed to the EU AI Act’s Code of Practice on Transparency of AI-Generated Content. Consequently...
You have probably heard by now. Claude Code burns through usage limits! But most of us live in the web app… distant from the terminal app, around which the buzz is about. Maybe that was enough to make you curious. Maybe you already knew exactly what it was and just want it running on your […]
The po...
Learn how to optimize your CI/CD pipeline for coding agents
The post How to Effectively Deploy Code With Claude Code appeared first on Towards Data Science.
Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick
A clear, math-first walkthrough of how VAEs learn to generate new data
The post Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick appeared first on Towards Data Science.
Prompt Caching vs. Fine-Tuning: A Cost and Latency Decision Framework
In this article, you will learn how prompt caching and fine-tuning differ as strategies for reducing cost and latency in agentic AI systems, and how...
SPP-Net Paper Walkthrough: Breaking the Fixed-Size Constraint
Learn how Spatial Pyramid Pooling enables CNNs to handle any image size, with a from-scratch PyTorch implementation
The post SPP-Net Paper Walkthrough: Breaking the Fixed-Size Constraint appeared first on Towards Data Science.
Top 5 Claude Skills for Marketing Ranked by GitHub Stars
Claude can write an ad or email from a prompt. This is usually done manually. Useful, but hardly a coherent system. The work still needs research, positioning, channel planning, quality checks, and reporting. Claude’s marketing skills add to those missing processes. However, search results mix dedic...
How to Implement Structured Output with Local LLMs
Why use it? How to implement it? What can we do when it fails?
The post How to Implement Structured Output with Local LLMs appeared first on Towards Data Science.
Before Q, K, and V: Reconstructing the Transformer
Many Transformer explainers start with the finished architecture. We ask why it looks the way it does.
The post Before Q, K, and V: Reconstructing the Transformer appeared first on Towards Data Science.
Matplotlib vs Plotly: Which Python Chart Tool Should You Choose?
From Static Plots to Interactive Data Exploration
The post Matplotlib vs Plotly: Which Python Chart Tool Should You Choose? appeared first on Towards Data Science.
Loop Engineering for Listing Questions: When the Answer Is Every Passage, Not the Top One
Enterprise Document Intelligence [Vol.1 #12] - The category of question most RAG pipelines silently fail on, and the pipeline shape that handles them
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The Problem with pandas Isn’t Performance. It’s Cognitive Overhead.
Faster dataframe engines are nice, but they don't reduce the amount of syntax an analyst has to hold in their head.
The post The Problem with pandas Isn’t Performance. It’s Cognitive Overhead. appeared first on Towards Data Science.
The real skill isn’t getting AI to answers! But to do so in a manner that fits our budgets and fulfils our requirements. It’s guiding it with clear context and turning its output into useful action. This list is built around a simpler idea. Instead of searching through thousands of skills, you start...
Claude Code Best Practices: 3 Lessons from 400,000 Sessions
I used to think Claude Code best practices were a matter of taste. Plan mode or not. Long CLAUDE.md or short. Pick what suits you, move on. Then Anthropic scored roughly 400k sessions from over 235k users against hard evidence of success. Tests passing, commits landing, users confirming they got wha...
Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.2’ Instead of the Actual Answer
Enterprise Document Intelligence [Vol.1 #11] - When the first answer points elsewhere in the document, the pipeline loops back to fetch the linked context
The post Loop Engineering for Cross-References: When RAG Answers ‘see Section 7.2’ Instead of the Actual Answer appeared first on Towards Data Sc...
How a Frontier Model Gets Built, Read from the Kimi K3 Report
An open, 2.8-trillion-parameter model shipped with 47 pages of its own recipe. Reading it tells you what building a frontier model now involves, and how little of it is the model.
The post How a Frontier Model Gets Built, Read from the Kimi K3 Report appeared first on Towards Data Science.
A primer about Semi-Supervised Learning, the approaches taken with different algorithms and the limitations of using unlabelled data.
The post Introduction to Semi-Supervised Learning appeared first on Towards Data Science.