Cross-provider PR review with Codex in GitHub Actions, and why a second opinion from a different lab beats any self-review
The post Don’t Let Claude Grade Its Own Homework appeared first on Towards Data Science.
How I Mastered Data Structures and Algorithms for ML (In 6 Weeks)
The strategies, questions, and process I used to ace coding interviews.
The post How I Mastered Data Structures and Algorithms for ML (In 6 Weeks) appeared first on Towards Data Science.
Inside the Claude Fable 5 System Prompt: A Full Breakdown
THE GIST ▸ What it is: A 3,826-line system prompt steering Claude Fable 5 inside the Claude app, pulled from a public GitHub archive. ▸ What’s in it: Rules about safety, tone and restraint. ▸ Why it matters: it shows a frontier “AI” is far more an engineered rulebook than a mysterious mind. Before y...
Prompts shape every interaction with a large language model. Clear instructions produce focused, useful responses, while vague ones often lead to inconsistent results. This becomes harder when teams need the same task completed repeatedly in a fixed format, tone, or structure. Meta-prompting asks th...
A Gentle Introduction to Autoencoders & Latent Space
Introduction Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of the principal approaches to mitigate this problem is to compress input data into a lower-dimensional representation whi...
LLM Evaluation Frameworks Compared: How to Actually Measure What Your Model Does
In this article, you will learn how to evaluate LLM applications using the three dominant open-source frameworks — RAGAS, DeepEval, and Promptfoo — and why...
Context Rot: Why Claude Code Sessions Decay, and How to Govern Them
Long sessions rot quietly, well before any token limit is reached. Here’s why, and how to govern your context in Claude Code.
The post Context Rot: Why Claude Code Sessions Decay, and How to Govern Them appeared first on Towards Data Science.
How to Measure Video Similarity: 6 Techniques I Tested (and the One I Shipped)
Two short clips. One question: how alike do they look? Sounds trivial, it isn’t, and I learned that the slow way. My setup: one reference clip, eight others to rank against it, all waterfalls (more on why in a second). I figured this was an afternoon job, grab a model, compute a number, move on. […...
RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each
Two techniques, two different problems, and why the question is not really "which one wins"
The post RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each appeared first on Towards Data Science.
RAG Evaluation Frameworks Compared: RAGAS vs TruLens vs DeepEval
LLMs are getting stronger every day, and building a RAG pipeline has never been easier. Knowing whether it actually works is not. Most teams ship a RAG system, see decent-looking answers, and call it done, until users hit hallucination, missing context, or irrelevant chunks. That’s where evaluation ...
That Is Embarrassing: Why Frontier AI Still Makes Things Up, and What to Do About It
The best AI models still hallucinate. These hallucinations are sometimes funny, and sometimes cause actual damage. In this post we will consider recent tales of AI hallucinations, and then look under the hood to understand why they happen.
The post That Is Embarrassing: Why Frontier AI Still Makes T...
RAG Was Always a Temporary Workaround. What is Next?
Vector databases are a temporary bridge. Discover why the next AI infrastructure revolution relies on persistent neural state and strict latency budgets, not on vector databases.
The post RAG Was Always a Temporary Workaround. What is Next? appeared first on Towards Data Science.
Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy
A measured look at distributed training, from DDP and FSDP to the ZeRO stages in between, and why the wiring between your GPUs matters as much as the strategy you choose
The post Behind the Scenes of Distributed Training and Why Your GPU Wiring Matters as Much as Your Strategy appeared first on Towa...
Find the optimal way to interact with your coding agents
The post How to Find the Optimal Coding Agent Interface appeared first on Towards Data Science.
Loop Engineering for Hierarchical Retrieval: Reading a Long Document by Its Table of Contents
Enterprise Document Intelligence [Vol.1 #7quater] - A 492-page document has a 358-entry table of contents. You can’t read it all, and top-k over every page mixes the answer with its neighbours. Route through the TOC instead: a bounded loop inside retrieval that saves tokens and lifts precision
The p...
7 Steps to Automating Descriptive Statistics with Python
Stop writing mean() and std() for every column. Learn how to automate descriptive statistics in Python and generate publication-ready summary tables in just a few steps.
Where Does an AI’s Personality Actually Come From?
They aren’t designed, you can’t help perceiving one anyway, and that makes them an engineering problem almost no one is solving.
The post Where Does an AI’s Personality Actually Come From? appeared first on Towards Data Science.
How to Clean Messy CSV Files with Python: A Beginner’s Guide
Learn how to clean CSV files with pandas by handling missing values, duplicate rows, messy text, wrong data types, mixed date formats, invalid emails, and currency values.
Inside the Subspace Where Spurious Correlations Are Born
Why small samples can produce large correlations by chance, and why large does not always mean meaningful
The post Inside the Subspace Where Spurious Correlations Are Born appeared first on Towards Data Science.