How to Choose Between Small and Frontier Models

Setting Up Your Own Large Language Model

Still a long way to go, but the future is promising The post Setting Up Your Own Large Language Model appeared first on Towards Data Science .

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.

LangChain vs LangGraph: 4 Key Differences and When to Use Each

A practical guide to choose the proper tool for your agentic workflows and systems The post LangChain vs LangGraph: 4 Key Differences and When to Use Each appeared first on Towards Data Science .

Understanding the capabilities, limitations, and societal impact of large language models

Economic impacts research at OpenAI

Call for expressions of interest to study the economic impacts of large language models.

How to Make Your Own JEV Model from an Open LLM

The article explains how to transform a small open‑source Qwen LLM into a fast, single‑pass text classifier by replacing its language‑modeling head with a JEV model. It provides a step‑by‑step guide to swapping the head, enabling the LLM to perform classification tasks efficiently. The process leverages the flexibility of open‑source models to create a lightweight, high‑performance classifier.

Three Kinds of RAG Corpus, and What It Costs to Build for the Wrong One

The article explains that enterprise document intelligence can be categorized into three distinct corpus types, each requiring a specific architecture. It outlines how to determine the shape of a document collection through three key questions. The piece also discusses the costs associated with building a system for the incorrect corpus type.

Backpropagation Explained for Beginners (Part 2): There Has to Be a Better Way

The idea that makes backpropagation possible. The post Backpropagation Explained for Beginners (Part 2): There Has to Be a Better Way appeared first on Towards Data Science .

How to Maximize GPT-6 Astra

The article titled "How to Maximize GPT-6 Astra" shares the author’s first impressions of OpenAI’s new frontier model. It discusses initial experiences and observations with GPT-6 Astra, offering insights into its capabilities and potential applications. The post was originally published on Towards Data Science.

Very Large Language Models and How to Evaluate Them

When to Use Claude Code and When to Use Codex

The article discusses the appropriate contexts for using Claude Code versus Codex, two coding agents. It explains the strengths and ideal use cases for each tool, helping readers decide which agent to employ for specific programming tasks. The post provides guidance on selecting the most suitable coding assistant based on the nature of the work.

Last Month’s Machine Learning Lessons Learned

The downside of conference travel The post Last Month’s Machine Learning Lessons Learned appeared first on Towards Data Science .