The article compares Claude Code and Cursor by presenting a field test that reveals differing token usage: one benchmark shows Claude Code using 33,000 tokens versus Cursor’s 188,000, while another benchmark shows the opposite. The post aims to provide a definitive comparison for teams deciding between the two tools.
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.
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Finding the right anchors for RAG: keyword, embedding, and TOC signals in parallel appeared first on Towards Data Science .
Testing fourteen engines on ninety-three human documents The post I Spent May Evaluating Different Engines for OCR appeared first on Towards Data Science .
Maximize your efficiency with Claude Code The post How to Efficiently Prompt Claude Code appeared first on Towards Data Science .
Enterprise Document Intelligence [Vol. 1 #7B] - Retrieval is filtering on structured tables: keywords first, TOC second, embeddings last The post Anchor Detection for RAG: Parallel Detectors, Then One LLM Call at the End appeared first on Towards Data Science .
The article titled "Why Claude Code Time Estimates Are Poor" discusses the challenges and shortcomings of using Claude, an LLM, for estimating code development time. It highlights how these estimates can be unreliable and offers insights into improving communication when working with LLM programming tools.
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.
I built four AI retrieval architectures on a laptop and benchmarked them against the same set of documents and questions. Here’s what the results taught me about the trade-offs between plain RAG, grap...
Increase the effectiveness of your coding agents through end-to-end testing. The post How to Run End-to-End Tests with Claude Code appeared first on Towards Data Science .
The article discusses how large language models (LLMs) undermine trust in documentation, software, and the organizations that produce them, making it easier to counterfeit signals of a mature system. It questions what maintainers can do to ensure software correctness and accurate documentation in the age of LLMs, and how they can demonstrate this quality to users.
Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does.
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 .