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 .
By Eivind Kjosbakken
The article "How to Effectively Solve 100+ Tasks with Claude Code" discusses strategies for working more efficiently with coding agents. It focuses on practical approaches to manage and complete a large number of tasks using Claude Code. The post was originally published on Towards Data Science.
By Eivind Kjosbakken
Get the most out of Claude Code with these four techniques The post 4 New Techniques to Maximize Claude Code appeared first on Towards Data Science .
By Eivind Kjosbakken
The paper introduces a dataset of the complete development history of a 21,000-line Python tool built entirely by Claude AI, accompanied by two code‑provenance tracing tools and three taxonomies for instruction intent, commit provenance, and response reliability. Analysis reveals that user CLI instructions differ from IDE‑chat instructions, focusing more on comprehension, planning, and consultation; code development is largely proactive; 14.3% of AI code‑generation events contain errors later caught by the AI‑authored test suite; and roughly one in four to five of the AI’s interactive responses contain factual errors.
A practical tutorial for recording model tool requests, real function results, patches, checks, screenshots, and a saved run log. The post How to Debug AI Coding Agents When They Change the Wrong Thing appeared first on Towards Data Science .
By Abdullahi Dattijo
arXiv:2608.30724v1 Announce Type: cross
Abstract: LLM agents are increasingly used to run autonomous ML experiments, iterating on target metrics with little human oversight. Prior work has documented...
By Pradyumna Shyama Prasad, Meiri Anto, Leon Eshuijs, Julian Moncarz, Kaustubh Kislay, Juan J. Vazquez