<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>ANTHROPIC USA — Evidence</title><description>Published analysis on verifying AI-generated code: methodology, measurement, and what the data actually supports.</description><link>https://anthropicusa.com/</link><language>en-us</language><item><title>The five questions to ask before your team adopts an AI coding assistant</title><link>https://anthropicusa.com/evidence/five-questions-before-adopting-an-ai-coding-assistant/</link><guid isPermaLink="true">https://anthropicusa.com/evidence/five-questions-before-adopting-an-ai-coding-assistant/</guid><description>Not whether to adopt — that decision is usually already made. These are the five measurements to take first, so you can tell later what actually changed.</description><pubDate>Sat, 15 Aug 2026 00:00:00 GMT</pubDate><category>adoption</category><category>measurement</category><category>engineering leadership</category></item><item><title>Measuring AI-generated code across open-source repositories: the method, published before the results</title><link>https://anthropicusa.com/evidence/measuring-ai-generated-code-in-open-source-method/</link><guid isPermaLink="true">https://anthropicusa.com/evidence/measuring-ai-generated-code-in-open-source-method/</guid><description>We are running a security and defect analysis across open-source repositories with significant AI-generated contribution. This is the methodology, published first — including what would prove us wrong.</description><pubDate>Wed, 12 Aug 2026 00:00:00 GMT</pubDate><category>method</category><category>AI code assurance</category><category>measurement</category></item></channel></rss>