<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2301.05108/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2301.05108/feed.xml" rel="self" type="application/rss+xml" /><item><title>Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning — mentioned by synthiq</title><link>https://news.ycombinator.com/item?id=36178122</link><guid isPermaLink="false">urn:hackerpapers:mention:2301.05108:36178122</guid><pubDate>Sat, 03 Jun 2023 16:39:45 GMT</pubDate><description>&lt;p&gt;For anyone interested in related research, I used https://mirrorthink.ai to find some background on the state-of-the-art. (disclaimer: this is AI generated, but grounded on contents of papers, with real references, so I&amp;#x27;d say it is still constructive) The state-of-the-art in code generation has seen significant advancements with the deployment of large language models (LLMs) in various code authoring tools. One such example is the study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT [1], which evaluates the code quality of these AI-assisted code generation tools. The study reveals that…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2301.05108/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.PL</category></item></channel></rss>
