<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Latent Space</title><description>Towards observable, reliable, scalable AI</description><link>https://jaesolshingithub-e7h0s5hlz-ysys143s-projects.vercel.app</link><language>en</language><managingEditor>Jaesol Shin</managingEditor><webMaster>Jaesol Shin</webMaster><category>Research</category><category>Artificial Life</category><category>Complexity Science</category><category>Computational Neuroscience</category><docs>https://www.rssboard.org/rss-specification</docs><generator>Astro + @astrojs/rss</generator><copyright>Copyright 2026 Jaesol Shin</copyright><lastBuildDate>Fri, 15 May 2026 19:18:34 GMT</lastBuildDate><item><title>Life As It Could Be</title><link>https://jaesolshingithub-e7h0s5hlz-ysys143s-projects.vercel.app/posts/alife_summary</link><guid isPermaLink="true">https://jaesolshingithub-e7h0s5hlz-ysys143s-projects.vercel.app/posts/alife_summary</guid><description>A guide to the field of Artificial Life, introduced at the ALife 2025 conference exhibition &apos;Life As It Could Be&apos;.</description><pubDate>Thu, 16 Oct 2025 00:00:00 GMT</pubDate><category>Research</category><tags>artificial life, alife, complexity science, emergence, conference</tags><wordCount>7877</wordCount><language>en</language><author>Jaesol Shin</author></item><item><title>Using Free AI Models with the GitHub Models Inference API</title><link>https://jaesolshingithub-e7h0s5hlz-ysys143s-projects.vercel.app/posts/github-inference-api</link><guid isPermaLink="true">https://jaesolshingithub-e7h0s5hlz-ysys143s-projects.vercel.app/posts/github-inference-api</guid><description>How to easily call modern AI models such as GPT-4.1 and DeepSeek R1 through GitHub&apos;s free model inference API</description><pubDate>Thu, 16 Oct 2025 00:00:00 GMT</pubDate><category>Development</category><tags>GitHub, Inference API, AI, API, machine learning, GPT-4, DeepSeek, OpenAI</tags><wordCount>1467</wordCount><language>en</language><author>Jaesol Shin</author></item><item><title>Experimenting with the GPT-5 Responses API Web Search Tool</title><link>https://jaesolshingithub-e7h0s5hlz-ysys143s-projects.vercel.app/posts/gpt5-web-search-api</link><guid isPermaLink="true">https://jaesolshingithub-e7h0s5hlz-ysys143s-projects.vercel.app/posts/gpt5-web-search-api</guid><description>An experimental record of implementing web search with OpenAI&apos;s GPT-5 Responses API, focused on tool-support differences between models and how parameters shape responses. The analysis centers on web search tool compatibility between gpt-5 and gpt-5-chat-latest.</description><pubDate>Thu, 16 Oct 2025 00:00:00 GMT</pubDate><category>AI</category><tags>AI, GPT-5, API, web search, OpenAI, tutorial</tags><wordCount>1022</wordCount><language>en</language><author>Jaesol Shin</author></item><item><title>Comparative Analysis of OpenAI Models — From GPT to the o-Series</title><link>https://jaesolshingithub-e7h0s5hlz-ysys143s-projects.vercel.app/posts/openai-models-comparison</link><guid isPermaLink="true">https://jaesolshingithub-e7h0s5hlz-ysys143s-projects.vercel.app/posts/openai-models-comparison</guid><description>An experimental record of quantitative performance measurements of reasoning ability, response time, and accuracy across OpenAI language models including GPT-4, GPT-5, o1, o3, and o4.</description><pubDate>Thu, 16 Oct 2025 00:00:00 GMT</pubDate><category>Research</category><tags>OpenAI, GPT, model comparison, AI, language models, reasoning models, performance analysis</tags><wordCount>2477</wordCount><language>en</language><author>Jaesol Shin</author></item></channel></rss>