<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Machine-Learning on Onur Baydaş</title><link>https://onurbaydas.com/en/tags/machine-learning/</link><description>Recent content in Machine-Learning on Onur Baydaş</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 17 Oct 2026 12:15:22 +0300</lastBuildDate><atom:link href="https://onurbaydas.com/en/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>AutoSynthData: Generating Training Data for Enterprise Agents</title><link>https://onurbaydas.com/en/news/2026-10-17-autosynthdata-enterprise-agent-training/</link><pubDate>Sat, 17 Oct 2026 12:15:22 +0300</pubDate><guid>https://onurbaydas.com/en/news/2026-10-17-autosynthdata-enterprise-agent-training/</guid><description>AutoSynthData is a tool focused on generating training data for enterprise agents. This tool aims to create synthetic data to improve the performance of AI models.</description></item><item><title>EmbeddingGemma 2: Open, Lightweight Multimodal Embedding Model</title><link>https://onurbaydas.com/en/news/2026-10-17-embedding-gemma-2-multimodal-model/</link><pubDate>Sat, 17 Oct 2026 12:15:22 +0300</pubDate><guid>https://onurbaydas.com/en/news/2026-10-17-embedding-gemma-2-multimodal-model/</guid><description>Google DeepMind announced EmbeddingGemma 2, a new open-source and lightweight multimodal embedding model. This model aims to represent different data types (text, image, etc.) in a single vector space.</description></item><item><title>Google EnvHarness Enables Self-Evolving Agents to Learn in Dynamics Environments</title><link>https://onurbaydas.com/en/news/2026-10-17-google-envharness-self-evolving-agents/</link><pubDate>Sat, 17 Oct 2026 12:15:22 +0300</pubDate><guid>https://onurbaydas.com/en/news/2026-10-17-google-envharness-self-evolving-agents/</guid><description>Google researchers open-sourced EnvHarness, a framework that dynamically adapts agent-training environments to an agent&amp;#39;s weaknesses. This system enables learning without modifying the agent, environment, or evaluation system.</description></item><item><title>Qwen3.8-27B-Defiance: Uncensored and Modded Local LLM</title><link>https://onurbaydas.com/en/news/2026-10-17-qwen-defiance-uncensored-llm/</link><pubDate>Sat, 17 Oct 2026 12:15:22 +0300</pubDate><guid>https://onurbaydas.com/en/news/2026-10-17-qwen-defiance-uncensored-llm/</guid><description>This model combines an uncensored version of Qwen3.8-27B with various &amp;#34;AI Construct&amp;#34; modes. Users can select a specific personality mode for each request, and the model provides uncensored responses.</description></item><item><title>Developer Adoption of AI Agents: The Role of Trust</title><link>https://onurbaydas.com/en/news/2026-10-16-ai-agent-adoption-trust/</link><pubDate>Fri, 16 Oct 2026 00:17:33 +0300</pubDate><guid>https://onurbaydas.com/en/news/2026-10-16-ai-agent-adoption-trust/</guid><description>Brendan O&amp;#39;Leary discusses how real-world AI adoption depends on building trust rather than solely chasing model benchmarks. This presentation highlights what 25 trillion tokens reveal about how developers actually adopt AI agents.</description></item></channel></rss>