<?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>Google on danilchenko.dev</title><link>https://www.danilchenko.dev/tags/google/</link><description>Recent content in Google on danilchenko.dev</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 08 May 2026 08:24:33 +0000</lastBuildDate><atom:link href="https://www.danilchenko.dev/tags/google/index.xml" rel="self" type="application/rss+xml"/><item><title>Gemini CLI Tutorial: Setup, Configuration, and a Real Python Project</title><link>https://www.danilchenko.dev/posts/gemini-cli-tutorial/</link><pubDate>Fri, 08 May 2026 08:24:33 +0000</pubDate><guid>https://www.danilchenko.dev/posts/gemini-cli-tutorial/</guid><description>Set up Google&amp;#39;s free Gemini CLI in 5 minutes, configure GEMINI.md, add MCP servers, and build a Python project — all on the 1,000 requests/day free tier.</description></item><item><title>How to Run Gemma 4 Locally With Ollama, llama.cpp, and vLLM</title><link>https://www.danilchenko.dev/posts/2026-04-07-run-gemma-4-locally-ollama-llama-cpp-vllm/</link><pubDate>Tue, 07 Apr 2026 06:00:00 +0000</pubDate><guid>https://www.danilchenko.dev/posts/2026-04-07-run-gemma-4-locally-ollama-llama-cpp-vllm/</guid><description>Step-by-step guide to running Google Gemma 4 locally on your hardware with Ollama, llama.cpp, and vLLM — including model picks, VRAM requirements, and real gotchas.</description></item><item><title>Google's TurboQuant Compresses LLM Memory 6x With Zero Accuracy Loss — Here's How It Works</title><link>https://www.danilchenko.dev/posts/2026-03-27-google-turboquant-llm-compression-6x-zero-accuracy-loss/</link><pubDate>Fri, 27 Mar 2026 06:00:00 +0000</pubDate><guid>https://www.danilchenko.dev/posts/2026-03-27-google-turboquant-llm-compression-6x-zero-accuracy-loss/</guid><description>Google&amp;#39;s TurboQuant algorithm compresses LLM KV cache memory by 6x with zero accuracy loss and no retraining needed. We break down the ICLR 2026 paper.</description></item></channel></rss>