This document details how to run Gemma models, covering framework selection, variant choice, and running generation/inference requests. It emphasizes considering available hardware resources and provides recommendations for beginners.
Google's Gemini 2.5 Flash model is a new, faster, and more cost-effective model with adjustable 'thinking' capabilities. The article details how to use it with llm-gemini, explores pricing differences compared to Gemini 2.0 Flash, and shares example SVG outputs.
Google’s John Mueller downplayed the usefulness of LLMs.txt, comparing it to the keywords meta tag, as AI bots aren’t currently checking for the file and it opens potential for cloaking.
Google Code Assist, now powered by Gemini 2.5, shows significant improvement in coding capabilities and introduces AI agents to assist across the software development lifecycle. The article details the features available in the free, standard, and enterprise tiers, and raises questions about agent availability and practical implementation.
Google releases Gemma 3, a new iteration of their Gemma family of models. It ranges from 1B to 27B parameters, supports up to 128k tokens, accepts images and text, and supports 140+ languages. This article details its technical enhancements (longer context, multimodality, multilinguality) and provides information on inference with Hugging Face transformers, on-device deployment, and evaluation.
The Gemini API documentation provides comprehensive information about Google's Gemini models and their capabilities. It includes guides on generating content with Gemini models, native image generation, long context exploration, and generating structured outputs. The documentation offers examples in Python, Node.js, and REST for using the Gemini API, covering various applications like text and image generation, and integrating Gemini in Google AI Studio.
Google's John Mueller discusses the issue of low-effort content that may look good but lacks genuine expertise, particularly noting the use of AI-generated images as a potential signal of low-quality content.
Google has released Gemini Code Assist for free, offering up to 180,000 code completions per month, significantly more than its competitors like GitHub Copilot.
This article explores the use of Google's NotebookLM (NLM) as a tool for research, particularly in analyzing the impact of the Aswan High Dam on schistosomiasis in Egypt. The author details how NLM can be used to create a research assistant-like experience, allowing users to 'have a conversation' with uploaded content to gain insights and answers from the material.
AI researchers at Stanford and the University of Washington trained an AI 'reasoning' model named s1 for under $50 using cloud compute credits. The model, which performs similarly to OpenAI’s o1 and DeepSeek’s R1, is available on GitHub. It was developed using distillation from Google’s Gemini 2.0 Flash Thinking Experimental model and demonstrates strong performance on benchmarks.
