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.
This blog post details an experiment testing the ability of LLMs (Gemini, ChatGPT, Perplexity) to accurately retrieve and summarize recent blog posts from a specific URL (searchresearch1.blogspot.com). The author found significant issues with hallucinations and inaccuracies, even in models claiming live web access, highlighting the unreliability of LLMs for even simple research tasks.
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 is upgrading Google Assistant users on mobile to Gemini, offering a new AI-powered assistant experience. The classic Google Assistant will no longer be accessible on most mobile devices later this year. Updates are also coming to tablets, cars, headphones, watches, and home devices.
Google is renaming 'Gemini Extensions' to 'apps' in the latest beta version of the Google app on Android. The change includes updates to the account menu and the full page for enabling and disabling each tool.
Google has enhanced Google Sheets with an AI-powered upgrade using its Gemini technology. This update allows users to automatically convert spreadsheets into charts, identify trends, and create advanced visualizations like heatmaps. Users can interact with the Gemini feature directly through a chat interface within Sheets.
An analysis of how well different AI systems perform in describing images and answering questions about them. The article compares ChatGPT, Gemini, Llama, and Claude using four images: a hand, a bottle of wine, a piece of pastry, and a flower.
Google has launched a public preview of Gemini Code Assist for individuals, offering up to 180,000 code completions per month, which is significantly more generous than competitors like GitHub Copilot. This tool is designed to support solo developers, students, hobbyists, freelancers, and startups with advanced AI capabilities, including generating entire code blocks and providing general coding assistance in various programming languages.
The paper "The Pursuit of Pseudocode Programming: Can LLMs Bridge the Gap?" explores the potential of Large Language Models (LLMs) to make pseudocode executable, addressing long-standing challenges in pseudocode programming. Pseudocode, known for its human-readable style, has been valuable for planning, communication, and education but has faced issues like lack of standardization, ambiguity, and limited expressiveness. LLMs offer new possibilities by handling ambiguity, generating code from pseudocode, and enhancing its expressiveness. Recent developments like SudoLang and pseudocode injection techniques demonstrate the potential of LLMs in this area. However, challenges remain in ensuring accuracy, reliability, and ethical considerations of LLM-generated code.
Key points:
- Pseudocode's benefits include improved efficiency, readability, and collaboration.
- Challenges include lack of standardization, ambiguity, and limited expressiveness.
- LLMs can interpret informal pseudocode, generate code, and enhance expressiveness.
- Developments like SudoLang and pseudocode injection show promise.
- Challenges include accuracy, debugging, and ethical considerations.
Key points:
- Pseudocode's benefits include improved efficiency, readability, and collaboration.
- Challenges include lack of standardization, ambiguity, and limited expressiveness.
- LLMs can interpret informal pseudocode, generate code, and enhance expressiveness.
- Developments like SudoLang and pseudocode injection show promise.
- Challenges include accuracy, debugging, and ethical considerations.
A web crawling project using Python, Selenium, Gemini, and Brightdata
- needs slight refactoring for openapi/llama.cpp integration
- needs slight refactoring for openapi/llama.cpp integration
