OpenAI has officially launched gpt-image-1, opening ChatGPT's powerful image generation capabilities to developers worldwide. Starting today, developers can utilize this new capability to create higher quality and diverse visual content.
gpt-image-1 provides several important features:
- **More accurate, high-fidelity images**: with advanced deep learning technology, the images generated are more detailed and realistic.
- **Diverse Visual Styles**: Supports a wide range of art styles and visual effects to meet the needs of different scenarios.
- **Precise Image Editing**: Provides powerful image editing tools for precise detail adjustments.
- **Rich World Knowledge**: Combined with OpenAI's large-scale training data, the images generated have a wide range of knowledge contexts.
- **Consistent Text Rendering**: Ensures consistency and readability of text in generated images.
These features will greatly enhance the quality and diversity of image generation, providing developers with more possibilities in creative design, digital art, advertising and marketing.
For more details and to get started, see the official guide: https://t.co/6XsgOj3oBO
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OpenAI recently released its latest multimodal image generation model, gpt-image-1, which is now open for use through the Images API, with support for the Responses API planned for the near future.This innovation offers developers and organizations the possibility to easily integrate high-quality image generation capabilities into applications and tools.
Currently, a number of well-known companies, including Adobe, Airtable, Figma, etc., are already utilizing gpt-image-1 to generate visual materials, thumbnails, avatars, and marketing materials for a variety of purposes; within a week of ChatGPT's launch, more than 130 million users have generated more than 700 million images through this technology.
Costs, gpt-image-1 is billed according to the number of tokens: $5 per million tokens for the prompt text, $10 per million tokens for the input image, and $40 per million tokens for the generated output image. The cost per image is approximately $0.02 for low quality images, $0.07 for medium quality images, and $0.19 for high quality images.
Additionally, developers worldwide now have access to the service and are able to adjust the APIs in theModeration parametersto control content review sensitivity (standard filtering or less restrictive). Note that some organizations may need to complete validation before they can start using it.
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We're excited to announce that our new course, Building Code Agents with smolagents, is now live!
The course was taught by our Agents Team Leader, Aymeric Roucher, and provided a great overview of the Agents field by Thom Wolf.
In this course, you will learn how to master the power of SmolAgents.
Currently, most AI agents use large language models to generate tool calls one at a time. Code agents, on the other hand, take a different approach.
Unlike tool-calling agents that follow a step-by-step process, code agents generate an entire piece of code at once, perform a series of operations, and then execute that piece of code all at once.
This approach can make agents more efficient, reliable, and better suited to handle complex tasks.
In this course, you will learn how to build code agents using the smolagents framework, how to safely run LLM-generated code through sandboxing and restricted execution, and how to evaluate your agents in single-agent and multi-agent systems.
Sign up now for free: https://t.co/KApfeYKUJw
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LlamaIndex integration with Milvus now supports full-text searches using BM25.
This function enables the Retrieval Augmented Generation (RAG) pipeline to utilize both vector search and traditional keyword matching techniques to improve the accuracy and efficiency of data retrieval.BM25 is a word frequency-inverse document frequency (TF-IDF)-based ranking function, which is widely used in the field of information retrieval, and is especially suitable for processing large-scale text data.
By combining these two search methods, users can achieve better performance when building intelligent Q&A systems, recommendation engines and content filtering platforms.
How to use this new feature:
- See the official tutorial for the implementation steps: tutorial link
- Explore further use cases for full-text search in Milvus: more information
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For the first time, Apple's iOS is getting a truly useful intelligent assistant, the Perplexity Assistant.
This AI app is capable of answering user questions and performing a variety of basic operations including playing media, drafting emails, mobile meeting scheduling, booking travel services, setting reminders and more. Its appearance marks a major breakthrough for iOS in the field of artificial intelligence.
**Perplexity Assistant** goes beyond simple information queries to help users with their daily tasks. For example, users can ask it to make restaurant reservations or adjust schedules to improve their lives and work efficiency.
With the continuous progress of technology, the application scenarios of intelligent assistants are becoming more and more extensive. This update makes it easier for iPhone users to utilize AI technology and enjoy a smarter life experience.
Interested users can try out this new feature by updating the Perplexity app.
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We are pleased to announce that our first web manipulation modelFIRE-1It is now officially available on Product Hunt.
FIRE-1 is a revolutionary tool designed to simplify web operations processes through automation and intelligence. It helps users to quickly accomplish a series of complex tasks such as data crawling, form filling and content generation. This model combines the latest natural language processing technology and machine learning algorithms to understand and execute the user's commands, which greatly improves work efficiency.
- Efficient data crawling capabilities: automatically extract key information from web pages
- Intelligent form filling: auto-fill forms based on pre-set templates
- Auto-generated content: generates relevant text based on user requirements
FIRE-1 has a wide range of application scenarios for market research, content creation, data analysis and many other fields. Whether you are a developer, market analyst or content creator, FIRE-1 can provide you with powerful support.
Come and experience this innovative tool! Visit the link: https://t.co/kvqmPxzLaJ
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To help developers better utilize Gemini 2.5 Flash for their projects, the Google AI Devs team recommends a number of valuable resources. Below are some of the key references:
First.Gemini API DocumentationProvides detailed API descriptions and usage examples to help developers get started quickly and gain insight into various features.
- **Gemini API documentation**
- **Code example**
- **Gemini Cookbook**
These resources cover a variety of application scenarios from basic to advanced, including data processing, model training and optimization.
In addition.Gemini CookbookIt also provides a series of practical code snippets and best practices to help developers solve common problems in real-world development and improve development efficiency.
Whether you are a beginner or an experienced developer, these resources will be an indispensable reference for you when working with Gemini 2.5 Flash.
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How to build an Agent Document Workflow (ADW) and when is it appropriate to do so?
ADW is the next step beyond the many prototype-stage RAG chatbots. Compared to traditional chatbots, ADW offers better scalability, seamless integration with existing software ecosystems, and superior error handling and human supervision capabilities.
In this article, we will delve into the following:
- ADW workflow forfour-stage
- of the ADW technology stackkey element
- A real-life ADW implementation case study
- Reference implementation of multiple ADW usage scenarios
Not all problems require an ADW solution, so we also analyze in detail how to calculate the return on investment (ROI) of an ADW implementation. With this information, organizations can better evaluate whether they should adopt this advanced approach to document processing.
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An open source tutorial called tiny-llm was recently discovered on GitHub, which details how to build a large language model from scratch. Based on the MLX implementation, this tutorial is particularly suitable for learning on Mac computers for systems engineers and developers interested in the underlying AI technology.
Instead of using complex high-level interfaces, the entire tutorial starts with the most basic pedagogical arithmetic and progresses to complex performance optimization. The main content includes:
- Three-week learning program: from simple Python code to complex performance optimization;
- Hands-on implementation of core components of the Big Language Model, such as attention mechanisms, positional encoding, etc;
- Detailed explanations of the principles behind each component with extensive references;
- The mainstream Qwen2 model is used as an example to help readers understand how big models work.
With this content, readers can gain insight into the workings of the Big Language Model and the technical details of its core components. In addition, the tutorial provides a wealth of references to help readers explore and research further.
This tutorial is a great learning resource for tech enthusiasts and engineers who want to dive deeper into the underlying technology of AI. Not only does it help beginners get started, but it also provides in-depth technical guidance for experienced developers.
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Find out how developer @haithehuman usesGemini Thinking Model, has innovatively built a series of programming education courses powered by artificial intelligence. By combining advanced natural language processing and machine learning technologies, these tutorials are able to provide personalized guidance to learners at different levels and help them master programming skills faster.
The Gemini Thinking Model is an artificial intelligence architecture that mimics the way humans think, and is capable of understanding complex concepts and generating logical explanations. The application of this model in the field of education not only improves students' learning efficiency, but also enhances their practical skills.
- **Personalized instruction**: course content tailored to each student's needs.
- **Real-time feedback**: instantly correct errors and provide suggestions for improvement.
- **Interactive experience**: Enhanced engagement in the learning process through a dialogic interface.
The methodology represents an important direction for the future of education technology, showing how AI can empower the traditional education industry to become more efficient and flexible.
