ChatGPT is about to launch a series of new shopping features designed to enhance the user's shopping experience in search responses.
These new features include:
- A rotating image containing the discovered product with an option to "Ask about this product" (called "Product Targeted Response")
- Product sidebars showing product images, purchase options, summaries of consumer reviews and why/what people think about the product
As it stands, of these linksNo affiliate marketing links attached. In addition, ChatGPT only automatically adds the referral URL to theutm_source=chatgpt[.] comThis tracking parameter to facilitate the analysis of traffic imported from ChatGPT search results.
It is important to note that all products shown are the result of independent selection rather than advertised promotions.
OpenAI is exploring an easy way for merchants to stream product information directly to ChatGPT, although only intent form submissions are accepted at this stage.
---
It's official, ChatGPT is officially launching a new shopping experience.
These improvements have begun a gradual rollout to Plus, Pro, and Free users, as well as non-logged-in users, globally. Full deployment is expected to be completed within a few days.
new featureThis includes smarter product recommendations, more accurate search results and a smoother shopping process, aiming to provide users with a more convenient and personalized shopping experience.
In addition, the search function has been significantly enhanced. The search function has become one of ChatGPT's most popular and fastest-growing features, processing more than one billion web search requests in the past week alone.
With these improvements, ChatGPT not only enhances the shopping experience of its users, but also further strengthens its competitiveness in the search engine market.
---
OpenAI has announced the introduction of a new shopping feature in its flagship product ChatGPT, an update powered by GPT-4o. The update is available to all user groups, including ChatGPT Pro, Plus and Free users, as well as global users who are not logged in.
The addition of the shopping feature will significantly enhance the utility of ChatGPT, allowing users to search for products, compare prices, and make purchases directly from the chat interface. This not only simplifies the shopping process, but also provides a more personalized shopping experience.
Key features include:
- Real-time product search and recommendation
- Multi-Platform Price Comparison
- One Click Purchase Link
In addition, GPT-4o's powerful natural language processing capabilities enable ChatGPT to better understand user intent and provide accurate merchandise suggestions.
This new feature will further promote the application of artificial intelligence in the field of e-commerce, providing users with a smarter and more convenient shopping experience.
---
A new technology called Model2Vec reduces the size of Sentence Transformers to one-fiftieth of their original size and makes them run 500 times faster.
This technology enables on-the-fly vector searches of millions of documents without pre-indexing by minimizing performance loss.Model2Vec not only improves processing efficiency, but also dramatically reduces storage requirements, making large-scale text processing more efficient and economical.
Model2VecFully open source, developers can easily integrate it into existing natural language processing projects to significantly improve system responsiveness and resource utilization.
Model2Vec has a wide range of applications in several fields, including but not limited to search engine optimization, recommender systems, chatbots, and more.
For more information about Model2Vec, please visit: https://t.co/OVJxpmTOYt
---
Recently, an open source company research tool based on GPT 4.1 and Firecrawl was launched. By simply typing in the name of a company, the tool can quickly obtain structured company data from the Internet.
This tool combinesOpenAIThe powerful natural language processing capability of the latest GPT-4.1 model and Firecrawl's data crawling technology can efficiently extract and organize relevant information. Users can easily access a wide range of information including company overview, financial data, market performance and more.
Wide range of application scenariosThe tool is suitable for investors, analysts, marketers, and any user who needs to gain insight into a specific company. In addition, the tool supports customized queries that provide more precise data based on the user's specific needs.
The tool is currently open for trial and interested users can visit the official website for a taste: https://t.co/of0cMt7sbF.
---
Legal professionals can now use the "Deep Researcher" template provided by create-llama to quickly generate detailed legal reports. Simply ask a question.Deep ResearcherA series of sub-questions are automatically generated to find the answers from your documents and eventually integrated into a complete report.
This tool not only greatly improves efficiency, but also ensures accurate and comprehensive reporting. For lawyers, legal advisors and researchers, this is undoubtedly a great boon.
- Quick report generation
- Automatic extraction of key information
- Increased efficiency
- Maintaining reporting accuracy
Experience this powerful feature immediately by simply typing in the command linenpx create-llamacan be started. For more details, visit the official website: https://t.co/xFsws3l1RE
particular thanksMarcus Schiesserhard work in this program.
---
While the community's reaction to the Llama 4 has been harsh, the story behind these models is more complex. A few weeks ago, Meta released the first Llama 4 models:
**Scout**: 109B total parameters, 17B active parameters, 16 specialists per level.
**Maverick**: 400B total parameters, 17B active parameters, 128 specialists per level.
**Behemoth**: 2T total parameters, 288B active parameters, 128 specialists per level.
Llama 4 is different in almost every way compared to Llama 3:
- Model architecture based on MoE (Mixed Expertise)
- Different approaches to multimodality (early fusion + native multimodality)
- Emphasis on model distillation during pre-training
- Different post-training processes
- Focus on long context capabilities
While Llama 3 focused on simplicity and scalability, Llama 4 strives to create a cutting-edge level of large language modeling to keep up with other top labs. Many of the design details of Llama 4 were inspired by other labs, such as OpenAI's adoption of the MoE architecture in GPT-4, Google's emphasis on native multimodality and long context capabilities in Gemini, and DeepSeek's emphasis on distillation and large-scale reinforcement learning.
So what's the problem? The development of large language models is an empirically driven and iterative process. In order to develop robust big models, we need to iteratively tune the models and build robust evaluation systems in order to detect meaningful changes. However, Llama 4 applies many major changes at once, a very risky approach. When the model was publicly released, large discrepancies emerged between internal and external assessments, revealing gaps in assessment capabilities.
What's next for Meta?Llama 4 was a reinvention of the Llama series.Meta completely shifted to a new research strategy, changing every aspect of the model in an attempt to create a cutting-edge level of big models. While Llama 4 failed to achieve this goal, it is not surprising. meta took a clear risk with Llama 4, which may pay off in the future.
To create large models at the frontier level, Meta simply needs to keep iterating and improving these models. Currently, the interval between major Llama releases is increasing, which is a problem. Models will continue to evolve and improve at a disturbing rate. The only way forward is to iterate quickly and fix existing gaps in assessment capabilities to avoid surprises like Llama 4.
To prevent users from being priced out, the Llama 4 model needs to be distilled into a number of smaller, denser models, such as DeepSeek-R1 and Qwen-2.5. Creating cutting-edge macromodels is a good goal, but Meta can succeed in the field of open macromodels without having to be state-of-the-art. After all, Llama has never been considered the "best" big model, and openness has always been the basis of its success.
---
Meta is actively advancing the development of LlamaCon and its AI web app. Several feature modules are already in the development stage in the upcoming update release.
Currently, the Meta AI Web App is already under development for the following features:
- **Reasoning** (based on the Llama 4 Reasoning model) - provides more powerful reasoning capabilities
- Canvas - Enhanced Creative Design Tools
- Research - tools to support scientific work
- Search - optimizing the search experience
- Talk - improved voice interaction
- New video - users can upload or record videos and regenerate content
- Connected apps - integration with other apps
- Memory - Enhanced memory storage and retrieval functions
These features will not only enhance the user experience, but also further promote the application and development of AI technology.
Meta is committed to providing smarter, more efficient and diversified services to users through these innovative features.
---
Watch Gemini 2.5 Pro realize a landmark Google DeepMind research paper.
The tool not only encodes reinforcement learning algorithms, but also enables real-time training visualization with debugging bugs. This is a huge step forward for researchers and developers as it provides full process support from code writing to debugging.
Key features include:
- Real-time training visualization: users can visualize the performance of the model during training.
- Automatic error debugging: the system can automatically identify and repair errors in the code to improve development efficiency.
- Integrated Development Environment (IDE): provides a complete development environment that simplifies the entire process from algorithm design to deployment.
The technology has a wide range of applications, from self-driving cars to intelligent robots, that can benefit from this efficient development tool.
In this way, Gemini 2.5 Pro not only demonstrates its power but also provides new tools and methods for research and development in the field of artificial intelligence.
Interested readers can find out more atGoogle DeepMind's official Twitter account to view the detailed video.
---
Join us tomorrow for a webinar exploring Command A, an updated generation model from Cohere designed for the enterprise. The model is optimized for enterprise users who require high-speed processing, safety and security, and high-quality output.
Command A combines advanced natural language processing technology to deliver superior performance in a variety of application scenarios including, but not limited to, customer support, content generation, and data processing.
Main features::
- Highly customizable to meet different business needs
- Strong security and privacy protection mechanisms
- Efficient data-processing capabilities for immediate output
By attending this webinar, you will have the opportunity to gain insight into how to leverage these highly effective AI models to create immediate value for your organization.
Register now:https://t.co/UYE0uRrKqK
