CATransformers is a carbon-driven framework for the co-design of neural architectures and system hardware. Using this framework, the research team discovered a greener CLIP model that reduces total carbon emissions by an average of 9.11 TP3T over its lifecycle, while maintaining its original accuracy (and even improving accuracy) and latency performance.
This is the first study to explore carbon-driven co-design of neural architectures and system hardware. The study was made possible by ACT, a new architectural carbon modeling tool developed by FAIR, which helps researchers better understand the environmental impact of different neural network architectures and optimize designs to reduce their carbon footprint.
Key results include:
- Developed the first architectural carbon modeling tool ACT
- Discovered a greener CLIP model that reduces carbon emissions and maintains or improves performance
- New directions and tools for sustainable AI research
More detailed information can be found at:Link to paper ➡️ https://t.co/uwyBQ1M2WF;code repository ➡️ https://t.co/TCTiqMZ5B2; and Other carbon design tools and research materials for sustainable AI ➡️ https://t.co/S0VH1DgVFS.
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Following a successful tutorial on the subject at ICML 2024, Meta's research arm, FAIR, has now released the fourth part of its Physics of Language Models research series. This research project aims to reveal universal laws in artificial intelligence through controlled experiments.
In Part IV, the researchers challenge the current paradigm of noise-based, real-world pre-training for architectural design by introducing a synthetic pre-training environment that makes comparisons between different model families more accurate and fair. This approach can be viewed as a "Galileo moment" in the design of large language models (LLMs), and just as the Leaning Tower of Pisa experiment pushed the boundaries of modern physics, the synthetic pretraining framework reveals the true limits of LLM architectures.
12 key findingsIncluding: The lightweight component Canon layer significantly improves the inference and generalization ability of Transformer, Mamba, GLA and other models by adding "horizontal residual links". These findings provide new ideas for future model design and may mark a new era in LLM research.
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Today, we officially launched theTemplate Functions, which is one of the easiest ways to get a project off the ground quickly. With a pre-built solution, developers have easy access to code snippets, online runtime environments and GitHub repositories provided by @firecrawl_dev and the community.
This new feature not only simplifies the development process, but also provides beginners with the opportunity to learn and practice. Whether it's a front-end framework, a back-end service or a full-stack application, the templates provide ready-to-use base code to help developers get to the actual development stage faster.
- **Code Snippets**: a selection of code snippets covering a wide range of programming languages and frameworks.
- **Online Runtime Environment**: You can run and debug the code directly in the browser.
- **GitHub repository**: contains the complete project structure and sample code for easy cloning and modification.
In addition, we provide detailed documentation and tutorials to help users better understand and use these templates. Visit our website today to experience this powerful tool!
See more at: https://t.co/lQXOPgKnkz
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Following MCP and A2A, AG-UI completed the protocol stack.
MCP enables agent-to-tool connectivity, and A2A enables agents to communicate with each other. Now, AG-UI bridges the gap between agents and users, providing the missing link between your agent backend and your user-facing UI application.
How it works:
- Client sends POST request to proxy endpoint
- Open an HTTP stream to get real-time events
- Each event has a type and some metadata
- The agent streams these events in real time
- UI updates immediately each time an event arrives
- The UI can also send its own events and context back to the agent
AG-UI supports almost all popular frameworks, such as CrewAI, LangGraph, Mastra, and others.
Key features include:
- Lightweight and open source
- Multiple transport methods: SSE, WS, Webhooks
- Bi-directional synchronization with support for real-time chat and tool calls
- Framework-agnostic: compatible with LangGraph, CrewAI, Mastra, etc.
- Loose pattern matching for seamless interoperability
- Bi-directional synchronization with support for chat, tool calls and real-time contexts
What's exciting is that AG-UI is completely open source and you can integrate it into your React apps right away.
For more details and a live demo of AG-UI, visit its official GitHub repository.
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The Google AI team recently released a new library, Keras Recommenders, designed to help developers easily build custom recommendation systems. The library provides APIs specifically designed for ranking and retrieval tasks, as well as examples for writing and training your first recommender.
The launch of Keras Recommenders fills a need in the market for an efficient, easy-to-use and powerful recommender system tool.The library not only simplifies the development of recommender systems, also provides a detailed introductory guide for beginners, reducing the learning curve.
In practice, recommender systems are widely used in e-commerce, social media, news and information, etc., through the analysis of user behavioral data, to provide users with personalized content recommendations. the emergence of Keras Recommenders allows developers to more quickly achieve these functions, and can be customized according to specific needs.
- API for ranking tasks
- APIs for retrieval tasks
- Rich sample code and documentation
Both organizations looking to improve user experience and researchers looking to explore recommendation algorithms in academic research can benefit from Keras Recommenders.
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With the rapid development of artificial intelligence technology, AI crawlers are increasingly widely used in data collection. For some enterprises, AI crawlers can bring rich data resources to help them conduct market analysis and product optimization.
However, for other organizations, frequent data crawling not only increases the burden on servers, but also may leak sensitive information, leading to unnecessary cost increases. To address this issue, Vercel has introduced a new feature - a one-click operation to block the data collection behavior of AI crawlers.This feature allows users to easily set up in the Vercel firewall without affecting normal AI search traffic.
This innovative feature provides organizations with greater flexibility and control so they can continue to enjoy the convenience of AI while keeping their data secure.
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Yes, I strongly believe that many interesting and useful enterprise applications can be created by deploying AI SDK agents on the Slack platform. This approach not only helps organizations interact with users more efficiently, but also enhances the user experience.
As AI technology continues to evolve, more and more organizations are looking to integrate AI into their daily work.Slack, a popular team collaboration tool, offers a rich set of API interfaces, making it an ideal platform for AI integration. By deploying AI agents in Slack, organizations can automate task processing, intelligent customer service, data analytics, and other functions to improve efficiency and responsiveness.
application scenarioIncluding but not limited to:
- Customer service: automated responses to frequently asked questions and 24/7 online support.
- Project management: track task progress and remind deadlines.
- Data Analysis: Monitor key indicators in real time and generate reports.
This innovative combination offers developers a new frontier of opportunity.
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It's so exciting to see people exploring new styles all the time.Make the web fun again with @v0, which is one of the most recent and highly anticipated projects in the field of front-end development. This project not only showcases the latest advances in front-end technology, but also provides a new platform for developers to create more interesting and interactive web experiences.
The results of @vercel and @v0's collaboration are widely recognized, especially in the front-end development community. They have made web building more efficient and flexible through a range of innovative techniques and tools. For example, their framework supports rapid iteration and deployment, allowing developers to focus more on creativity and user experience rather than tedious technical details.
Also, @rauchg's contribution in this project is worth mentioning. His efforts not only drove the success of the project, but also inspired more developers to participate in this wave of innovation. This spirit of open collaboration is exactly what the modern technology community needs to promote knowledge sharing and technological advancement.
With the application of these new technologies, we can expect the Internet to become even more colorful in the future.
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Recently, an internet user shared his unique way of celebrating his cousin's academic achievement. His cousin had just passed her 10th grade exams with flying colors and as a reward, he created a personal website for her to showcase her poetry work.
This website is not only a loving gift, but also an affirmation of your cousin's talent. In today's digital age, having a personal website can greatly enhance the impact of your personal brand.Website creationUsedWeb Development Tools@v0, a powerful platform that enables users to easily build beautiful and feature-rich websites.
This personalized gift not only encourages young people to continue pursuing their interests and dreams, but it also creates fond memories as they grow up.
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Our own Dr. R. Lance Martin officially launched the LangGraph 101 workshop at Interrupt.
This workshop explains concepts such as workflows and agents, and humans in the loop in depth, enabling participants to clearly understand the core of these technologies. In addition, he demonstrated the power of LangGraph Studio, a tool that helps developers iterate and build reliable agent systems efficiently.
LangGraphis a graph-based natural language processing framework that represents and processes textual data through graph structures to achieve more efficient and accurate language understanding and generation. This technology has a wide range of applications in several fields such as intelligent customer service, content generation and data analytics.
Future workshops are scheduled as follows:
- More advanced applications of LangGraph
- How to Design and Optimize Complex Tasks with LangGraph
- Real-world case sharing: building a complete NLP program from scratch
For those interested, you can follow the subsequent links for more information.
