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Nvidia

296

Image Credit: Nvidia

Hugging Face and NVIDIA to Accelerate Open-Source AI Robotics Research and Development

Hugging Face and NVIDIA have announced a collaboration to accelerate robotics research and development.
The collaboration aims to bring together their open-source robotics communities to drive advances in various industries.
Hugging Face's LeRobot platform and NVIDIA's AI and robotics technology will enable researchers and developers to access open-source frameworks for robot training, simulation, and inference.
The collaboration will streamline the development process and create a data flywheel for the robotics community.

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Hackaday

289

Image Credit: Hackaday

Open-Source Robot Transforms

The CYOBot is a transforming quadruped robot with four articulating arms and wheels.
It is based on the previous version called the CYOCrawler but with added wheels.
The robot is open-source and modular, allowing for customization and expansion.
Most parts of the robot are common or 3D printed, making it accessible for development.

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Marktechpost

155

Tencent Releases Hunyuan-Large (Hunyuan-MoE-A52B) Model: A New Open-Source Transformer-based MoE Model with a Total of 389 Billion Parameters and 52 Billion Active Parameters

Tencent has released Hunyuan-Large, an open-source Transformer-based MoE model with a total of 389 billion parameters and 52 billion active parameters.
Hunyuan-Large is designed to handle large contexts of up to 256K tokens and rivals other leading models in performance.
The model incorporates technical advancements such as pre-training on diverse data, mixed expert routing strategy, key-value cache compression, and expert-specific learning rate.
Hunyuan-Large outperforms existing models on NLP tasks and addresses the need for long-context understanding in AI applications.

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Hackernoon

247

Image Credit: Hackernoon

How Accumulating TODOs Turns Into Technical Debt (and My Open Source Tool to Manage It)

TODO comments can accumulate and become technical debt, impacting project health and maintainability.
Todoctor is a tool that makes technical debt visible and actionable by tracking and managing TODO comments.
TODO comments can lead to invisible technical debt, decreased code quality, and loss of context.
Todoctor identifies TODO hotspots, visualizes technical debt, and encourages accountability to address outstanding tasks.

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Hackernoon

336

Image Credit: Hackernoon

ChartDB: Pioneering the Future of Database Visualization ✨

ChartDB is a database visualization tool that has gained popularity on GitHub, with over 1,500 stars.
It offers an intuitive interface for easy database visualization and management.
Future plans for ChartDB include AI-driven features, community-powered growth, and expanding functionality.
The founders are focused on innovation and setting a new standard in database visualization.

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Image Credit: Fb

Open Source AI Can Help America Lead in AI and Strengthen Global Security

Meta's open source Llama models are increasingly being used by a broad community for AI development. Therefore, they are being made available to U.S. government agencies like defence and national security applications and private sector partners supporting their work for streamlining complicated logistics and planning. The responsible and ethical uses of open source AI models like Llama will not only support the prosperity and security of the United States but also establish US open-source standards in the global race for AI leadership.
Llama is being fine-tuned to support specific national security team missions, such as planning operations and identify adversaries' vulnerabilities for critical repairs. AWS and Microsoft Azure are hosting our models on their secure cloud solutions for sensitive data to support governments. Moreover, it is in both America and the wider democratic world's interest for American open-source models to excel and succeed over models from China and elsewhere.
As Open source models become more capable and widely adopted, a global open-source standard for AI models is likely to emerge and embedded in technology, infrastructure, manufacturing, global finance and e-commerce. The world builds on high standards of openness, transparency and accountability, based on American leadership and commitment to international law for global peace and security, is significant.
Open source technology leads to faster innovation, reduced costs and better products, thanks to contributions from developers worldwide, helping the United States retain its technological edge while spreading access to AI globally and ensuring that resulting innovations are responsible and ethical supporting geopolitical interests of the United States and its closest allies. In addition, public-private collaborations can help governmental agencies and organizations improve their operations and make a greater impact for the people they serve.
Llama is supported by private sector partners including Accenture, Amazon Web Services, Anduril, Booz Allen, Databricks, Deloitte, IBM, Leidos, Lockheed Martin, Oracle, Palantir, Scale AI, and Snowflake to enable widespread adoption of American open source AI models. Other countries including China and America's competitors are investing heavily to leap ahead of the US.
Meta's open source AI models like Llama will not only support the prosperity and security of the United States but establish US open source standards in the global race for AI leadership. Public-private collaborations like this can help agencies and organizations improve their operations and make a greater impact for the people they serve, Deloitte being one of them.
Meta has partnered with the US State Department and led industry voices to promote safe, secure, and reliable AI systems that address societal challenges. They worked with the United Nations Educational, Scientific, and Cultural Organization (UNESCO) on a translation interface built on Meta's No Language Left Behind (NLLB) AI model to support high-quality translation in 200+ languages, including those that are low-resourced and marginalized.
Meta wants to play its part in supporting the safety, security and economic prosperity of America and its closest allies too. Therefore, it is the responsibility of countries leveraging AI for national security to deploy AI ethically, responsibly, and in accordance with relevant international law and fundamental principles.
Meta is humbled to see such huge enthusiasm around the world for the vast possibilities of this freely available technology. Working closely with partners, it hopes to play a part in securing America's technological leadership and ensuring the world's next generation of digital infrastructure is rooted in democratic values and safeguards.

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The New Stack

Image Credit: The New Stack

To Solve Kubernetes Sprawl, Try Kubernetes ‘All the Way Down’

Kubernetes is becoming the dominant app platform for delivering a range of applications but it also leads to increasing 'sprawl' of Kubernetes clusters, with large businesses using it everywhere. To manage this, Kubernetes can become a natural control point for managing other clusters of it. Using Kubernetes as a control plane can manage the 'thin layer' of Kubernetes, providing a single control point for managing Kubernetes clusters across different providers and achieve the Holy Grail of “hybrid multicloud."
Increasing sprawl of Kubernetes clusters is a growing concern as organizations struggle to manage Kubernetes at scale.
Kubernetes is emerging as the default standard for delivering a range of applications from cloud-native to legacy monolithic applications and virtualization.
In large organisations, Kubernetes clusters can sprawl across multiple providers and location, making it difficult to manage.
Using Kubernetes as a control point can manage the 'thin layer' of Kubernetes, providing a single control point for managing Kubernetes clusters across different providers and achieve the Holy Grail of “hybrid multicloud."
Cluster API (CAPI) and its providers allow Kubernetes to manage Kubernetes due to its control point capability.
Kubernetes has a baked-in architecture that assists with operations, allowing proper tiering of payloads (pods), extensible API, and more.
Kubernetes was not the first attempt at an application platform; WebLogic, WebSphere and virtualization came before it.
As a control point, Kubernetes can manage other control points and legacy control points were far less likely to have an API-enabled control point.
Kubernetes is considered the 80-90% solution that effectively solves application packaging, deployment, and operations problems due to its extensible architecture and friendly API.

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Amazic

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Image Credit: Amazic

5 Challenges with Open Source and How to Overcome Them with OpenLogic

Open-source software offers cost-effective, adaptable, and customizable solutions, but with its broad usage come challenges.
Migration from legacy systems, compatibility issues, and adaptation of configuration settings are the primary migration and integration challenges.
Managing third-party dependencies is a continuous challenge; vulnerabilities in third-party libraries can affect the overall security of the open-source software.
The open-source ecosystem is known for its rapid pace of innovation, leading to frequent updates and introducing valuable features and improvements.
The open-source ecosystem comprises different technologies, and troubleshooting issues can be complex due to unique configurations.
OpenLogic provides expert support for updating legacy systems, migration to open-source technologies, and integration support.
OpenLogic’s team at regular intervals scans for vulnerabilities and detects and responds to emerging threats proactively.
Finding and retaining personnel with the necessary technical expertise constitutes a challenge; engaging support providers specializing in open-source technologies can alleviate troubleshooting difficulties.
Open source software has various licensing requirements and compliance considerations, which can be complex to manage.
OpenLogic's team monitors security advisories and implements fixes to address potential threats, offering peace of mind to organizations concerned about security.
Through collaboration with OpenLogic, organizations can manage the challenges associated with open-source software and maximize the benefits of these solutions.

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Marktechpost

274

Meet Hertz-Dev: An Open-Source 8.5B Audio Model for Real-Time Conversational AI with 80ms Theoretical and 120ms Real-World Latency on a Single RTX 4090

Hertz-Dev is an open-source 8.5 billion parameter audio model for real-time conversational AI.
It achieves a theoretical latency of 80 milliseconds and a real-world latency of 120 milliseconds on a single NVIDIA RTX 4090 GPU.
The model is designed to be accessible, powerful, and efficient, democratizing the field of conversational AI.
Hertz-Dev reduces latency to a level where human-machine interactions are nearly indistinguishable from human-to-human interactions.

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Medium

Image Credit: Medium

A Product Manager’s Guide to Tackling Social Challenges

Understanding the root causes and contextual nuance of social challenges is crucial for product managers.
Defining the problem clearly helps identify actionable insights and measurable outcomes.
Co-creating solutions with the community ensures relevance and practicality.
Leveraging technology requires assessing feasibility, accessibility, and inclusivity.

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Insider

Image Credit: Insider

Sam Altman explains OpenAI's shift from open to closed AI models

OpenAI CEO Sam Altman explained the company's shift from open to closed AI models in a Reddit AMA.
OpenAI wants to hit the safety threshold for its AI models and believes closed models offer an easier way to achieve this.
Altman acknowledged the importance of open-source models but emphasized the value of powerful and easy-to-use APIs and services.
OpenAI has faced criticism for not open-sourcing its AI models, with cofounder Elon Musk being one of the vocal critics.

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TechCrunch

Image Credit: TechCrunch

Chinese military researchers reportedly used Meta’s AI to develop a defense chatbot

Chinese military researchers reportedly used Meta’s AI to develop a defense chatbot.
Researchers affiliated with a PLA R&D group utilized Meta’s Llama 2 AI model.
The chatbot, named ChatBIT, gathers and processes intelligence for operational decision-making.
Meta states that the use of the Llama model was unauthorized and against its acceptable use policy.

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Medium

Image Credit: Medium

10 Open Source Projects You Will Be Amazed to Discover ‍

Blender is an open-source software for creating 3D models and animations.
React.js is a popular JavaScript library for building interactive UIs.
Google's TensorFlow is a comprehensive platform for building AI models.
Docker is a leading tool for containerization to ensure consistent performance across environments.
Node.js is a server-side runtime for scalable and high-performance applications.
Microsoft's Visual Studio Code is an open-source editor known for its versatility.
Ansible is a powerful IT automation tool for configuration management and application deployment.
PostgreSQL is an open-source relational database system known for its stability and advanced features.
Redis is a versatile, in-memory data store used as a database, cache, and message broker.
OpenCV is an open-source library for real-time computer vision and image processing.

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Medium

223

Image Credit: Medium

[ 9CM ] Newly in for November

Newly in for November
Sales Period: 2024/11/01 ~ 2024/11/30 23:59:59 UTC
Price: $9.99
Purchase Limit: 10 times per account

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Marktechpost

184

Run AI Open Sources Run:ai Model Streamer: A Purpose-Built Solution to Make Large Models Loading Faster, and More Efficient

Run AI recently announced an open-source solution called Run AI: Model Streamer, aimed at improving the loading time of inference models.
Run AI: Model Streamer provides a high-speed, optimized approach to loading models, making the deployment process faster and more seamless.
The tool offers up to six times faster model loading and is compatible with various storage types and popular inference engines.
Run AI: Model Streamer enhances the efficiency and reliability of AI systems, especially those requiring quick inference for real-time use cases.

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