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Cloudblog

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Introducing the next generation of AI inference, powered by llm-d

Efficient AI inference is crucial as the world moves towards deploying AI at scale, leading to the need for better processing power.
Open-source inference engines like vLLM are being utilized to address these challenges, with full support across various Google Cloud platforms.
A new project called llm-d is introduced, aiming at making AI inference more scalable and cost-effective through Kubernetes-native distributed and disaggregated inference.
llm-d incorporates advanced serving technologies and aims at providing low-latency, high-performance inference by leveraging Google Cloud's resources and AI integrations.

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Medium

430

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Top 10 Open Source Projects Every Developer Should Watch in 2025

Diving into open-source projects is essential for developers to enhance their skills and streamline their workflow by leveraging existing work.
Contributing to open-source projects enables coders to improve their coding abilities and save significant time.
A curated list of valuable open-source repositories for developers in 2025 can provide direct utility, serve as study material, or inspire new ideas.
Open-source repositories play a crucial role in modern software development by fostering faster innovation, facilitating learning, and delivering better tools for all users.

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Hackernoon

278

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Open-Source Data Catalogs: A New Chance to Implement Data Mesh

Open-source data catalogs have revolutionized the implementation of data mesh architecture by simplifying data management across departments.
Data mesh promotes decentralized architecture, allowing faster access to relevant data for various teams, akin to streaming services vs. traditional broadcast television.
Key principles of data mesh include domain-oriented data ownership, treating data as a product, self-service data infrastructure, and federated computational governance.
Open-source data catalogs like Polaris Catalog and Unity Catalog provide centralized metadata repositories, governance policies, data lineage tracking, and access controls that align with data mesh principles.
Advantages of open-source data catalogs in data mesh architecture include cost-effectiveness, flexibility, community support, and enabling data observability.
Implementing data mesh with open-source data catalogs involves assessing needs, trying out different tools, onboarding domains, defining governance policies, integrating with existing infrastructure, training teams, and maintaining the infrastructure.
While data mesh offers speed and flexibility, organizations need to prioritize data security, privacy, and compliance when integrating these architectures.
Open-source data catalogs have unlocked new opportunities for medium-sized organizations to optimize costs and prepare for future growth through decentralized data management.
The community support and flexibility of open-source tools enable organizations to develop new plugins, unlock multiple use cases, and enhance data observability.
Data mesh integration requires technical knowledge and might not be suitable for smaller organizations or those with on-premise setups or multi-cloud environments.

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AWS Blogs

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AWS Weekly Roundup: Strands Agents, AWS Transform, Amazon Bedrock Guardrails, AWS CodeBuild, and more (May 19, 2025)

Last week featured AI Week in Italy, and this week includes AWS Community Day in Zurich and AWS Cloud Infrastructure Day on May 22.
Strands Agents SDK was introduced for building AI agents with ease, supporting various use cases and models.
AWS Transform for .NET launched with new capabilities for modernizing .NET applications.
Amazon Bedrock Guardrails now supports cross-Region inference for enhanced resilience during peak demand.
AWS EC2 instances now support ENA queue allocation, optimizing network traffic management.
New Amazon EC2 P6-B200 instances with NVIDIA Blackwell GPUs for AI training and HPC applications.
AWS Control Tower introduces account-level reporting for baseline APIs.
AWS CodeBuild introduces Docker Server capability for faster CI/CD pipelines.
AWS CodePipeline now supports deploying to AWS Lambda with traffic shifting.
Amazon Cognito introduces support for OIDC prompt parameter for user reauthentication.

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VentureBeat

255

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Why Microsoft Fabric has already been adopted by 70% of the Fortune 500 — and what’s next

Microsoft Fabric is a unified data platform that aims to tackle enterprise data complexity by offering a common data layer across Microsoft's data and analytics tools.
CosmosDB, Microsoft's NoSQL document database, is a headline addition to Microsoft Fabric and powers many high-profile AI applications.
Microsoft Fabric now has over 21,000 paying customers worldwide, including 70% of the Fortune 500, showing rapid growth and strong adoption.
Microsoft is integrating various database options into Microsoft Fabric, including Azure SQL and CosmosDB, to streamline deployment and management for organizations.
Fabric enables organizations to deploy NoSQL databases like CosmosDB without the need to manage complex infrastructure, enhancing performance and reducing latency.
Microsoft's Fabric strategy includes maintaining a highly performant cache and synchronization mechanism to ensure millisecond response times for AI applications.
OneLake technology within Fabric stores the data, handling multiple data types and formats from SQL, NoSQL, and unstructured data in open source data formats.
Microsoft is open sourcing technologies like DiskANN for vector search capabilities, contributing to the AI ecosystem and enabling developers to build high-performance applications.
By unifying data products and leveraging open source standards, Microsoft Fabric eliminates data fragmentation and provides a unified architecture for various services.
Enterprises leveraging Microsoft Fabric can focus on creating AI applications that deliver business value, surpassing competitors struggling with fragmented architectures.

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TechCrunch

420

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Microsoft open-sources a command-line text editor and more at Build

Microsoft open-sourced a command-line text editor called Edit at Build 2025 conference.
Open source software serves as market research and can lead to paid applications and services.
Edit will allow developers to edit files in the command line via the 'edit' command to minimize context switching.
GitHub Copilot in VS Code and Windows Subsystem for Linux (WSL) will also be open-sourced by Microsoft.

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VentureBeat

397

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Quantum Machines launches Qualibrate open source framework to speed quantum computer calibration

Quantum Machines has launched Qualibrate, an open-source framework for calibrating quantum computers, reducing calibration time from hours to minutes.
Qualibrate enables fast, modular calibration, fosters a global ecosystem for sharing calibration protocols, and provides a comprehensive solution for executing and sharing protocols across different quantum computing platforms.
The framework transforms quantum calibration into a collaborative system, allowing researchers and quantum engineers to focus on quantum system logic rather than low-level details.
Qualibrate's open-source nature and modular architecture allow for immediate sharing and validation of new calibration protocols within the quantum computing community.

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Insider

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Marc Andreessen says the US needs to lead open-sourced AI: 'Imagine if the entire world — including the US — runs on Chinese software'

Venture capitalist Marc Andreessen warns that the US needs to lead open-source AI to avoid ceding control to China in the global AI race.
China has favored open-source models, while US tech giants have taken a more proprietary approach to AI development.
The debate over open- and closed-source AI is intensifying amid US-China tech rivalry and concerns over technological sovereignty.
Andreessen believes that open-source AI could become the global standard, shaping values embedded in AI models that will 'intermediate' key institutions like education, law, and medicine.

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Medium

242

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Why open-source is essential for true Private AI

Open-source models like BLOOM, Falcon, and Meta’s Llama family have shown the potential to compete with proprietary AI in scale and capability.
Advancements like DeepSeek and Google’s Gemma models using Quantization-Aware Training enhance accessibility and efficiency of AI models.
Open-source AI communities can innovate and compete with proprietary models, providing architectural guarantees for data privacy and security.
Fine-tuning open-source models offers enterprises control over their AI, ensuring data privacy and ownership of resulting models.
Using public APIs for fine-tuning risks data privacy leakage and vendor lock-in, especially for sensitive data in regulated industries.
Fine-tuning with public AI providers can lead to exposure of proprietary data and lack of ownership of the resulting fine-tuned model.
Fine-tuning open-source models within your infrastructure preserves data privacy, security, and ownership of artifacts, IP, and the process.
Open-source models offer flexibility, control, and freedom but require responsible sourcing, evaluation, and governance to avoid bias and risks.
Trust in the source of open-source models is crucial, and enterprises should vet and approve models from reputable organizations.
Engaging with upstream communities, contributing code/docs, and participating in open-source development accelerates innovation and builds goodwill.

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Medium

297

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A Path Down Linux Lane

Linux initially relied on CLI but later introduced GUIs like KDE Plasma and GNOME to enhance accessibility.
The flexibility of Linux distros allows users to choose between CLI and GUI interfaces.
Linux dominates in server, cloud, supercomputers, IoT, and embedded systems, showcasing its versatility and reliability.
With its reliability, security, and strong community support, Linux is a worthwhile choice for both beginners and tech enthusiasts.

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Medium

128

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Quantized Qwen2.5-Omni-7B Just Dropped and It’s FREE

The Qwen2.5-Omni-7B is a 7-billion-parameter multimodal AI model developed by Alibaba’s Qwen team, offering enhanced capabilities across text, speech, and vision modalities with quantization in formats like BF16, GPTQ, and AWQ.
Quantization optimizes the model for performance and memory efficiency, reducing its memory footprint significantly for various inference durations and GPU resources, making it suitable for consumer-grade hardware.
The open-source model is available on platforms like Hugging Face and ModelScope, enabling free download, fine-tuning, and deployment by anyone, with detailed performance benchmarks across different evaluation tasks and datasets.
The quantized versions of Qwen2.5-Omni-7B show competitive performance in automatic speech recognition, text-to-speech, text-to-text, speech-to-text, and multimodal tasks, offering efficiency gains with slight accuracy trade-offs.
The AWQ version in particular stands out for its memory efficiency, closely approaching BF16's accuracy while using significantly less memory, making it a strong choice for developers prioritizing both efficiency and accuracy.
The release of quantized Qwen2.5-Omni-7B democratizes access to powerful multimodal AI models, providing high performance across various modalities and lowering the entry barrier for developers via free availability on platforms like Hugging Face and ModelScope.
This release signifies a significant advancement in open-source AI, offering developers flexibility and power in building applications that require text, speech, and vision processing, making it a valuable asset for a wide range of projects.
Download Qwen2.5-Omni-7B today from Hugging Face or ModelScope to leverage its cutting-edge technology in your projects, benefiting from its low memory footprint, high accuracy, and versatile capabilities at no cost.

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Siliconangle

Image Credit: Siliconangle

AWS open-sources Strands Agents SDK to ease AI agent development

AWS has open-sourced Strands Agents SDK, a toolkit for developing AI agents.
Strands Agents SDK allows integration of large language models and supports tools like Amazon Bedrock and Anthropic PBC's API.
The SDK includes prebuilt tools for tasks and managing data processed by AI agents.
AWS plans to enhance Strands Agents SDK with support for multi-agent collaboration and data orchestration in the future.

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Medium

352

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How I Built a Local AI Assistant for Obsidian — No Cloud, No API Keys

Obsidian has become a popular tool for managing knowledge in a local-first manner.
Many existing AI integrations with Obsidian require API keys and rely on cloud services, compromising privacy and control.
To address this, a local AI assistant was developed, focusing on privacy, control, and offline usability.
The project aimed to create a fast, offline AI assistant for note-based workflows without cloud dependencies.
The assistant is implemented as a Python CLI tool that loads a local model and executes queries locally.
Installation is straightforward, requiring only Python 3.6+ and pip, with no need for internet connectivity post-setup.
Use cases include local note summarization, semantic search, writing assistance, and offline research support.
The assistant respects privacy, operates instantly, is hackable, and works in air-gapped environments.
Challenges in development included managing model size, memory execution, antivirus concerns, and balancing simplicity with functionality.
Future plans include adding features like ChromaDB-powered search, model flexibility, prompt templates, an Obsidian plugin bridge, and a GUI launcher.

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Marktechpost

178

Salesforce AI Releases BLIP3-o: A Fully Open-Source Unified Multimodal Model Built with CLIP Embeddings and Flow Matching for Image Understanding and Generation

Multimodal modeling aims to understand and generate content across visual and textual formats, integrating image recognition and generation into a unified system to enhance interactions.
BLIP3-o, developed by Salesforce Research in collaboration with academic institutions, introduces a family of unified multimodal models using CLIP embeddings and a sequential training approach for image understanding and generation.
The model leverages CLIP embeddings and a diffusion transformer for image synthesis, employing a dual-stage training strategy that enhances alignment and visual fidelity.
BLIP3-o outperforms in various benchmarks, achieving top scores in image generation alignment, reasoning ability, and image understanding, showcasing its superiority in subjective quality assessments.

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Insider

Image Credit: Insider

Meta's Llama has reached a turning point with developers as delays and disappointment mount

Meta's Llama 4 models faced a lukewarm start with limited adoption compared to previous models, leading to questions about its relevance among developers.
At Meta's LlamaCon conference, developers expressed disappointment as expected advanced models weren't announced, settling for traditional models instead.
The release of Llama 4 Scout and Llama 4 Maverick, with a delayed Llama 4 Behemoth, struggled to compete against alternatives like DeepSeek's V3 and Qwen.
While Meta claimed state-of-the-art performance, criticism grew around the fading momentum of Meta's open-source models in technical performance and mindshare.
Competitors like DeepSeek, Qwen, and OpenAI are outpacing Meta in reasoning, tool use, and real-world deployment, prompting doubts about Llama's progress.
Meta reassured its commitment to improving its models and features based on community feedback, but concerns linger over Llama falling behind in the AI landscape.
While Llama 3's launch in the past was widely praised for its breakthrough, Llama 4's reception has been less enthusiastic, raising questions about Meta's direction.
Criticism of Llama 4 extended beyond technical evaluations, highlighting its diminished tool-calling capabilities for agentic AI compared to rival models like OpenAI.
Llama's absence of a reasoning model and decline in developer interest indicate a loss of pace in the AI market, despite its continued presence in various AI applications.
Industry experts note that while Llama may be losing ground to proprietary models, it still holds value for enterprises seeking open-source solutions for specific tasks at a lower cost.
Despite competition from proprietary models, Llama's role remains relevant for many developers due to its cost-effectiveness and adaptability to diverse use cases within AI applications.

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