https://github.com/dstackai/dstack
dstack is an open-source platform that simplifies AI workload orchestration across multi-cloud and on-premises environments. [tldr; k8s alternative, vllm orchestrator]
Pros:
AI-Centric Design: Tailored specifically for AI tasks, dstack offers an intuitive experience compared to general-purpose orchestrators.
Multi-Cloud and On-Prem Support: Integrates seamlessly with various cloud providers and on-premises servers, offering flexibility in resource management.
Ease of Setup: Requires minimal configuration, allowing quick environment setup without the complexity of platforms like Kubernetes.
Cons:
Maturity and Community Support: As a newer tool, dstack may lack the extensive community support found with established platforms like Kubernetes.
Feature Set Limitations: While optimized for AI workloads, dstack might lack some advanced features found in general-purpose orchestrators, potentially limiting its applicability in non-AI scenarios.
Comparison with Docker and Kubernetes:
Docker: Focuses on building and running individual containers. dstack complements Docker by managing and orchestrating these containers specifically for AI workloads, offering features like native accelerator support and simplified deployment.
Kubernetes: Designed to manage complex, distributed applications at scale, Kubernetes can introduce unnecessary complexity for AI-specific tasks.
Integration with vLLM:
dstack can deploy models using vLLM, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). This integration allows for efficient AI model serving across various environments.
dstack is an open-source platform that simplifies AI workload orchestration across multi-cloud and on-premises environments. [tldr; k8s alternative, vllm orchestrator]
Pros:
AI-Centric Design: Tailored specifically for AI tasks, dstack offers an intuitive experience compared to general-purpose orchestrators.
Multi-Cloud and On-Prem Support: Integrates seamlessly with various cloud providers and on-premises servers, offering flexibility in resource management.
Ease of Setup: Requires minimal configuration, allowing quick environment setup without the complexity of platforms like Kubernetes.
Cons:
Maturity and Community Support: As a newer tool, dstack may lack the extensive community support found with established platforms like Kubernetes.
Feature Set Limitations: While optimized for AI workloads, dstack might lack some advanced features found in general-purpose orchestrators, potentially limiting its applicability in non-AI scenarios.
Comparison with Docker and Kubernetes:
Docker: Focuses on building and running individual containers. dstack complements Docker by managing and orchestrating these containers specifically for AI workloads, offering features like native accelerator support and simplified deployment.
Kubernetes: Designed to manage complex, distributed applications at scale, Kubernetes can introduce unnecessary complexity for AI-specific tasks.
Integration with vLLM:
dstack can deploy models using vLLM, a high-throughput and memory-efficient inference and serving engine for large language models (LLMs). This integration allows for efficient AI model serving across various environments.
GitHub
GitHub - dstackai/dstack: Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU,…
Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal. - dstackai/dstack
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https://github.com/questdb/questdb
//gud for ohlcv, trading data, will share later review after running in prod soon
QuestDB is the fastest growing open-source time-series database offering blazingly fast, high throughput ingestion and dynamic, low-latency SQL queries. The entire high-performance codebase is built from the ground up in Java, C++ and Rust with no dependencies and zero garbage collection.
QuestDB achieve high performance via a column-oriented storage model, parallelized vector execution, SIMD instructions, and low-latency techniques. In addition, it is hardware efficient, with quick setup and operational efficiency.
//gud for ohlcv, trading data, will share later review after running in prod soon
QuestDB is the fastest growing open-source time-series database offering blazingly fast, high throughput ingestion and dynamic, low-latency SQL queries. The entire high-performance codebase is built from the ground up in Java, C++ and Rust with no dependencies and zero garbage collection.
QuestDB achieve high performance via a column-oriented storage model, parallelized vector execution, SIMD instructions, and low-latency techniques. In addition, it is hardware efficient, with quick setup and operational efficiency.
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https://github.com/cosmoscout/cosmoscout-vr
🌌 A virtual universe which lets you explore, analyze and present huge planetary datasets and large simulation data in real-time.
CosmoScout VR is a modular virtual universe developed at the German Aerospace Center (DLR). It lets you explore, analyze and present huge planetary data sets and large simulation data in real-time.
🌌 A virtual universe which lets you explore, analyze and present huge planetary datasets and large simulation data in real-time.
CosmoScout VR is a modular virtual universe developed at the German Aerospace Center (DLR). It lets you explore, analyze and present huge planetary data sets and large simulation data in real-time.
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Перевод статьи блога Google._.pdf
142 KB
https://github.com/google/A2A
https://soundcloud.com/rinerd/a2a
буду здесь иногда выкладывать переводы с аудио на англ
anyway imo this is extremely important topic, enjoy
https://soundcloud.com/rinerd/a2a
буду здесь иногда выкладывать переводы с аудио на англ
anyway imo this is extremely important topic, enjoy
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rin files
Перевод статьи блога Google._.pdf
i was doing some research on a2a last weeks and will be posting about it much more next month
https://soundcloud.com/rinerd/a2a-101
https://soundcloud.com/rinerd/a2a-101
SoundCloud
Hear the world’s sounds
Explore the largest community of artists, bands, podcasters and creators of music & audio
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https://github.com/Kilo-Org/kilocode
The Kilo-Org/kilocode repository hosts Kilo Code, an open-source AI coding assistant. 🤖
This tool is designed to help developers with planning, building, and fixing code. It combines and extends the features of two other projects, Roo and Cline, into a single, more powerful application.
Key Features
AI-Powered Assistance: Utilizes the latest AI models like Claude 3 Sonnet, Opus, and Gemini 1.5 Pro to assist with coding tasks.
No API Keys Needed: Offers a free tier with access to powerful models without requiring users to provide their own API keys.
Extensible Marketplace: Includes an MCP Server Marketplace that allows users to extend the agent's capabilities with new tools and features.
System Notifications: Keeps you updated on the status of your tasks, even when the application is not in focus.
The Kilo-Org/kilocode repository hosts Kilo Code, an open-source AI coding assistant. 🤖
This tool is designed to help developers with planning, building, and fixing code. It combines and extends the features of two other projects, Roo and Cline, into a single, more powerful application.
Key Features
AI-Powered Assistance: Utilizes the latest AI models like Claude 3 Sonnet, Opus, and Gemini 1.5 Pro to assist with coding tasks.
No API Keys Needed: Offers a free tier with access to powerful models without requiring users to provide their own API keys.
Extensible Marketplace: Includes an MCP Server Marketplace that allows users to extend the agent's capabilities with new tools and features.
System Notifications: Keeps you updated on the status of your tasks, even when the application is not in focus.
GitHub
GitHub - Kilo-Org/kilocode: Kilo is the all-in-one agentic engineering platform. Build, ship, and iterate faster with the most…
Kilo is the all-in-one agentic engineering platform. Build, ship, and iterate faster with the most popular open source coding agent. - Kilo-Org/kilocode
rin files
Photo
im sorry for sharing crypto stuff even if its my own, i promised no crypto here, it won't happen again
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