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Изворен канал @pythonotes · Post #402 · 22 дек.

Отдельно разберём TaskGroup, который пришел на замену gather в Python 3.11. Ключевые отличия ▫️create_task() возвращает объект asyncio.Task, у которого есть соответствюущие методы управления. То есть у нас больше контроля ▫️это контекстный менеджер, который гарантирует что все таски будут остановлены по выходу из контекста ▫️ошибка автоматически отменяет незавершенные задачи, ▫️except* передает нам ExceptionGroup, в котором каждую ошибку можно обработать отдельно import asyncio import random async def do_it() -> str: if random.random() < 0.1: raise ValueError('Oops') delay = random.uniform(0.5, 1.5) await asyncio.sleep(delay) return delay async def main(): try: async with asyncio.TaskGroup() as tg: for _ in range(10): tasks.append(tg.create_task(do_it())) for t in tasks: print(t.result()) except *ValueError as e: for err in e.exceptions: print(err) asyncio.run(main()) Рекомендую изучить страницу Coroutines and Tasks из документации, где представлено больше интересных примеров и механизмов - таймауты - отмена задач - создание задач из другого потока #async

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@githubtrending · Post #15062 · 15.08.2025 г., 12:30

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#python#mllm#point_clouds#scene_understanding#spatial_intelligence SpatialLM is a powerful 3D language model that turns complex 3D point cloud data from videos, RGBD images, or LiDAR into clear, structured 3D scene layouts showing walls, doors, windows, and objects with labels. It works without needing special equipment and can detect user-specified object categories. This helps you understand and analyze indoor spaces better, useful for robotics, navigation, and 3D design. You can run it on your data, visualize results, and even customize detection tasks easily, making 3D scene understanding more accessible and flexible for many applications. https://github.com/manycore-research/SpatialLM

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#python #mllm #point_clouds #scene_understanding #spatial_intelligence

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@githubtrending · Post #15528 · 28.02.2026 г., 12:00

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#python#agent#android#app#automation#copilot#gui#mllm#mobile#mobile_agents#multimodal#multimodal_agent#multimodal_large_language_models Mobile-Agent-v3.5 is Alibaba's top GUI agent family using GUI-Owl 1.5 models (2B to 235B sizes) for automating desktop, mobile, and browser tasks like stock checks, bookings, or document creation with planning, reflection, and memory. Try free online demos on ModelScope or Bailian, or use limited-time APIs—no setup needed. It leads 20+ benchmarks for real-world use. You benefit by saving time on repetitive tasks, boosting productivity, and handling complex operations hands-free across devices. https://github.com/X-PLUG/MobileAgent

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#python #agent #android #app #automation #copilot #gui #mllm #mobile #mobile_agents #multimodal #multimodal_agent #multimodal_large_language_models

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@githubtrending · Post #14639 · 27.04.2025 г., 13:00

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#python#agent_computer_interface#ai_agents#computer_automation#computer_use#grounding#gui_agents#in_context_reinforcement_learning#memory#mllm#planning#retrieval_augmented_generation Agent S2 is a smart AI assistant that handles computer tasks by breaking them into smaller steps and using specialized tools for each part, making it highly adaptable and efficient across different systems like Windows and Android. It outperforms other AI tools in completing complex tasks, learns from experience, and adjusts plans as needed, helping users automate digital work more reliably and effectively. https://github.com/simular-ai/Agent-S

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#python #agent_computer_interface #ai_agents #computer_automation #computer_use #grounding #gui_agents #in_context_reinforcement_learning #memory #mllm #planning #retrieval_augmented_generation