The Gmail vocabulary import is the one thread connecting it back to Google's ecosystem. Everything else stays local.
AI
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Cheers: Unified Multimodal Model for Image Understanding Generation
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AI could understand and generate images from a single, efficient model!
— 机器之心 JIQIZHIXIN (@jiqizhixin) 8 avril 2026
Tsinghua University, Xi'an Jiaotong University, and University of Chinese Academy of Sciences present Cheers!
This unified multimodal model decouples fine image details from their core semantic meaning.… pic.twitter.com/s0MgsejA97AI could understand and generate images from a single, efficient model! Tsinghua University, Xi'an Jiaotong University, and University of Chinese Academy of Sciences present Cheers! This unified multimodal model decouples fine image details from their core semantic meaning. This new architecture stabilizes AI's understanding while boosting image generation fidelity by selectively re-injecting those details. Cheers matches or outperforms advanced unified multimodal models in both visual understanding and generation. It notably beats Tar-1.5B on GenEval and MMBench, using only 20% of the training cost and achieving 4x token compression. Breakthrough efficiency! Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation Project: github.com/AI9Stars/Cheers Model: huggingface.co/ai9stars/Chee… Paper: arxiv.org/abs/2603.12793 Our report: mp.weixin.qq.com/s/EK6cyCJz5… 📬 #PapersAccepted by Jiqizhixin
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Better way to track AI community updates on X
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I built a better way to keep up with the AI community here on X:
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Asia surpasses Europe technologically according to Alexandre Tsicopoulos
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L’Asie a pris l’avantage technologiquement sur l’Europe.
— Michel Levy Provençal (@mikiane) 8 avril 2026
Extrait de mon interview avec @Alex_Tsico pic.twitter.com/7mLU62whyNAsia has gained a technological advantage over Europe. Excerpt from my interview with @Alex_Tsico [Translated from EN to English]
→ View original post on X — @alex_tsico, 2026-04-08 08:00 UTC
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Multi-Agent Systems Accelerate Large Code Refactoring Tasks
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Multi-agent on large refactors changed what's possible in a single day.
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Anthropic Contributes Patches to FFmpeg Open Source Project
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FFmpeg was the last frontier of human resistance :D. FFmpeg (@FFmpeg) Thank you to @AnthropicAI for sending FFmpeg patches — https://nitter.net/FFmpeg/status/2041595801483264002#m
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Building More Human-Like Artificial Intelligence Systems
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It is about building a more human like AI.
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WideSeek-R1: Multi-Agent Framework Achieves DeepSeek-R1 Performance
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Still waiting for DeepSeek?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 8 avril 2026
Here comes WideSeek-R1.
Researchers from Tsinghua University and Infinigence AI introduce "width scaling," an innovative lead-agent and subagent framework.
Instead of a single powerful AI working through a problem sequentially, WideSeek-R1… pic.twitter.com/OOb3Azq6G3Still waiting for DeepSeek? Here comes WideSeek-R1. Researchers from Tsinghua University and Infinigence AI introduce "width scaling," an innovative lead-agent and subagent framework. Instead of a single powerful AI working through a problem sequentially, WideSeek-R1 orchestrates multiple smaller AIs to work in parallel. This system is trained with multi-agent reinforcement learning, allowing for scalable coordination and simultaneous execution using a shared large language model, but with each sub-agent having specialized tools and isolated contexts. WideSeek-R1-4B achieves an item F1 score of 40.0% on the WideSearch benchmark, a performance comparable to the much larger, single-agent DeepSeek-R1-671B. WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning Paper: arxiv.org/abs/2602.04634 Project: wideseek-r1.github.io Our report: mp.weixin.qq.com/s/qgGe51Rcw… 📬 #PapersAccepted by Jiqizhixin
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Python Tools You Need for AI Projects
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Python Tools You Need for #AI Projects by @Python_Dv #ArtificialIntelligence #MachineLearning #ML #MI
→ View original post on X — @ronald_vanloon, 2026-04-08 07:48 UTC