2025: Agents got a shared language (MCP)
2026: Agents learned to talk to each other (A2A)
2027: Agents got wallets
2028: Agents started hiring other agents By 2030, most of the economy runs machine-to-machine. Humans won’t be replaced.
They just won’t be required.
@godofprompt
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AI agents timeline: MCP to machine-to-machine economy by 2030
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Chinese AI dominates video models: 7 of 8 top, faster, cheaper
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But here's what nobody's saying: Chinese AI just dominated the leaderboard.
7 of 8 top video models: China. Faster. Cheaper. Better. While everyone waited for Sora, they shipped the future. -
AI-native experiences: evolving games, interactive cinema, responsive demos
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The unlock: AI-native games where worlds evolve from player actions
Interactive cinema where viewers control the story
Live product demos that respond to questions
Streaming with environments that shift on command -
Alibaba-backed AI (100M users) launches omni-model, memory attention, instant generation
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Alibaba-backed. 100M users. Launched Jan 13.
— God of Prompt (@godofprompt) 17 janvier 2026
Three breakthroughs nobody else has:
Omni-model (everything speaks one language)
Memory attention (infinite worlds that remember)
1-4 step generation (instant response)
They unified what Google, OpenAI, Meta built separately. pic.twitter.com/irDCNCm78ZAlibaba-backed. 100M users. Launched Jan 13.
Three breakthroughs nobody else has: Omni-model (everything speaks one language)
Memory attention (infinite worlds that remember)
1-4 step generation (instant response) They unified what Google, OpenAI, Meta built separately. -
From waiting and hating to live interactive generation
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Old way: • write prompt
• wait
• get clip
• hate it
• start over New way: • think it
• see it build LIVE at 1080P
• change anything mid-generation
• it never stops The render bar is dead. -
PixVerse R1: Real-time world model replaces AI video generation
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AI video just died. What comes next is so much better it's not even the same category. PixVerse R1 isn't a video generator. It's a real-time world model that responds to your thoughts instantly. This changes everything.
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C2C outperforms traditional Text-to-Text on four benchmarks
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8. C2C was rigorously tested on four major challenges: MMLU-Redux, OpenBookQA, ARC-Challenge, and C-Eval. It significantly outperformed the traditional "Text-to-Text" approach.
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Efficient Cache Improves Model Performance Without Retraining
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7. The paper demonstrated that improving a model's performance is possible by utilizing a more efficient cache. This enhancement does not require adding extra words or retraining, maintaining the same input length while enhancing understanding.
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C2C introduces neural Fuser to connect model KV-Caches
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5. Each model possesses a memory storage referred to as KV-Cache. C2C introduces a small neural "Fuser" that connects the memory of one model to another, facilitating information sharing and collaboration.