pendant ce temps le livreur gagne plus que l'auteur
GENERATIVE AI
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Amazon’s AI Coding Boom Creates Development Challenges
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Amazon’s AI coding boom is creating a mess. Vibe coding their way to chaos.
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DeepSeek Raises $300M at $10B Valuation
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The company that proved you don't need billions to build world-class AI is now asking for money. DeepSeek is raising outside capital for the first time. The target: at least $300 million at a valuation north of $10 billion. Until now, founder Liang Wenfeng funded everything
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Hybrid approach: combining proprietary and open AI models
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The second misconception I keep seeing: Too many teams think they have to choose between open models and proprietary models. They do not. The smarter path is hybrid. → proprietary models for scale and broad capability
→ open models for flexibility and control
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Models as Components: The Infrastructure Behind AI Agents
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My biggest takeaway: Models are becoming components, not products. What matters now is the system around them: → runtimes
→ memory
→ tool access
→ orchestration
→ secure execution environments That is what turns a model into an agent, and an agent into something the -
Anthropic Research Reveals Subliminal Learning in AI Model Training
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/4 Anthropic demonstrates subliminal learning where models inherit behavior from seemingly unrelated training data. Anthropic co-authors new research on subliminal learning, published in Nature. You train models on outputs from other models, assuming only visible content
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TDM-R1: Fast AI Image Generator Learning from Human Feedback
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What if fast AI image generators could learn from simple human likes and object counts as easily as they learn from complex math? Researchers from HKUST, CUHK Shenzhen, and Xiaohongshu present TDM-R1 to do just that. Most lightning-fast AI models struggle to use real-world
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Meta SAM Audio Makes Interactive Sound Design Intent-Based
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My takeaway: Audio is finally becoming interactive.
You don’t mix sound anymore, you point at intent. That’s what Meta’s SAM Audio project is pointing toward, and it has real implications for creators, product teams, and anyone building with multimodal AI. Watch the full video -
AI Model Splits Audio Into Target Sound and Background
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Under the hood, the model does one powerful thing: It splits sound into two parts → → The target sound
→ Everything else That single capability unlocks a lot → → Faster editing for creators
→ Cleaner, more controllable data for AI training
→ Multimodal systems that -
AI Audio Tool Adapts to Human Intent Through Multiple Interfaces
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The big unlock is simple but profound: Instead of forcing humans to adapt to audio tools, the model adapts to human intent. You can guide it three ways → → Type what you want, like “keep only the voice”
→ Click where the sound happens in the video
→ Mark a moment in time