GPT Image 2 + Sider AI is INSANE → You search for a product on Google
→ You pass the photo to Sider with 1 prompt
→ Professional brand kit instantly All without switching tabs Prompt below
AI
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GPT Image 2 + Sider AI workflow creates instant brand kits
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Tenstorrent Demos Faster Than Real-Time AI Video Generation
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Preview our faster than real-time AI video generation demo. See the rest for yourself on May 1st at TT-Deploy. Watch the livestream: https://
tenstorrent.com/deploy -
Qwen-based Model Achieves Pareto Optimality in Accuracy-Cost
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We’ve post trained a model on top of Qwen that achieves Pareto optimality on accuracy-cost curves. Unlike our previous post trained models, this model has been trained to be good at search and tool calls simultaneously, allowing us to unify the tool call router and
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Unitree Robotics: China Supply Chain Mastery Threatens US Competition
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Sam is right about the importance of robotics for the US but Unitree offers a real lesson to US companies hoping to compete at the bleeding edge. It has risen thanks to a true mastery of China’s supply chain.
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Challenges in Manufacturing World-Class Affordable Robot Actuators
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If only it were that easy to make world-class cheap robot actuators… https://t.co/0iO1tfZ8vf
— Will Knight (@willknight) 22 avril 2026If only it were that easy to make world-class cheap robot actuators…
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Integrated AI creative workflow with GPT Image and Seedance
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On parle beaucoup de “meilleures IA”.
— Jouhatsu | AI Influence Operator (@Jouhatsu_ai) 22 avril 2026
Mais le vrai game changer… c’est quand tout fonctionne ensemble.
Plus besoin de jongler entre 10 outils pour créer quelque chose de propre.
GPT Image 2.0 × Seedance 2.0 sur Higgsfield, c’est enfin un vrai workflow de création. https://t.co/jFcNPrvCvV pic.twitter.com/53YjgwI0eUOn parle beaucoup de “meilleures IA”. Mais le vrai game changer… c’est quand tout fonctionne ensemble. Plus besoin de jongler entre 10 outils pour créer quelque chose de propre. GPT Image 2.0 × Seedance 2.0 sur Higgsfield, c’est enfin un vrai workflow de création.
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Perplexity’s Pipeline Improves Base Model Accuracy and Efficiency
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This pipeline is why the same base model produces more accurate, better-cited, and more efficient answers inside Perplexity than out of the box. Read our research:
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Reward Design Balances Correctness Preference Efficiency
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Our reward design combines correctness, preference, and efficiency. Preference only counts when the answer is correct. This keeps the model from optimizing for better-sounding wrong answers.
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Fine-tuning and On-Policy RL for Model Optimization
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We first fine-tune the model to follow instructions, stay within guardrails, and keep language consistent. Then we run on‑policy RL to improve search accuracy and tool efficiency while preserving those behaviors.