Why Nvidia’s Acquisition of Hugging Face Would Reshape Open-Source AI
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OpenAI's Rogue AI Agents Were Probing Hugging Face Two Months Before Hack
An independent researcher found the agents hijacked Hugging Face accounts and mapped the platform's defenses as early as May 13—activity OpenAI's own incident report never fully described.
Nvidia buys Hugging Face for $12.9B in push into AI software
Nvidia agreed to buy Hugging Face for $12.93 billion, gaining a platform used by more than 18 million developers to share and deploy AI models.
Hugging Face hack exposes the open-weight AI cybersecurity paradox
Hugging Face relies on open weight Chinese models to defend itself from rogue AI agents. But a lack of safety guardrails makes those models potentially dangerous too.
Hugging Face Explores $13 Billion Sale a Month After a Rogue OpenAI Agent Hacked It
The open-source AI hub is fielding buyout interest at nearly triple its 2023 valuation, weeks after a security breach and days after Stripe's OpenRouter deal reset the price of AI infrastructure.
JPMorgan: Open source weight commercialization exhibits a "winner-takes-all" phenomenon; Zhipu target price raised to HK$2,000, MiniMax target price cut to HK$300.
According to BlockBeats, on July 8th, JPMorgan Chase released a research report stating that currently competitive models in the market can expand adoption through open-source weighting and continue to monetize through official APIs, partner channels, enterprise deployments, and workflow products; while weaker models face faster price comparisons and traffic fragmentation. JPMorgan Chase raised its revenue forecasts for Zhipu from 2026 to 2030 by 3% to 9%, and narrowed its adjusted loss forecasts for 2026 and 2027 to RMB 3.711 billion and RMB 3.141 billion respectively. The 2028 forecast was revised from a loss of RMB 1.287 billion to a profit of RMB 2.367 billion. The target price was raised from HKD 1800 to HKD 2000, maintaining an "Overweight" rating. The report believes that the performance of GLM-5.5/6, KimiK3, and DeepSeekV4.1 will be key indicators of whether Zhipu can maintain its leading position. JPMorgan lowered its revenue forecasts for MINIMAX-W (2027-2030) by 2% to 8%, and reduced its target price from HK$400 to HK$300, while maintaining a "neutral" rating. The company noted that the M3 model offers a permanent 50% discount, reflecting that the model has not yet created a significant capability premium for leading domestic competitors. JPMorgan believes that if MiniMax can narrow the capability gap, normalize the discount, maintain API usage, and demonstrate stronger workflow stickiness through MiniMaxCode, its outlook could turn positive.
MIRA, an open-source 5B multiplayer world model, uses DINOv3 representation to mitigate long-term drift and can simulate 2v2 battles in Rocket League in real time.
According to Beating, AI research firm General Intuition, in collaboration with French AI lab Kyutai and Epic Games, has launched MIRA, a multiplayer interactive world model. As a generative game simulator supporting real-time multiplayer interaction, MIRA can simulate 2v2 battles in Rocket League in real time, based solely on historical footage and player button presses, without requiring a physics engine, rendering engine, or explicit 3D representation for inference. Unlike the "decoupling of logic computation and image rendering" approach adopted by companies like Odyssey, MIRA takes a generative simulation approach based on video latent space. MIRA boasts 5 billion parameters, and its core design builds the latent prediction space on a frozen general-purpose visual encoder, DINOv3-L. Leveraging pre-trained visual features, the generated latent states can more stably fall within the effective representation space, significantly mitigating image drift and divergence during long-term prediction. For multi-screen alignment, MIRA stitches the latent images from four player perspectives into a unified grid, enabling spatial attention mechanisms to operate naturally across viewpoints and improving the spatial consistency of vehicles, the soccer ball, and key events across multiple perspectives. The Action Dropout introduced during training also helps the system complete the game behavior of vehicles not controlled by commands when parts of the motion flow are missing. Currently, MIRA can run in real time at 20 frames per second on a single NVIDIA B200 graphics card. The team has open-sourced the training and inference code and released the Rocket Science dataset, which contains 1,000 hours of matches, approximately 4,000 hours of video, motion flow, and physics data from four perspectives; the complete training of the model used approximately 10,000 hours of clean match data.