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Google's Gemini Omni Flash debuts at #1# on the Artificial Analysis Text to Video and Image to Video Leaderboards, edging out ByteDance's Seedance 2.0 on both Gemini Omni Flash is the first model in Google's Gemini Omni family, unveiled at Google I/O in May and opened to developers in public preview on June 30. Google positions Omni as a natively multimodal model that can "create anything from any input", starting with video: it accepts text, images, and video as input, generates clips with native audio, and supports conversational editing, where prompts change a video while preserving the rest of the scene. Gemini Omni Flash generates 3 to 10 second clips at 720p and 24 FPS, in 16:9 or 9:16, with longer durations coming soon. In the Artificial Analysis Video Arena, Gemini Omni Flash debuts at #1# on both the Text to Video and Image to Video Leaderboards, narrowly ahead of ByteDance's Seedance 2.0 on each. Gemini Omni Flash is priced at $0.10 per second of generated video ($6.00 per minute), matching Veo 3.1 Fast. The rate is the same for Text to Video and Image to Video. It is available now in the Gemini API, Google AI Studio, and the Gemini Enterprise Agent Platform, in the Gemini app and Google Flow for consumers, and at no cost in YouTube Shorts and the YouTube Create app. Congratulations to @GoogleDeepMind on the release! See below for comparisons between Gemini Omni Flash and other leading models in the Artificial Analysis Video Arena 🧵

From Micron Technology edging past the market valuation of Meta Platforms — and briefly Tesla — for the first time, to Apple's memory-linked price hikes, here is a roundup of the big financial stories of the week

From Micron Technology edging past the market valuation of Meta Platforms — and briefly Tesla — for the first time, to Apple's memory-linked price hikes, here is a roundup of the big financial stories of the week

We're introducing GLM-5.2, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and, for the first time, delivers that capability on a solid 1M-token context. GLM-5.2's new capabilities include: Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20% Pure Open: An MIT open-source license — no regional limits, technical access without borders Supporting long-horizon tasks starts with making long context engineering-usable: the model must maintain quality across long, messy coding-agent trajectories, not just accept more tokens. A 1M context is easy to claim, but much harder to keep reliable under real engineering pressure. To this end, we substantially expanded 1M-context training for coding-agent scenarios, covering large-scale implementation, automated research, performance optimization, and complex debugging. The result is a long-context system that is not only wide in scope, but solid in execution: a practical substrate for sustained engineering work. This capability is reflected in GLM-5.2's performance on three long-horizon coding benchmarks. FrontierSWE measures whether an agent can complete open-ended technical projects at the scale of hours to tens of hours, spanning systems optimization, large-scale code construction, and applied ML research. On this benchmark, GLM-5.2 trails Opus 4.8 by only 1%, while edging out GPT-5.5 by 1% and Opus 4.7 by 11%. On PostTrainBench, where each agent is given an H100 GPU and evaluated by how much it can improve small models through post-training, GLM-5.2 outperforms both Opus 4.7 and GPT-5.5, ranking second only to Opus 4.8. On SWE-Marathon, an ultra-long-horizon software engineering benchmark covering tasks such as building compilers, optimizing kernels, and developing production-grade services, GLM-5.2 still has room to grow, trailing Opus 4.8 by 13% while remaining second only to the Opus series. Across all three benchmarks, GLM-5.2 is the highest-ranked open-source model, showing that its 1M context has translated into practical long-horizon delivery capability.

Feels like it’s finally time for round 2 on $ALT. It’s one of the first protocols actually designed around HyperEVM’s native strengths: speculation, leverage, perp culture, and fast-moving onchain liquidity. For months, HYPE whales have been waiting for real volume to arrive on HyperEVM. Now there’s finally a product that gives traders a reason to stay onchain instead of just farming points and rotating capital. Meme launches tied directly to leveraged market exposure feels extremely native to the Hyperliquid ecosystem. - BTC longs. - HYPE leverage plays. - NVDA directional bets. - SPX edging All wrapped inside launchpad mechanics. That’s the kind of product that creates sticky volume instead of temporary hype. And the bigger thing people are missing: becomes more interesting as volatility increases. Because unlike normal launchpads, the underlying leverage exposure itself affects the bonding curve dynamics. Feels less like a temporary meta and more like one of the first protocols HyperEVM actually needed.

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DeWanna Bonner, who abandoned the Fever so she could play with her fiancée is losing her lesbian mind & the security guard is trying his darnest not to laugh at Sophie Cunningham egging her on 😂