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Okay, the @VulcanBench results for Qwen3.8-Max are in, and it is not what I expected. First, for anyone new to VulcanBench, here's a quick TL;DR on the eval suite: 23 frontier-hard software engineering tasks taken from real merged OSS PRs, run in a Docker sandbox, 3 runs per task across all three of its effort levels. No puzzles, no random abstract stuff, all real things engineering teams would do with these models. It looks like Qwen3.8-Max has a major overthinking problem, it uses a LOT of tokens and is very slow, period, no other way to see it. My cost to run this benchmark was $126.25, to run the exact same eval suite with DeepSeek V4-Flash was only $13.60. This makes Qwen3.8-Max an insanely expensive model. The tasks Qwen genuinely can't solve fail at every effort level, extra reasoning didn't help. The regression is almost all in work it already handles: six tasks that low solves every single time account for 83% of the 26-point drop, three of them collapsing to zero. It's not losing the hard problems. It's losing the ones it already knows how to do. Since Qwen3.8-Max hit a lot of wall clock budget caps, I thought I'd share more about this. - VulcanBench caps both steps (50–200) and wall clock (5–60 min), each scaled by repo size. - This is aligned with how comparable harnesses bound agents, DeepSWE caps rollouts at 100 environment steps, sitting right inside my step range; Terminal-Bench enforces a per-task wall clock; SWE-bench Verified scaffolds typically allow 20–60 min per instance with 250–350 step limits. - Every model on my chart gets the identical budget, and Qwen is the slowest model I've tested at 20–25 min/task. Soooo... Alibaba positions Qwen3.8-Max as trailing only Claude Fable 5. But on the kind of real coding work engineering teams would actually throw at it, under a fixed budget, its best setting lands mid-pack and its default lands last, so common. If you want to optimize for accuracy, Grok 4.5 is the move. If you want accuracy per dollar, DeepSeek V4-Flash is hard to beat, heck it's 10× cheaper than Qwen and you get higher accuracy. Qwen just isn't in the game at this point, this is not a model I could see engineering teams using for daily coding work.

As we continue to advance our commitment to responsible growth, strong governance, environmental responsibility, employee well-being, and creating lasting value for the communities we serve, we are pleased to announce the launch of the VFS Global Sustainability Report 2025.

Seedance 2.0 on higgsfield Prompt: A photorealistic smartphone selfie vlog that looks exactly like a real mobile phone recording. A young woman (image = her face and hair) visits a cozy thrift store on a sunny afternoon, filming everything herself with a handheld smartphone in selfie mode. The video features natural hand shake, realistic autofocus, slight exposure changes, authentic smartphone stabilization, and true-to-life colors. Smiling at the camera, she says, "Today's thrift store challenge—I'm only buying the first cute thing I find!" She walks through colorful racks of vintage clothes, shelves of accessories, plush toys, and home décor until she spots a random adorable oversized hat. Laughing, she immediately tries it on in front of a mirror, making funny poses and bursting into genuine laughter at how silly she looks. She decides to buy it, walks out of the store holding a small shopping bag, proudly shows her surprise find to the camera, smiles brightly, waves, and says, "See you in the next vlog. Bye!" before reaching toward the phone to stop the recording. The video should feel completely real with natural human movement, consistent facial features, realistic hand interactions, authentic thrift store lighting, no beauty filters, no CGI, no AI-plastic appearance, no subtitles, no logos, no watermark, and no background music—only real store ambience, footsteps, quiet conversations, clothing rustling, and natural environmental sounds.

Software quality now depends on the constraints you set around your agents. When humans manually wrote most of the code we could look at the code itself for signs of quality. Is it clean? Is it thoughtful? Is it fast? Can another engineer understand it? Does it have tests? Agents can now generate more code than people can read. When code generation scales beyond review, quality - checks for one or more of correctness, maintainability, security, performance etc - increasingly has to live somewhere else. It moves into the harness, environment and operating system around the agent. This can be the tests and deterministic checks that decide what the system is allowed to do (amongst others). Your constraints are what may eventually enable loops of agents to deliver production software reliably. They can include unit tests, property tests, acceptance tests, mutation testing and quality metrics. This back-pressure lets the system resist bad work before it becomes somebody elses problem. Set your constraints. They decide whether the code your agents generate is good enough to ship.

Fairfax County School Board member Marcia St. John-Cunning speaks out on the critical importance of fostering inclusive, safe environments for LGBTQIA+ youth. #FairfaxCounty# #LGBTQIA# #PrideMonth# #SchoolBoard# #InclusiveSchools#

Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: Tech report: Tech blog:

A humanoid robot is fundamentally an embodied AI system. While motors, actuators, batteries, and sensors provide the body, neural intelligence provides the mind. Without neural computation, a humanoid is simply an advanced machine. With it, the robot becomes capable of adapting to unpredictable environments, understanding language, recognizing objects, planning tasks, and continuously improving through experience. This shift positions "Humanoid Neural" as a foundational concept within the robotics ecosystem. Neural Intelligence is the Core of Future Humanoids. Modern humanoids rely on neural-network-based systems to perform nearly every cognitive function. These include: Visual perception Speech recognition Language understanding Object identification Human pose estimation Motion planning Reinforcement learning Dexterous manipulation Long-term memory Decision making Navigation Emotional recognition Social interaction As robots become more capable, nearly every subsystem transitions from traditional programming toward learned neural models. This makes "neural" less of a niche AI term and more of an umbrella for robot intelligence. A Broad and Scalable Brand One of the strongest characteristics of is that it is not confined to a single product category. #Languageunderstanding# #neuralnetwork# #socialinteraction# #neuralntelligence# #smarthumanoids# #iq#

Important Notice After a careful evaluation of the Company's operating conditions, market environment, and future strategic direction, BitMart has made the difficult decision to commence an orderly wind-down of its trading platform operations. We deeply regret having to make this decision. Key dates: • Jul 26, 2026, 01:30 UTC: New registrations, deposits, and new trading orders will begin to be suspended. • Aug 26, 2026, 01:00 UTC: All trading services will be discontinued. • Jan 31, 2027, 15:59 UTC: Platform operations will officially cease. Withdrawal services will remain available. We strongly encourage all users to close positions, complete KYC (if needed), and withdraw assets as early as possible. Please read the full announcement for important timelines and instructions: 🔗 Thank you for your trust and support over the years. ❤️