Warren identified the deeper problem almost a century before Ludwig von Mises and Friedrich Hayek made it famous in academic economics. When everyone owns everything, no one owns anything. When labor earns the same reward regardless of effort, effort disappears. When prices vanish, no one knows what anything is worth.

In 1825, a rich Welsh industrialist bought an entire American town to prove that socialism could work. He had the money, the buildings, the theory, and hundreds of eager followers waiting to move in. Two years later, it was over. 🧵 https://t.co/z0HqcbTQ8P

Paraguay is entering a new era of connectivity 🇵🇾 Paraguay is working with Starlink through MITIC and COPACO to bring high-speed internet to more than 1,600 schools in isolated areas This will connect 50,000+ students and teachers to online learning, digital libraries, collaboration tools, and new opportunities A satellite in orbit can now change the future of a classroom full of students on the ground

🚨BREAKING: ANTHROPIC COOKING ITS OWN AI INFERENCE CHIP In early talks with SAMSUNG for its 2nm process + advanced packaging Samsung already invested in Anthropic’s Series H btw https://t.co/KYPjR5AGJF

This is actually one of the cleanest workflows I’ve seen for live singing + choreography sync using Seedance 2.0. The color-coded shot language + layering IPA/FACS is really smart. Especially like how they separate body movement, camera, and lighting directions. https://t.co/XKR1Osit93

do you know about the mosquito bite deleter? press it onto the bite. push button. beep. gets hot for a few seconds. boop. protein is cooked under the skin. itching stops immediately & permanently https://t.co/20hd8o1C3k

Which countries are leading in AI development? The Global AI Vibrancy Tool lets you compare AI capabilities across 66 countries using 42 indicators, from research output to infrastructure to policy frameworks. Explore our interactive tool: https://t.co/lH1G3zrdab

Fable 5 isn't nerfed, it's SLAUGHTERED. the problem isn't even the model itself, but the hard guardrails Anthropic has set in place. https://t.co/h1QgD9SzvK

@libertus you can start here: https://t.co/1CPTNoCbxK and if you have the appetite for it, just do a couple of searches online

Bridgewater used their unique financial knowledge and partnered with us on @tinkerapi to fine-tune a model that helps their analysts focus on what's important. Experts improving AI that empowers experts. https://t.co/6RJITMG2BJ

Coding agents are real users of the @huggingface Hub! They're searching for models, building and pushing datasets, training models on Jobs, spinning up Spaces... Now there's public data: each agent's share of Hub traffic, updated monthly 👇 https://t.co/ZpJDoXPPsq

AI Infra Day | SGLang × Sarvam with Hugging Face India's AI infra community is coming together. A day of deep-dives and technical discussions with researchers and engineers building the future of AI infrastructure. Bangalore | 11th July | 12:00–4:00PM Register Now: https://t.co/StSC0dxac9

Update on our long-horizon AI R&D evals: In April, we launched CRUX, a project to regularly run open-world evaluations. These long, messy, real-world tests of what AI agents can actually do. Our second evaluation is underway, and we ask: AI agents automate AI research? There is a lot of interest in studying AI research automation. But most of the systems built so far follow one of three patterns. 1) keep a human in the loop to guide the agent and course-correct along the way. 2) focus on narrow problems where ground truth is clear and progress is easy to verify, as in AutoResearch. 3) use scaffolds engineered for one specific type of research question, so strong results may say more about the scaffold than about the agent's general research ability. These efforts are helpful, but a lot of AI research is much broader. Success is not immediately clear or verifiable. Researchers need to test and reject promising hypotheses, backtrack, consider new or unconventional approaches, and do a lot more to make progress on answering research questions. In CRUX #2, we are trying to test whether agents can answer novel, open-ended AI research questions. - One major risk in such a task is contamination. We want the agent to have access to the internet and all the tools it needs to solve the task, so we can't use research questions from publicly available papers. At the same time, we want high quality papers to serve as the source of challenging research questions. - To address this, we partnered with AI researchers from UKAISI, UToronto, Princeton, and other institutions who have written high-quality papers that aren’t yet public, so there’s no risk of contamination. - The authors pose open-ended research questions without giving away answers. The agent must produce a NeurIPS-quality paper and a reproducible codebase, which the authors of the papers then review. - We built a general-purpose scaffold on OpenClaw and Opus 4.8. (We would have loved to use Fable 5, but given the filters on AI R&D capabilities, we don't want to confound results.) - Agents get generous resource budgets set in consultation with the original authors, such as access to VMs, GPUs, and any other compute needed to answer the question. They also have $3,000 in API credits per paper. We evaluate them on week-long time horizons to make progress on answering the research question, far more than typical agent evals. - The agent needs to manage its own budget. It can track its spend and stay within its limits, and it can modify its scaffold and reasoning effort as it sees fit. - In addition to the final artifacts, such as the paper's code, we are also evaluating the agent's trajectories in depth. When we announced CRUX, we planned to conduct an open-world eval every month. Given the scope and ambition of this project, we have spent a lot more time making sure we are confident in our setup and results. That said, the early results we have are exciting, and we look forward to sharing them soon.

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