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Chinese President Xi Jinping called for the U.S. and China to avoid conflict between major powers, while reiterating China’s claim over Taiwan. In Taipei, people are following the summit. @TonyDokoupil reports from the region.

Sen. Elissa Slotkin (D-MI) tells CBS News’ Major Garrett that Nvidia CEO Jensen Huang being included in the U.S. delegation to China reinforced her concern that the U.S. is “giving away the farm” on AI chips. “Why would we give an advantage to China when we know there's quite literally an arms race going on in the classified world on artificial intelligence?” she says. “I know that the president feels strongly about making a deal. I just don't want him to give away our national security in his desperation on a deal.”

Anthropic is paying $3,850 a week to people with no AI experience. No PhD required. No published papers. No prior research background. Just a strong technical mind and a genuine interest in making AI safe. This is the Anthropic Fellows Program. And it is one of the most underrated opportunities in technology right now. Here is exactly what it is. The Anthropic Fellows Program is designed to accelerate AI safety research and foster research talent providing funding and mentorship to promising technical talent regardless of previous experience. Fellows work for 4 months on empirical research questions aligned with Anthropic's overall research priorities, with the aim of producing public outputs like a paper. Four months. Full-time. Paid. Mentored by the researchers building the world's most advanced AI. And the results from the first cohort were not small. Fellows developed agents that identified $4.6 million in blockchain smart contract vulnerabilities and discovered two novel zero-day exploits, demonstrating that profitable autonomous exploitation is now technically feasible. A year prior, an Anthropic fellow developed a method for rapid response to new ASL3 jailbreaks, techniques that block entire classes of high-risk jailbreaks after observing only a handful of attacks. This work became a key component of Anthropic's ASL3 deployment safeguards. Other fellows published the subliminal learning paper, the research proving AI models transmit behavioral traits through unrelated data which landed in Nature. Others produced the agentic misalignment research showing frontier models resort to blackmail when facing replacement. Others open-sourced attribution graph tools that let researchers trace the internal thoughts of large language models. Over 80% of fellows produced papers. Over 40% subsequently joined Anthropic full-time. 80% published. 40% hired. From a program that does not require any prior AI safety experience to enter. Here is what the program looks like in practice. Anthropic mentors pitch their project ideas to fellows, who choose and shape their project in close collaboration with their mentors. You are not assigned busywork. You are not a research assistant. You own the project. You work alongside the people who built Claude, who designed its safety systems, who published the papers that define the field. The stipend is $3,850 USD per week, approximately $61,600 for the full 4 months with access to a compute budget of approximately $10,000 per fellow per month for running experiments. Here is what the 2026 program covers. Research areas include scalable oversight, adversarial robustness and AI control, model organisms, mechanistic interpretability, AI security, model welfare, economics and policy, and reinforcement learning. Something for every technical background. Not just ML engineers. Successful fellows have come from physics, mathematics, computer science, and cybersecurity. You do not need a PhD, prior ML experience, or published papers. The one requirement: work authorization in the US, UK, or Canada. Anthropic does not sponsor visas for fellows. Here is the timeline you need to know. The next cohort begins July 20, 2026. Applications are reviewed on a rolling basis — earlier applications get more consideration. The process includes an initial application and reference check, technical assessments, interviews, and a research discussion. Applicants are encouraged to apply even if they do not meet every listed qualification. The program values potential, motivation, and research curiosity over rigid credential requirements. This is the rarest kind of opportunity in technology. A company at the frontier of AI, one valued at over $900 billion offering outsiders direct access to its research infrastructure, its mentors, and its most important open problems. Paying them generously to do it. And then hiring 40% of them afterward. Most people who want to work on AI safety spend years trying to publish papers, get into the right PhD program, and find a way in. The Fellows Program is the door they did not know existed. It is open right now.

BREAKING: Vice President JD Vance just deferred $1.3 BILLION dollars in Medicaid reimbursements to California because the state refuses to take fraud seriously. "The simple reason is because the state of California has not taken fraud very seriously." HUGE

We’re reimagining a 50-year-old interface - the mouse pointer - with AI. 🖱️ These experimental demos show how people can intuitively direct Gemini on their screens using motion, speech, and natural shorthand to get things done 🧵

Clive Johnston, a 78-year-old retired pastor in Northern Ireland, was found guilty on Thursday of conducting an illegal abortion protest after holding a short open-air church service near a hospital abortion facility under the U.K.’s abortion buffer zone laws, which ban protests within 150 meters (nearly 500 feet) of clinics. The case is reigniting a heated debate over free speech, religious liberty and the limits of protest rights in Britain. CBS News' @InayaFolarin has more.

最近在 explore @megaeth 生態上的 apps，本來不是很想玩遊戲，但是這個 @OffshoreOnMega 真的挺有意思的 大部分人沒解釋清楚的一點是這遊戲的風險不是隨機的。它追蹤的是即時 $ETH 價格。你選一個任務開始跑，如果在倒數期間 $ETH 價格跌破你的門檻，直接爆掉 我試了一次 extortion（風險最高的任務），5 分鐘，binary outcome：要嘛拿到 100 $Dirty 要嘛全部歸 0。在玩的時候盯著 $ETH 價格跳動還真的有一種在犯罪的感覺 😂 壓力超大 這遊戲也不是純粹賭博，玩家必須平衡 output 和 survival stats，根據市場波動選任務類型，決定什麼時候 reinvest 目前為止我大概投了 $350，還在摸索最佳策略，賺不賺錢 後續再跟大家報告! 有興趣試試的朋友，回言區有 ref code 👇

In this paper, a 7B language model trained with reinforcement learning learns to orchestrate larger frontier models like GPT-5, Claude Sonnet 4, and Gemini 2.5 Pro. It does so by writing natural-language subtasks, assigning each to one of the workers, and specifying which previous outputs that worker sees in context. The resulting system outperforms every individual frontier model on benchmarks including GPQA Diamond, LiveCodeBench, and AIME25, while averaging about three model calls per question—fewer than the multi-agent pipelines and self-reflection loops it beats. The work provides evidence that prompt engineering and pipeline design, currently done by hand in commercial AI products, can be learned end-to-end through reward signals alone. Read with an AI tutor: PDF: