Anthropic, the mind behind ChatGPT competitor Claude, is joining the industry-wide charge into education, as the tech company announces a new university and classroom partnerships that will put their educational chatbot into the hands of students of all ages. Announced today, Claude for Education will be entering more classrooms and boosting its peer-reviewed knowledge bank, as it integrates with teaching and learning software Canvas, textbook and courseware company Wiley, and video learning tool Panopto. "We're building toward a future where students can reference readings, lecture recordings, visualizations, and textbook content directly within their conversations," the company explained. Students and educators can connect Wiley and Panopto materials to Claude's data base using pre-built MCP servers, says Anthropic, and access Claude directly in the Canvas coursework platform. In summary: students can use Claude like a personal study partner.

AI tools have become deeply embedded in how many students learn and complete schoolwork--and that usage is only poised to increase. On Tuesday, the American Federation of Teachers announced an AI training hub for educators, backed by 23 million from Microsoft, OpenAI, and Anthropic. The AFT is the second-largest teachers' union, representing 1.8 million teachers and educational staffers across the country. Their training hub will open in New York City this fall, featuring workshops that will educate teachers on how to use AI tools for tasks like generating lesson plans and quizzes, or writing emails to parents. Microsoft is providing 12.5 million for AI teacher training over the next five years.

You know the Rubik's Cube -- the one that's been tormenting coffee tables and classrooms since the '80s. This is the Rubik's Connected Pro, and it's what happens when a nostalgic brain teaser gets a serious tech upgrade. Whether you've always wanted to learn how to solve the cube or you're already a bit of a speedcuber looking to shave seconds off your time, the Rubik's Connected is designed to help you learn, improve, and compete -- all from your smartphone or tablet. And right now, you can get two smart cubes for just 79.99, so you'll always have one to battle your sibling, roommate, or that one coworker who insists they're "just naturally good at puzzles." Let's start with the basics.

The nation's largest teachers' union -- representing millions of staff within America's education system -- has joined forces with some of the world's top players in AI to ready another generation of tech-savvy educators. Announced Tuesday, July 8, by the American Federation of Teachers (AFT) and New York City-based affiliate United Federation of Teachers, along with tech giants Microsoft, OpenAI, and Anthropic, the new National Academy for AI Instruction will funnel 23 million toward free AI training and curriculum for all 1.8 million union members. The goal of the program and its brick-and-mortar Manhattan facility -- the brainchild of venture capitalist Roy Bahat and modeled after other high-tech training centers -- is to create a "national model for AI-integrated curriculum," according to the coalition, focused on skills-based workshops, online courses, and hands-on training. Microsoft will invest 12.5 million into the training program, with an additional 8 million in funding from OpenAI and 500,000 from Anthropic, the New York Times reports. OpenAI will also provide 2 million in technical resources.

After a period of beta testing, version 120 of the Opera browser is now being rolled out to the public. The biggest piece of news in this particular update is a new built-in translation feature with support for over 40 languages, and the browser is now based on Chromium 135. To protect privacy, the new Translator feature doesn't pass any information on to third parties, and the translation itself is processed using AI (in partnership with Lingvanex) on Opera's European-based servers. Other improvements in Opera 120 include improved password management, enhancements to Split Screen mode, refinements to Tab Islands, a new Miniplayer for videos, and VPN Pro. Lastly, Opera 120 includes a fix for a serious zero-day vulnerability (labeled CVE-2025-6554) in the V8 JavaScript engine.

"I teach computer science, and that is all," wrote Boaz Barak, of Harvard University, in a recent op-ed in The New York Times.a The main point of the op-ed was to protest the growing politicization of U.S. higher education, especially at elite universities, where we have seen many faculty members proceed from scholarship to advocacy. But in spite of the provocative title, the content of Barak's op-ed is quite more nuanced. "We should not normalize bringing one's ideology to the classroom," wrote Barak, and I could not agree more. But he also wrote that "The interaction of computer science and policy sometimes arises in my classes, and I make sure to present multiple perspectives." Here, Barak is advocating fairness and balance, rather than neutrality and avoidance of non-technical topics.

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These free online courses don't include certificates of completion or direct instructor messaging, but you still get unrestricted access to all the video content. So there's no unpleasant catch to worry about. Find the best free AI courses on Udemy.

Specifically, this non-iterative paradigm allows us to conduct inner-level optimization (value estimation) in training, while performing outer-level optimization (policy extraction) in testing. Naturally, such a paradigm raises three core questions that are not fully answered by prior non-iterative offline RL counterparts like rewardconditioned policy: Q1) What information should we transfer from the inner-level to the outer-level? Q2) What should we pay attention to when exploiting the transferred information for safe/confident outer-level optimization? Q3) What are the benefits of concurrently conducting outer-level optimization during testing? Motivated by model-based optimization (MBO), we propose DROP (Design fROm Policies), which fully answers the above questions. Specifically, in the inner-level, DROP decomposes offline data into multiple subsets and learns an MBO score model (A1). To keep safe exploitation to the score model in the outer-level, we explicitly learn a behavior embedding and introduce a conservative regularization (A2). During testing, we show that DROP permits test-time adaptation, enabling an adaptive inference across states (A3). Empirically, we find that DROP, compared to prior non-iterative offline RL counterparts, gains an average improvement probability of more than 80%, and achieves comparable or better performance compared to prior iterative baselines.

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