There are some jobs human bodies just weren't meant to do. Unloading trucks and shipping containers is a repetitive, grueling task ' and a big reason warehouse injury rates are more than twice the national average. The Pickle Robot Company wants its machines to do the heavy lifting. The company's one-armed robots autonomously unload trailers, picking up boxes weighing up to 50 pounds and placing them onto onboard conveyor belts for warehouses of all types. The company name, an homage to The Apple Computer Company, hints at the ambitions of founders AJ Meyer '09, Ariana Eisenstein '15, SM '16, and Dan Paluska '97, SM '00. The founders want to make the company the technology leader for supply chain automation. The company's unloading robots combine generative AI and machine-learning algorithms with sensors, cameras, and machine-vision software to navigate new environments on day one and improve performance over time. Much of the company's hardware is adapted from industrial partners. You may recognize the arm, for instance, from car manufacturing lines ' though you may not have seen it in bright pickle-green....

Among high school students and adults, girls and women are much more likely to use traditional, step-by-step algorithms to solve basic math problems ' such as lining up numbers to add, starting with the ones place, and 'carrying over' a number when needed. Boys and men are more likely to use alternative shortcuts, such as rounding both numbers, adding the rounded figures, and then adjusting to remove the rounding. But those who use traditional methods on basic problems are less likely to solve more complex math problems correctly. These are the main findings of two studies our research team published in November 2025. This new evidence may help explain an apparent contradiction in the existing research ' girls do better at math in school, but boys do better on high-stakes math tests and are more likely to pursue math-intensive careers. Our research focuses not just on getting correct answers, but on the methods students use to arrive at them. We find that boys and girls approach math problems differently, in ways that persist into adulthood....

Reducing the visibility of polarizing content in social media feeds can measurably lower partisan animosity. To come up with this finding, my colleagues and I developed a method that let us alter the ranking of people's feeds, previously something only the social media companies could do. Reranking social media feeds to reduce exposure to posts expressing anti-democratic attitudes and partisan animosity affected people's emotions and their views of people with opposing political views. I'm a computer scientist who studies social computing, artificial intelligence and the web. Because only social media platforms can modify their algorithms, we developed and released an open-source web tool that allowed us to rerank the feeds of consenting participants on X, formerly Twitter, in real time. Drawing on social science theory, we used a large language model to identify posts likely to polarize people, such as those advocating political violence or calling for the imprisonment of members of the opposing party. These posts were not removed; they were simply ranked lower, requiring users to scroll further to see them. This reduced the number of those posts users saw....

Claude Opus 4.5 is Anthropic's new flagship model in the Claude 4.5 family, and it's very clearly optimized around a single thesis: large language models are no longer just chatbots, they're operating systems for agents. It's positioned as Anthropic's best model for coding, agents, and computer use, and the design choices all point in that direction'long context, deep reasoning, powerful tool use, and strong safety scaffolding tuned for real work in spreadsheets, codebases, browsers, and enterprise workflows. At the core, Opus 4.5 is a large decoder-only transformer trained with next-token prediction on a broad mixture of internet text, code, documents, and synthetic data, continuing the Claude lineage. Anthropic doesn't publish layer counts or parameter numbers, but from its behavior and public documentation it's clear that the model combines high capacity with careful optimization for long contexts and tool-driven interaction. Within the 4.5 family, Opus sits above Sonnet and Haiku as the top-tier variant, aimed at the hardest reasoning and automation tasks rather than being just a general-purpose assistant....