Dylan Cope

"Oxford, Cambridge, Imperial, Manchester and University College London — are collectively enrolling about five Chinese Stem postgrads for every four Brits. In engineering, there are some 3,300 Chinese postgrads versus 1,900 Brits; in maths, 700 Chinese versus 500 Brits." MAD

"There's no scientific evidence to prove that a black woman and a white woman are genetically different." This level of biological illiteracy is almost impossible to comprehend. This person thinks skin color (beyond a tan) is environmentally determined?

Holy shit… this might be the most unreal academic-writing upgrade I’ve ever seen 🤯 A team from NUS just dropped PaperDebugger an in-editor, multi-agent system that lives inside Overleaf and rewrites your paper with you in real time. Not copy-paste. Not a sidebar chatbot. Actual agentic editing inside your LaTeX editor. Here’s why this is insane 👇 → You highlight a messy paragraph, and it launches a full critique + rewrite pipeline → Returns clean before–after diffs like Git, then patches your document instantly → Runs Reviewer, Enhancer, Scoring, and Researcher agents in parallel → Uses Kubernetes pods to scale multi-agent reasoning inside the editor → Taps an MCP toolchain for literature search, reference lookup, and section-level enhancement Deep research mode is even crazier: It pulls relevant arXiv papers, summarizes them, compares your method against them, and generates citation-ready tables… all inline while you're writing. It’s basically a mini committee of reviewers embedded in your document rewriting, critiquing, sourcing, and polishing without ever breaking flow. If this scales, Overleaf stops being an editor… and becomes a full AI-assisted research environment.

do americans actually microwave water for tea?

Update: I looked closer - it’s actually two Black men and a Black woman, not just two men. So I re-ran the math. With that correction, the odds of this exact group (Asian woman + 2 Black men + 1 Black woman + one-legged man) randomly walking together in 1880 Chicago? About 1 in 2.4 million. Netflix didn’t just bend history - they straight-up violated statistics.

🚨 This MIT paper just broke everything we thought we knew about AI reasoning. These researchers built something called Tensor Logic that turns logical reasoning into pure mathematics. Not symbolic manipulation. Not heuristic search. Just tensor algebra. Here's how it works: Logical propositions become vectors. Inference rules become tensor operations. Truth values propagate through continuous transformations. Translation? Deduction and neural computation finally speak the same language. This isn't symbolic AI bolted onto deep learning. It's not deep learning pretending to do logic. It's a unified framework where both happen simultaneously. Every major AI model today hits a wall with consistency because logic is discrete and gradients are continuous. You can't backpropagate through "true or false." Tensor Logic erases that boundary completely. The system embeds Boolean reasoning, probabilistic inference, and predicate logic inside a single differentiable framework. That means you can train it end-to-end like a neural network while maintaining logical guarantees. In experiments, the system performs logical inference as matrix operations. Neural nets can now reason with symbolic precision. Symbolic systems can learn from data like neural nets. The numbers are wild. The system handles complex logical queries with the same computational efficiency as matrix multiplication. No expensive search. No combinatorial explosion. But here's the part that should terrify the incumbents: this scales. Traditional symbolic AI chokes on ambiguity. Neural networks hallucinate logical structures. Tensor Logic gets both right simultaneously. If this approach spreads, we might finally get models that don't just predict truths they can prove them. Systems that reason with mathematical certainty while learning from messy real-world data. The implications go way beyond academic AI. Every system that needs both learning and guarantees autonomous vehicles, medical diagnosis, financial systems, legal reasoning just got a new foundation. Current AI is either good at learning or good at logic. Never both. That dichotomy just ended. The fusion of logic and learning isn't coming. It's already here.

I personally work on automating labor because I expect the benefits of vast abundance and new product variety created from AI automation will outweigh the negative effects on human employment. Cost-benefit reasoning is unlikely to win you many friends, but it's far more honest.