@AnthropicAI latest Interpretability paper is gold ! Understand and vizualze it easily with deepmanim.com Keep up the good work @claudeai !

What happens when a civilization consumes all the energy of its planet? Then its star? Then its galaxy? This DeepManim animation visualizes the terrifying climb from: Type I → Type II → Type III civilizations. Try out DeepManim ! #mars #exoplanet #planet #manim

Transformers process words in parallel. So how do they know the difference between: “The cat ate the fish” and “The fish ate the cat” ? This video is a visual explanation of positional encoding: the elegant mathematical trick that gives Transformers a sense of order and distance. We explore: • why embeddings alone fail • the positional encoding problem • sinusoidal embeddings • multi-frequency fingerprints • rotational geometry • how attention learns relative distance Video made with DeepManim (Try it out ! )

I asked DeepManim: “Teach me Kolmogorov Complexity.” And it generated a complete visual explanation of: – randomness – compression – shortest programs – incomputability – Berry’s paradox Not slides. Not stock visuals. Actual mathematical storytelling. The crazy part is that Kolmogorov Complexity is one of those topics most people only encounter through dense theory papers. But once visualized, the intuition becomes obvious: Complexity is just the length of the shortest recipe. This feels like a new way to learn difficult ideas. Less memorization. More direct understanding. Try DeepManim

Math explanations are usually static. A theorem. A graph. A paragraph. But mathematical understanding is dynamic. So I tested something with DeepManim: I asked it to teach convergence rates. It generated a full animated learning experience: – sequences racing toward limits – shrinking error terms – polynomial vs exponential decay – quadratic convergence “teleporting” precision The interesting part isn’t just the animation. It’s that this came from a prompt. We’re getting closer to a future where complex ideas become instantly visualizable. DeepManim is exploring that direction.

Ever wondered what speculative decoding is ? It's a clever technique to speedup llms token generation Here is DeepManim giving a satisfying explanation ! Try DeepManim ! #speedup #ai #manim #token

Ever wondered what the advantages of MoE over dense models are? Here is an explanation made by DeepManim, it took 120B models as exemple Just ask DeepManim #MoE #speedup #llm #manim

DeepManim casualy explaining "What is quantization aware training and why it is useful" Try DeepManim

What is quantum entanglement? Just ask DeepManim and marvel on the satisfying video ! Try DeepManim #quantum #entanglement #ai #manim

Paul Erdős collaborated with over 500 mathematicians and left a legacy of deep, deceptively simple problems. With recent AI systems like GPT-5.5 making progress on Erdős-style questions, we may be entering a new era of mathematical discovery. Here is a DeepManim video to break down his story and why this moment matters. Try DeepManim ! #erdos #ai #maths

From characters ➝ subwords ➝ tokens This new DeepManim animation shows how Byte Pair Encoding (BPE) bridges human language and AI understanding. Simple, visual, and surprisingly satisfying 👇 #Token #AI #LLM

DeepManim is magical, Here is it casually making an introduction to information theorie Try it out !

I asked DeepManim "What is model steering ?" It came out with a satisfaying animated video explaning it. You can just consume Knowledge i guess Try DeepManim ! #anthropic #llm #ai #safety

Try DeepManim !

You will love DeepManim ! Basic explanation of cpu vs gpu :

Are we alone in the universe ? Is the great filter behind or ahead of us ? Here is deepmanim.com take on the mater Try it yourself !!!

MIT just dropped ScienceClaw Infinite: 300 autonomous agents conducting distributed scientific discovery. We visualized it with DeepManim.com

TurboQuant AI models waste massive memory on vectors. Compressing them usually adds overhead defeating the purpose. Google's new paper uses just 1 extra bit to eliminate that overhead. Result: same accuracy, way less memory. Accepted at ICLR 2026. The trick? Random rotations a 50-year-old math theorem. Here is a deepmanim.com overview of the paper. #manim

Spot on, love it !

So satisfying and clear !

Cursor's AI agents spend most of their time doing one thing: grep. A tool from 1973. When your codebase is small, it's fine. But Enterprise customers have monorepos where a single grep takes 15 seconds. So Cursor built a new index, from scratch. Here is an overview made by deepmanim.com