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Screen Awesome is a Chrome screen recording extension with zero host permissions, making video uploads architecturally impossible. Free, no watermarks, with auto-zoom, vector annotations, and scrolling screenshots.

OpenAI's next-gen model reportedly solves 10 long-standing open math problems for just $2,000 in token costs, evolving from knowledge carrier to knowledge producer.

Deep dive into how an 80B-parameter LLM runs on Mac with only 4.3GB memory, covering ultra-low-bit quantization, sparsity, memory mapping, and implications for privacy and edge AI.

A systematic AI engineer learning roadmap covering programming, math, ML, and data engineering foundations, plus frontier AI technologies like LLM, RAG, Agents, and MCP with free open-source resources.

DeepMind has top math AI systems like AlphaGeometry and AlphaProof but trails OpenAI on general math benchmarks. We analyze the specialized vs. general-purpose model divide and what benchmarks miss.

Curated collection of free, open-source ML lecture notes from MIT, Stanford, and Harvard—more current than textbooks, with GitHub list and selection criteria explained.

OpenAI's internal model codenamed Astra reportedly solved 10 major open math problems. We examine the claim's credibility, AI math reasoning capabilities, and a rational evaluation framework.

When AI starts proving theorems, how do mathematicians view their own value? Exploring the existential anxiety AI brings to mathematics and the future of human-AI collaboration.

Explore how mechanical strain breaks material symmetry to induce chiral structures. This article analyzes the physical mechanisms, advantages, and applications in programmable metamaterials, pharmaceuticals, and flexible electronics.

GPT-5.6 Sol conquers frontier math but struggles on ARC-AGI-3 puzzles. The fix? Not a smarter model, but two API settings that tripled scores and cut token costs 6x.

In-depth analysis of open-source AI models' latest progress in mathematical reasoning, exploring evaluation challenges like data contamination and benchmark saturation, and how formal verification and chain-of-thought methods drive more objective assessment.

Reddit leaks OpenAI's internal model codenamed Astra, claiming ten advances in math and theoretical CS. We analyze the rumor's credibility and its implications for AI reasoning.

OpenAI's internal model Astra reportedly achieved 10 breakthroughs in math and theoretical CS. We analyze the rumors, compute infrastructure trends, real AI research assistant experiences, and AI's limits in original research.

A systematic guide to learning MARL from theory to code, covering CleanRL, PettingZoo, PyMARL tools, IQL/VDN/QMIX/MADDPG algorithm progression, and practical tips for bridging theory and implementation.

An in-depth look at ten major advances in mathematics and theoretical computer science, covering complexity theory, combinatorics, and derandomization, and how they impact cryptography, AI training, and quantum computing.

OpenAI has allegedly completed the first construction of a nonsofic group in mathematical history. If proven valid, this would resolve a core open problem in group theory that has stood for over twenty years.

When RL continuously optimizes models to please reward models, do soaring Elo scores truly represent capability gains? A deep dive into Reward Hacking in RLHF, Goodhart's Law in AI, and industry countermeasures.

Deep analysis of the dilemma in AI model competition where reasoning gaps and pricing imbalances force vendors to excel at either capability or cost-effectiveness to survive.

Decoding signals like "frontiermogging" to analyze upcoming AI frontier model leaps, Agent automation deployment, and developer ecosystem expansion trends.

A developer shares their real experience with Composer 2.5, from budget pick to daily go-to. Deep comparison with Sonnet 5 in debugging scenarios reveals the gap between benchmark scores and real productivity.