Anthropic launches Claude Design, powered by Claude Opus 4.7 visual model, allowing users to create interactive prototypes, wireframes, presentations, and marketing materials through conversation without any design experience. The coolest feature is one-click handoff to Claude Code for direct implementation, completing the entire process from idea to production code within the Anthropic ecosystem. This tool targets not professional designers but PMs, founders, and marketers who previously didn’t use Figma, directly challenging Figma’s 80-90% market share in UI/UX design.

NVIDIA releases the world’s first open-source quantum AI model family “Ising,” designed to tackle quantum computing’s two core bottlenecks: calibration and error correction. Ising Calibration compresses calibration time from days to hours, while Ising Decoding boosts error correction speed by 2.5x and accuracy by 3x. Jensen Huang states that AI will become the quantum computer’s operating system, ushering in a new era of quantum-GPU supercomputing.

Google DeepMind has released Gemini Robotics-ER 1.6, an embodied reasoning model purpose-built for robotics. It enables robots to accurately understand spatial relationships, read analog gauges, determine task completion, and is now available to all developers through the Gemini API and Google AI Studio. What does this mean for AI-powered robotics?

Google introduces Skills feature for Chrome, supporting saving and one-click reuse of Gemini prompts, marking the browser’s official transition into an AI workflow platform.

A step-by-step guide to AI quantitative trading covering five core building processes: data collection & cleaning, strategy design, backtesting, out-of-sample validation (OOS), and deployment & monitoring. Explains three key advantages of AI over manual trading - emotional stability, processing speed, and consistency - and shares essential techniques to bridge the gap between backtesting and live performance.

Mike Bellafiore says elite traders run on Playbooks, not instincts. Reading his book, I suddenly realized my AI system had been doing exactly that all along — just written in code instead of a notebook.

AI trading bots face five major security threats: supply chain attacks, API key leaks, Prompt Injection, model poisoning, and exchange API vulnerabilities. This article breaks down each attack vector from an engineering perspective and provides actionable defense strategies and security checklists to help developers build truly secure automated trading systems.

A firsthand account of deploying MiniMax M2.7 into a multi-agent AI system — why we switched from GPT-4o, the real cost difference between subscription and per-token billing, and three pitfalls of running open-source LLMs in a production agent environment.

From a raw RSI idea to real-money live trading — where AI genuinely accelerated our workflow, and where it hit a wall completely. A look at the real development process and mental hurdles behind our 5-line architecture.

Built a trading agent in three days — AI sped up iteration, debugging, and documentation, but strategy intuition and risk judgment are still on you. A solo developer’s real observations, without the hype.