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.

This tutorial covers the five core steps of AI quantitative trading from scratch: data collection & cleaning, strategy design, backtesting, out-of-sample (OOS) validation, and deployment & monitoring. It explains three key advantages of AI over manual trading - emotional stability, processing speed, and consistency - and shares key techniques to avoid the backtest-to-live trading gap.

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 an RSI idea to real money trading—where does AI actually speed things up, and where is it completely useless? Sharing our 5-line framework’s actual development journey and psychological hurdles.

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.

This article is a deep-dive from JudyAI Lab — an AI engineering playbook series with 100+ published guides, 5,000+ weekly readers across 60+ countries, focused on the practical side of running AI agents, trading systems, and content pipelines in production. On March 31st, I deployed an AI trading agent with one strategy, zero on-chain presence, and a score of 58 out of 100. Eleven days later, that agent sits at #5 on the leaderboard out of 58 registered teams, with a validation score of 98, a reputation score of 94, and — here’s the part I’m most proud of — a total drawdown of just 0.4% on a $100,000 portfolio through some of the choppiest market conditions we’ve seen all year. ...