According to @DeepLearningAI, Andrew Ng emphasizes disciplined evaluations followed by error analysis as the core path to accelerate agentic AI systems, offering traders concrete benchmarks to track progress across AI equities and AI-linked crypto narratives; Source: @DeepLearningAI. According to @DeepLearningAI, OpenAI has strengthened ties with AMD, creating a clear event catalyst for GPU supply-chain exposure such as AMD shares and related options activity as traders assess partnership-driven demand signals; Source: @DeepLearningAI. According to @DeepLearningAI, DeepSeek has cut inference prices, directly impacting model-serving unit economics and providing a data point for monitoring cost-per-token trends and margin assumptions across AI infrastructure plays; Source: @DeepLearningAI. According to @DeepLearningAI, Tinker aims to make multi-GPU fine-tuning easier, indicating operational improvements that can shorten model update cycles and serve as a measurable driver for deployment velocity and developer adoption metrics; Source: @DeepLearningAI. According to @DeepLearningAI, the issue also highlights robots that draw the route before moving, signaling planning advances in autonomy that traders can map to edge AI and robotics supplier sentiment checks; Source: @DeepLearningAI. According to @DeepLearningAI, these developments form a practical watchlist: AMD for chip-partnership headlines, AI infrastructure names for inference pricing shifts, and tooling that accelerates fine-tuning for release cadence tracking, with crypto traders monitoring AI-theme sentiment as a macro catalyst without directional bias; Source: @DeepLearningAI.