According to @AndrewYNg, a new PyTorch for Deep Learning professional certificate taught by Laurence Moroney is now available and is described as a definitive program for learning PyTorch, which he notes is one of the main frameworks researchers use to build breakthrough AI systems (source: Andrew Ng on X, Oct 29, 2025). For trading context, this announcement underscores the ongoing centrality of PyTorch in cutting-edge AI workflows, offering a timely datapoint on developer upskilling that market participants can track when evaluating AI-exposed equities and crypto AI infrastructure themes (source: Andrew Ng on X, Oct 29, 2025).

According to @paoloardoino, many centralized AI companies are effectively functioning as an Artificial Intelligence Treasury (AIT), concentrating control and value within walled platforms (source: @paoloardoino on X, Oct 26, 2025). According to @paoloardoino, AI "won't be caged for long in walled gardens," underscoring a directional bias toward open, decentralized AI stacks that intersect with crypto-native infrastructure and incentives; the post does not reference specific tokens or provide quantitative signals (source: @paoloardoino on X, Oct 26, 2025).

According to @StockMKTNewz, Sam Altman’s Abundant Intelligence post says OpenAI plans to build a factory capable of producing a gigawatt of new AI infrastructure every week, requiring innovation across chips, power, buildings, and robotics, and taking years to execute. Source: @StockMKTNewz The post adds that OpenAI will share partner details in the coming months and financing later this year, emphasizing that increasing compute is the key to increasing revenue and aiming to build much of the capacity in the US. Source: @StockMKTNewz For traders, the disclosed gigawatt-per-week target and compute–revenue linkage focus attention on AI chip supply and power-intensive data center buildouts, while crypto participants may track AI-linked narratives given the scale of compute expansion signaled. Source: @StockMKTNewz

According to @karpathy, he resurfaced a paragraph from the 2021 GSM8K paper in a Sep 13, 2025 X post, highlighting ongoing attention to LLM reasoning evaluation (source: Andrej Karpathy, X post on Sep 13, 2025). GSM8K is a grade‑school math word‑problem benchmark designed to assess multi‑step reasoning in language models, making it a primary metric for tracking verified reasoning improvements (source: Cobbe et al., GSM8K paper, 2021). Because the post does not announce a new model, dataset, or benchmark score, there is no immediate, verifiable trading catalyst for AI‑linked crypto assets at this time (source: Andrej Karpathy, X post on Sep 13, 2025). Traders should wait for measurable GSM8K score gains or product release notes before positioning, as GSM8K is specifically used to quantify reasoning progress (source: Cobbe et al., GSM8K paper, 2021).

According to @scottshics, Kite is built for an agentic future and AI agents’ reliable execution time is doubling roughly every 7 months, highlighting fast performance improvement, source: @scottshics on X, Aug 19, 2025. The post references a research report by @0xPrismatic that informed this view, source: @scottshics on X, Aug 19, 2025. For crypto traders, this acceleration in agent capabilities is a relevant datapoint for the AI-agent and on-chain automation narrative within digital assets, source: @scottshics on X, Aug 19, 2025.