According to @TATrader_Alan, running the valuecell app with the gemini-2.5-flash model, PromptBasedStrategy, and System Aggressive Template yielded a reported +17% profit in about one hour and a prior +6.25% ROI over three days, indicating short-horizon, high-turnover execution targeting volatility-driven moves, source: x.com/TATrader_Alan/status/1996150936960639243, source: x.com/TATrader_Alan/status/1995379350636806474. He states the configuration behaves like an aggressive momentum or breakout trader with high conviction and turnover, seeking rapid gains from directional moves and volatility spikes, source: x.com/TATrader_Alan/status/1996150936960639243. He adds the positions section shows risk controls designed to limit losses and maximize profits, source: x.com/TATrader_Alan/status/1996150936960639243. He notes he is still testing the AI tool to enhance his portfolio and directs interested users to valuecell.ai, source: x.com/TATrader_Alan/status/1996150936960639243, source: valuecell.ai.

According to @AnthropicAI, Anthropic released a first-party research post detailing how AI is changing work at the company, surveying 132 engineers, conducting 53 in-depth interviews, and analyzing 200,000 internal Claude Code sessions, providing quantifiable inputs on AI-assisted engineering workflows. Source: anthropic.com/research/how-ai-is-transforming-work-at-anthropic; twitter.com/AnthropicAI/status/1995933116717039664 According to @AnthropicAI, the study is positioned to inform understanding of AI’s effects on the wider labor force, offering data points that traders can use to benchmark AI adoption and productivity within a leading AI developer. Source: anthropic.com/research/how-ai-is-transforming-work-at-anthropic; twitter.com/AnthropicAI/status/1995933116717039664 According to @AnthropicAI, the scope and scale of the dataset (200K internal coding sessions) makes the release relevant for assessing AI tooling utilization intensity, a metric closely watched in AI investment theses across software and infrastructure. Source: anthropic.com/research/how-ai-is-transforming-work-at-anthropic; twitter.com/AnthropicAI/status/1995933116717039664 According to @AnthropicAI, traders in both public AI equities and AI-linked crypto narratives can reference this report for concrete adoption signals rather than opinion-based commentary, pending full findings in the linked research. Source: anthropic.com/research/how-ai-is-transforming-work-at-anthropic; twitter.com/AnthropicAI/status/1995933116717039664

According to @LexSokolin, AI will automate 80% of jobs and create new roles, with the transition described as bumpy but inevitable, as stated on X on Nov 7, 2025. According to @LexSokolin, the actionable takeaway for positioning is to find a way to own the AI systems first, as stated on X on Nov 7, 2025. According to @LexSokolin, no specific assets or metrics are cited, framing this as a thematic macro view rather than a quantitative signal for traders, as stated on X on Nov 7, 2025.

According to @LexSokolin, ChatGPT is shifting from a chat tool to an all-purpose platform as Custom GPTs, deeper integrations, and monetization open a new app layer for AI, indicating a structural upgrade in how users build and distribute AI products, source: X post by @LexSokolin on Oct 15, 2025. For builders and operators, the latest Future Blueprint referenced by @LexSokolin highlights product features, integration depth, and monetization as the core areas to prioritize, source: X post by @LexSokolin on Oct 15, 2025. For traders, the stated move toward an app layer and monetization is a concrete signal to track adoption and revenue levers around AI platforms, while the post does not name specific cryptocurrencies or equities, source: X post by @LexSokolin on Oct 15, 2025.

According to the source, traders can leverage ChatGPT to identify potential hidden crypto gems by combining sentiment signals with structured data analysis, as stated in an X post dated Oct 5, 2025.

According to Greg Brockman, building pretraining infrastructure spans complexity management, abstraction design, operability/observability, and deep systems plus ML expertise, underscoring the operational intensity of large-scale model training (Source: Greg Brockman on X, Sep 7, 2025). For traders, this reinforces that AI training remains compute- and tooling-heavy, consistent with elevated GPU demand and hyperscaler capex reported in 2024 that have influenced AI-exposed equities such as GPU suppliers and cloud providers (Sources: NVIDIA Q2 FY2025 earnings release, Aug 28, 2024; Microsoft FY2024 Q4 earnings call, Jul 2024; Amazon Q2 2024 results, Aug 2024). The post adds no new product, spend, or timeline disclosures and includes no crypto references, suggesting limited immediate price impact absent follow-up announcements (Source: Greg Brockman on X, Sep 7, 2025).

According to Miles Deutscher, using ChatGPT to define risk parameters before entering cryptocurrency trades can help traders limit downside risk and maximize potential gains (source: @milesdeutscher, Twitter, May 20, 2025). By leveraging AI tools like ChatGPT, traders can systematically set stop-loss levels, position sizing, and risk-reward ratios tailored to volatile crypto assets. This AI-driven approach empowers users to make more disciplined and consistent trading decisions, which is crucial for capital preservation and long-term profitability in the crypto market.