According to @balajis, much of practical AI work is about loading the context first and only then expecting the system to reason, highlighting the importance of stable prompts (source: X post by @balajis on Dec 10, 2025). According to @balajis, AIs, like humans, get confused if you switch context mid-stream, which can degrade reasoning quality and output reliability (source: X post by @balajis on Dec 10, 2025). According to @balajis, this framing is a metaphor and biological brains may work differently, but the operational takeaway for AI systems remains the need for consistent context (source: X post by @balajis on Dec 10, 2025). Based on this point from @balajis, keeping prompts and analysis threads consistent is important when deploying AI in crypto trading workflows to avoid confusion-driven errors (source: X post by @balajis on Dec 10, 2025).

According to Andrej Karpathy, large language models should be treated as simulators that channel multiple perspectives rather than as entities with their own opinions, source: Andrej Karpathy on X. He advises replacing you centric questions with prompts that ask what different groups would say, which is directly applicable to structuring crypto market research, source: Andrej Karpathy on X. Applying this to trading workflows, practitioners can prompt simulated bulls, bears, and market makers to generate scenario narratives for BTC and ETH without assuming the model holds a personal view, source: Andrej Karpathy on X. He adds that forcing a you voice only makes the model adopt a personality implied by finetuning data statistics, reinforcing role based simulation as the correct mental model for AI assisted analysis, source: Andrej Karpathy on X.

According to @DeepLearningAI, its Generative AI for Software Development skills certificate is now available and teaches how to assign roles to LLMs to guide tone, detail, and perspective for more controllable outputs (source: @DeepLearningAI). The announcement highlights practical prompt engineering methods—specifically role setting—to improve response clarity for both beginner and expert developers seeking efficiency (source: @DeepLearningAI). The source does not mention cryptocurrencies, tokens, or market impacts, indicating no direct crypto trading catalyst from this update (source: @DeepLearningAI).

According to Miles Deutscher, DeepSeek is surprisingly capable and worth experimenting with using specific prompts that produce outputs vastly different from standard ChatGPT/Gemini prompting (source: Miles Deutscher on X, Dec 3, 2025, https://twitter.com/milesdeutscher/status/1996359907084538161). He directs users to a prompt thread by @aiedge_ for testing and experimentation (source: Miles Deutscher on X, Dec 3, 2025, https://x.com/aiedge_/status/1996274497973043495). The post focuses on AI tooling and experimentation and does not reference cryptocurrencies, tokens, or price action, providing no direct crypto trading signals (source: Miles Deutscher on X, Dec 3, 2025, https://twitter.com/milesdeutscher/status/1996359907084538161). For traders tracking AI narratives, the key takeaway is that influencer attention is highlighting alternative AI model workflows, but this specific post contains no market-impact claims or asset mentions (source: Miles Deutscher on X, Dec 3, 2025, https://twitter.com/milesdeutscher/status/1996359907084538161).

According to @milesdeutscher, he published an Alpha AI thread on X outlining five combined tips to get the best ChatGPT responses, source: X post by @milesdeutscher on Nov 28, 2025 at https://twitter.com/milesdeutscher/status/1994464154061775038. He states that stacking these five tips improves ChatGPT output quality, source: X post by @milesdeutscher on Nov 28, 2025 at https://twitter.com/milesdeutscher/status/1994464154061775038. He directs readers to the thread link at https://x.com/aiedge_/status/1994462554459181436 for details, source: X post by @milesdeutscher on Nov 28, 2025 at https://twitter.com/milesdeutscher/status/1994464154061775038. Positioned as an Alpha AI thread, the post signals potential process edge for market participants who use ChatGPT in crypto research workflows, source: the phrase "Alpha AI thread" in @milesdeutscher’s X post on Nov 28, 2025 at https://twitter.com/milesdeutscher/status/1994464154061775038.

According to the source, a new study proposes a simple pre-prompt that coaxes alignment-trained large language models to reveal multiple possible answers instead of a single response, restoring diversity reduced by alignment training (source: X post dated Oct 15, 2025). According to the source post, no model list, benchmark metrics, peer-review status, or market impact data were provided, so there is no direct evidence yet of price effects in AI-linked crypto or equities (source: X post dated Oct 15, 2025).

According to the source, an X post dated Oct 15, 2025 highlights an article claiming a 'magic prompt' can make ChatGPT more creative and smarter, but the post itself provides no quantitative benchmarks, datasets, or reproducible tests to validate performance (source: X post on Oct 15, 2025). According to the source, there are no model version details, deployment contexts, or peer-reviewed evaluations that traders could track for verification (source: X post on Oct 15, 2025). According to the source, the post makes no mention of crypto assets, AI-related tokens, or partnerships, implying no immediate, source-backed fundamental catalyst for AI-focused cryptocurrencies (source: X post on Oct 15, 2025). Given the absence of measurable impact in the source, traders may wait for independently verified benchmarks or enterprise adoption disclosures before adjusting exposure to AI narrative tokens (source: X post on Oct 15, 2025).

According to the source, a Penn State University study reports that using blunt or rude prompts led large language models to produce sharper, more accurate answers versus polite phrasing in controlled evaluations. Source: Penn State University. This finding challenges the common assumption that polite prompts improve model accuracy and instead highlights tone as a measurable lever in prompt engineering. Source: Penn State University. Prior research shows prompt strategy materially affects LLM task performance, reinforcing that instruction style can shift accuracy outcomes in reasoning and QA tasks. Source: Google Research, Chain-of-Thought Prompting (Wei et al., 2022) and Kojima et al., 2022. Because a majority of institutional traders cite AI and machine learning as the most influential technology in markets, prompt techniques that measurably raise model accuracy are operationally relevant to research workflows, trading assistants, and crypto-market analytics tied to the AI narrative. Source: J.P. Morgan e-Trading Trends Survey 2024.

According to @AnthropicAI, a new Engineering Blog post explains that beyond prompt engineering, developers need context engineering to get the most out of AI agents, and the post details how it works, directing readers to the official write-up for methodology and implementation guidance, source: @AnthropicAI. According to @AnthropicAI, the announcement is positioned for developers building AI agents and points to a structured approach to context setup that can inform production workflows in domains such as data retrieval, tool use, and decision pipelines, which is directly relevant to builders of automated research and execution systems in crypto and finance, source: @AnthropicAI.

According to @LexSokolin, the AI grid is being built now, with compute centers as the new power plants, API calls as the new power lines, and prompts as the new switches, highlighting where infrastructure value may concentrate, source: @LexSokolin. According to @LexSokolin, this framing directs traders to focus on capacity, throughput, and reliability at the compute, API, and prompt layers when constructing exposure, source: @LexSokolin. According to @LexSokolin, the call to “bet accordingly” implies positioning in the infrastructure stack rather than purely application-layer bets as the intelligence “electrification” proceeds, source: @LexSokolin. According to @LexSokolin, crypto market participants can map this thesis to infrastructure-aligned themes that mirror power plants, grids, and switches, focusing on decentralized compute, data, and interface layers that align with the buildout he describes, source: @LexSokolin.

According to Miles Deutscher, he released a short AI prompting guide distilled from two years of daily AI use and framed prompting as the most valuable skill in 2025 for leveling up outputs (source: Miles Deutscher on X, September 2, 2025). For crypto and stock traders using AI in research and execution, applying the guide’s prompting techniques can improve response quality and speed in market scanning, on-chain research writeups, and report drafting, aligning with the author’s stated goal to enhance outputs (source: Miles Deutscher on X, September 2, 2025).

According to Miles Deutscher, traders and analysts can achieve a 'high-ROI' by creating a 'prompt library' for AI tools. He suggests a workflow where users can have the AI model Grok interpret text from images of prompts, and then save these organized prompts in a database like Notion for future use. For traders, this method can streamline the process of research and analysis by having a ready-to-use library of effective prompts for market data, sentiment analysis, or strategy formulation, thus enhancing efficiency and potential profitability.