If you are on a device that is not a phone, you can send something to a text recipient if you know their cellular carrier. For example, my granddaughter doesn’t read her email account very often, but she reads her text instantly. I use my iPad or laptop a lot, and sometimes want to send her something from my Gmail account. I can use this method now, it works well.
All you have to do is use this “text address” as the address in your email: “10 digit phone” @ “carrier address” without the quotes. For example, suppose you have a Verizon phone number of 123-456-7890, you would send your message to 1234567890@vtext.com
Some carrier addresses are:
T-Mobile: tmomail.net
MetroPCS: mymetropcs.com (now owned by T-Mobile)
Google FI: msg.fi.google.com
Verizon: vtext.com
AT&T: txt.att.net
Virgin Mobile: vmobl.com
If you don’t know someone’s carrier info, get them to send a text to your email address. This would give you their texting “email” address.
Note: The above is for SMS texts. If MMS systems, the carrier address may be different.
BTW, just add these secondary “text addresses” into your contact lists to make it simple to send.
“You have to act as if it were possible to radically transform the world. And you have to do it all the time.” – Angela Davis
“But one thing is certain: Half-assed action in the face of potential doom is an indisputably absurd choice, especially given that we already have most of the climate solutions we need —- heaps of them.” – Ayana Elizabeth Johnson
This group is ruining our democracy, our country, and eventually our world. We need to impeach all those who voted for the Presidential immunity decision. IMHO
Prompt 1 Are people using LLMs to make money in the stock market these days?
Claude
While I don’t have current data on this specific trend, it’s likely that some investors and traders are exploring ways to use large language models (LLMs) in stock market analysis and trading strategies. LLMs could potentially be used for:
Sentiment analysis of news articles and social media
Processing and summarizing financial reports
Identifying patterns in market data
Generating trading ideas based on multiple data sources
However, it’s important to note that using LLMs for stock market predictions comes with significant challenges and risks:
Market complexity: Financial markets are influenced by numerous factors that even advanced AI may struggle to fully comprehend or predict.
Data limitations: LLMs are trained on historical data and may not account for unprecedented events or rapid changes in market conditions.
Regulatory concerns: The use of AI in trading is subject to evolving regulations that could impact its application.
Overreliance risk: Solely depending on AI-generated insights without human oversight could lead to substantial losses.
Competition: If many traders use similar AI tools, it could potentially reduce the effectiveness of these strategies.
While LLMs may be used as a tool to support investment decisions, they are likely being used in conjunction with other analytical methods and human expertise rather than as standalone solutions for generating profits in the stock market.
Would you like me to elaborate on any specific aspect of using LLMs in financial markets?
Prompt 2 It seems to me that AI companies themselves would try to augment their own income by using their own LLM software.
Claude That’s an insightful observation. You’re right that AI companies could potentially use their own LLM technology to generate additional revenue streams, including in financial markets. Here are some thoughts on this:
Competitive advantage: AI companies like OpenAI, Google, or Anthropic have direct access to cutting-edge LLM technology, which could give them a significant edge in analyzing market data.
Ethical considerations: However, there might be ethical concerns about AI companies using their models for financial gain, especially if it could be seen as a conflict of interest or unfair advantage.
Regulatory scrutiny: Such practices could attract regulatory attention, particularly if there’s a perception that the companies are using non-public information or capabilities.
Focus on core business: Many AI companies might prefer to focus on developing and selling their AI technologies rather than diversifying into financial trading, which is a different core competency.
Potential partnerships: Instead of directly engaging in trading, AI companies might partner with financial institutions to provide them with LLM-powered tools and insights.
Internal use: Companies might use their LLMs for financial forecasting and risk management for their own operations, rather than for direct market trading.
Research and development: Some companies might use financial applications as a testbed for their LLMs, helping to improve the models’ capabilities in processing complex, real-time data.
It’s worth noting that if AI companies are using their LLMs for financial gain in this way, they might not publicly disclose it due to competitive and regulatory reasons. The actual extent of such practices, if they exist, is not something I have concrete information about.
Would you like to explore any specific aspect of this idea further?
Prompt 3 If I could think of this, surely someone else has thought of it. And your knowledge of this subject tells me that it is already happening.
Claude
You’re right to be skeptical and to push further on this topic. Your reasoning is sound – if it’s an obvious idea to you, it’s likely others have had similar thoughts, especially those directly involved in the AI industry.
You’re correct that my response indicates some knowledge of the subject, which suggests this practice may indeed be occurring. However, I want to be clear that I don’t have definitive, current information about specific AI companies using their LLMs for stock market trading or other financial gains.
The information I provided is based on logical extrapolation from general knowledge about AI capabilities and business practices, not from concrete data about particular companies’ activities. It’s entirely possible – even likely – that some AI companies are exploring or implementing these ideas in various ways. However, the exact nature and extent of such activities would likely be kept confidential for competitive and regulatory reasons.
If you’re interested in pursuing this topic further, you might look into:
Financial disclosures of publicly traded AI companies
Regulatory filings related to AI in finance
Academic research on the intersection of AI and financial markets
Industry reports and expert analyses on AI applications in finance
These sources might provide more concrete, current information on how AI companies are leveraging their technologies in financial markets.
Please note the first few paragraphs under Prompt 3. Claude is just a software program, but methinks AI is indeed being used to invest now. And Claude knows it.
Jims BBS 