LangChainBitcoin: A Perfect Combination of Large Language Models and Bitcoin

Author: CheKohler. Compilation: Cointiem

Author: CheKohler. Compilation: Cointiem.comQDD

Large Language Models (LLMs) are artificial intelligence (AI) that are trained through a large amount of text and code datasets. This enables them to perform various tasks, such as generating text, translating languages, and writing different types of creative content. In the past year, LLMs have become increasingly popular, and OpenAI's GPT-3 model has become one of the fastest growing applications in the world, open to the public.

Although ChatGPT may be mistaken for LLM, there are actually many competitors, some are closed source and some are open source. Google's Bard, Meta's LLaMA and other LLMs are also keen to explore the possibility of this new technology, and use their own data sets, Web scraping tools, weights and improvements to improve their functions.

LLMs have been introduced, and as they are gradually being used by more people to input prompts and provide information, these models provide value to users and continue to improve over time. Now it may seem like cumbersome gimmicks that can only solve certain specific problems, but they may become universal tools that replace most of the software and services we use today.

Although the future of LLMs is difficult to predict, existing LLMs have seen an influx of early adopters and their usage continues to grow. There are many reasons why LLMs have become so popular.

Firstly, they can access and process a large amount of information, enabling them to quickly learn and adapt to new situations, and perform various functions, including writing content, summarizing content, translating content, providing research services, and even coding and debugging.

twoSecondly, they can generate texts indistinguishable from human written texts, making them very suitable for various applications, such as Chatbot, customer service and marketing.

Finally, they are constantly updated and improved, which means that their functions are constantly expanding.

Popular LLMs are centrally controlled and funded by large technology companies

The popular LLMs with which you interact the most today are currently centrally controlled by a few large technology companies, and few people run LLMs on their own devices. Most users are not yet invested in this technology, and they prefer to have these models run on cloud servers and use them when needed.

This means that these companies have significant power over the usage and access permissions of these models. In addition, developing and training LLMs is expensive, which means that these companies can use their vast financial resources to subsidize the costs of these models. Companies such as Microsoft and Google are willing to spend money on these efforts to improve their models and establish a customer base that they can monetize when their user base is strong enough.

This concentration of power raises several issues.

Firstly, it may lead to LLMs being maliciously used for malicious purposes such as spreading incorrect information.

Secondly, it may unfairly prioritize large technology companies over small businesses and startups, and promote large-scale integration or even larger monopolies.

Finally, it may make it difficult for users to control their data and privacy.

Lack of clear monetization path

One of the biggest challenges faced by LLMs is the need for a clear monetization path. Although these models can generate various valuable products and services, it is still necessary to clarify how to sell these products and services in a profitable and sustainable manner to achieve feasibility on a global scale.

Enterprises building LLMs can choose to build their products into large LLM providers like OpenAI, and believe that they will maintain a leading position in the market; You can also take the risk of independently building your own model; Or try to figure out how to build an artificial intelligence that utilizes popular modeling software such as GPT, LLaMA, and BARD.

In addition, these enterprises also need to position themselves in specific fields; For example, an LLM that is only used for customer relationship management (CRM) customer surveys would be popular globally, but introducing it to businesses around the world in a cost-effective manner would be more complex. Although the product is completely digital and physical, monetization through legal channels carries risks, involving a range of custodians and payment processors, as well as foreign exchange and settlement risks.

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Once an LLM company can clearly display user growth, user retention rate, regular revenue, and customer lifetime value, traditional investors will flow in to fund these companies as they can now rely on indicators they understand.

The Future of Large Language Models

The Future of Large Language Models

Firstly, the cost of training and deploying LLMs may continue to decrease. This will enable more enterprises and organizations to use these models, thereby promoting the development of new innovative applications.

Secondly, the functionality of LLMs continues to expand. As these models become more powerful, they will be able to perform a wider range of tasks. This will make them more valuable to businesses and organizations.

Finally, the development of LangChain agents may help address some concerns about the concentration and abuse of LLMs. By making these models more decentralized and transparent, LangChain agents can ensure that they are used for good rather than malicious purposes.

What is a LangChain agent?

Although LLMs are very useful in general applications, they do not always have datasets, weights, and user feedback suitable for each segmented market. With the increasing number of LLM models, different companies or individuals are improving these models for different use cases. These improved models do not interact with each other and often exist in isolation, which limits their scope of influence.

It is unrealistic to expect users to register for each LLM model to handle specific queries, so it is necessary to find a method to connect different models together, which is the role of LangChain. No, it is not a blockchain and does not have tokens, so you can rest assured.

The core idea of this library is that we can "chain" different components together to create more advanced LLM use cases. A chain may consist of multiple components from multiple modules. The chain goes beyond a single LLM call and involves a series of calls (whether to LLM or other utilities). LangChain provides a standard interface for chains, integrates with other tools, and provides end-to-end chains for common applications.

LangChain agents will make it easier for LLMs to communicate, leverage professional knowledge and training from the global market, and quickly reduce the cost of artificial intelligence learning. LangChain ensures that you do not need to reinvent the wheel on certain training, but rather through model requests and responses in the market, but it does have one drawback.

Money cannot flow as quickly as API calls between AI models, at least not the currency we are accustomed to. However, if a network can settle payments in globally recognized pricing units in real-time and automatically trigger certain requests, it will be a game-changing factor.

This payment network can be combined with LangChain to unlock a new market based on AI requests and responses, achieving monetization through real-time payments. This is where LangChainBitcoin is a possible solution.

What is LangChainBitcoin?

LangChainBitcoin is a set of tools that enable LangChain agents to directly interact with Bitcoin and Lightning Network.

LangChainBitcoin includes two main functions:

oneLLM Proxy Bitcoin Tool: Using the newly launched OpenAIGPT-3/4 function call and the built-in abstract toolset in LangChain, users can create agents that can hold bitcoin balances (on the chain and Lightning Network), send/receive bitcoins on Lightning Network, and interact with Lightning Nodes (LND).

twoL402HTTPAPI traversalLangChainL402 is a Python project that allows users using the requests package to easily browse APIs that require L402 based authentication. The project also includes a wrapper that is compatible with LangChainAPIChain, allowing LangChain agents to interact with APIs that require L402 and Macaroons for payment or authentication. This allows the agent to access the real resources behind the lightning metering API.

This means that anyone who creates LLM locally, provides data to LLM, or provides assistance in response can pay for this information and allow larger LLMs and their customers to access it and make micro payments.

A company or individual can sell a prompt by restricting access to APIs that require L402 authentication, while potential buyers can request their own local agents to evaluate the response based on a set of standards. If the agent approves the response, further responses can be purchased.

For customers, they can use their favorite LLM model and connect Lightning Wallet to it to pay for requests outside of the current service provider's toolset, or if they want to compare the prompt responses of different models.

How does this relate to Lightning Network?

If Lightning Network becomes the de facto standard basis for settlement and payment between different AI models and their global customers.

These payments will require more liquidity and routing paths, which will encourage AI companies to establish nodes, while ordinary lightning node operators can provide assistance by creating paths between popular Lightning wallets and various AI lightning nodes.

By monetizing API calls in the form of satoshis on Lightning Network, you can now make instant micro payments between different databases, suppliers, models and customers worldwide. No matter where they are in the world, this new demand for continuous micro payment will also generate more costs on the Lightning Network and help make routing nodes more attractive business practices.