LLMs as Building Blocks for Next-Gen Software
Large Language Models (LLMs) are transforming the software industry with their sophisticated natural language processing and understanding capabilities. As essential components in the development of next-generation software, LLMs are increasingly relevant to the future of programming. This blog post will explore the potential of LLMs in-depth, from GitHub repositories like Langchain to the use of vector search databases and the concept of self-reflection in LLM agent compositions. However, it's important to remain aware of the limitations of LLMs and the role of human oversight in ensuring their reliability.
LLMs, such as OpenAI's GPT series, have opened new horizons in the software industry, enabling developers to create advanced and versatile applications. LLMs are pretrained on massive datasets and can process and generate human-like text, allowing them to understand and respond to complex inputs. This functionality makes LLMs a valuable building block for creating next-generation software solutions that are efficient, intelligent, and adaptable to various use cases, including chatbots, code generation, and content creation.
Langchain is a GitHub repository that streamlines the integration of LLMs with traditional software. By providing a platform for developers to compose software using LLMs, Langchain enables the creation of powerful applications that harness the full potential of these models. For example, Langchain allows developers to create intelligent code generation tools by combining LLMs with syntax-aware models, resulting in software that can generate code snippets based on natural language input while maintaining syntactic correctness.
Vector search databases, such as FAISS or PineconeDB, play a crucial role in augmenting the capabilities of LLMs. These databases store and retrieve high-dimensional data vectors, allowing LLMs to quickly and accurately process information. By incorporating vector search databases into LLM-based software, developers can improve performance and efficiency while expanding the potential applications of their creations. For instance, LLMs can be combined with vector search databases to develop advanced recommendation systems that analyze user preferences and generate personalized content.
Self-reflection is a key aspect of LLM agent compositions, enabling models to analyze and evaluate their own behavior. This introspection allows LLMs to learn from their mistakes and become more reliable over time. For example, incorporating self-reflection into the design of LLMs can help create intelligent agents that adapt and grow in response to user feedback or new data. This results in more robust and dependable software solutions that better serve the needs of users in various domains, from customer support to healthcare.
Generative agent architecture. Source: https://arxiv.org/pdf/2304.03442.pdf
While LLMs have many practical applications, they can also be used creatively to push the boundaries of software development. For example, LLMs can be employed in the creation of Non-Player Character (NPC) world simulations in gaming, generating lifelike interactions and immersive environments. Developers can use LLMs to simulate dialogues, generate storylines, or even create in-game tutorials that dynamically adapt to player choices. This is just one of the many inventive ways LLMs can be used, and developers are encouraged to think outside the box when considering their potential applications.
Despite their impressive capabilities, it's important to recognize the limitations of LLMs. Many examples showcased on Twitter often display the "happy paths" of LLMs, but in reality, these models are not always reliable. For instance, LLMs can generate inappropriate or biased content due to the nature of their training data. Human oversight is essential in the development process to ensure the quality and accuracy of LLM-based software. Implementing strategies such as rule-based filters, reinforcement learning from human feedback, or fine-tuning LLMs on specific domain data can help mitigate these limitations.
It's crucial to maintain a realistic perspective and be cautious of skewed perceptions when evaluating the true potential of LLMs. For instance, when developing AI-based moderation tools, it is essential to recognize that LLMs alone cannot guarantee perfect content filtering, and human moderators are still needed to make the final call in ambiguous cases.
LLMs have undoubtedly emerged as a powerful building block for the next generation of software. With innovative platforms like Langchain and the incorporation of vector search databases, LLMs are poised to transform the software industry. However, it's crucial to recognize their limitations and the importance of human oversight in ensuring their reliability. As developers continue to experiment and explore the potential of LLMs, we can anticipate the emergence of groundbreaking software solutions that will shape the future of technology. By balancing the excitement around LLMs with a healthy dose of realism, we can truly unlock their full potential and create a better, more intelligent digital landscape.
Some Knowledge on Zero Knowledge
Zero Knowledge technology (zk) is a powerful enabler that holds the potential to revolutionize not only Web 3 but also various other industries. As a versatile technology, it offers a multitude of use cases. However, we are still in the early stages of discovering all the applications that zk can enable. Some evident zk use cases, such as transaction privacy and data compression (i.e., rollups), have already found real-world applications. Nevertheless, there are numerous potential use cases and technological advancements that must be realized to bring zk to mainstream adoption.
In this expanded analysis, we will explore the different applications of Zero Knowledge Proofs (ZKPs), discuss what could enable the next phase of this technology, and suggest some startup ideas that could benefit from zk.
Zero Knowledge Proofs (ZKPs) have made significant inroads in the crypto industry since their inception. ZKPs are essentially a cryptographic method that allows an entity to prove they possess specific knowledge or have completed a task correctly without revealing the information or details of the task execution. Due to their nature, ZKPs have been primarily employed in privacy-focused crypto networks. They have also been utilized to provide validity proofs for Ethereum layer 2 (L2) transactions on Ethereum's L1, introducing the concept of zk rollups. Additionally, ZKPs have found niche applications within various projects.
ZKPs enable privacy, particularly in decentralized networks lacking a central authority that can act as the source of truth. They allow a Web 3 user (the prover) to demonstrate to the network validators (the verifiers) that their transaction is valid (e.g., sufficient balance) without revealing transaction details, such as the amount, sender, or receiver addresses. ZKPs were initially developed to support shielded (private) payments in the Zcash network and later expanded to other networks, including privacy-focused L1s like Zcash, Horizon, Aleo, and Iron Fish; privacy smart contracts on general chains like Tornado Cash; and privacy-focused L2s like Aztec.
The other significant use case of ZKPs is generating rollup validity proofs on the underlying L1. General-purpose rollups optimize for throughput by not utilizing the privacy features of ZKPs. In this tradeoff, ZKP acts only as a proof of correctness for L2 transaction execution.
Furthermore, ZKPs have found applications in other blockchain protocols, such as Mina, which uses ZKPs to compress the blockchain state to a minuscule size, and Filecoin, which employs ZKPs to ensure storage providers correctly store the data they claim to store. Celo Plumo uses ZKPs to enable the creation of ultra-light network clients for use on resource-limited devices, while Dark Forest represents the most popular application of ZKPs in the gaming sector.
Currently, the zk development stack is not as well-developed as the AI stack. However, efforts are underway to build these abstractions. At the bottom of the stack are low-level ZKP libraries such as PLONK and STARK. Above that layer, high-level languages like Noir aim to abstract away the underlying zk cryptography, helping application developers focus on the application logic. Circom is another popular ZKP language that sits between these two layers, as it can be used to create complex zk backends and develop ZKP-based applications.
Another example of ZKP abstractions in Web 3 is StarkWare's Cairo language, which allows developers to implement general smart contracts that use STARK proving under the hood. To provide further abstraction, Nethermind's Warp tool enables Solidity developers to convert their Solidity code directly to Cairo. Using Warp, it was possible to transpile Uniswap V3 code to Cairo with minimal changes to the original Solidity code.
To foster the growth and development of ZKP technology, new startups can explore various opportunities, including:
Developing optimized execution engines for ZKPs: Startups can focus on creating dedicated GPU libraries or exploring FPGA and ASIC solutions to accelerate ZKP computations and reduce proving times.
Building software abstraction layers and tooling: Companies can invest in developing high-level languages, frameworks, and tools that simplify the zk application development process, making it more accessible to a broader range of developers.
Exploring novel applications of ZKPs: With zk technology offering a myriad of use cases, startups can identify and develop innovative applications in various industries such as healthcare, supply chain management, digital identity, and more.
Enhancing interoperability and cross-chain compatibility: Startups can work on solutions that enable seamless interaction between various zk-enabled networks and platforms, promoting a more connected and efficient Web 3 ecosystem.
As the technology matures and gains mainstream adoption, the potential of Zero Knowledge technology will become more evident. With continued research, development, and collaboration across industries, ZKPs could reshape the future of privacy, security, and trust in the digital world.
Merge aftermath
The Merge may have launched smoothly, but the Proof of Work (PoW) Ethereum fork, ETHPoW, aimed at die-hard miners, is off to a troubled start. In its first 24 hours, the ETHW coin has lost 75% of its value and users report they cannot access the network using the information provided by the ETHPoW team. Miners, the targeted users of the network, appear to be earning only a tenth of what they were when mining PoW Ethereum.
Ethereum underwent its largest ever upgrade on Sept. 15, replacing the PoW consensus mechanism with Proof of Stake (PoS), reducing the network's energy consumption by 99.8% and dropping Ether issuance by more than 87%. Large Chinese miners planned to fork Ethereum and maintain the chain after The Merge, capturing headlines.
However, ETHPoW's debut faced criticism for delaying its launch until hours after The Merge. The project failed to prepare a block explorer, wallet, and public Remote Procedure Call software well in advance of launching. When ETHPoW published its network specifications around eight hours after The Merge, users immediately reported problems accessing the network due to its Chain ID already being used by another project.
ETHPoW's rocky start coincided with a bloodbath in the ETHW market, with the price plunging 75% to $12.29 over the past 24 hours. Most miners appear unconvinced by ETHPoW, with the network hosting only 70.34 terahashes per second (TH/s) of hashing power, compared to Ethereum Classic's 242 TH/s.
Despite hosting less than one-twelfth of Ethereum's pre-merge hash rate, ETHPoW is failing to provide EtHash miners with a profitable refuge. Daily ETHPoW issuance is worth just $167,000, equating to 0.8% of the value generated each day under PoW Ethereum. With only 8% of Ethereum's hash rate migrating to ETHPoW, ETHW miners' earnings have fallen 90% compared to when they were mining Ethereum.
The Merge transitioned Ethereum from a mining-based PoW blockchain to a more energy-efficient, scalable PoS system by joining Ethereum's mainnet with its PoS-based system, the Beacon Chain. This shift laid the foundation for future upgrades, like sharding, which can only work under a PoS consensus model.
The ETHPoW fork's troubled start has raised concerns among miners and the crypto community. The lack of organization, poor preparation, and issues with network access have resulted in many questioning the fork's viability and long-term potential.
As Ethereum moves towards a more energy-efficient, scalable PoS system, ETHPoW's struggles to gain traction among miners highlight the industry's shifting priorities. The Merge has already significantly reduced energy consumption on the network and laid the groundwork for future developments, such as sharding, which will further enhance Ethereum's capabilities.
In contrast, the ETHPoW fork seems to be struggling to offer a viable alternative for miners looking to continue with PoW mining. With drastically reduced rewards and mounting challenges, it remains uncertain whether ETHPoW will be able to establish itself as a sustainable option for miners in the long run.
While some miners may be unhappy with the transition to PoS, the overall trend in the industry is moving towards more environmentally friendly and scalable solutions. As Ethereum continues to develop and refine its PoS system, it will likely attract more developers and users, further solidifying its position as a leading blockchain platform.
In the meantime, ETHPoW will need to address its current issues and prove its value proposition to the mining community if it hopes to survive in the rapidly evolving cryptocurrency landscape. However, as the industry moves towards more sustainable and scalable solutions, the future of PoW-based forks like ETHPoW remains uncertain.