Aperture Finance Explained: A Breakthrough in UX for Web3
While most protocols improve UX through UI and click optimisation, Aperture is coming at it from a different angle: interacting with all protocols and chains through an AI chatbot
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Introduction
1. Aperture Overview
2. Aperture Architecture
3. Team
4. Backers
Conclusion
Introduction
Aperture is a long time purveyor of automation solutions in the DeFi , the history of Aperture Finance dates back to 2021 when it was introduced as a product that aggregated investment opportunities from various blockchain networks into one place, offering a universal solution for DeFi users while bridging the gap between chains. A dive into the current iteration comes from 2022, when they automated complex yield strategies into simple one-click vaults and presented it on YouTube so every user could see it happen visually.
Today, according to their Litepaper from February 2024, Aperture seems to have transformed into something else: LLMs, Intent based architectures, managing positions and strategies in DeFi through a chatbot. Aperture is working on a generalized Intent infrastructure (similar to Anoma or Essential) complete with various types of Solvers. Let's try to piece everything together and understand what Aperture does. A little spoiler: it appears to be a significant breakthrough in how users interact with web3, but full implementation still requires time.
1. Aperture Overview
When visiting the Aperture Finance website, the first thing we'll see is: Engineered by Silicon Valley's finest, Aperture Finance offers seamless automation for both traditional finance (TradFi) funds and DeFi enthusiasts. Sounds quite straightforward, doesn't it? It seems like you could simply take a few different strategies, define the logic of interaction between them, and easily combine them. Or better yet, just text them to a bot and let it do all the work. The issue here is that in reality, connecting different strategies together, let alone having the ability to rearrange different components among them, is a non-trivial task. We've previously discussed such a phenomenon as looping in the Contango review, which offers to automate looping strategies and offer synthetic cPerps. This means that automated strategies, replacing manual loops, involve obtaining a futures loan, swapping it for the desired asset, lending the asset along with the user's margin, borrowing against it within certain limits, and reimbursing the initial amount of the futures loan. And that sounds quite complex for the end user.
But what if the end user doesn't even need to delve into these actions and understand them? What if the end user doesn't need to manually assemble their strategy, clicking buttons and signing Tx across multiple protocols with monitoring and tracking all actions in a spreadsheet?
This is exactly what Aperture is currently working on - the team is creating a new user interface of a chatbot, based on the underlying Intents infrastructure, which will allow users to "declare their goals" in natural language and connect to a network of solvers to achieve better execution and better prices than possible within the current transactional paradigm. Within the current DeFi paradigm, a limited but familiar "transactional approach" is used to achieve desired end states. This approach assumes that users interact with the dApp interface, initiate actions, and hope for desired results.
And this is called "Intent Based Architecture." As stated on The Block by McDavid Stoddard from Aperture:“Intent-based architectures allow the average DeFi user to access ‘execution types and prices that were previously only available to well-capitalized shops with their own team of developers’. Even a dedicated DeFi user armed with a premium YouTube account and tracking spreadsheets won't be able to achieve the level of execution and expressiveness that is possible with Intent-based DeFi UX.”
How it looks: At the current base level, the Aperture Liquidity Intents solution is built on Uniswap V3 (and PancakeSwap V3). And the user, through the interface, specifies the end goal for their LP position, and the transaction is executed only if its result matches the desired outcome voiced by the user.
For instance, a user can sign a message indicating they want their liquidity position to rebalance once it reaches 3,000 USDC for ETH, rebalancing in a 50:50 ratio within the range of 2,800 to 3,200 USDC, and specify an acceptable range of influence for swaps and gas fees on the price. Then, the decentralized network of solvers identifies potential transaction flows and sends them to the Aperture smart contract, where they are simulated and ranked based on modeling.
Alternatively, a simpler analogy can be drawn: the UX transition from LLM to DSL is akin to placing an order at a pizzeria over the phone. The customer can place an order in a very conversational language: "Give me your all-meat pizza in your largest size." The operator on the other end can mirror back: "You want our meat lover's pizza in a very large size?" The user easily understands this transformation and agrees: "Yes, I didn't know what it was called, but that's exactly what I need."
In essence, the Aperture UX ultimately allows the user to interact with DeFi in a similar manner. David Stoddard presented it as follows in an interview with The Block: "The user will say, 'I want you to rebalance me into the highest-yielding pool,' and then the Intents chatbot will reflect back to them a well-formatted DSL output that the user can review and confirm."
Currently, Aperture offers a unified interface for various DEXs and chains, but its IntentsGPT does not yet manage positions on behalf of the user. The full functionality is still under development, and more details will be available soon. Nevertheless, the primary Aperture Solver (more on them in the next section) showed good volumes as of March 8th:
2. Aperture Architecture
As previously outlined, Aperture comprises several components and participants in the protocol. Now, let's delve into what Solvers, Intent-based infrastructure, LLM, DSL, and so forth entail.
From the end-user perspective, the real UX Intents has three critically important foundational components, which are currently lacking in most decentralized DeFi applications (execution UX):
A new interface for declaring transactional objectives. Users no longer enumerate a list of steps taken with a simple "yes" or "no," but rather declare the desired end state.
A network of solvers that can compete to achieve the specified transactional objective while maintaining the flexibility of the transactional approach used to reach that end state.
An execution arbiter that can accurately rank solutions proposed by solvers and assist in executing the solutions (recipes) proposed by the winning solver. This network's responsibilities include selecting solutions based on ranking, holding solvers accountable, and implementing solutions in decentralized applications and blockchains ("global composability").
DSL is a unique domain-specific language (DSL) to which the interface can translate intentions, which can then be executed and verified on the blockchain.
LLM serves as the core of Aperture along with Solvers. It's a large language model, essentially, within the Aperture ecosystem.
The Intents-based infrastructure can be broken down into several components:
Application Layer: Solvers - these are specialized agents responsible for providing solutions in exchange for incentives from the intent author. This component operates based on Account Abstraction technology. The resolution function, operating based on the wallet's account abstraction, enables Solvers to demand any distributions on behalf of the user. They play a decisive role in managing the system's complexity and are aggregated into the Solver DAO. Solvers can be categorized into two main types:
Public and transparent solvers: These solvers are authorized to transparently handle standard intents, making them suitable for general transactions where confidentiality is not the primary concern.
Privileged solvers: These solvers are intended to handle confidential intents and have special trust privileges. Their privileged status allows them to manage transactions requiring a higher level of security and confidentiality. Additionally, Solvers compete by offering the best conditions for the user, and multiple Solvers can respond to a single request: if Solver A covers the Dymension distribution and Solver B covers the Celestia distribution, then both Solvers can earn a fee for servicing our user.
Intent Information Exchange Hub (Mempool): serves as a preliminary intermediate stage for user intents. It is designed to efficiently organize and queue these intents for processing using algorithms that prioritize based on various criteria such as urgency and resource requirements. The information hub ensures safe and orderly management of intents before they are transmitted to the blockchain.
ZK-Simulate for Data Validity: this is a necessary resource for verifying certain intents and corresponding solutions that rely on off-chain data. Zero-knowledge proof can be used to verify the authenticity of this data. By utilizing advanced cryptographic tools such as Brevis or Axiom, Aperture can generate ZKP for on-chain historical data that are part of the proposed solution by the Solver. This method allows for thorough verification of the Solver's output data, ensuring they are accurate, complete, and compliant with specified constraints and intents, without compromising transaction data privacy.
Verification Smart Contracts: for each type of intended use, a smart contract will be required for modeling, verifying, and controlling the proposed solution.
Ranking and Execution Mechanism: each group of verified intents must be ranked based on the results and evaluation of the Solver, and subsequently executed. The most important aspect of this execution mechanism is its ability to ensure accountability. In case of any malicious actions, such as canceled transactions or other malicious events, the execution mechanism is designed to penalize responsible Solvers through deduction or other means. This not only protects the integrity of transactions but also mitigates potential malicious behavior from Solvers.
3. Team
Lian Zhu LinkedIn - Cofounder, CEO. Lian Zhu is the Co-founder and CEO of Aperture Finance, a DeFi platform for structured products. Prior to starting Aperture, he had served as a Senior Product Manager at Amazon Web Services (AWS) and a Technical Program Manager at Netflix for 3 years respectively. Lian founded Internest Inc., a Natural Language Processing (NPL) focused startup that empowers multi-media marketing, localization, and publishing. He obtained his Executive MBA degree from UC Berkeley in 2022. An American Association of Chinese Writers member, he has published over 30 books, including the Chinese translation of Revival by Stephen King and the Diary of A Wimpy Kid series.
Peiqian Li LinkedIn - Co-founder and Co-CTO. Prior to his current role, Peiqian served as a Senior Software Engineer at Google for over six years, honing his expertise in real-time engagement modeling and streaming data processing infrastructure for Google News & Discover content recommendation. Transitioning to a pivotal role within YouTube Music, he contributed significantly to the development of user profile infrastructure, solidifying his reputation as a versatile and innovative technologist. His academic pursuits mirror his commitment to excellence, with a Master of Science in Computer Science from Stanford University, achieved through the Honors Cooperative Program while employed full-time at Google. He also holds a Bachelor of Science in Computer Engineering from Columbia University, graduating magna cum laude, and a Bachelor of Science in Computer Science from Wittenberg University, where he graduated summa cum laude and was an active member of the Wittenberg Choir.
Gao Han LinkedIn Co-founder and Co-CTO His journey into the realm of tech began at Google, where he held the mantle of Software Engineer for over five years in the bustling San Francisco Bay Area. There, he delved into the intricacies of news intelligence and ranking, coupled with ML-based user interest prediction, fostering an environment of innovation and growth. Prior to his tenure at Google, Gao honed his craft at Amazon Web Services (AWS), refining his skills as a Software Engineer, focusing on application deployment orchestration within the CodeDeploy team. Gao's passion for technology blossomed early on, evident during his time as a Software Developer at Rubicon International, where he spearheaded projects leveraging Machine Learning techniques and constructed revenue maps for client visualization. His academic journey boasts a Bachelor of Science in Computer Science from Cornell University, followed by a stint at the University at Buffalo, where he furthered his studies in Computer Science, laying the groundwork for his illustrious career in the tech industry.
Kavi Saglani LinkedIn CMO Kavi Saglani commands the realm of marketing and communications as the Senior Vice President at Matrixport, based in vibrant Singapore. With a track record spanning diverse fintech ventures, Kavi has been instrumental in shaping brand narratives and driving strategic initiatives. From his tenure at Cake DeFi to his pivotal role as the Global Head of Brand & Communications at a prominent crypto exchange, Kavi has consistently delivered impactful campaigns that resonate with audiences worldwide. Prior to his foray into fintech, Kavi honed his expertise at leading communications agencies like Weber Shandwick and Nelson Bostock Unlimited, where he crafted compelling narratives and spearheaded integrated campaigns for global brands. His journey into the tech industry began with roles at Aura Communication Global Ltd. and Muso.com, where he orchestrated marketing and PR strategies with finesse, contributing significantly to business growth. Armed with a degree in Media and Communications from Birmingham City University, Kavi continues to trailblaze the intersection of finance, technology, and communications with unparalleled vision and expertise.
4. Backers
Backers of Aperture Finance include Dewhales Capital, Arrington Capital, ParaFi Capital, Divergence Ventures, Costanoa Ventures, MEXC, Double Peak, Rarestone Capital, Krypital Group, PrimeBlock Ventures, Staked VC, Athena Ventures.
Conclusion
Recently, it has become increasingly common to develop with projects, one of the main goals of which is to ensure the smoothest possible user interaction with web3. Usually the emphasis is on UI/UX in terms of interface, reducing the number of clicks and confirmations, and simplifying the presentation of information visualisation. But this does not get rid of the most important problem: the layout of complex strategies, which makes users need to understand different chains, different protocols and strategies within protocols.
Aperture Finance, on the other hand, offers a completely different approach: it can be called user and protocol abstraction. There is a reason why TG trading bots have become so widespread, after all, because of the ease of interaction within them. And Aperture offers to convert web3 interaction into a dialogue format with Account Abstraction, which will save users from a large number of interactions with protocols. And as we have seen, the simplicity of the idea hides underneath complex processes that are tightly coupled.
Aperture Finance links:
Website | Twitter | Discord | Telegram | Medium | Documentation