aelf

aevatar Intelligence: aelf's new AI agent framework. 🔵 http://aevatar.ai No-code. Cross-chain. Cross-platform.

📊aelf Ventures Spotlight: How AI Agents Achieve Goals🤖 #AIagents are essentially large language models (#LLMs). Traditional LLMs generate responses rooted in the data they were trained on and are bounded by knowledge and reasoning constraints. Today, sophisticated agentic technology is able to leverage backend tool integration to access real-time information, streamline workflows and automate tasks to achieve complex goals effectively. AI agents can now adapt to user expectations by learning from past interactions and planning future actions to provide comprehensive and personalised responses, even without human intervention. Achieving user goals involves 3️⃣ key stages: 🔵Goal Initialisation and Planning🌟 While AI agents can operate autonomously in decision-making, they still rely on human-defined goals, tools and environments. Given the user’s objectives and available resources, these agents generate a series of tasks and subtasks, refining their outputs at each step to achieve complex goals efficiently. 🔵Resources for Agent Reasoning📚 AI agents make decisions based on perceived information, yet often lack the complete knowledge and resources needed to accomplish complex tasks. To bridge this gap, they utilise tools like external datasets, web searches, APIs and other agents to fill in missing information and update their knowledge base, enabling continuous reassessment and self-correction during task execution. This interaction between tools allows AI agents to tackle a wider range of tasks compared to traditional AI models, providing more general-purpose assistance. 🔵Reinforcement Learning🧠 AI agents improve the accuracy of their responses through feedback from other AI agents and human-in-the-loop (HITL) input. For instance, after presenting a prediction to the user, the agent saves the learned information and user feedback to refine future performance, adapting to user preferences. Feedback from multiple agents minimises the need for user direction, while feedback from users ensures better alignment of outcomes with intended goals. This iterative refinement process enhances the agent’s reasoning and accuracy, while storing past solutions in a knowledge base helps to avoid repeated mistakes. 💻Future of the AI World AI agents marks a major step towards the agentic web. By integrating with tools and real-time resources while learning from feedback, they can dynamically reassess and adapt their strategies to navigate complex goals with higher precision and personalisation. They are set to transform the digital assistant landscape, offering innovative solutions across various domains and enhancing productivity in unprecedented ways.

⏪ 2024 RECAP ⏪ It has been an exciting ride from the #Bitcoin bull run and rise of AI Agents, to TON Foundation's domination of the Telegram Messenger Mini App space. As we welcome 2025, let's reflect back on the events that shook the #AI, #blockchain and #crypto world this year!🔥 🔗https://lnkd.in/g2U5thWr

🌊aelf Ventures Weekly News Highlight: 27 December 2024💡 1️⃣aelf Launches aevatar Intelligence: No-Code Framework Empowering AI Agent Development aelf has introduced aevatar intelligence, an open-sourced, no-code framework designed to simplify the creation and deployment of AI agents to enable both novices and experts to build cross-platform AI agents without extensive developer knowledge. aevatar intelligence offers multiple large-language models (#LLMs) as customisation options for #AIagents and is looking to create an open and collaborative platform. Furthermore, it aims to enable seamless cross-chain and cross-platform inter-agent communication through its partnership with AetherLink. An AI pump pad is also set to launch, showcasing aevatar intelligence's versatility and paving the way for more innovative applications to be built on aelf blockchain. 2️⃣Meme Coins Capture 31% of Investor Focus in 2024 In 2024, meme coins experienced a fourfold increase in #crypto investor mindshare compared to last year. While meme coins are primarily driven by cultural virality rather than traditional fundamentals, there has been growing investor appetite for speculative opportunities, as meme coins now dominate 14.36% of investor narratives, making it on par with crypto AI. Meme coins, particularly those on Solana Foundation and Coinbase's Base platform, emerged as one of the most profitable narratives with average returns of 201% annually, outperforming AI tokens which saw an 11.6% decline. Despite rising interests in #AI and real-world assets, the meme coin craze reflects a potential shift in investor focus within the blockchain landscape. 3️⃣Gen Z Crypto Adoption Surges Post US Election Following Trump’s presidential win, crypto exchange Bitget reported a dramatic 683% rise in Gen Z users in November, highlighting a significant shift in crypto demographics. Gen Z's proportion of new users soared from 26.2% in October to 53.8%, likely driven by Trump's pro-Bitcoin and crypto stance. This influx of Gen Z users highlights the influence of political narratives on financial decisions. Globally, this shift mirrors broader trends as nations like El Salvador and Brazil explore #Bitcoin-friendly policies, suggesting political events will continue to shape future crypto adoption. 4️⃣Utility Meme Coins Predicted to Propel DeSci Sector's Growth MV Global forecasts that the #DeSci sector will expand significantly in 2025, propelled by utility meme coins. DeSci’s primary aim was to revolutionise scientific research through blockchain and has diversified beyond #DAO-driven funding to include health data management and open research initiatives. Pump.Science has enabled the creation of DeSci-specific meme coins like Rifampicin and Urolithin A, which have grown to $73M and $59M in market cap respectively. These tokens fund research and provide updates, linking token ownership to scientific advancements with strong community support.

🗣️aelf Ventures' Voices: Challenges Associated with AI Agents💬 🔷Hallucinations📉 LLMs are prone to "hallucinations," generating misleading or nonsensical content, due to biases in training data, processing errors and limited real-world understanding which could result in poor output quality of complex, multi-step processes. Addressing these hallucinations is crucial to ensure accountability and trust, especially in financial sectors where transparent decision-making is essential. 🔷Scalability🌐 While the industry has made strides in scalability, existing blockchains weren't designed to support the growing number of on-chain AI agents performing frequent, multi-step transactions. Although developers are working on advanced scaling solutions to support seamless global usage, they remain in a continuous race to keep up with the rapidly evolving demands. 🔷Cross-Chain Compatibility⛓️ Despite efforts to create bridges and composable layers, the crypto landscape remains fragmented, with compatibility issues hindering seamless cross-chain interactions. Introducing numerous AI agents would worsen congestion and complexities. Resolving these compatibility challenges is crucial for building a more integrated and efficient blockchain network, particularly as AI agent usage grows. 🔷Tools and Infrastructure🛠️ Current blockchain tools and infrastructure need significant development to meet the complex needs of AI agents, particularly in communication and interaction. Unlike Web2 systems with standardised protocols like HTTPS and Swift, Web3 lacks a unified inter-agent communication standard. Reports are created and exchanged in each inter-agent interaction, where each agent refines and expands on prior reports, which is lengthier and costlier. 🔷aelf Ventures’ Take💡 The journey to developing fully autonomous AI agents with robust real-world crypto expertise is still ongoing, requiring significant enhancements in functionality and adaptability. Beyond blockchain challenges, the Web2 environment also presents similar hurdles due to its lack of standardisation; different platforms use diverse API formats, authentication methods and data structures, which complicates the access to data and task execution options for AI agents. As these systems are not inherently designed for seamless AI integration, a solid infrastructure is crucial for those projects striving to bridge AI with Web2 or Web3 effectively and seamlessly. At aelf, we recently launched our very own AI agent framework—aevatar Intelligence, that features a no-code model, enabling everyone to easily create AI agents that can intelligently interact and collaborate cross-platform and cross-blockchain in real time. The race to develop scalable, adaptable AI agents is not without challenges, but those who succeed will redefine the landscape of technological innovation.

📊aelf Ventures Spotlight: Use Cases of AI Agents🤖 Let’s explore some compelling use cases where #AIagents illustrate their potential to revolutionise fields such as creative production, marketing, finance and more!⬇️ 🔵AI Agents as Creative Directors🖼️ AI agents such as Zerebro could aid in the production of text, images, video and audio, significantly enhancing efficiency and innovation within the creative industry. This enables seamless creation of bespoke marketing materials and creative assets like #NFTs, allowing creative professionals to concentrate on strategic tasks. This approach not only supports artistic expression but also introduces innovative economic models with more sophisticated copyright management systems. 🔵AI Agents as Influencers and KOLs🌟 Luna by Virtuals Protocol is an example of AI agents that are transforming audience engagement by providing transparent, data-driven insights while maintaining continuous interaction. Unlike human influencers that are limited by their availability, these agents are capable of operating around the clock, offering personalised and targeted interactions globally. This could revolutionise social media marketing through consistent messaging and interaction, thereby enhancing brand reach and loyalty. With data analytics, AI agents can tailor content to meet diverse audience preferences, setting a new standard for engagement in the digital marketing landscape. 🔵AI Agents as Data Analysts📊 Traders can deploy AI agents like AIXBT to enhance decision-making by analysing market data to predict trends and manage risks. Their ability to identify opportunities and generate insights is vital in the #DeFi sector, where market dynamics can be volatile. By adopting a proactive approach, these agents equip investors with insights needed to swiftly and effectively adapt to changing market landscapes, ensuring more informed investment decisions. 🔵AI Agents as Fund Managers💰 AI agents such as Aimonica revolutionise trading and asset management with advanced data analysis and automated decision-making. They could fine-tune portfolio strategies, execute transactions seamlessly and conduct real-time market assessments to mitigate risks, offering higher investment returns. Investors could enjoy improved operational efficiency, making AI-driven portfolio management a valuable asset in the financial landscape. 🔵AI Agents as Enterprise Resource Planning Tools💡 AI agents enhance productivity in organisations by converting large datasets into actionable insights and automating tasks to empower business leaders to make strategic decisions on resource allocation. They are especially useful in roles with routine tasks or require the analysis of large datasets, such as customer support, supply chain management and financial forecasting. With this, organisations could become more operationally efficient and more strategic in allocating human resources to more complex, value-driven activities.

aelf's v1.12.0 upgrade, originally scheduled for 25 Dec 2024, will be postponed. While verifying the upgrade code, we identified areas for further improvement. As such, the team requires additional implementation time. The updated timeline will be announced soonest. Meanwhile, we appreciate the community's patience and understanding in this matter.💙 https://lnkd.in/gMpaYK76

🚨 Introducing aevatar.ai: Your AI agent framework for seamless collaboration across platforms and blockchains.🌐 CORE CONCEPT 📍 ➡️Different LLMs are integrated into aevatar intelligence, so you can select your preferred AI engine. ➡️Through its no-code model, aevatar intelligence allows users to create AI agents with very customisable functions. ➡️ With AetherLink, an AI Oracle built on aelf, AI agents can access near-infinite data streams across different platforms and blockchains. ⚙️Repo: https://lnkd.in/g_JhU2WX For more details, refer to this thread: https://lnkd.in/gZrZppZr

Now, we have full control and access to AI agents with aevatar Intelligence—the brand-new AI agent framework built by aelf. 🔵 https://aevatar.ai Our revolutionary cloud-native AI agent framework features a no-code model, enabling everyone to create AI agents that can intelligently interact and collaborate cross-platform and cross-blockchain in real time. Ready up for the next wave of #AI #blockchain enablement!⚡ Read the full announcement via the link: https://lnkd.in/gpxR35CN

⚙️ We've gone live with a smart contract upgrade that further strengthens users' experience. Get the details via the link! https://lnkd.in/gH7zs3uG