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Top Cryptocurrencies to Watch in Early 2026

contact codearies / January 14, 2026

Read 6 MinEarly 2026 is gearing up to be a crucial year for the crypto world, with institutional capital flowing into ETFs and real world applications changing the landscape of which coins truly matter. Instead of getting caught up in every new meme, serious investors and builders are honing in on a select group of cryptocurrencies that boast robust networks, clear narratives, and increasing adoption across DeFi, payments, Web3, and tokenization. Here’s a look at key categories and the top cryptocurrencies to keep an eye on, along with how Codearies supports founders in these ecosystems. Remember, this isn’t investment advice, always do your own research and manage your risks. Bitcoin and Ethereum the blue chip foundation Bitcoin and Ethereum continue to be the backbone of the market, influencing nearly every other crypto trend in 2026. Bitcoin BTC Digital reserve asset Many analyses still regard Bitcoin as the market’s bellwether, holding about sixty percent of the total crypto market cap and enjoying strong institutional demand through spot ETFs. Institutional flows and supply squeeze Reports indicate that ETFs are set to absorb more than one hundred percent of new BTC supply, while exchange reserves are hovering near multi year lows, reinforcing the long term hold narrative. Macro positioning With central banks easing liquidity and a more crypto friendly policy stance in the U.S. for 2026, BTC is positioned as a prime risk asset and a hedge in many investment portfolios. Ethereum ETH Backbone of Web3 Ethereum remains the foundational layer for DeFi, NFTs, DAOs, and tokenization, supported by the largest developer community in the crypto space. Staking and L2 growth Following the proof of stake upgrades, staking yields are transforming ETH into a yield bearing asset, while rollups like Arbitrum and Optimism enhance throughput and draw more activity into the Ethereum ecosystem. Institutional narrative Outlook reports suggest that Ethereum based real world asset tokenization and staking ETFs will be significant catalysts in 2026. High performance smart contract L1s Solana Avalanche and Sui Fast Layer 1s that prioritize user experience and consumer applications are set to be a major focus in 2026, as chains lacking real users are likely to fade away. Solana SOL Consumer and DeFi chain Solana stands out as a lightning fast blockchain geared for mass adoption, offering incredibly quick and affordable transactions that are boosting NFT gaming, DeFi, and mobile centric applications. Institutional validation With the CME launching SOL futures and options, along with a growing interest in ETFs, it’s clear that institutional confidence in Solana is on the rise following its resurgence in 2025. Avalanche AVAX Custom chains and tokenization Avalanche is making waves in various 2026 predictions as a frontrunner in custom blockchains and tokenization subnets tailored for enterprises and institutions. Real world assets Its ability to create specialized subnets aligns perfectly with the current trends in institutional RWA tokenization and regulated DeFi, driving this cycle forward. Sui SUI New generation performance chain Sui is gaining attention as one of the top coins to watch, thanks to its object centric design and a strong push from developers in the DeFi gaming and consumer dApp space. Growing ecosystem With its modular architecture and strategic partnerships, Sui is positioning itself as a serious player in the high TPS Layer 1 arena. Scaling Ethereum Polygon and Arbitrum Layer 2 and scaling focused projects are becoming increasingly vital as demand for Ethereum continues to rise. Polygon POL formerly MATIC Scaling Ethereum for the world Polygon consistently appears on 2026 watch lists as a leading platform for scaling and building ecosystems for mainstream brands, DeFi, and Web3 gaming. Enterprise and brand adoption Collaborations with major brands, along with advancements in zk rollups, reinforce the idea that Polygon will remain essential in bridging Web2 to Web3. Arbitrum ARB Leading L2 for DeFi Arbitrum is recognized as one of the top Ethereum Layer 2 solutions, thanks to its significant DeFi adoption, Ethereum’s security, and lower transaction costs. DAO influence With a substantial DAO treasury and a strong governance role, ARB is a token to keep an eye on for ecosystem coordination and governance. Interoperability and data Chainlink Polkadot and Cosmos Connecting data and applications across different chains is essential as the ecosystem becomes more fragmented with multiple chains. Chainlink LINK Real world data bridge Chainlink is often highlighted as a utility token to keep an eye on because it secures oracle data feeds for DeFi, real world assets (RWAs), insurance, and more. CCIP and tokenization With the Cross Chain Interoperability Protocol and partnerships in RWA tokenization, LINK is positioned right at the intersection of institutional DeFi and on chain finance. Polkadot DOT Interoperability at scale Polkadot is making waves in 2026 as a leading multi chain framework, featuring parachains and shared security for specialized blockchains. Modular future Its long term vision of connecting numerous app specific chains through a central relay aligns perfectly with trends in institutional tokenization and modular stacks. Cosmos ATOM Internet of blockchains Cosmos is all about sovereign chains linked through IBC, boasting a modular architecture that optimizes zones for DeFi, gaming, and infrastructure. Interchain expansion As more projects embrace IBC and app chains, the ATOM and Cosmos ecosystem continue to play a pivotal role in the interoperability narrative. Payments stablecoins and real world value Stablecoins and payment focused networks are emerging as one of the most significant structural trends heading into 2026, as they evolve into the internet’s dollars and settlement rails. Ripple XRP Cross border and enterprise rails XRP is making its mark on top 2026 lists thanks to its cross border payment use cases and legal clarity milestones that have sparked renewed interest from institutions. Stablecoin and RWA ecosystems ONDO and RWA platforms Tokens like ONDO are gaining attention as key players in tokenized treasuries and the adoption of institutional RWAs, which are expected to surge as banks begin to tokenize assets and utilize on chain settlement. Meme and culture coins Dogecoin and Shiba Inu Even in an institution led structure retail driven culture remains a significant force and

Blockchain, Utility Token

Top Utility Tokens With Real World Use Cases to Watch in 2026

contact codearies / January 14, 2026

Read 7 MinUtility tokens are gearing up for 2026, poised to become the driving force behind real world crypto adoption. They’re transforming blockchains from mere speculative playgrounds into robust platforms that facilitate payments, identity verification, supply chains, advertising, gaming, and AI infrastructure. Instead of just gathering dust in wallets, utility tokens are now intricately woven into products, where they handle fees, unlock features, reward users, and secure networks across finance, logistics, media, and Web3 applications. Let’s take a closer look at what defines a top utility token in 2026, which projects are making waves with real world applications, and how Codearies is helping founders create and launch impactful utility token ecosystems What makes a top utility token in 2026 Not every token with a whitepaper can claim the title of a top utility token. The standout tokens of 2026 share several key traits that set them apart from the noise. Analysts and industry experts have pinpointed these characteristics among their top picks. Key characteristics Clear core utility A top utility token has a clear purpose within its product, whether that’s covering gas fees, securing the blockchain, enabling governance, or purchasing specific services, no vague promises about the future here. Real user adoption These tokens have real users and partners actively utilizing them for payments, staking, or accessing services, rather than just traders speculating on centralized exchanges. Strong ecosystem integrations A leading utility token is integrated across various applications, partnerships, or enterprise deployments, making it part of a larger ecosystem rather than a standalone dApp. Sustainable Tokenomics The tokenomics are designed to prevent runaway inflation, featuring emissions burn mechanics and fee flows that reward long term usage instead of just catering to early airdrop hunters. Regulatory and narrative resilience The design emphasizes utility over unregistered profit promises, aligning with the emerging frameworks for classifying tokens into utility, security, stablecoin, and real world asset categories. With this perspective, let’s explore some of the most significant utility tokens with real world applications to keep an eye on in 2026 across various sectors. BNB powering one of the largest ecosystems BNB started out as a simple discount token for trading fees, but it has since transformed into a powerhouse for one of the most dynamic multi chain ecosystems. This includes the BNB Smart Chain, BNB Beacon Chain, and an expanding Web3 stack. It blends the features of both currency and utility tokens, serving purposes like gas fees, staking, participation in launchpads, and making payments across thousands of decentralized applications (dApps). Why BNB matters in 2026 Core utilities BNB plays a crucial role in transaction fees for DEX swaps on the BNB Smart Chain, staking, validator delegation, launchpad allocations, and even offers fee discounts within the Binance exchange ecosystem. Real world and Web3 reach You’ll find that merchants, Web3 games, and DeFi protocols readily accept BNB, while the chain itself boasts a significant portion of global on chain users, making it a go to option for many new projects. Ecosystem effect With a robust array of tools, wallets, centralized exchange (CEX) support, and developer infrastructure, BNB stands out as one of the easiest tokens to integrate for payment solutions and access features. For builders, BNB is a shining example of how a token can evolve from a one dimensional discount coin into a versatile utility asset within a thriving ecosystem. Ethereum ETH the programmable money standard ETH is often seen as a blue chip asset, but it’s also one of the most crucial utility tokens out there. Why? Because it’s needed to pay gas fees and interact with smart contracts throughout the Ethereum ecosystem. As rollups and Layer 2 solutions grow, ETH continues to play a vital role in settling transactions and securing the network. Why ETH is still a top utility token Gas and settlement Every transaction on Ethereum requires ETH as gas, making it indispensable for DeFi, NFTs, and a multitude of dApps, even when those interactions occur through rollups that eventually settle back on Ethereum. Collateral and staking ETH helps secure the network through proof of stake and serves as high quality collateral in lending protocols, derivatives markets, and restaking products. Composability Since Ethereum is the primary settlement layer for many protocols, ETH is intricately woven into the fabric of DeFi, Web3 infrastructure, and real world asset tokenization. ETH exemplifies how a native gas token with substantial liquidity can become the cornerstone of an entire smart contract economy. VeChain VET enterprise supply chains and sustainability VeChain’s VET token stands out as one of the most reliable enterprise focused utility tokens, with applications in supply chain traceability, carbon tracking, and compliance. VeChain ToolChain enables businesses to implement use cases without needing extensive blockchain knowledge, while VET and its associated tokens facilitate transactions and promote data integrity Why VET is compelling in 2026 Real world deployments VeChain has partnered with major players like Walmart China, BMW, Renault, DNV, and San Marino to track food supply chains, vehicle maintenance, ESG reporting, and national carbon credit initiatives. Utility in logistics and compliance VET anchors data authenticity, rewards ecosystem participants, and covers operational costs on the VeChainThor blockchain. Sustainability narrative As ESG requirements become more stringent, tokens that effectively track emissions and compliance data are drawing interest from both enterprises and regulators. VET illustrates that utility tokens can support real supply chain events and government programs, not just digital transactions. Basic Attention Token BAT fixing digital advertising BAT is integrated into the Brave browser and stands out as a prime example of a utility token that transforms the digital advertising and attention industry. It rewards users for engaging with privacy focused ads, ensures fair compensation for publishers, and allows advertisers to run campaigns with clear metrics. Why BAT still matters User rewards Brave users can earn BAT by choosing to view ads, turning their attention into a valuable resource that they control. Payments and tipping Users have the option to tip YouTubers, bloggers, and websites directly in BAT through Brave Rewards, supporting creators without the need for invasive

Uncategorized

Top 10 Technology Trends That Will Define 2026

contact codearies / January 12, 2026

Read 7 MinTechnology in 2026 is all about an AI first approach, where smart agents, interconnected infrastructure, and sustainable engineering are transforming the way we live, build, and grow businesses. The key trends have evolved beyond mere buzzwords, they now work together as systems. AI agents are coordinating tasks, cloud and edge computing are powering operations, secure blockchains are tracking value, and green technology is ensuring everything stays aligned with climate goals. Here are the top ten technology trends that will shape 2026, along with how Codearies can help companies turn these trends into innovative products and growth. 1 AI agents and autonomous workflows AI agents are taking traditional AI to the next level, evolving from simple question answering tools into goal oriented digital workers that can plan, act, and coordinate across various applications. Businesses are deploying fleets of specialized agents for support, finance, DevOps, marketing, and operations, seamlessly integrating them with CRMs, ticketing systems, and cloud platforms. Key points These agents understand business objectives and break them down into tasks, calling APIs and looping until everything is completed, rather than just handling one prompt at a time. According to Gartner and McKinsey, agentic AI and multi agent systems are emerging as essential strategic trends that will permeate most enterprise stacks by 2026. Companies that blend human expertise with AI agents in hybrid teams experience quicker execution, reduced costs, and round the clock operations. 2 Generative AI everywhere Generative AI is moving from pilot projects to becoming a core part of the infrastructure across content creation, design, coding, and analytics. These tools are now available on devices, in browsers, and within vertical SaaS applications, meaning most users will interact with generative AI through their existing apps rather than standalone models. Key points Generative AI is driving the creation of text, images, videos, and code, all integrated into office suites, CRM tools, design platforms, and developer IDEs. Application specific models and domain tuned language models are emerging for sectors like legal, finance, healthcare, retail, and gaming, enhancing accuracy and building trust. Companies are making significant investments in AI safety, data governance, and copyright aware tools to leverage generative AI at scale without legal complications. 3 Cloud plus edge computing Cloud computing continues to be the backbone of AI, but edge computing is making significant strides as models are executed closer to devices, enhancing speed, privacy, and reliability. Companies are now crafting architectures where intensive training takes place in large data centers, while inference and decision making happen on smartphones, vehicles, factories, and IoT devices. Key points According to McKinsey, global demand for data center capacity is expected to grow by about 19 to 22 percent each year through 2030, largely fueled by AI workloads. Edge AI helps cut down on latency and bandwidth costs, enabling predictive maintenance, real time monitoring, and offline capabilities in sectors like manufacturing, automotive, and healthcare. Hybrid cloud and edge computing patterns are becoming the go to reference architectures for CIOs looking to implement AI on a large scale. 4 Advanced connectivity 5G and early 6G Advanced connectivity through mature 5G private cellular networks and early research into 6G is laying the groundwork for many trends expected in 2026, connecting sensors, robots, vehicles, and AR devices. Network slicing and satellite connectivity are helping to provide high quality coverage even in remote areas. Key points 5G private networks are being deployed across factories, ports, hospitals, and campuses, allowing for low latency control of machinery and mission critical IoT applications. Research into 6G is concentrating on AI driven network management, ultra high bandwidth, and the integration of digital twins for smart cities and enhanced mobility. Satellite to smartphone services are bridging the last mile connectivity gaps, making the dream of global, always on connectivity a more achievable reality for both consumers and businesses. 5 Cybersecurity digital trust and provenance In today’s world, as AI and connectivity grow, so does the potential for cyberattacks, making cybersecurity and digital trust technologies top priorities for boards. Organizations are now embracing confidential computing, AI driven threat detection, and digital provenance systems to ensure the authenticity of their data and content. Key points According to Gartner, digital trust, cyber resilience, and digital provenance are among the essential strategies for safeguarding enterprise value in an AI driven future. AI powered security tools can sift through logs, emails, and network flows on a large scale, spotting anomalies much quicker than traditional human only SOC teams. Techniques like cryptographic proofs, watermarks, and on chain records play a crucial role in verifying the origins of data models and media, helping to mitigate risks associated with deepfakes and fraud. 6 Decentralized and digital trust technologies Web3 and tokenization Decentralized tech is evolving beyond mere speculation, as blockchains now facilitate identity verification, payments, DeFi, and asset tokenization across finance, supply chains, and media. Real world asset tokens, utility tokens, and digital identity solutions are establishing ownership and provenance in multi cloud, multi agent environments. Key points The tokenization of real world assets, ranging from treasuries to real estate and carbon credits, is becoming a significant trend in institutional finance and infrastructure. Utility tokens and stablecoins are the backbone of many payment and loyalty systems, connecting Web2 applications with Web3 frameworks. Companies are looking into permissioned or hybrid blockchain solutions that maintain compliance while reaping the benefits of programmability and transparency. 7 Robotics 2.0 and physical AI Robotics is stepping up its game by merging with AI sensors and computer vision, making it possible to automate more intricate tasks across warehouses, factories, retail spaces, and healthcare settings. Now, cobots and autonomous mobile robots are teaming up with humans, moving beyond just working in isolation. Key points Physical AI empowers robots to sense their surroundings, grasp tasks, and adjust to new situations rather than sticking to strict scripts. Robotics 2.0 platforms offer modular automation cells that can be easily reconfigured for new products, which helps cut down on capital expenditures and speeds up deployment. Drones and robots are becoming essential in logistics, inspection,

Blockchain

Why Are Institutions Finally Entering DeFi?

contact codearies / January 9, 2026

Read 6 MinInstitutions are finally stepping into the DeFi arena, and it’s about time! The landscape has evolved from just experimental yield farms to a robust infrastructure that provides genuine yields, tokenized real world assets, and compliant pathways that align with existing regulations. With clearer guidelines, institutional grade custody, KYC enabled access, and DeFi products that mirror familiar financial instruments like ETFs, money markets, and repos, banks, funds, and corporations can now confidently allocate significant capital at scale. What changed DeFi from wild west to institutional venue In the early days, DeFi was largely the playground of anonymous teams, unaudited contracts, and retail investors chasing yields, which kept regulated institutions at a distance. However, several key developments have shifted the risk reward equation. Regulated options like spot crypto ETFs and DeFi themed ETFs are now giving institutions the on ramps they need to fit their investment mandates. For instance, a European pension fund made headlines by investing in a regulated DeFi ETF using Coinbase Custody in early 2025. Meanwhile, DeFi’s total value locked (TVL) surged back above $123 billion in 2025, marking a 41% year over year increase, largely fueled by tokenized Treasuries and leading lending protocols like Aave, which alone boasted over $14.6 billion in TVL. Now, consultancies and banks are starting to view DeFi as a complementary infrastructure that can ease settlement friction and open up new markets, rather than as a shadowy parallel system. Thought leaders from firms like Oliver Wyman and J.P. Morgan are outlining institutional DeFi models that blend smart contracts with essential safeguards for AML, KYC, governance, and custody. This shift in perspective equips risk committees and boards with the language they need to see DeFi as a pathway to enhancement rather than a looming threat. Regulatory clarity and compliance rails For many institutions, the biggest hurdle has been the uncertainty surrounding who is accountable for compliance when dealing with decentralized protocols. In recent years, we’ve seen clearer guidelines emerge regarding digital assets and DeFi, thanks to frameworks from the US and EU, along with specific legislation like the CLARITY Act, which have helped to ease concerns about legal risks. Surveys indicate that while a whopping eighty six percent of institutional investors are either investing in or planning to invest in digital assets, the gap between their interest and actual investment is largely due to the complexities of regulations and operations, issues that these new rules are designed to tackle. On the technical front, AML and KYC tools for DeFi have made significant strides. Institutions are now employing wallet risk scoring, on chain analytics, and permissioned systems to comply with Bank Secrecy Act requirements while still tapping into protocol liquidity. Guides on AML compliance in DeFi highlight the importance of KYC, KYB, transaction monitoring, and DAO governance design to align with global standards. They also show how projects can incorporate decentralized identity and analytics to keep bad actors at bay without sacrificing the essence of decentralization. This evolving toolkit empowers compliance teams to approve interactions with smart contracts rather than shutting down DeFi altogether. New products tailored to institutional needs Institutions are no longer chasing the high APYs that initially drew in retail investors. Instead, they’re on the lookout for scalable, transparent, and well managed returns that can easily fit into their existing portfolios. The DeFi landscape now offers a variety of options to meet these needs. For instance, tokenized real world assets like on chain US Treasuries, commercial paper, and credit products have quickly become one of the fastest growing segments, allowing funds to hold yield-bearing instruments with the benefits of on chain settlement and composability. Institutional DeFi offerings also encompass regulated DeFi ETFs, structured yield notes, and integrated custody access to lending pools and DEX liquidity. Platforms such as Fireblocks and Coinbase serve as gateways to institutional DeFi, combining multi party computation (MPC) custody, policy engines, and curated protocol lists. This setup enables desks to engage in staking, lending, and liquidity provision with workflows that can be audited. Reports indicate that, spot Bitcoin ETFs like BlackRock’s IBIT could surpass eighty six billion dollars in assets, acting as a bridge for future DeFi exposure through tokenized positions and derivatives. We’re also seeing the rise of hybrid models that merge traditional finance (TradFi) with DeFi. Banks and asset managers are experimenting with DeFi protocols behind the scenes for lending, trading, or collateral management, all while providing clients with familiar interfaces and documentation. This approach simplifies the complexities of DeFi, wrapping them in institution friendly formats and turning protocols into backend solutions rather than consumer facing brands. Why institutions care yields liquidity and efficiency There are several key reasons why institutions are increasingly drawn to DeFi. For starters, the transparency of on chain yields can often surpass what traditional money markets offer, especially when utilizing tokenized treasuries and over collateralized lending instead of those murky structured products. Then there’s the fact that DeFi provides continuous markets with nearly instant settlement, which can significantly cut down on counterparty risk and operational hassles for things like collateral swaps, FX, and basis trades. Moreover, DeFi opens up new avenues for liquidity. Take asset managers, for instance, they can put tokenized funds or real world assets into automated liquidity pools, tapping into a global pool of investors without having to rely solely on centralized exchanges or OTC desks. Plus, the concept of composability allows institutions to create programmable workflows where collateral can shift automatically between strategies based on set rules, all without the need for intermediaries to manually reconcile ledgers. Banks and hedge funds view this as a chance to prototype next gen infrastructure while still adhering to their risk frameworks. Remaining challenges and risks Despite the progress we’ve made, there are still some significant hurdles to overcome. The regulatory landscape is fragmented, meaning that rules vary from one jurisdiction to another, which creates legal and operational challenges for global institutions. When it comes to anti money laundering (AML) and knowing your customer (KYC) practices in truly permissionless protocols,

AI, Blockchain

The Rise of AI Trading Agents: Will DeFAI Disrupt Human Traders?

contact codearies / January 8, 2026

Read 8 MinAI trading agents are emerging as a new breed of autonomous systems that can monitor markets around the clock, analyze thousands of signals every second, and execute strategies across both centralized and decentralized exchanges without the emotional biases or fatigue that humans experience. In the world of crypto, this trend is increasingly referred to as DeFAI, which blends decentralized finance with AI driven trading logic. These agents operate on blockchain networks or around smart contracts, directly interacting with DeFi protocols. They pose a significant challenge to traditional discretionary traders and even many systematic human traders by drastically reducing reaction times, capitalizing on tiny inefficiencies, and scaling strategies to levels that no manual trading desk can match, all while introducing new types of systemic risk. From bots to AI trading agents Basic trading bots have been around for years, executing simple rules for market making, arbitrage, or trend following. They depend on hard coded conditions and often fail when market conditions change or data becomes erratic. AI trading agents take it a step further. They leverage machine learning models to identify patterns in price movements, order books, on chain flows, and even off chain news sentiment. These agents can adapt their strategies over time, learning which signals are significant in various volatility environments and adjusting their allocations accordingly. In the DeFi space, AI agents can connect directly to smart contracts, providing liquidity to automated market makers (AMMs), adjusting positions in lending markets, hunting for on chain arbitrage opportunities, and rebalancing portfolios in near real time. Instead of a human monitoring dashboards, an agent keeps an eye on the mempool, liquidity pools, and oracle feeds, executing complex multi step transactions seamlessly. This blend of autonomy, speed, and composability is what sets DeFAI apart from traditional bot based trading setups. Why AI is so powerful in trading Markets are constantly churning out massive streams of data, think tick data, order books, liquidations, funding rates, social media chatter, and macroeconomic news. It’s a lot for human traders to keep up with on a continuous basis. That’s where AI models come in, especially those using deep learning and reinforcement learning. They can handle vast, multi dimensional datasets and spot complex, non linear relationships between various inputs and future returns or risk profiles. By analyzing factors like volatility clusters, order book imbalances, whale wallet movements, and correlated asset shifts, they can predict short term price movements. AI also helps eliminate emotional biases that often plague human traders. Emotions like fear of missing out, loss aversion, and the tendency to overtrade after a loss can cloud judgment. Well designed AI agents, on the other hand, adhere to data driven strategies and risk management rules. They know when to pull back on exposure if the signals start to weaken, rather than doubling down on losing trades. Over time, this disciplined approach can lead to significant performance advantages, especially in high frequency or intraday trading, where human emotions and reaction times can be major drawbacks. How DeFAI agents operate in on chain markets In the world of decentralized finance, AI trading agents engage with protocols in a variety of ways. One common approach is autonomous market making. These agents keep an eye on volume, volatility, and order flow on automated market makers (AMMs), adjusting liquidity ranges, fees, or pool allocations in real time. For instance, an AI agent might decide to concentrate liquidity closely around the current price or spread it out to minimize impermanent loss. They can also shift liquidity between different pools or chains based on yields and risk assessments. Another strategy involves cross protocol arbitrage and rebalancing. An AI agent continuously scans for price differences between decentralized exchanges (DEXs), centralized exchanges (CEXs), and derivatives markets. When it identifies mispricings, it can execute complex multi leg trades, including flash loans, to secure profits. Additionally, it can rebalance collateral and borrowing across lending protocols, optimizing funding costs for a treasury or investment fund based on current rates and utilization. Portfolio style DeFAI agents are designed to handle longer term investments. They typically spread their allocations across blue chip tokens, DeFi governance tokens, stablecoins, and yield strategies, all based on risk models that take into account on chain analytics like protocol total value locked (TVL), governance activity, emission schedules, and whale movements. These agents regularly rebalance their portfolios and may use options or perpetual contracts to hedge when necessary. Will AI agents replace human traders AI trading agents are set to take over many roles in trading, but they won’t replace everything. Routine tasks like basic arbitrage, passive market making, and straightforward trend strategies are already being handled by algorithms in traditional finance, and this trend is only speeding up in the crypto space. As DeFAI continues to evolve, the proportion of trading volume managed by autonomous agents is expected to increase, putting pressure on discretionary traders who don’t have a distinct informational or structural advantage. That said, markets are intricate and adaptive systems. Human creativity is still vital for crafting innovative strategies, shaping new narratives, and grasping regime shifts that disrupt previous correlations. People are particularly good at interpreting complex geopolitical events, regulatory changes, or technological advancements that models may not have encountered before. The most successful trading organizations will likely blend human strategic insight with AI agents for execution, scanning, and optimization, creating a hybrid model where humans and machines work together rather than one completely replacing the other. Another significant limitation is that models rely on historical data for training. When markets venture into truly uncharted territory, AI can falter dramatically if not properly managed. Human oversight is essential for tracking performance, stepping in when assumptions fail, and determining when to retire or retrain models. Therefore, DeFAI is more likely to shift human traders into roles as supervisors and designers of agent ecosystems rather than eliminate them altogether. New risks introduced by DeFAI As AI agents continue to gain traction, several systemic risks start to surface. Herding and correlation: When numerous agents are trained on

Blockchain

The Next Generation of L1 Blockchains: Modularity, DA & High TPS

contact codearies / January 7, 2026

Read 6 MinThe next wave of layer one blockchains is being shaped by three key concepts: modularity, specialized data availability, and genuinely high throughput that can cater to mainstream applications without compromising on security. These trends are changing the way founders approach base layers, moving away from rigid, monolithic chains to more flexible stacks. In this new landscape, execution, settlement, and data availability can evolve independently, all while providing a seamless experience for users and developers. For builders, this translates to more options in trade offs and greater freedom to design chains tailored for specific use cases like DeFi, gaming, AI, or real world assets, rather than trying to create a one size fits all solution. From monolithic L1s to modular architectures The first generation of L1s bundled execution, consensus, settlement, and data storage into a single, tightly integrated system. While this made them robust, it also made scaling difficult without either raising fees or sacrificing decentralization. The new wave of chains embraces modularity, allowing for the separation of concerns. This means components like execution environments, data availability layers, and interoperability protocols can be swapped, upgraded, or specialized over time. We can see this in ecosystems that support parallel chain rollups or app specific instances, all while relying on a shared base for security and finality. Modular design brings two major benefits. First, it allows performance to scale horizontally across multiple execution environments instead of just vertically through hardware upgrades. Second, it gives different applications the flexibility to choose the right mix of latency, cost, and security without forcing the entire network to conform to the same parameters. As more L1s embrace this approach, the competitive landscape is shifting towards who can offer the best developer experience and the most seamless abstraction over what is, in reality, a complex multi layer stack. Data availability as a first class design choice Data availability, once just a background detail, has now taken center stage as a crucial design element for the next generation of blockchains. High throughput applications and rollups require a dependable method to publish transaction data affordably and securely, ensuring that anyone can reconstruct the state even if execution nodes fail or go offline. Specialized data availability layers and data aware Layer 1s are stepping up, providing high bandwidth data publication with varying security and cost profiles. This allows rollups and application chains to offload storage while still benefiting from the guarantees of the base layer. This shift in focus on data availability is reshaping the economics of scaling. Rather than having every node store all data redundantly, some networks are adopting sampling, erasure coding, or economic incentives to maintain availability at a lower cost per byte, all while resisting censorship and data withholding attacks. Consequently, builders can now consider workloads like high frequency trading, gaming event streams, and AI data applications that would have been too costly under traditional full replication models. Being aware of data availability is quickly becoming a vital factor for teams when deciding which Layer 1 or Layer 1 plus data availability combination to build upon. High TPS and real world performance When it comes to raw transactions per second (TPS), many figures are more about marketing than reality. However, for consumer scale applications and on chain finance, the trifecta of high effective throughput, low latency, and predictable fees is what truly matters. Modern Layer 1s are experimenting with sharding, parallelized execution, optimized consensus protocols, and hardware aware networking to push live throughput far beyond what the first wave of chains could manage. Some are utilizing dynamic state sharding, while others are leveraging high performance virtual machines or parallel transaction schedulers to allow non conflicting transactions to run simultaneously. The key metric isn’t just peak theoretical TPS, it’s about sustained real world performance under load, with modest hardware requirements and stable fees. Layer 1s that can keep fees low even during peak activity open the door to product categories like micropayment streams, in game transactions, and machine to machine commerce, which simply wouldn’t be viable if a single transaction costs more than a cup of coffee. As more L1s reach thousands of TPS in production developers increasingly weigh ecosystem maturity and tooling as heavily as benchmark scores. Interoperability and multi chain futures One of the standout features of the next generation of Layer 1 blockchains is their native interoperability. Instead of betting on a single chain to dominate, many new architectures are now envisioning a future where various specialized Layer 1s and Layer 2s can coexist, linked together through bridges, relay chains, or messaging protocols. Take Polkadot, for example, with its parachains, or modular ecosystems that support application specific chains, these illustrate the shift towards a network of independent yet interconnected chains that can share security and liquidity. For developers, this means creating applications that can seamlessly communicate across different chains from the get go, whether it’s for sharing liquidity, exchanging data, or managing cross chain governance. Layer 1s that offer strong interoperability features lessen the reliance on fragile external bridges, making it simpler to view the multi chain landscape as a single programmable ecosystem. In the coming years, success won’t just hinge on the strength of an individual Layer 1 but also on how effectively it integrates into this expansive network of networks. Developer experience and ecosystem gravity Even the most sophisticated architecture can fall short without a robust ecosystem of developers and users. The competition among next gen Layer 1s is increasingly focused on the quality of their tools, programming languages, SDKs, documentation, and grant opportunities, rather than solely on consensus mechanisms. Some chains are attracting existing Ethereum developers by offering EVM compatibility or multi VM environments, while others are introducing new programming languages designed for safety or parallel execution. Ecosystem gravity starts to take shape when wallets, exchanges, infrastructure providers, and dApp frameworks rally around specific Layer 1s, making it easier to launch new projects there compared to more isolated networks. By 2026, research is expected to highlight a cluster of leading Layer

Why AI Agents Are the Next Big Frontier in Automation

contact codearies / January 6, 2026

Read 8 MinAI agents are stepping up as the next big thing in automation. They’re not just about responding to single prompts or following fixed scripts, they can actually plan, act, and adapt throughout entire workflows. Unlike traditional bots or rule based systems, these modern agents grasp goals, analyze context, select tools known as APIs, and keep iterating until they achieve the desired outcome. This transformation turns software from a passive responder into an active collaborator, capable of managing projects, running operations, and coordinating with humans and other systems with minimal oversight. From static automation to autonomous agents In the past, traditional automation has been all about handling narrow, repetitive tasks like sending emails, updating records, or transferring data between systems. These processes can be quite fragile. If something changes or an error pops up outside the predefined rules, the automation fails, and humans have to step in. AI agents change the game by blending language understanding, reasoning, and tool usage. They can make sense of vague requests, determine what needs to be done, and break tasks down into manageable steps, even when conditions shift. Picture this: you tell an agent to research a new market, identify fifty promising leads, draft personalized outreach messages, and input them into your CRM. A traditional script would require strict templates and flawless inputs. But an agent can search, synthesize information, spot duplicates, adjust the tone for different segments, and recover from hiccups like missing emails or API limits. Over time, it learns which prospects are more likely to convert and fine tunes its own strategy. Automation evolves from handling single tasks to achieving comprehensive outcomes. Key capabilities that make AI agents different There are several key features that set agents apart from older automation methods and simple chat assistants. Goal orientation is a major one. Agents work towards objectives rather than just following instructions. You might say, “Grow newsletter signups this month” or “Reduce customer response time,” and they’ll choose the best tactics within the set boundaries. They don’t just provide answers, they actively pursue goals. Tool use: Agents have the ability to tap into external tools, apps, and APIs. They can execute database queries, trigger workflows, send messages, or even run code. This capability allows them to connect human language with digital systems seamlessly. Memory and context: Agents keep track of their ongoing tasks in a working memory, and sometimes they even remember long term details like preferences, history, and rules. This continuity enables them to manage multi step projects instead of just handling one off interactions. Self evaluation: Many agent frameworks incorporate loops where the agent reviews its own work, measures outcomes against goals, and tries again with a refined approach. This reflective process marks a significant advancement beyond the traditional fire and forget automation. Coordination: Agents can communicate with other agents or humans, negotiating tasks and sharing information. This collaboration paves the way for digital teams of specialized agents to work together, much like different departments in a company. Why businesses care now The timing of this shift is no coincidence. Several trends are converging to bring agents into the limelight. Language models have become sophisticated enough to understand complex requests and create detailed plans across various fields. Tooling ecosystems now offer connections to CRMs, ERPs, marketing platforms, development tools, and more. Thanks to cloud infrastructure and vector databases, it’s now feasible to store context and run agents efficiently and affordably at scale. Meanwhile, talent shortages and cost pressures are driving organizations to find leverage wherever they can. Many companies have already tapped out the easy gains from traditional RPA and low code workflows. While those tools still have their place, they struggle with messy, unstructured tasks like reading contracts, summarizing customer feedback, synthesizing market research, or coordinating cross team initiatives. This is where AI agents come in, providing the leverage needed when conventional automation reaches its limits. High impact use cases for AI agents In various industries, agents are carving out their own niches across several categories. Knowledge work copilots can step in as project managers for tasks that involve planning research, drafting documents, gathering approvals, and keeping track of progress. Take a marketing agent, for instance, they can oversee an entire campaign, from researching the target audience to managing content calendars and reporting, only bringing in humans for those big strategic decisions. When it comes to customer operations, agents can sort through support tickets, review past interactions, pull data from internal systems, suggest solutions, and either handle low risk issues directly or draft responses for human agents to approve. As they gain experience, they start to recognize common patterns, which helps reduce the average handling time and allows human staff to focus on more complex cases. In the realm of sales and growth, agents can pinpoint potential customers and enhance their profiles using public data, score leads, craft personalized outreach messages, and coordinate follow ups across various channels. They also keep CRM records up to date, maintain clean pipelines, and generate weekly summaries for managers. For engineering and DevOps, agents can sift through issue queues, logs, and metrics to suggest fixes, write code, run tests, and submit merge requests. Infrastructure agents can monitor incidents, scale resources, and create post mortems with minimal human input. In back office and finance, agents can reconcile transactions, categorize expenses, follow up on invoices, prepare summaries, and assist with audits by linking every line item to the necessary documentation. What ties these cases together is a common thread, they all deal with unstructured information, multiple systems, and decision making that goes beyond simple if then rules, this is where agents truly excel. Architecture of modern AI agents Under the surface, most agents operate on a similar framework, even if the specifics of their implementations vary. There’s a planning layer that takes the user’s goal and breaks it down into smaller tasks, often using a chain of thought approach. Then, a tool layer provides access to various APIs, including those for email, calendars, CRMs, payment

Blockchain, Solana

Solana vs. Other L1s: Why Builders Are Migrating

contact codearies / January 5, 2026

Read 7 MinSolana is quickly becoming one of the most appealing layer one blockchains for developers in 2026. This rise is fueled by its impressive throughput, ultra low fees, consumer friendly applications, and a rapidly growing developer community. While many competing L1s are grappling with fragmentation or sluggish user growth, Solana is making strides. Although Ethereum still holds the crown in terms of overall maturity and developer numbers, an increasing number of projects are opting for Solana as their primary execution layer or are shifting consumer apps from other chains to take advantage of its speed, straightforward architecture, and expanding user base. Why performance and cost matter for builders When it comes to dApps, most consumers prioritize latency, cost, and reliability over ideological concerns. Solana consistently processes effective user transactions at around one thousand to fourteen hundred transactions per second (TPS) under real world conditions, with peak stress tests exceeding one hundred thousand TPS. Meanwhile, it maintains median transaction fees close to just $0.00025, over ten thousand times cheaper than the typical Ethereum mainnet fees, which hover around one to six dollars per transaction, and often exceed five cents on popular L2s. For applications like high frequency trading, payments, order book DEXs, gaming, and social platforms, where users expect web2 level responsiveness, these differences are significant, they can determine whether a product feels scalable and usable. In contrast to ecosystems that rely on multiple L2s and sidechains for scaling, Solana employs a single, highly optimized L1 design. This approach means that all activities share the same state and liquidity, simplifying things for developers who would otherwise have to manage bridges, fragmented liquidity, and cross rollup user experiences. Reports indicate that by late 2025 and into 2026, Solana is handling more daily on chain trades and active addresses than most other chains, creating a vibrant environment that feels alive rather than just a testing ground. Developer momentum and ecosystem depth Developer traction is a solid indicator of future success. Recent data from Electric Capital and other tracking sources reveals that in the first nine months of 2025, Solana welcomed over 11,500 new developers, marking an impressive 83% increase year over year. This brings its active developer count to about 17,700, while Ethereum still holds the lead with nearly 32,000, albeit at a slower growth rate. The main difference lies in focus, Solana developers are increasingly working on payments, gaming, consumer apps, and DEX infrastructure, rather than just financial primitives or enterprise pilots. Ecosystem metrics back up this narrative. Solana ranks high in DeFi TVL, stablecoin volume, and developer activity, with some reports placing it second in TVL, second in developer activity, and third in stablecoin transfer volumes. Retail facing infrastructure is also advancing quickly, with initiatives like Solana phones, a robust wallet ecosystem, and integrations with major exchanges and brokers enhancing visibility and usability for everyday users. Solana vs other L1s what is different When compared to other high performance Layer 1s like Avalanche, Cardano, Polkadot, and newer challengers, Solana presents a unique set of trade offs. Avalanche highlights subnet flexibility, while Cardano emphasizes formal methods and cautious upgrades. In contrast, Solana focuses on aggressive performance optimization and a unified base layer. This approach has drawn projects that prioritize shared liquidity and composability over custom app chains. Benchmarks really showcase this performance advantage. Current stats indicate that Solana can theoretically handle up to sixty five thousand transactions per second (TPS) with fees hovering around just $0.00025. In comparison, Avalanche and Polygon manage around four thousand and seven thousand TPS, respectively, but with slightly higher fees. While Ethereum’s mainnet is still the go to for premium settlements, more and more everyday transactions are shifting to rollups and sidechains, which can add some mental overhead for users and complexity for development teams. The perception of Solana has also evolved. In the past, reliability issues and outages led some critics to doubt its resilience. However, by late 2025 and into 2026, updates and improvements in tooling have prompted many analysts to label it as battle tested. Cboe Global Markets even filed for Solana linked ETFs, and major brokers have started supporting SOL trading, signaling a growing confidence from institutional players. This kind of endorsement is still quite rare among most Layer 1 competitors. Why builders migrate from other chains Developers tend to flock to platforms where they can quickly deliver value, delight users, and minimize operational risks. Three key factors stand out. First up is user experience. Builders on Solana strive to keep things simple for users, avoiding the need to choose networks, manage bridges, or deal with lengthy confirmation times. For consumer applications like gaming, live social feeds, or on chain order books, even minor improvements in user experience can lead to better retention. Numerous reports highlight Solana as one of the most utilized chains for real commerce and payments, with some payment processors ranking SOL among the top seven cryptocurrencies for actual spending volume worldwide. The second factor is ecosystem liquidity and composability. Many projects that initially launched on slower Layer 1s are finding more active liquidity and partnership opportunities by transitioning to Solana, where decentralized exchange (DEX) volume and NFT activity remain robust compared to its peers. Discussions on Reddit and various forums reveal that builders feel Solana better meets the needs of their applications, especially when it comes to managing thousands of small user actions per minute at a low cost. Third, When it comes to developer tools and the learning curve, Rust and Solana’s unique paradigms can feel a bit daunting at first. However, the ecosystem has made significant strides with improved documentation, frameworks, and SDKs. Thanks to modern frameworks, much of the complexity is hidden away, allowing teams to whip up production ready prototypes in no time. On the flip side, some Layer 1 chains struggle with less developed tools and smaller developer communities, which can slow down debugging and hiring processes. Tradeoffs and risks of choosing Solana Solana isn’t without its risks. Its architecture is more intricate than

Blockchain, Crypto, Meme Coin

Why Meme Coins Are Becoming the Gateway to Crypto Adoption

contact codearies / January 2, 2026

Read 8 MinMeme coins are quickly becoming the entry point for many into the world of crypto. They break down the psychological and technical barriers that often keep everyday folks from diving into Bitcoin, Ethereum, and the more complex DeFi protocols. With their fun, affordable, and culturally relatable vibe, meme coins attract people who might never bother reading a whitepaper or delving into tokenomics. Instead, they download a wallet, join a community, and make their first trade. From there, a good number of them gradually venture into centralized exchanges, DeFi apps, NFTs, and on chain games, transforming what started as a joke into a full fledged crypto journey. Why meme coins attract first time users Traditional crypto projects can seem pretty daunting, filled with complex discussions about decentralization, consensus mechanisms, and yield strategies. Meme coins turn that narrative on its head by blending speculation with internet humor and community storytelling. People are more inclined to try out a coin associated with a dog, cat, frog, or a celebrity parody because it feels more like joining a viral trend than making a serious financial decision. Price psychology plays a big role too. While the unit price of a coin doesn’t mean much without considering its supply, users often feel that buying millions of units of a low cost meme coin offers more potential than owning a tiny slice of a blue chip asset. This lottery ticket mentality, combined with stories of early Dogecoin or Shiba Inu investors turning small amounts into life changing profits, creates a strong sense of FOMO and curiosity. That curiosity drives users to learn how to create wallets, transfer tokens, and track charts, essential skills for participating in the crypto space. Social media is also a key player in this phenomenon. Influencers, KOLs, and online communities are always sharing memes, price screenshots, and inside jokes that spread far beyond the usual crypto Twitter crowd. This virality allows meme coins to reach people who may have never set foot on a crypto news site but spend hours on TikTok, Telegram, or Reddit. Once those users start asking how to buy, they often find themselves going through the entire crypto onboarding process without ever having to begin with Bitcoin education or DeFi tutorials. The role of community and identity Crypto has always thrived on community, but meme coins take that to a whole new level. Owning a meme coin becomes a part of who you are online. People switch up their avatars, add coin tickers to their usernames, and treat Discord or Telegram channels like their own digital tribes. For many newcomers, that sense of community is way more motivating than any technical details about the coin itself. These communities host contests, airdrops, meme battles, and even raids where everyone works together to post content across different platforms in perfect sync. New users quickly grasp the basics like liquidity pools, centralized exchanges, and slippage just by joining in on these events and asking questions in the chat. What might seem dull in a textbook turns into an adrenaline rush when it’s the difference between winning a contest or missing out on a price surge. This feeling of belonging helps users push through the challenges of learning about seed phrases, network fees, and the risks of making irreversible mistakes. They don’t feel like isolated individuals facing a complicated system, instead, they’re part of a group with common goals. That group dynamic is why meme coins often outshine more serious but less social projects when it comes to bringing in new users and keeping them engaged. Meme coins as marketing funnels for the entire ecosystem When a meme coin skyrockets in popularity, centralized exchanges scramble to list it because trading volume and new account signups soar. These exchanges then promote other products to the same users, like margin trading, staking, and even educational modules on safer investing and diversification. The meme coin essentially becomes the gateway for the entire exchange’s business. On the decentralized front, meme coin liquidity pools introduce users to DEXs, bridging, and yield farming. Someone who starts by buying a token on a DEX will quickly notice options to stake LP tokens, farm rewards, or get involved in launchpads. Many DeFi protocols intentionally embrace meme liquidity because it draws in traffic and fees that can later benefit more structured products. NFTs and gaming ecosystems are definitely reaping the rewards. Meme coin communities often kick off NFT collections, games, or metaverse experiences that showcase their beloved mascots. Those who initially jumped in for a quick profit often find themselves buying NFTs, diving into game economies, or trying out cross chain bridges. In this way, meme coins become cultural brands that guide users through various aspects of Web3. Education through speculation Critics have a point when they say meme coins can be volatile, risky, and often lack solid fundamentals. However, even when these coins take a nosedive, many users walk away with invaluable knowledge. They pick up skills like securing wallets, spotting scams, reading contract addresses, and grasping the basics of tokenomics. This hard earned experience equips them to be more savvy participants when they later engage with more serious projects. Nowadays, many communities have educational channels that cover essential topics like risk management, doing your own research (DYOR), fundamental analysis, and security tips. Some teams even collaborate with educators or influencers to host live sessions that break down how liquidity works, why slippage is important, and how to steer clear of honeypots. Ironically, the fear of getting rekt drives a deeper understanding than any abstract academic material ever could. For younger users and those in emerging markets, meme coins might be their first encounter with candlestick charts or the concept of macro cycles. These experiences are shaping a generation that is more financially and technologically savvy, even if their initial trades don’t always hit the mark. The double edged sword risks and downsides While meme coins might seem appealing at first glance, they come with some pretty significant risks. A

AI, Blockchain

How AI Is Reinventing DeFi Through Autonomous Smart Contracts

contact codearies / December 31, 2025

Read 8 MinAI is shaking up the world of DeFi by transforming smart contracts from rigid rule based systems into flexible, self sufficient entities that can gauge market conditions, learn from data, and adjust their actions with minimal human oversight. Gone are the days of fixed interest rates, strict collateral requirements, and manual strategy crafting. Now, DeFi protocols are beginning to harness AI agents to enhance liquidity yields, manage risk, and execute trades in real time, making decentralized finance not only more efficient but also a bit more intricate and risky. This evolution paves the way for exciting new applications like self optimizing lending pools, autonomous market makers, and dynamic liquidation systems, but it also brings up important concerns about transparency, trust, and governance, especially when the code can adapt through learning. From static smart contracts to autonomous agents Traditional DeFi smart contracts operate on set logic, if the collateral ratio dips below a certain point, liquidate, if the price feed indicates X, then adjust the rate to Y. While these contracts are powerful, they lack the ability to adapt to context. Enter AI driven autonomous smart contracts, which introduce three key enhancements. They can gather more data from on chain activities, cross chain movements, and off chain signals. They learn from this data using techniques like reinforcement learning or predictive analytics. And they take action by tweaking parameters, choosing strategies, or initiating flows without waiting for manual governance decisions. In practical terms, this means that the behavior of protocols can evolve over time. Lending platforms can identify the best collateral factors for various assets by monitoring volatility and user actions. Automated market makers can adjust their fee structures based on changing volumes and volatility. Liquidation bots can determine which positions to liquidate when gas prices surge or liquidity is low. The outcome is a more agile DeFi ecosystem that functions less like a static spreadsheet and more like a constantly evolving trading desk, all built on chain. Where AI plugs into the DeFi stack AI isn’t here to take over smart contracts at their core. Instead, smart contracts continue to serve as the reliable foundation for managing asset custody and settlement. Typically, AI finds its place in agents that interact with or adjust these contracts. A few interesting patterns are starting to emerge. One of these patterns involves AI governed parameters. In this setup, governance determines which metrics an AI agent can manage, like interest rate curves, fee multipliers, or reward schedules. The agent operates off chain but regularly updates on chain contracts with new values through secure configuration calls. Another pattern is AI powered executors. These agents monitor the markets and carry out transactions such as arbitrage rebalancing or liquidations, all while adhering to predefined cap rules and safety checks stored on chain. A third pattern features AI enhanced oracles and risk engines. Oracles can use anomaly detection to weed out unreliable price data, while risk engines can predict overall protocol risk through simulations and machine learning. These components don’t directly hold assets, but they significantly influence how smart contracts respond to real world events. AI optimized lending and liquidity Lending protocols are among the biggest winners when it comes to AI. Currently, most lending markets depend on static risk parameters like loan to value ratios, liquidation thresholds, and reserve factors, which governance updates periodically based on human analysis. This method is often slow and can lead to overreactions. With AI, protocols can continuously assess risk for each asset, user cohort, and market condition. For instance, the system can learn that a specific token tends to become highly volatile during major events and can automatically tighten collateral requirements in advance. It can also identify concentration risk when one borrower dominates a pool and adjust incentives to encourage a more diverse mix of lenders or borrowers, reducing that risk. When it comes to liquidity, AI can really help determine how much of the reserves should be lent out versus what should be kept as a safety net. It can also create dynamic interest curves that adjust based on usage and volatility in a nonlinear fashion, enhancing capital efficiency without compromising safety as much as traditional static curves tend to do. Smarter automated market makers Automated market makers (AMMs) initially relied on straightforward bonding curves that don’t need a centralized order book, but they often face issues like impermanent loss and can be less effective in volatile or thin markets. With AI driven liquidity management, these AMMs can become significantly smarter. An AI agent can continuously track volume fluctuations and order flow, making real time decisions about where to allocate liquidity along a curve or across various pools. It might shift liquidity closer to the current price during stable market conditions and spread it out more when volatility increases. Additionally, it can adjust fees on the fly, raising them during turbulent times to better reward liquidity providers and lowering them during quieter periods to draw in more traders. Over time, an AI powered AMM can learn the microstructure patterns of the market on each chain and trading pair, uncovering optimal configurations that would be nearly impossible to fine tune manually. For liquidity providers, this means potentially higher net returns and reduced uncompensated risk. For traders, it can lead to less slippage, especially with long tail assets. AI driven liquidations and risk mitigation Liquidations are one of the most delicate functions in DeFi. If they’re too aggressive, users face unnecessary liquidations, if they’re too slow, protocols can end up with bad debt. Traditional liquidation bots operate on basic rules, often competing against each other and wasting gas in the process. With autonomous smart contract ecosystems, AI agents can plan liquidations in a more strategic manner. They can simulate future price movements and gas conditions to determine the best timing and order for liquidating positions. They can also route liquidations across multiple decentralized exchanges (DEXs) to minimize slippage and even coordinate partial liquidations to protect user health and reduce systemic shock. AI isn’t

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