Ready

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The competition in the blockchain space is heating up as new and innovative platforms emerge, challenging established players. One such contender is Coldware (COLD), a Web3 platform that’s taking a bold step in the decentralized finance (DeFi) and Internet of Things (IoT) spaces. Coldware's unique approach to integrating IoT with blockchain technology is beginning to capture the attention of investors and market participants. With the growing interest in Coldware (COLD)’s presale, it seems that the emerging blockchain is poised to rival Avalanche’s market position. Coldware (COLD) enters the scene with a Web3 and IoT-ready blockchain that caters to a rapidly growing demand for smart solutions in decentralized applications. This emerging blockchain platform has positioned itself as an attractive alternative to AVAX by introducing features that cater to the next generation of blockchain technology. Coldware (COLD)’s focus on IoT devices and mobile-first decentralized applications gives it a competitive edge over AVAX, making it especially appealing to new investors and developers looking for scalability, simplicity, and enhanced usability. While Avalanche (AVAX) remains an attractive option for those building decentralized applications (dApps), Coldware (COLD) offers a broader spectrum of use cases by allowing seamless integration with IoT devices. The flexibility of Coldware’s platform is transforming how decentralized applications can be deployed in industries ranging from supply chain management to smart cities. This focus on IoT integration means Coldare could potentially lead the way in bringing real-world use cases into the blockchain space. The Shift Towards New Opportunities Despite the introduction of exciting initiatives like VanEck's Avalanche ETF filing, the price of AVAX has been on a sharp decline in 2025, losing more than 55% of its value since January. As institutional interest remains, the current performance of Avalanche has left many investors questioning its ability to maintain growth momentum. With AVAX trading at approximately $17.61, far from its historic highs, the sentiment surrounding the network has been shaken. Avalanche (AVAX) once stood out as one of the fastest and most scalable blockchain solutions, often compared with Ethereum due to its focus on decentralization and speed. However, as the market evolves, some of its key attributes have started to show limitations, particularly in scalability and adoption. The high-performance promises made by Avalanche are now challenged by newer players like Coldware (COLD), which brings additional features, including integration with IoT and a mobile-first platform that is more accessible to a global audience. How Coldware’s Web3 Focus Aligns with Market Demands Coldware (COLD)’s presale has been attracting attention from investors, particularly in Asia, where blockchain adoption is growing rapidly. The platform's Web3 approach makes it an attractive proposition for newcomers, as it aims to simplify the process of entering the decentralized finance (DeFi) ecosystem. Coldware (COLD)’s ability to seamlessly blend IoT, blockchain, and mobile solutions could accelerate the transition to Web3, positioning the platform as a key player in the future of the decentralized economy. As more developers and investors look for new opportunities in the blockchain space, Coldware’s (COLD) focus on accessibility, scalability, and integration with IoT could help it build a solid community of users, developers, and institutional investors. With Ethereum and Avalanche currently facing significant challenges, Coldware presents a fresh approach that is likely to capture the attention of both retail and institutional investors looking for a more versatile and inclusive platform. Coldware's Growth Potential As the market reevaluates Avalanche’s place in the blockchain ecosystem, Coldware's ability to attract a new generation of users through its IoT integration and mobile-first approach positions it as a strong competitor. The declining performance of AVAX, combined with the launch of Coldware (COLD)’s innovative solutions, could signal the beginning of a shift in market dominance. Coldware's growing presale momentum and its entry into the IoT space indicate that it is ready to challenge Avalanche’s position in the market. Conclusion In the fast-evolving world of blockchain technology, Coldware’s IoT-ready, mobile-first approach has quickly captured the attention of new market participants. As Avalanche (AVAX) grapples with a steep decline in value, Coldware (COLD)’s innovative solutions present a serious challenge to AVAX’s market share. With a strong presale and a growing community, Coldware (COLD) could emerge as the blockchain of choice for investors seeking the next big opportunity in decentralized finance and IoT applications. For more information on the Coldware (COLD) Presale: Visit Coldware (COLD) Coldware (COLD) Join and become a community member: https://t.me/coldwarenetwork https://t.me/coldwarenetwork https://x.com/ColdwareNetwork https://x.com/ColdwareNetwork This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions. 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Coldware’s IoT-Ready Blockchain

Can decentralized finance (DeFi) on Bitcoin finally deliver the automation that investors have been waiting for? Bitflow, a decentralized Bitcoin exchange, seems to think so. Today, the company announced the launch of Automated Dollar-Cost Averaging (DCA) for Bitcoin and Runes tokens on Stacks, marking a significant step toward AI-powered DeFi infrastructure. The introduction of Automated DCA allows users to accumulate Bitcoin (BTC) and popular Runes tokens without relinquishing custody of their assets. By leveraging Bitflow Keepers, the platform’s smart automation engine, this new feature brings recurring, trustless investment strategies directly onto the Stacks ecosystem. The development could signal a turning point for Bitcoin DeFi, long overshadowed by Ethereum’s more robust ecosystem. Unlocking Automation for Bitcoin DeFi Automated DCA isn’t just about convenience—it addresses a fundamental challenge in crypto investing: timing the market. With this feature, users can automate purchases of BTC, stablecoins, STX, sBTC, SIP-10 tokens, and top Runes assets like $DOG (DOG•GO•TO•THE•MOON), Stacks’ leading memecoin. The goal? To simplify long-term crypto accumulation and reduce reliance on manual trading. "Bitcoin DeFi is finally unlocking real automation with Automated DCA. Utilizing Bitflow Keepers, this powerful new feature enables users to program their BTC investments in a fully decentralized and trustless manner," said Dylan Floyd, Bitflow Co-Founder and Lead Developer. For the first time on Stacks, DeFi users can execute systematic investment strategies while retaining full control over their assets. All transactions occur on-chain, fully non-custodial, eliminating reliance on third-party intermediaries—a key principle in the decentralized finance ethos. The Broader Implications for Bitcoin’s Role in DeFi This move could redefine how Bitcoin functions within the DeFi landscape. Traditionally seen as a static store of value, Bitcoin’s role is evolving into that of a productive asset. Bitflow’s automation framework enhances this utility by allowing users to implement profit-taking strategies and recurring purchases based on predefined conditions—without needing to time the market manually. Moreover, the support for Runes and SIP-10 tokens broadens the utility of the Stacks Layer 2 (L2) network, positioning it as a key player in Bitcoin-native finance. The introduction of AI-driven automation could attract users looking for sophisticated tools typically associated with Ethereum’s DeFi offerings. Looking Ahead: AI and Cross-Layer Integration Bitflow’s roadmap for DeFi automation doesn’t end here. The company plans to introduce automated yield strategies that will optimize BTC-based yield farming without manual intervention. These strategies aim to unlock new avenues for generating returns on Bitcoin holdings, advancing the asset’s role beyond passive investment. Additionally, upcoming features like market-triggered swaps will enable users to execute trades automatically based on price trends and volatility indicators. Perhaps most significantly, Bitflow is working on facilitating cross-layer asset flows between Bitcoin’s Layer 1 and Stacks Layer 2. Such integration would improve liquidity efficiency and enable seamless movement of BTC-based assets across layers, enhancing the scalability and accessibility of Bitcoin DeFi. Final Thoughts: Is Bitcoin DeFi Finally Catching Up? The launch of Bitflow’s Automated DCA feature could represent a critical inflection point for Bitcoin-native finance. By bringing AI-powered, non-custodial automation tools to the Stacks ecosystem, Bitflow is challenging the perception that Bitcoin’s utility is limited to being a store of value. While Ethereum continues to dominate the DeFi conversation, Bitflow’s efforts may help Bitcoin close the gap by introducing more accessible, automated financial strategies. As Bitcoin continues to establish itself as a yield-generating asset, innovations like Automated DCA might pave the way for a more active, productive role in the decentralized economy. The question now is whether users and developers will embrace this shift. If they do, Bitcoin could finally become more than digital gold—it could become the foundation for a truly decentralized, automated financial future. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Can decentralized finance (DeFi) on Bitcoin finally deliver the automation that investors have been waiting for? Bitflow , a decentralized Bitcoin exchange, seems to think so. Today, the company announced the launch of Automated Dollar-Cost Averaging (DCA) for Bitcoin and Runes tokens on Stacks, marking a significant step toward AI-powered DeFi infrastructure. Bitflow The introduction of Automated DCA allows users to accumulate Bitcoin (BTC) and popular Runes tokens without relinquishing custody of their assets. By leveraging Bitflow Keepers, the platform’s smart automation engine, this new feature brings recurring, trustless investment strategies directly onto the Stacks ecosystem. The development could signal a turning point for Bitcoin DeFi, long overshadowed by Ethereum’s more robust ecosystem. Unlocking Automation for Bitcoin DeFi Unlocking Automation for Bitcoin DeFi Automated DCA isn’t just about convenience—it addresses a fundamental challenge in crypto investing: timing the market. With this feature, users can automate purchases of BTC, stablecoins, STX, sBTC, SIP-10 tokens, and top Runes assets like $DOG (DOG•GO•TO•THE•MOON), Stacks’ leading memecoin. The goal? To simplify long-term crypto accumulation and reduce reliance on manual trading. "Bitcoin DeFi is finally unlocking real automation with Automated DCA. Utilizing Bitflow Keepers, this powerful new feature enables users to program their BTC investments in a fully decentralized and trustless manner," said Dylan Floyd, Bitflow Co-Founder and Lead Developer. For the first time on Stacks, DeFi users can execute systematic investment strategies while retaining full control over their assets. All transactions occur on-chain, fully non-custodial, eliminating reliance on third-party intermediaries—a key principle in the decentralized finance ethos. The Broader Implications for Bitcoin’s Role in DeFi The Broader Implications for Bitcoin’s Role in DeFi This move could redefine how Bitcoin functions within the DeFi landscape. Traditionally seen as a static store of value, Bitcoin’s role is evolving into that of a productive asset. Bitflow’s automation framework enhances this utility by allowing users to implement profit-taking strategies and recurring purchases based on predefined conditions—without needing to time the market manually. Moreover, the support for Runes and SIP-10 tokens broadens the utility of the Stacks Layer 2 (L2) network, positioning it as a key player in Bitcoin-native finance. The introduction of AI-driven automation could attract users looking for sophisticated tools typically associated with Ethereum’s DeFi offerings. Looking Ahead: AI and Cross-Layer Integration Looking Ahead: AI and Cross-Layer Integration Bitflow’s roadmap for DeFi automation doesn’t end here. The company plans to introduce automated yield strategies that will optimize BTC-based yield farming without manual intervention. These strategies aim to unlock new avenues for generating returns on Bitcoin holdings, advancing the asset’s role beyond passive investment. Additionally, upcoming features like market-triggered swaps will enable users to execute trades automatically based on price trends and volatility indicators. Perhaps most significantly, Bitflow is working on facilitating cross-layer asset flows between Bitcoin’s Layer 1 and Stacks Layer 2. Such integration would improve liquidity efficiency and enable seamless movement of BTC-based assets across layers, enhancing the scalability and accessibility of Bitcoin DeFi. Final Thoughts: Is Bitcoin DeFi Finally Catching Up? Final Thoughts: Is Bitcoin DeFi Finally Catching Up? The launch of Bitflow’s Automated DCA feature could represent a critical inflection point for Bitcoin-native finance. By bringing AI-powered, non-custodial automation tools to the Stacks ecosystem, Bitflow is challenging the perception that Bitcoin’s utility is limited to being a store of value. While Ethereum continues to dominate the DeFi conversation, Bitflow’s efforts may help Bitcoin close the gap by introducing more accessible, automated financial strategies. As Bitcoin continues to establish itself as a yield-generating asset, innovations like Automated DCA might pave the way for a more active, productive role in the decentralized economy. The question now is whether users and developers will embrace this shift. If they do, Bitcoin could finally become more than digital gold—it could become the foundation for a truly decentralized, automated financial future. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

Is Bitcoin DeFi Ready for Full Automation? Bitflow’s New Automated DCA Feature Suggests It Might Be

هل Bitcoin DeFi جاهز للأتمتة الكاملة؟ تشير ميزة DCA الآلية الجديدة في Bitflow إلى أنه قد يكون كذلك

¿Está Bitcoin DeFi lista para una automatización total? La nueva función DCA automatizada de Bitflow sugiere que podría serlo

האם Bitcoin DeFi מוכן לאוטומציה מלאה? תכונת ה-DCA האוטומטית החדשה של Bitflow מציעה שזה יכול להיות

Bitcoin DeFi è pronto per l'automazione completa? La nuova funzionalità DCA automatizzata di Bitflow suggerisce che potrebbe esserlo

ビットコインDeFiは完全自動化の準備ができているか？Bitflowの新しい自動DCA機能はそれが可能であることを示唆している

Bitcoin DeFi მზად არის სრული ავტომატიზაციისთვის? Bitflow-ის ახალი ავტომატური DCA ფუნქცია ვარაუდობს, რომ ეს შეიძლება იყოს

Adakah Bitcoin DeFi Bersedia untuk Automasi Penuh? Ciri DCA Automatik Baharu Bitflow Mencadangkan Ia Mungkin

Este Bitcoin DeFi gata pentru automatizare completă? Noua funcție automată DCA a Bitflow sugerează că ar putea fi

Да ли је Битцоин ДеФи спреман за потпуну аутоматизацију? Нова аутоматизована функција ДЦА Битфлов-а сугерише да би то могло бити

Handa na ba ang Bitcoin DeFi para sa Buong Automation? Ang Bagong Awtomatikong DCA na Feature ng Bitflow ay Iminumungkahi na Maaaring Ito

Ngaba i-Bitcoin DeFi ilungele ukuZitshintsha ngokupheleleyo? Inqaku elitsha le-Bitflow elizenzekelayo le-DCA licebisa ukuba inokuba

比特币 DeFi 是否已准备好实现完全自动化？Bitflow 的新自动化 DCA 功能表明情况可能如此

When people think of generative AI, many envision it working as part of a “system of engagement” — a customer service agent, a supply chain management tool, or a way to intelligently interact with and search an organization’s PDFs and other proprietary data. That’s an accurate view – for the next year or two, applications that intelligently create content by leveraging large language models (LLMs) will remain a primary AI focus for enterprises. But consider this: Most of the code written at enterprises sits within business processes — systems like inventory planning, which sit between the engagement layer and the more rigid systems of record (an organization’s data, etc.) How will GenAI benefit that layer? How can an organization improve its business processes with this pervasive technology? Agentic AI is the answer. While AI agents are built to do specific tasks or automate specific, often repetitive tasks (like updating your calendar), they generally require human input. Agentic AI is all about autonomy (think self-driving cars), employing a system of agents to constantly adapt to dynamic environments and independently create, execute, and optimize results. When agentic AI is applied to business process workflows, it can replace fragile, static business processes with dynamic, context-aware automation systems. Let’s take a look at why integrating AI agents into enterprise architectures marks a transformative leap in the way organizations approach automation and business processes, and what kind of platform is required to support these systems of automation. What Agents Are Doing Now When you provide an agent with context, the agent then feeds that context to an LLM and asks it to complete and respond to it. AI agents can also use capabilities to complete tasks on behalf of users. These AI agents can perform several key functions guided by instructions and information derived from context: Tool use — The agent uses external functions, APIs, or tools to extend its capabilities and perform specific tasks. This can include calling predefined functions or interfacing with external services (like making web requests using cURL or accessing RESTful APIs) to obtain context or execute actions beyond its inherent functionalities. Decision-making — The agent evaluates available information and selects the most appropriate action to achieve its goals. This involves analyzing context, weighing possible outcomes, and choosing a course of action that aligns with the desired objectives. Planning — The agent formulates a sequence of actions or strategies to achieve a specific goal. Reasoning — The agent analyzes the available context, draws conclusions, predicts outcomes of actions, and makes informed decisions about the optimal steps to take to reach the desired outcome. These latter kinds of functions – decision-making, planning, and reasoning – often involve multiple agents working together toward a goal. The agents could seek to refine generated code for correctness, debate whether an agentic decision is biased, or plan the use of other agentic capabilities to complete a task. Orchestrating an Agentic Network Models that power networks of agents are essentially stateless functions that take context as an input and output a response, so some kind of framework is necessary to orchestrate them. Part of that orchestration could be simple refinements (the model requests more information). This might sound analogous to retrieval-augmented generation (RAG) – and it should, because RAG is essentially a simplified form of agent architecture: It provides the model with a single tool that gets it additional information, often from a vector database. But multiagentic model frameworks take this further; they broker requests for additional information or provide a response that’s designed to be fed to another agent for refinements. One agent could, say, write some Python, while another then reviews it. Or an agent could express a goal or idea, and then a second agent’s job could be to break that up into a set of tasks or review the idea to find problems that the first agent can review, and then refine the idea. Your results start to get better and better. OK, but How Do I Build Agentic AI? In the near future, a lot of software engineers will become agentic process authors. They’ll build these processes by mixing and matching components: models, user input, goals – and critical business services. An example of one of these components is the stock in an inventory management system. What if you connected that system to an agent that could help optimize inventory levels during the holiday season? With the help of another agent who has done some historical inventory level analysis, you could ensure that there is just enough inventory to meet seasonal demand yet leave little inventory after the holiday rush. This might disappoint fervent after-Christmas sale shoppers, but it would also help prevent retailers from selling their wares at a loss. But how will developers build these systems? Agentic processes can, of course, be expressed with code, but it also helps to visualize them as “agentic flows” – one agent’s output becomes the input of another agent, and so on. Tools available now are already providing a lot of value in this effort to simplify building agentic systems. One such solution is Langflow, a visual, low-code builder for creating agentic AI applications and complex AI workflows by dragging and dropping different components, without the need for much coding. Langflow enables developers to define anything as a tool, including components like a prompt, data source, model, APIs, tools, or any other agents. We’ve recently seen significant demand for building “flows” with agents, as developers are creating a lot of applications that include several multiagent capabilities: Agents are the most popular type of component that developers are inserting into flows with Langflow. Wrapping up: From copilot to Pilot Agentic workflows bring together enterprise data, AI, and APIs, forming systems of automation that empower domain experts to scale their abilities and make enterprises work better through AI. Integrating AI agents into enterprise architectures marks a big leap in how organizations approach automation and business processes. These agents, empowered by LLMs and agentic frameworks, transcend traditional boundaries by seamlessly operating across processes, workflows, and code. Adopting agentic workflows promises to enhance efficiency, scalability, and responsiveness across business operations. They’ll manage entire workflows, handle complex tasks with greater adaptability, and improve customer experiences by providing more personalized and timely interactions. As automation becomes embedded in enterprise systems, AI copilots will graduate to pilots, and organizations that employ agentic AI will be better positioned to innovate, compete, and deliver value. For more details on agentic AI, read the free whitepaper, “Systems of Automation: The Future of Enterprise Architecture Is Agentic.” And check out this page to learn more about Langflow. Written by Ed Anuff, chief product officer, DataStax When people think of generative AI, many envision it working as part of a “system of engagement” — a customer service agent, a supply chain management tool, or a way to intelligently interact with and search an organization’s PDFs and other proprietary data. That’s an accurate view – for the next year or two, applications that intelligently create content by leveraging large language models (LLMs) will remain a primary AI focus for enterprises. leveraging large language models (LLMs) leveraging large language models (LLMs) But consider this: Most of the code written at enterprises sits within business processes — systems like inventory planning, which sit between the engagement layer and the more rigid systems of record (an organization’s data, etc.) How will GenAI benefit that layer? How can an organization improve its business processes with this pervasive technology? Agentic AI is the answer. While AI agents are built to do specific tasks or automate specific, often repetitive tasks (like updating your calendar), they generally require human input. Agentic AI is all about autonomy (think self-driving cars), employing a system of agents to constantly adapt to dynamic environments and independently create, execute, and optimize results. When agentic AI is applied to business process workflows, it can replace fragile, static business processes with dynamic, context-aware automation systems. Let’s take a look at why integrating AI agents into enterprise architectures marks a transformative leap in the way organizations approach automation and business processes, and what kind of platform is required to support these systems of automation. What Agents Are Doing Now When you provide an agent with context, the agent then feeds that context to an LLM and asks it to complete and respond to it. AI agents can also use capabilities to complete tasks on behalf of users. These AI agents can perform several key functions guided by instructions and information derived from context: Tool use — The agent uses external functions, APIs, or tools to extend its capabilities and perform specific tasks. This can include calling predefined functions or interfacing with external services (like making web requests using cURL or accessing RESTful APIs) to obtain context or execute actions beyond its inherent functionalities. Decision-making — The agent evaluates available information and selects the most appropriate action to achieve its goals. This involves analyzing context, weighing possible outcomes, and choosing a course of action that aligns with the desired objectives. Decision-making — Planning — The agent formulates a sequence of actions or strategies to achieve a specific goal. Planning — Reasoning — The agent analyzes the available context, draws conclusions, predicts outcomes of actions, and makes informed decisions about the optimal steps to take to reach the desired outcome. Reasoning — These latter kinds of functions – decision-making, planning, and reasoning – often involve multiple agents working together toward a goal. The agents could seek to refine generated code for correctness, debate whether an agentic decision is biased, or plan the use of other agentic capabilities to complete a task. Orchestrating an Agentic Network Models that power networks of agents are essentially stateless functions that take context as an input and output a response, so some kind of framework is necessary to orchestrate them. Part of that orchestration could be simple refinements (the model requests more information). This might sound analogous to retrieval-augmented generation (RAG) – and it should, because RAG is essentially a simplified form of agent architecture: It provides the model with a single tool that gets it additional information, often from a vector database. retrieval-augmented generation (RAG) retrieval-augmented generation (RAG) But multiagentic model frameworks take this further; they broker requests for additional information or provide a response that’s designed to be fed to another agent for refinements. One agent could, say, write some Python, while another then reviews it. Or an agent could express a goal or idea, and then a second agent’s job could be to break that up into a set of tasks or review the idea to find problems that the first agent can review, and then refine the idea. Your results start to get better and better. OK, but How Do I Build Agentic AI? In the near future, a lot of software engineers will become agentic process authors. They’ll build these processes by mixing and matching components: models, user input, goals – and critical business services. An example of one of these components is the stock in an inventory management system. What if you connected that system to an agent that could help optimize inventory levels during the holiday season? With the help of another agent who has done some historical inventory level analysis, you could ensure that there is just enough inventory to meet seasonal demand yet leave little inventory after the holiday rush. This might disappoint fervent after-Christmas sale shoppers, but it would also help prevent retailers from selling their wares at a loss. But how will developers build these systems? Agentic processes can, of course, be expressed with code, but it also helps to visualize them as “agentic flows” – one agent’s output becomes the input of another agent, and so on. Tools available now are already providing a lot of value in this effort to simplify building agentic systems. One such solution is Langflow , a visual, low-code builder for creating agentic AI applications and complex AI workflows by dragging and dropping different components, without the need for much coding. Langflow Langflow Langflow enables developers to define anything as a tool, including components like a prompt, data source, model, APIs, tools, or any other agents. We’ve recently seen significant demand for building “flows” with agents, as developers are creating a lot of applications that include several multiagent capabilities: Agents are the most popular type of component that developers are inserting into flows with Langflow. Wrapping up: From copilot to Pilot Agentic workflows bring together enterprise data, AI, and APIs, forming systems of automation that empower domain experts to scale their abilities and make enterprises work better through AI. Integrating AI agents into enterprise architectures marks a big leap in how organizations approach automation and business processes. These agents, empowered by LLMs and agentic frameworks, transcend traditional boundaries by seamlessly operating across processes, workflows, and code. Adopting agentic workflows promises to enhance efficiency, scalability, and responsiveness across business operations. They’ll manage entire workflows, handle complex tasks with greater adaptability, and improve customer experiences by providing more personalized and timely interactions. As automation becomes embedded in enterprise systems, AI copilots will graduate to pilots, and organizations that employ agentic AI will be better positioned to innovate, compete, and deliver value. For more details on agentic AI, read the free whitepaper, “Systems of Automation: The Future of Enterprise Architecture Is Agentic.” And check out this page to learn more about Langflow. For more details on agentic AI, read the free whitepaper, “ Systems of Automation: The Future of Enterprise Architecture Is Agentic. ” And check out this page to learn more about Langflow . Systems of Automation: The Future of Enterprise Architecture Is Agentic. Systems of Automation: The Future of Enterprise Architecture Is Agentic. this page this page to learn more about Langflow Written by Ed Anuff, chief product officer, DataStax Written by Ed Anuff, chief product officer, DataStax

AI Agents: How They're Getting Ready to Blow Up the Business Process Layer

وكلاء الذكاء الاصطناعي: كيف يستعدون لتفجير طبقة العمليات التجارية

Agents d'IA: com s'estan preparant per explotar la capa de processos empresarials

AI-agenter: Sådan bliver de klar til at sprænge forretningsproceslaget

Agentes de IA: cómo se están preparando para hacer estallar la capa de procesos de negocios

Agents IA : comment ils se préparent à faire exploser la couche des processus métier

AI エージェント: ビジネス プロセス層を爆破する準備

AI agentai: kaip jie ruošiasi susprogdinti verslo procesų sluoksnį

Agenci AI: Jak przygotowują się do wysadzenia warstwy procesów biznesowych

Agenti AI: Ako sa pripravujú vyhodiť do vzduchu vrstvu obchodných procesov

AI Agents: ከመይ ጌሮም ነቲ ናይ ቢዝነስ መስርሕ ንጣብ ንምፍንፋሕ ይዳለዉ ኣለዉ።

AI agentlari: ular biznes-jarayon qatlamini portlatish uchun qanday tayyorlanishmoqda

Ama-AI Agents: Balungela Kanjani Ukuqhumisa Isendlalelo Senqubo Yebhizinisi

Navigating Digital Classrooms As Educators—Are We Ready to Adapt?

We now find ourselves fully immersed in the age of Trump 2.0, and it’s fair to say that waters have been choppier than usual. As markets attempt to anticipate the famously unpredictable President’s every move, every statement has become a potential key event on Wall Street. Although Trump’s America First agenda, which appears to be heavily focused on deregulation, the cutting of international aid, and other expenses deemed unnecessary by the returning President, appears to be heavily growth-focused, the widespread uncertainty surrounding his every utterance is leading to widespread volatility. Despite this, the S&P 500 climbed 4.61% between Trump’s resounding election win on November 5, 2024, and his inauguration on January 21, 2025, the recent announcement of 25% tariffs on Canada and Mexico, as well as an additional 10% on Chinese imports has seen Wall Street struggle for momentum in the early days of the President’s second term. Unpicking The Fallout from Trump’s Tariffs One notable case of global market volatility came in the form of Trump’s non-announcement and subsequent announcement of trading tariffs in the early days of his White House return. Markets had initially calmed after the President didn’t announce the tariffs promised during his inauguration, only for jitters to resume when he suggested that they would be introduced on February 1, 2025. Even in the hours leading up to the imposition of the tariffs, a false report that a delay until March 1, 2025, caused more market confusion before being quickly denied by Trump’s press secretary. The uncertainty until the end as to whether Trump would follow up on his tariff threats saw the S&P 500 dip 1.6% in the early hours of trading following the announcement despite the threat of their imposition being long factored into markets. Tariffs can carry a long-term impact on market volatility due to the additional taxes that are required on imports, the threat of retaliatory tariffs as part of a larger international trade war, and the prospect of companies seeking alternative supply chains to ease the economic impact. Markets can also be confounded by the impact of inflation, which may occur if US importers pass on higher supply chain costs as a result of tariffs on to consumers. This would rapidly increase the cost of living and damage consumer spending power. This, coupled with Trump’s proposed mass deportations, could undermine American industry, shrinking labor pools and pushing more upward pressure on prices. While these events are speculative, they will see more institutions focus their attention on reacting quickly to emerging market data surrounding CPI and US labor insights, which paves the way for more dramatic market responses to confounding datasets. What Will Happen to US Financial Markets? Macro strategy risk taker Russel Matthews has suggested that markets will become more erratic, random, and chaotic in the age of Trump 2.0, and this should be a core factor to keep in mind when it comes to risk management. While this could pave the way for institutions to adopt a more cautious, less greedy approach to profit-taking, we’re set to see more nuanced strategies among investors to overcome the threat of volatility. Likewise, J.P. Morgan’s global investment strategy team has suggested a diversified approach to withstand the uncertainties that could occur during Trump’s second term, prioritizing a resilient portfolio to help achieve long-term financial goals. But is there really a tangible threat to the long-term progress of US financial markets? It’s worth noting that the first Trump administration posted a healthy 83% cumulative return for the S&P 500 between 2017 and 2020, with one drawdown of around 15% in 2018 and the pandemic crash of 35% in early 2020. This serves as a timely reminder that the stock market generally trends upward over time, regardless of who’s in the White House. However, it’s also worth noting that Trump’s control of the House of Representatives, Congress, and the Senate means that the returning President has far more room to enact his most ambitious policies this time with less scrutiny. “Over time - as markets adapt - the effects of Trump's policies may stabilize, depending on the success of their implementation and broader economic conditions,” explains Maxim Manturov, head of investment research at Freedom24. “For example, if tax breaks successfully stimulate growth without causing excessive inflation or trade wars to escalate into protracted conflicts negatively impacting global supply chains - investors may find that relative stability returns after initial spikes in volatility.” “Therefore, while short-term reactions may be dramatic following Trump's statements/actions, understanding how they translate into long-term trends becomes crucial for investors seeking to effectively mitigate risk in the face of continued uncertainty surrounding his presidency.” Opportunities in Deregulation Amid concerns over the impact of tariffs and trade wars, it’s easy to forget that Trump is attempting to instill a pro-growth outlook for US industries. The announcement of the SoftBank, OpenAI, and Oracle joint venture called ‘Stargate’ was unveiled by Trump and is an example of the United States’ bid to use deregulation to boost leadership in key areas like artificial intelligence. With the initial investment totaling $100 billion, with the aim to increase to at least $500 billion over the course of Trump’s term, we can expect the initiative to pave the way for AI stocks to build their global market dominance in a way that can support long-term growth on Wall Street. Trump’s controversial statement in December, promising that any person or company investing at least $1 billion into the US would receive fully expedited approvals and permits, including all environmental approvals, is another example of the President’s focus on market growth in a deregulation environment. Volatility to Remain We can be certain that higher levels of market volatility will remain throughout Trump’s second term, but the President’s growth-focused approach appears likely to uncover fresh investment opportunities for institutional and retail investors alike. Overall, the impact of Trump’s tariffs and subsequent trade wars may play a deciding role in the health of US markets, but Wall Street’s historical long-term growth should offer a sense of comfort to investors who could be caught up in short-term price shifts over the next four years. We now find ourselves fully immersed in the age of Trump 2.0, and it’s fair to say that waters have been choppier than usual. As markets attempt to anticipate the famously unpredictable President’s every move, every statement has become a potential key event on Wall Street. Although Trump’s America First agenda, which appears to be heavily focused on deregulation, the cutting of international aid, and other expenses deemed unnecessary by the returning President, appears to be heavily growth-focused, the widespread uncertainty surrounding his every utterance is leading to widespread volatility. Despite this, the S&P 500 climbed 4.61% between Trump’s resounding election win on November 5, 2024, and his inauguration on January 21, 2025, the recent announcement of 25% tariffs on Canada and Mexico, as well as an additional 10% on Chinese imports has seen Wall Street struggle for momentum in the early days of the President’s second term. Unpicking The Fallout from Trump’s Tariffs One notable case of global market volatility came in the form of Trump’s non-announcement and subsequent announcement of trading tariffs in the early days of his White House return. Markets had initially calmed after the President didn’t announce the tariffs promised during his inauguration, only for jitters to resume when he suggested that they would be introduced on February 1, 2025. Even in the hours leading up to the imposition of the tariffs, a false report that a delay until March 1, 2025, caused more market confusion before being quickly denied by Trump’s press secretary. The uncertainty until the end as to whether Trump would follow up on his tariff threats saw the S&P 500 dip 1.6% in the early hours of trading following the announcement despite the threat of their imposition being long factored into markets. S&P 500 dip 1.6% S&P 500 dip 1.6% Tariffs can carry a long-term impact on market volatility due to the additional taxes that are required on imports, the threat of retaliatory tariffs as part of a larger international trade war, and the prospect of companies seeking alternative supply chains to ease the economic impact. Markets can also be confounded by the impact of inflation, which may occur if US importers pass on higher supply chain costs as a result of tariffs on to consumers. This would rapidly increase the cost of living and damage consumer spending power. This, coupled with Trump’s proposed mass deportations, could undermine American industry, shrinking labor pools and pushing more upward pressure on prices . upward pressure on prices upward pressure on prices While these events are speculative, they will see more institutions focus their attention on reacting quickly to emerging market data surrounding CPI and US labor insights, which paves the way for more dramatic market responses to confounding datasets. What Will Happen to US Financial Markets? Macro strategy risk taker Russel Matthews has suggested that markets will become more erratic, random, and chaotic in the age of Trump 2.0, and this should be a core factor to keep in mind when it comes to risk management. While this could pave the way for institutions to adopt a more cautious, less greedy approach to profit-taking, we’re set to see more nuanced strategies among investors to overcome the threat of volatility. Likewise, J.P. Morgan’s global investment strategy team has suggested a diversified approach to withstand the uncertainties that could occur during Trump’s second term, prioritizing a resilient portfolio to help achieve long-term financial goals. But is there really a tangible threat to the long-term progress of US financial markets? It’s worth noting that the first Trump administration posted a healthy 83% cumulative return for the S&P 500 between 2017 and 2020, with one drawdown of around 15% in 2018 and the pandemic crash of 35% in early 2020. 83% cumulative return 83% cumulative return This serves as a timely reminder that the stock market generally trends upward over time, regardless of who’s in the White House. However, it’s also worth noting that Trump’s control of the House of Representatives, Congress, and the Senate means that the returning President has far more room to enact his most ambitious policies this time with less scrutiny. “Over time - as markets adapt - the effects of Trump's policies may stabilize, depending on the success of their implementation and broader economic conditions,” explains Maxim Manturov, head of investment research at Freedom24. “For example, if tax breaks successfully stimulate growth without causing excessive inflation or trade wars to escalate into protracted conflicts negatively impacting global supply chains - investors may find that relative stability returns after initial spikes in volatility.” “Therefore, while short-term reactions may be dramatic following Trump's statements/actions, understanding how they translate into long-term trends becomes crucial for investors seeking to effectively mitigate risk in the face of continued uncertainty surrounding his presidency.” Opportunities in Deregulation Amid concerns over the impact of tariffs and trade wars, it’s easy to forget that Trump is attempting to instill a pro-growth outlook for US industries. The announcement of the SoftBank, OpenAI, and Oracle joint venture called ‘Stargate’ was unveiled by Trump and is an example of the United States’ bid to use deregulation to boost leadership in key areas like artificial intelligence. With the initial investment totaling $100 billion , with the aim to increase to at least $500 billion over the course of Trump’s term, we can expect the initiative to pave the way for AI stocks to build their global market dominance in a way that can support long-term growth on Wall Street. investment totaling $100 billion investment totaling $100 billion Trump’s controversial statement in December, promising that any person or company investing at least $1 billion into the US would receive fully expedited approvals and permits, including all environmental approvals, is another example of the President’s focus on market growth in a deregulation environment. fully expedited approvals fully expedited approvals Volatility to Remain We can be certain that higher levels of market volatility will remain throughout Trump’s second term, but the President’s growth-focused approach appears likely to uncover fresh investment opportunities for institutional and retail investors alike. Overall, the impact of Trump’s tariffs and subsequent trade wars may play a deciding role in the health of US markets, but Wall Street’s historical long-term growth should offer a sense of comfort to investors who could be caught up in short-term price shifts over the next four years.

The Age of Volatility: Readying for the Unpredictability of Trump 2.0

Ерата на нестабилността: Подготовка за непредсказуемостта на Тръмп 2.0

The Age of Volatility: Připravujeme se na nepředvídatelnost Trumpa 2.0

Das Zeitalter der Volatilität: Vorbereitung auf die Unberechenbarkeit von Trump 2.0

L'era della volatilità: prepararsi all'imprevedibilità di Trump 2.0

Het tijdperk van volatiliteit: voorbereiden op de onvoorspelbaarheid van Trump 2.0

Era zmienności: przygotowanie na nieprzewidywalność Trumpa 2.0

Table of Links Abstract and I. Introduction II. Award Form and Content III. Electronic Signatures, Writing Requirement, and Authentication A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award
B. How could an Electronic Award be Considered to be "In Writing"
C. Could a Multisig E-Award be Duly Authenticated IV. Can Multisig Arbitration be Seen as an Autonomous Legal Order? V. Conclusion Appendix A: Multisig Transaction on Bitcoin Testnet Example Appendix B: Breakdown of BTC Stored in 2/3 Multisig Accounts V. CONCLUSION Multisig is an exciting technology that could change how merchants resolve cross-border disputes. The multisig protocol functions similar to escrow, but a key difference is that no one party, including the arbitrator, has sole possession of the funds. In a 2/3 multisig account, at least two parties must authorize a transfer by signing data with their respective private keys. This scheme provides a more efficient and trust-less mechanism for trading. However, there is some uncertainty whether or not multisig can be recognized as an arbitral award in some jurisdictions. The New York Convention requires awards to be “duly authenticated.” In civil law systems, the authentication of signatures is traditionally performed by public authorities or notaries. Moreover, the lex arbitri may impose additional form and document legalization requirements. If parties select the law of England as the seat of arbitration, they are free to agree on the form of an award under Section 52(1) of the Arbitration Act 1996. Through the concept of party autonomy, parties can restrict arbitrators from following guidelines on the content of awards and authorize the issuance of an award in electronic form. The parties should explicitly opt-out of a reasoned award as provided for under some arbitration rules. In addition, names and wallet addresses should be associated with parties at the time when the arbitration agreement is made. By doing this, an expert witness is able to certify the identity of parties and authenticate electronic signatures. Under ECA 2000, Section 7(1) (a), electronic signatures are admissible as evidence and under section 7(3), any person can certify the authenticity of such signatures. Multisig ought to be viewed as compatible within the international arbitration legal order, where the process is defined in an arbitration agreement and the raw transaction data is the actual arbitral award in an electronic form. Much in the same way courts have been able to adapt the law regarding paper contracts to the emergence of technologies like telegram, telex, fax, and e-mail, blockchain-based applications will be another method for entering into contractual relations. The technological novelty of multisig does not justify characterizing it as a separate legal order. Although multisig is compatible with the aims of international arbitration, in that it could provide a fast, cost-effective, and final method for resolving disputes privately, it is unlikely it will be widely used at the present. For one, there is a steep learning curve for merchants and their legal representatives to learn how to adequately use the technology. Secondly, arbitration institutions might be reluctant or slow to modernize rules to allow for conducting arbitration by electronic means. And finally, the multisig protocol is not intuitive enough for ordinary commercial use. Author:
(1) A.J. Santos, B.A. (UTSA), J.D. (STCL), Department of Private International Law, Ankara Yıldırım Beyazıt University, Faculty of Law (ajsantos@protonmail.com). This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Award Form and Content II. Award Form and Content III. Electronic Signatures, Writing Requirement, and Authentication A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award B. How could an Electronic Award be Considered to be "In Writing" C. Could a Multisig E-Award be Duly Authenticated A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award A. Is it possible for an Arbitrator o Digitally Sign an Arbitral Award B. How could an Electronic Award be Considered to be "In Writing" B. How could an Electronic Award be Considered to be "In Writing" C. Could a Multisig E-Award be Duly Authenticated C. Could a Multisig E-Award be Duly Authenticated IV. Can Multisig Arbitration be Seen as an Autonomous Legal Order? IV. Can Multisig Arbitration be Seen as an Autonomous Legal Order? V. Conclusion V. Conclusion Appendix A : Multisig Transaction on Bitcoin Testnet Example Appendix A Appendix A : Multisig Transaction on Bitcoin Testnet Example Appendix B : Breakdown of BTC Stored in 2/3 Multisig Accounts Appendix B Appendix B : Breakdown of BTC Stored in 2/3 Multisig Accounts V. CONCLUSION Multisig is an exciting technology that could change how merchants resolve cross-border disputes. The multisig protocol functions similar to escrow, but a key difference is that no one party, including the arbitrator, has sole possession of the funds. In a 2/3 multisig account, at least two parties must authorize a transfer by signing data with their respective private keys. This scheme provides a more efficient and trust-less mechanism for trading. However, there is some uncertainty whether or not multisig can be recognized as an arbitral award in some jurisdictions. The New York Convention requires awards to be “duly authenticated.” In civil law systems, the authentication of signatures is traditionally performed by public authorities or notaries. Moreover, the lex arbitri may impose additional form and document legalization requirements. If parties select the law of England as the seat of arbitration, they are free to agree on the form of an award under Section 52(1) of the Arbitration Act 1996. Through the concept of party autonomy, parties can restrict arbitrators from following guidelines on the content of awards and authorize the issuance of an award in electronic form. The parties should explicitly opt-out of a reasoned award as provided for under some arbitration rules. In addition, names and wallet addresses should be associated with parties at the time when the arbitration agreement is made. By doing this, an expert witness is able to certify the identity of parties and authenticate electronic signatures. Under ECA 2000, Section 7(1) (a), electronic signatures are admissible as evidence and under section 7(3), any person can certify the authenticity of such signatures. Multisig ought to be viewed as compatible within the international arbitration legal order, where the process is defined in an arbitration agreement and the raw transaction data is the actual arbitral award in an electronic form. Much in the same way courts have been able to adapt the law regarding paper contracts to the emergence of technologies like telegram, telex, fax, and e-mail, blockchain-based applications will be another method for entering into contractual relations. The technological novelty of multisig does not justify characterizing it as a separate legal order. Although multisig is compatible with the aims of international arbitration, in that it could provide a fast, cost-effective, and final method for resolving disputes privately, it is unlikely it will be widely used at the present. For one, there is a steep learning curve for merchants and their legal representatives to learn how to adequately use the technology. Secondly, arbitration institutions might be reluctant or slow to modernize rules to allow for conducting arbitration by electronic means. And finally, the multisig protocol is not intuitive enough for ordinary commercial use. Author: (1) A.J. Santos, B.A. (UTSA), J.D. (STCL), Department of Private International Law, Ankara Yıldırım Beyazıt University, Faculty of Law (ajsantos@protonmail.com). Author: Author: (1) A.J. Santos, B.A. (UTSA), J.D. (STCL), Department of Private International Law, Ankara Yıldırım Beyazıt University, Faculty of Law (ajsantos@protonmail.com). This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. This paper is available on arxiv under ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL license. available on arxiv available on arxiv

Why Multisig Might Not Be Ready for Widespread Arbitration Use

One of the most interesting side effects of the TikTok ban saga was how it prompted everyone to pause and reflect on TikTok’s place in our culture and the profound influence it has had. Love it or hate it, TikTok has reshaped the way we consume content, catapulting creators to fame, defining cultural trends, and building communities around niche interests. Now, with TikTok’s future more stable, we can start to unpack its legacy—and one area stands out: the mainstream acceptance of TikTok Shop in the United States. TikTok turned out to be the platform to bring livestream commerce to an American audience at scale, and it has shown this isn’t just a passing trend or something that, as I wrote in 2023, is for niche audiences like baseball card sellers. For American companies, the message is clear—now is the time to harness this momentum and adapt to the future of connected, real-time shopping experiences. From Content to Commerce: The FOMO Effect in Livestream Shopping Livestream shopping taps into something deeply human: the desire to connect in real-time and the fear of missing out. TikTok’s success with its Shop feature demonstrated how content creators can build buzz, cultivate trust, and spark a buying frenzy within minutes of going live. As marketing executive Andrew Garson shared in a recent conversation, “For followers, livestream shopping is not just shopping—it’s an event, a shared experience that blends entertainment with exclusivity, and it’s another way to build connections online.” At the heart of livestream commerce is a simple yet powerful psychology: viewers tune in not just to buy products but to be part of a moment. When a creator recommends a product during a livestream, it doesn’t feel like a hard sell—it feels like a trusted friend sharing something they love. This kind of engagement transforms shopping from a solitary transaction into a communal experience, leveraging the same dynamics that make social media so engaging. For brands, this shift represents an extraordinary opportunity. By aligning with the right creators and speaking authentically to their audiences, businesses can build not just sales but deep, lasting relationships with their customers. Platforms like Whatnot and Loupe have embraced this approach, focusing on niche markets like collectibles and sports memorabilia, where passion runs high and trust drives every transaction. A Blueprint for Success: Authenticity Over Hard Sales Livestream shopping success isn’t about discounts or flashy promotions—it’s about authenticity. The brands and creators that thrive are those who understand their audience and focus on storytelling. Instead of simply presenting a product, they show how it fits into a customer’s life or solves a real problem. It’s marketing that feels personal, not transactional. It’s this authenticity that has allowed niche platforms like Whatnot to raise hundreds of millions to expand their live shopping capabilities, as they prove that the format works when it aligns with the needs of communities. The Future Is Now: Adapting to a New E-Commerce Era American brands are at a critical juncture as they approach engagement with what may be a new era in e-commerce. “This era of e-commerce will be known as one that prioritizes real-time interaction, community-driven trust, and shared experiences over traditional product displays,” shared marketing executive Andrew Garson, “The companies that succeed will be the ones that focus on building authentic connections and turning shopping into something fun, something that people want to be a part of.” Just where that will take place has become the biggest question as we enter 2025. As it appears increasingly likely the TikTok ban will remain in effect at the end of the month, the question has become which platform - existing or otherwise - will rise to meet the moment. The opportunity is here, waiting for someone to seize it. One of the most interesting side effects of the TikTok ban saga was how it prompted everyone to pause and reflect on TikTok’s place in our culture and the profound influence it has had. Love it or hate it, TikTok has reshaped the way we consume content, catapulting creators to fame, defining cultural trends, and building communities around niche interests. Now, with TikTok’s future more stable, we can start to unpack its legacy—and one area stands out: the mainstream acceptance of TikTok Shop in the United States . TikTok turned out to be the platform to bring livestream commerce to an American audience at scale, and it has shown this isn’t just a passing trend or something that, as I wrote in 2023, is for niche audiences like baseball card sellers . the mainstream acceptance of TikTok Shop in the United States for niche audiences like baseball card sellers For American companies, the message is clear—now is the time to harness this momentum and adapt to the future of connected, real-time shopping experiences. For American companies, the message is clear—now is the time to harness this momentum and adapt to the future of connected, real-time shopping experiences. From Content to Commerce: The FOMO Effect in Livestream Shopping From Content to Commerce: The FOMO Effect in Livestream Shopping Livestream shopping taps into something deeply human: the desire to connect in real-time and the fear of missing out. TikTok’s success with its Shop feature demonstrated how content creators can build buzz, cultivate trust, and spark a buying frenzy within minutes of going live. As marketing executive Andrew Garson shared in a recent conversation, “For followers, livestream shopping is not just shopping—it’s an event, a shared experience that blends entertainment with exclusivity, and it’s another way to build connections online.” marketing executive Andrew Garson marketing executive Andrew Garson At the heart of livestream commerce is a simple yet powerful psychology: viewers tune in not just to buy products but to be part of a moment. When a creator recommends a product during a livestream, it doesn’t feel like a hard sell—it feels like a trusted friend sharing something they love. This kind of engagement transforms shopping from a solitary transaction into a communal experience, leveraging the same dynamics that make social media so engaging. For brands, this shift represents an extraordinary opportunity. By aligning with the right creators and speaking authentically to their audiences, businesses can build not just sales but deep, lasting relationships with their customers. Platforms like Whatnot and Loupe have embraced this approach, focusing on niche markets like collectibles and sports memorabilia, where passion runs high and trust drives every transaction. A Blueprint for Success: Authenticity Over Hard Sales A Blueprint for Success: Authenticity Over Hard Sales Livestream shopping success isn’t about discounts or flashy promotions—it’s about authenticity. The brands and creators that thrive are those who understand their audience and focus on storytelling. Instead of simply presenting a product, they show how it fits into a customer’s life or solves a real problem. It’s marketing that feels personal, not transactional. It’s marketing that feels personal, not transactional. It’s this authenticity that has allowed niche platforms like Whatnot to raise hundreds of millions to expand their live shopping capabilities, as they prove that the format works when it aligns with the needs of communities. The Future Is Now: Adapting to a New E-Commerce Era American brands are at a critical juncture as they approach engagement with what may be a new era in e-commerce. “This era of e-commerce will be known as one that prioritizes real-time interaction, community-driven trust, and shared experiences over traditional product displays,” shared marketing executive Andrew Garson, “The companies that succeed will be the ones that focus on building authentic connections and turning shopping into something fun, something that people want to be a part of.” fun, Just where that will take place has become the biggest question as we enter 2025. As it appears increasingly likely the TikTok ban will remain in effect at the end of the month, the question has become which platform - existing or otherwise - will rise to meet the moment. The opportunity is here, waiting for someone to seize it. someone

Livestream Shopping: Ready to Explode in 2025

**Utrecht, Netherlands, January 9th, 2025/Chainwire/--**Nexera announces the launch of its Layer 1 blockchain purpose-built to meet the demands of institutional on-chain capital markets. By embedding compliance, interoperability, and scalability at its core, Nexera Chain delivers a comprehensive solution for real-world asset tokenization, bridging traditional and decentralized finance. This innovative blockchain integrates advanced AI-driven tools for compliance, enabling automated Know Your Customer (KYC), Know Your Business (KYB), Know Your Transaction (KYT), Anti-Money Laundering (AML), and Travel Rule enforcement. Nexera Chain ensures every participant and transaction adheres to global regulatory standards, providing institutions with the confidence to operate in a rapidly evolving regulatory landscape. Its design aligns with the principles of frameworks such as the EU’s Markets in Crypto-Assets (MiCA), offering a strong foundation for compliance without compromising efficiency or innovation. Nexera Chain stands out by addressing the long-standing challenges of blockchain adoption in institutional markets. Its compliance-first infrastructure eliminates the inefficiencies and security gaps caused by fragmented solutions, ensuring seamless integration of regulatory requirements. The blockchain’s omnichain interoperability enables fluid connections across public and private networks as well as legacy systems. This removes barriers to liquidity and data flow, paving the way for institutions to scale their tokenization efforts while maintaining the highest standards of compliance and security. The platform also simplifies tokenization for developers and enterprises with its developer-friendly APIs, SDKs, and white-label solutions. These tools streamline the process of bringing both tangible and digital assets on-chain, ensuring compliance across the entire asset lifecycle. Central to Nexera Chain is the ERC-7208 standard, a universal framework for managing tokenized assets, data, and identity across multiple networks. By standardizing these components, ERC-7208 supports consistent cross-chain activity and unlocks new opportunities for businesses to innovate within a scalable, interoperable environment. With over $1 billion in aggregate value represented across the Nexera ecosystem, including applications in tokenized real estate, art, carbon credit, GPUs, and more, Nexera Chain demonstrates its ability to deliver measurable value. Its unified infrastructure bridges the gap between traditional financial systems and blockchain technology, creating a compliant, scalable, and efficient platform for institutional adoption. "Over the past six years, we’ve meticulously developed advanced modules for compliance, tokenization, and data. With the launch of Nexera Chain, we’re uniting blockchain technology and modern capital markets in a single robust ecosystem. By integrating low-cost transactions and institutional-grade compliance at the protocol level, Nexera Chain enables secure, efficient on-chain financial operations. Nexera Chain redefines how capital markets embrace digital assets and decentralized finance." said Rachid Ajaja, Founder & CEO of Nexera Nexera Chain’s launch represents an important leap forward for blockchain adoption in institutional markets. By embedding compliance and interoperability into its core, it lays the groundwork for secure, scalable, and compliant participation in global tokenized markets. About Nexera Chain Nexera Chain is the first compliance-ready Layer 1 blockchain purpose-built to meet the demands of institutional on-chain capital markets. By embedding compliance, interoperability, and scalability at its core, Nexera Chain delivers a comprehensive solution for real-world asset tokenization, bridging traditional and decentralized finance. Contact PR Manager Ramsey Shallal Nexera Network ramsey@gelocubed.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here **Utrecht, Netherlands, January 9th, 2025/Chainwire/--**Nexera announces the launch of its Layer 1 blockchain purpose-built to meet the demands of institutional on-chain capital markets. By embedding compliance, interoperability, and scalability at its core, Nexera Chain delivers a comprehensive solution for real-world asset tokenization, bridging traditional and decentralized finance. This innovative blockchain integrates advanced AI-driven tools for compliance, enabling automated Know Your Customer (KYC), Know Your Business (KYB), Know Your Transaction (KYT), Anti-Money Laundering (AML), and Travel Rule enforcement. Nexera Chain ensures every participant and transaction adheres to global regulatory standards, providing institutions with the confidence to operate in a rapidly evolving regulatory landscape. Its design aligns with the principles of frameworks such as the EU’s Markets in Crypto-Assets (MiCA), offering a strong foundation for compliance without compromising efficiency or innovation. Nexera Chain stands out by addressing the long-standing challenges of blockchain adoption in institutional markets. Its compliance-first infrastructure eliminates the inefficiencies and security gaps caused by fragmented solutions, ensuring seamless integration of regulatory requirements. The blockchain’s omnichain interoperability enables fluid connections across public and private networks as well as legacy systems. This removes barriers to liquidity and data flow, paving the way for institutions to scale their tokenization efforts while maintaining the highest standards of compliance and security. The platform also simplifies tokenization for developers and enterprises with its developer-friendly APIs, SDKs, and white-label solutions. These tools streamline the process of bringing both tangible and digital assets on-chain, ensuring compliance across the entire asset lifecycle. Central to Nexera Chain is the ERC-7208 standard , a universal framework for managing tokenized assets, data, and identity across multiple networks. By standardizing these components, ERC-7208 supports consistent cross-chain activity and unlocks new opportunities for businesses to innovate within a scalable, interoperable environment. ERC-7208 standard ERC-7208 standard With over $1 billion in aggregate value represented across the Nexera ecosystem, including applications in tokenized real estate, art, carbon credit, GPUs, and more, Nexera Chain demonstrates its ability to deliver measurable value. Its unified infrastructure bridges the gap between traditional financial systems and blockchain technology, creating a compliant, scalable, and efficient platform for institutional adoption. "Over the past six years, we’ve meticulously developed advanced modules for compliance, tokenization, and data. With the launch of Nexera Chain, we’re uniting blockchain technology and modern capital markets in a single robust ecosystem. By integrating low-cost transactions and institutional-grade compliance at the protocol level, Nexera Chain enables secure, efficient on-chain financial operations. Nexera Chain redefines how capital markets embrace digital assets and decentralized finance." said Rachid Ajaja, Founder & CEO of Nexera "Over the past six years, we’ve meticulously developed advanced modules for compliance, tokenization, and data. With the launch of Nexera Chain, we’re uniting blockchain technology and modern capital markets in a single robust ecosystem. By integrating low-cost transactions and institutional-grade compliance at the protocol level, Nexera Chain enables secure, efficient on-chain financial operations. Nexera Chain redefines how capital markets embrace digital assets and decentralized finance." said Rachid Ajaja , Founder & CEO of Nexera Rachid Ajaja Rachid Ajaja Nexera Chain’s launch represents an important leap forward for blockchain adoption in institutional markets. By embedding compliance and interoperability into its core, it lays the groundwork for secure, scalable, and compliant participation in global tokenized markets. About Nexera Chain Nexera Chain is the first compliance-ready Layer 1 blockchain purpose-built to meet the demands of institutional on-chain capital markets. By embedding compliance, interoperability, and scalability at its core, Nexera Chain delivers a comprehensive solution for real-world asset tokenization, bridging traditional and decentralized finance. Nexera Chain Nexera Chain Contact PR Manager Ramsey Shallal Nexera Network ramsey@gelocubed.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Nexera Announces First Compliance-Ready Layer 1 For Institutional On-Chain Capital Markets

Nexera oznamuje první vrstvu 1 připravenou na dodržování předpisů pro institucionální kapitálové trhy v řetězci

Nexera anuncia la primera capa 1 preparada para el cumplimiento normativo para los mercados de capitales institucionales en cadena

Nexera-k kate instituzionaleko kapital-merkatuetarako betetzeko prest dagoen lehen geruza iragartzen du

Nexera اولین لایه آماده انطباق را برای بازارهای سرمایه در زنجیره سازمانی اعلام کرد

Nexera、機関投資家向けオンチェーン資本市場向けの初のコンプライアンス対応レイヤー1を発表

Nexera ប្រកាសការអនុលោមតាមលំដាប់ថ្នាក់ដំបូង 1 សម្រាប់ទីផ្សារមូលធនតាមខ្សែសង្វាក់របស់ស្ថាប័ន

Nexera ogłasza pierwszą warstwę 1 gotową do zgodności dla instytucjonalnych rynków kapitałowych w łańcuchu

Nexera Itangaza Iyubahirizwa Ryambere-Yiteguye Urwego 1 Kubigo Kumurongo-Urunigi Isoko Rikuru

Nexera Inozivisa Yekutanga Kuteerera-Yakagadzirira Layer 1 YeInstitutional On-Chain Capital Markets

“Nexera” institusional zynjyrly maýa bazarlary üçin ilkinji laýyklyk 1-nji gatlagy yglan edýär

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Master Company Name Matching in Minutes with Python's Ready-Made Libraries

Building a Global Production Ready Marketplace With Medusa JS

Introduction Building generative AI requires working with a variety of developing machine learning and AI technologies. It also requires buckets of patience. These two requirements are not unrelated. Through working alongside extremely talented machine learning experts from different firms building generative AI applications, it’s clear that there are a number of ‘yikes’ aspects to developing fully realized generative AI tools. In this article, I’ll briefly summarise five pitfalls to avoid when developing a generative AI application. Don’t Assume the Outputs of LLMs Will Be Accurate This may seem obvious, but I was surprised at how unreliable the output of commercial LLMs could be. When testing major LLMs (in particular, ChatGPT, Anthropic’s Claude AI, and Amazon Bedrock) on complex financial datasets, I noted that they can generate errors or hallucinations at a rate of one per page. LLMs are guilty of often generating information that sounds plausible but is false. For example, when testing financial data, I found that LLMs produced financial ratios like ‘Non-operating income and expense’. These sound legitimate but don’t exist in the document and are completely hollow financial terms. Though effective prompt engineering can mitigate some of these hallucinations, many of them occur for initially unexplainable reasons (hence why every LLM has a recommendation for manually reviewing its output somewhere in its user interface and documentation). OCR (Optical Character Recognition)—the technology that converts images to machine-readable text—also generates a high rate of errors. It is challenging to develop a mechanism to combat OCR infidelity for each model. AI-based adaptive learning for user-flagged errors can be a helpful strategy but requires huge amounts of manual intervention. Overall, the lack of a calibrated confidence score in commercial LLMs is a huge limitation. Application developers must develop their own methods for calculating confidence and validating errors. 2. Don’t Be Complacent With Your Production Credits Any generative AI dependent on LLM production credits (such as Google’s Tensor Processing Units (TPUs)) needed to be technologically regulated to avoid spending more than accounted for - potentially bankrupting the company or developer. Bankruptcy through overenthusiastic spending of production credits might seem like an urban myth - certainly, there are no noted cases of production credits running amok - but better safe than sorry, right? There are several ways to stretch out your production credits to their fullest potential: Take advantage of short-lived, discounted, preemptible VMs from your cloud application provider (but only if your application is designed to handle interruptions and then resume from checkpoints). Leverage spot instances for non-critical workloads to significantly reduce costs while automatically shutting down idle instances. Cloud provider tools can be very helpful for this. Where appropriate, you could experiment with open-source frameworks that don’t require production credits. I’m very pro the democratic approach of open source models and their potential for collaborative innovation. And, of course, open-source models can be accessed for free. 3. Don’t Forget to Test User Experience (UE) Again, it may seem obvious, but when stuck looking at a profusion of code, it’s easy to forget that a real human being will be using the product. In a production environment, consider the pathways the user will take, even in complex and code-heavy environments. Don’t be like Google Bard, whose generative AI model couldn’t answer simple user questions about space upon release or, later, what to put on pizza (hint: it’s not glue). Many traditional UX testing tools can be adapted for generative AI products - such as a card sorting tool. Another effective way to test the technology is with a human-in-the-loop system, like beta testers or reviewers. At the moment, the company I’m with is using human beta testers to test their new generative AI technology. The testers use the tool for free - and they can harvest the usage patterns. 4. Don’t Forget the Importance of Training Data Clean, normalize, and potentially enrich your data to improve the training process. Techniques like tokenization and feature engineering might be helpful. Generally, the more data, the better (the AI algorithms I used were from a document store of 25 million documents, for example), but too much data may result in overfitting or computational bottlenecks. Slightly off-topic, but in the future, it’s possible that wrangling with training data may not be an issue. Promising advancements like AutoML automatically cleanse training data and assess the model’s performance using a number of techniques like meta-learning, Bayesian inference, and neural architecture search. For SMEs with limited coding resources, AutoML might be a promising innovation. 5. Don’t Choose the Wrong Scale Modeling Strategy There are many ways to scale a model. For example, you might split the model across multiple machines (model parallelism) or replicate the model across multiple machines (data parallelism). Complex models will likely benefit from the model’s parallelism’s device distribution; larger datasets or models with smaller architectures may benefit from increased throughput. Another consideration is whether to upgrade compute resources on a single machine, such as by increasing GPU memory (vertical scaling) or adding more resources (e.g. GPUs) - also known as horizontal scaling. Don’t forget to containerize your generative AI application to ensure consistent behaviors across different environments. Of course, rigorous testing and validation after model scaling is a must. To ensure it can handle the increased load, consider trying a variety of by-the-book testing plus load and stress testing. Summary It’s unrealistic to expect building a generative AI product to work without encountering at least one of these challenges. Ultimately, each of these challenges presents a fork in decision-making. At each juncture, the choice you make gets you one step closer to building a mature, production-ready model. Working with the latest advances in academic and industrial machine learning will help counter some of the typical frustrations, as machine learning’s competitive landscape is constantly pushing out new innovations. I organize the London Machine Learning Meetup (the largest community of AI experts in Europe), which is a free community that hosts events that unpack the latest technical advances in machine learning. Above all, accuracy and cost-effectiveness don’t have to be mutually exclusive. With controlled and strategic experimentation, building a generative AI product can be more rewarding and less frustrating than you might think. Good luck! Introduction Introduction Building generative AI requires working with a variety of developing machine learning and AI technologies. It also requires buckets of patience. These two requirements are not unrelated. Through working alongside extremely talented machine learning experts from different firms building generative AI applications, it’s clear that there are a number of ‘yikes’ aspects to developing fully realized generative AI tools. In this article, I’ll briefly summarise five pitfalls to avoid when developing a generative AI application. Don’t Assume the Outputs of LLMs Will Be Accurate This may seem obvious, but I was surprised at how unreliable the output of commercial LLMs could be. When testing major LLMs (in particular, ChatGPT, Anthropic’s Claude AI, and Amazon Bedrock) on complex financial datasets, I noted that they can generate errors or hallucinations at a rate of one per page. LLMs are guilty of often generating information that sounds plausible but is false. sounds For example, when testing financial data , I found that LLMs produced financial ratios like ‘Non-operating income and expense’. These sound legitimate but don’t exist in the document and are completely hollow financial terms. testing financial data testing financial data Though effective prompt engineering can mitigate some of these hallucinations, many of them occur for initially unexplainable reasons (hence why every LLM has a recommendation for manually reviewing its output somewhere in its user interface and documentation). effective prompt engineering effective prompt engineering OCR (Optical Character Recognition)—the technology that converts images to machine-readable text—also generates a high rate of errors. It is challenging to develop a mechanism to combat OCR infidelity for each model. AI-based adaptive learning for user-flagged errors can be a helpful strategy but requires huge amounts of manual intervention. OCR OCR Overall, the lack of a calibrated confidence score in commercial LLMs is a huge limitation. Application developers must develop their own methods for calculating confidence and validating errors. 2. Don’t Be Complacent With Your Production Credits 2. Don’t Be Complacent With Your Production Credits Any generative AI dependent on LLM production credits (such as Google’s Tensor Processing Units (TPUs)) needed to be technologically regulated to avoid spending more than accounted for - potentially bankrupting the company or developer. Bankruptcy through overenthusiastic spending of production credits might seem like an urban myth - certainly, there are no noted cases of production credits running amok - but better safe than sorry, right? There are several ways to stretch out your production credits to their fullest potential: Take advantage of short-lived, discounted, preemptible VMs from your cloud application provider (but only if your application is designed to handle interruptions and then resume from checkpoints). Take advantage of short-lived, discounted, preemptible VMs from your cloud application provider (but only if your application is designed to handle interruptions and then resume from checkpoints). Leverage spot instances for non-critical workloads to significantly reduce costs while automatically shutting down idle instances. Cloud provider tools can be very helpful for this. Leverage spot instances for non-critical workloads to significantly reduce costs while automatically shutting down idle instances. Cloud provider tools can be very helpful for this. Leverage spot instances Where appropriate, you could experiment with open-source frameworks that don’t require production credits. I’m very pro the democratic approach of open source models and their potential for collaborative innovation. And, of course, open-source models can be accessed for free. Where appropriate, you could experiment with open-source frameworks that don’t require production credits. I’m very pro the democratic approach of open source models and their potential for collaborative innovation. And, of course, open-source models can be accessed for free. open-source frameworks 3. Don’t Forget to Test User Experience (UE) Again, it may seem obvious, but when stuck looking at a profusion of code, it’s easy to forget that a real human being will be using the product. In a production environment, consider the pathways the user will take, even in complex and code-heavy environments. Don’t be like Google Bard, whose generative AI model couldn’t answer simple user questions about space upon release or, later, what to put on pizza (hint: it’s not glue ). simple user questions about space simple user questions about space it’s not glue it’s not glue Many traditional UX testing tools can be adapted for generative AI products - such as a card sorting tool. Another effective way to test the technology is with a human-in-the-loop system, like beta testers or reviewers. At the moment, the company I’m with is using human beta testers to test their new generative AI technology. The testers use the tool for free - and they can harvest the usage patterns. card sorting tool. card sorting tool. 4. Don’t Forget the Importance of Training Data Clean, normalize, and potentially enrich your data to improve the training process. Techniques like tokenization and feature engineering might be helpful. tokenization tokenization Generally, the more data, the better (the AI algorithms I used were from a document store of 25 million documents, for example), but too much data may result in overfitting or computational bottlenecks. Slightly off-topic, but in the future, it’s possible that wrangling with training data may not be an issue. Promising advancements like AutoML automatically cleanse training data and assess the model’s performance using a number of techniques like meta-learning, Bayesian inference, and neural architecture search. For SMEs with limited coding resources, AutoML might be a promising innovation. AutoML AutoML 5. Don’t Choose the Wrong Scale Modeling Strategy There are many ways to scale a model. For example, you might split the model across multiple machines (model parallelism) or replicate the model across multiple machines (data parallelism). Complex models will likely benefit from the model’s parallelism’s device distribution; larger datasets or models with smaller architectures may benefit from increased throughput. Another consideration is whether to upgrade compute resources on a single machine, such as by increasing GPU memory ( vertical scaling ) or adding more resources (e.g. GPUs) - also known as horizontal scaling. Don’t forget to containerize your generative AI application to ensure consistent behaviors across different environments. vertical scaling vertical scaling Of course, rigorous testing and validation after model scaling is a must. To ensure it can handle the increased load, consider trying a variety of by-the-book testing plus load and stress testing. Summary It’s unrealistic to expect building a generative AI product to work without encountering at least one of these challenges. Ultimately, each of these challenges presents a fork in decision-making. At each juncture, the choice you make gets you one step closer to building a mature, production-ready model. Working with the latest advances in academic and industrial machine learning will help counter some of the typical frustrations, as machine learning’s competitive landscape is constantly pushing out new innovations. I organize the London Machine Learning Meetup (the largest community of AI experts in Europe), which is a free community that hosts events that unpack the latest technical advances in machine learning. London Machine Learning Meetup London Machine Learning Meetup Above all, accuracy and cost-effectiveness don’t have to be mutually exclusive. With controlled and strategic experimentation, building a generative AI product can be more rewarding and less frustrating than you might think. Good luck!

Building Production-Ready Generative AI: Five Things Not to Do

The Journey of Using AI to Help Me Code, But Is It Production-Ready? Using AI for coding has quickly become more than just a prototype. It is now part of the developer toolkit. I still remember when I first came across AI-generated code. It was GitHub Copilot, and I thought to myself, “Could this help me write better code?” Initially, I was skeptical. Something that began in jest, as an absurd test, became the realization that with proper use of AI, it genuinely could change the way we coded. Like most developers, I had a pretty standard process when dealing with a new problem. It was to gather the requirements, outline a plan, get down to writing the code, and then debug when necessary. Introducing AI into this workflow felt odd at first. It wasn’t just faster writing of code; it was how AI reshaped how I thought about coding. It wasn’t fill-in-the-blank; it was suggesting code structures that I may not have thought of pointing out possible inefficiencies, helping see different approaches to the same problem. For example, I was working on a backend project using. NET. The task was to optimize a database query, which was pulling in a lot of data. Normally, I would dive into the query, break it up, and then refactor it until it was as efficient as possible. And so this all changed the day I started writing the query out. GitHub Copilot gave me options & much more efficient ways to do pagination and filtering that I hadn’t even thought of. And here’s the crazy thing: these weren’t just suggesting something completely random; they were really good improvements. AI saved me time but taught me optimization techniques in real-time. The few first attempts at working on AI tools proved that I still needed to understand the logic of codes that were being suggested to me. There is a real temptation to just accept the output that AI spits out, especially when you are running behind a deadline, but that’s a slippery slope. AI can help you code faster but isn’t a substitute for knowing why you’re writing that code. And that is where developers can get it wrong — too heavy on AI, not enough actual due diligence. A perfect case in point is that I was trying to work out how to integrate Kafka with a minimal API. I knew that this kind of asynchronous consumption of messages could be optimized. Of course, it’s still not for you to put it into the correct architecture. This time, I feel I need to share it with everyone & AI can make your life more efficient. You must learn when to accept the AI’s decision and when to adjust its suggestions in the light of your project’s context. The beauty of coding with AI is that it does not focus solely on the small things — those rote, boilerplate tasks. Over time, I realized how AI was guiding me through other deep design decisions, like How do I structure an application?
When should I scale? It's almost like having a junior developer who’s good at the basics, but instead of just taking the AI’s output at face value, you could guide it, mentor it even, and adapt its suggestions to your needs. That’s where AI shines, the tool for exploration and learning. It’s not like “Here’s how you write a for loop,” but more “Have you considered this alternative approach?” Whether it’s optimizing that PostgreSQL query, implementing bulk copy functionality with NPGSQL, or tweaking connection pooling in EF Core, AI gives you options you might not have otherwise thought of on your own. It tests your assumptions and pushes you to think outside of the box. AI still has a very long way to go before it can get the subtleties of every development environment. I once asked ChatGPT to help me solve the issue of race conditions in a multi-threaded application. It gave me an answer but failed to account for edge cases that I knew would be a certain trouble in production. It reminded me that AI tools are only as good as the data that trains them. This is still more of a human intuition and experience play in many complex scenarios. This fusion of the strengths of both AI and human talents will define the future of coding. Applying AI the right way involves gaining the limitations of its usage and playing to its strengths. I now see AI as a partner in coding and not as a person writing code for me but as one that helps me code smarter. It can help me get ideas, clean up the code structure, and carry out repetitive tasks, but I still make the final calls. To developers who haven’t yet embraced AI, I understand that. It is a natural fear of the tools that seem to take over part of our job. Nevertheless, I always advise beginners to start small. Let AI help with unit tests for instance, or even refactoring suggestions. As you grow more comfortable, start using them in larger contexts — architecture decisions, system optimizations, or debugging. It is very important to treat AI as an evolving tool. Like how we learn new frameworks or languages, AI systems such as Codex and GitHub Copilot improve every day. The more you use them, the better they get at understanding your coding style and preferences, along with how you think about problems. The magic of such a system is in how it evolves with you by giving progressively more valuable insights the more you work with it. What is unmagical about this is that it doesn’t do the work for you. It’s like learning to code all over again, but you have an assistant. You wouldn’t let someone else write all your codebase, but you would listen if they offered some brilliant new way to approach a problem. That’s how I use AI now as a crutch, but as a way to broaden my horizons and improve my craft. Now, with maturing AI, I expect the future of coding to lie not in how machines are going to replace us but in helping machines become some kind of tool that makes us better developers. If you do AI right, it is not only about making you more productive but also about making you a better coder, opening up new possibilities, pushing you to think deeper, solving problems faster, and delivering more robust solutions. And, in the end, that is what code is all about. Reference GitHub Copilot Link
OpenAI Codex Link
PostgreSQL Npgsql Link
Kafka Integration Link The Journey of Using AI to Help Me Code, But Is It Production-Ready? Using AI for coding has quickly become more than just a prototype. It is now part of the developer toolkit. I still remember when I first came across AI-generated code. It was GitHub Copilot, and I thought to myself, “Could this help me write better code?” Initially, I was skeptical. It was GitHub Copilot, and I thought to myself, “Could this help me write better code?” Initially, I was skeptical. It was GitHub Copilot, and I thought to myself, “Could this help me write better code?” Initially, I was skeptical. Something that began in jest, as an absurd test, became the realization that with proper use of AI, it genuinely could change the way we coded. Like most developers, I had a pretty standard process when dealing with a new problem. It was to gather the requirements, outline a plan, get down to writing the code, and then debug when necessary. Introducing AI into this workflow felt odd at first. It wasn’t just faster writing of code; it was how AI reshaped how I thought about coding. It wasn’t fill-in-the-blank; it was suggesting code structures that I may not have thought of pointing out possible inefficiencies, helping see different approaches to the same problem. For example, I was working on a backend project using. NET. The task was to optimize a database query, which was pulling in a lot of data. Normally, I would dive into the query, break it up, and then refactor it until it was as efficient as possible. And so this all changed the day I started writing the query out. GitHub Copilot gave me options & much more efficient ways to do pagination and filtering that I hadn’t even thought of. And here’s the crazy thing: these weren’t just suggesting something completely random; they were really good improvements. AI saved me time but taught me optimization techniques in real-time. AI saved me time but taught me optimization techniques in real-time. AI saved me time but taught me optimization techniques in real-time. The few first attempts at working on AI tools proved that I still needed to understand the logic of codes that were being suggested to me. There is a real temptation to just accept the output that AI spits out, especially when you are running behind a deadline, but that’s a slippery slope. There is a real temptation to just accept the output that AI spits out, especially when you are running behind a deadline, but that’s a slippery slope. There is a real temptation to just accept the output that AI spits out, especially when you are running behind a deadline, but that’s a slippery slope. AI can help you code faster but isn’t a substitute for knowing why you’re writing that code. And that is where developers can get it wrong — too heavy on AI, not enough actual due diligence. A perfect case in point is that I was trying to work out how to integrate Kafka with a minimal API. I knew that this kind of asynchronous consumption of messages could be optimized. Of course, it’s still not for you to put it into the correct architecture. This time, I feel I need to share it with everyone & AI can make your life more efficient. You must learn when to accept the AI’s decision and when to adjust its suggestions in the light of your project’s context. You must learn when to accept the AI’s decision and when to adjust its suggestions in the light of your project’s context. You must learn when to accept the AI’s decision and when to adjust its suggestions in the light of your project’s context. The beauty of coding with AI is that it does not focus solely on the small things — those rote, boilerplate tasks. Over time, I realized how AI was guiding me through other deep design decisions, like How do I structure an application? When should I scale? How do I structure an application? When should I scale? It's almost like having a junior developer who’s good at the basics, but instead of just taking the AI’s output at face value, you could guide it, mentor it even, and adapt its suggestions to your needs. That’s where AI shines, the tool for exploration and learning. It’s not like “Here’s how you write a for loop,” but more “Have you considered this alternative approach?” Whether it’s optimizing that PostgreSQL query, implementing bulk copy functionality with NPGSQL, or tweaking connection pooling in EF Core, AI gives you options you might not have otherwise thought of on your own. It tests your assumptions and pushes you to think outside of the box. AI still has a very long way to go before it can get the subtleties of every development environment. I once asked ChatGPT to help me solve the issue of race conditions in a multi-threaded application. AI still has a very long way to go before it can get the subtleties of every development environment. I once asked ChatGPT to help me solve the issue of race conditions in a multi-threaded application. AI still has a very long way to go before it can get the subtleties of every development environment. I once asked ChatGPT to help me solve the issue of race conditions in a multi-threaded application. It gave me an answer but failed to account for edge cases that I knew would be a certain trouble in production. It gave me an answer but failed to account for edge cases that I knew would be a certain trouble in production. It gave me an answer but failed to account for edge cases that I knew would be a certain trouble in production. It reminded me that AI tools are only as good as the data that trains them. This is still more of a human intuition and experience play in many complex scenarios. This fusion of the strengths of both AI and human talents will define the future of coding. Applying AI the right way involves gaining the limitations of its usage and playing to its strengths. I now see AI as a partner in coding and not as a person writing code for me but as one that helps me code smarter. It can help me get ideas, clean up the code structure, and carry out repetitive tasks, but I still make the final calls. To developers who haven’t yet embraced AI, I understand that. It is a natural fear of the tools that seem to take over part of our job. Nevertheless, I always advise beginners to start small. Let AI help with unit tests for instance, or even refactoring suggestions. As you grow more comfortable, start using them in larger contexts — architecture decisions, system optimizations, or debugging. It is very important to treat AI as an evolving tool. Like how we learn new frameworks or languages, AI systems such as Codex and GitHub Copilot improve every day. The more you use them, the better they get at understanding your coding style and preferences, along with how you think about problems. Codex GitHub Copilot The magic of such a system is in how it evolves with you by giving progressively more valuable insights the more you work with it. What is unmagical about this is that it doesn’t do the work for you. It’s like learning to code all over again, but you have an assistant. You wouldn’t let someone else write all your codebase, but you would listen if they offered some brilliant new way to approach a problem. That’s how I use AI now as a crutch, but as a way to broaden my horizons and improve my craft. Now, with maturing AI, I expect the future of coding to lie not in how machines are going to replace us but in helping machines become some kind of tool that makes us better developers. If you do AI right, it is not only about making you more productive but also about making you a better coder, opening up new possibilities, pushing you to think deeper, solving problems faster, and delivering more robust solutions. And, in the end, that is what code is all about. Reference GitHub Copilot Link OpenAI Codex Link PostgreSQL Npgsql Link Kafka Integration Link GitHub Copilot Link Link OpenAI Codex Link Link PostgreSQL Npgsql Link Link Kafka Integration Link Link

Is AI Actually Writing Production-Ready Code?

Next week will be extremely important for financial markets. On September 18, the Fed will announce its interest rate decision and start the new rate cut cycle. Markets expect that the most probable scenario is a 25 bps rate cut. It should be noted that traders do not rule out the possibility of a 50 bpd cut. From a big-picture point of view, the Fed is expected to cut rates by 75 - 100 bpd by the end of this year to provide support to the labor market, which has already started to show signs of weakness. Rate cuts improve the liquidity situation in the markets and make money cheaper, which usually serves as support for riskier assets. However, markets do not look too bullish right now. Crypto markets have settled well below highs that were seen back in March and remain under some pressure. The tech-heavy NASDAQ index has pulled back from this year’s highs by roughly 10%. Why are markets not rallying ahead of the start of the new rate-cut cycle? Markets are worried about the potential slowdown of the global economy. The situation in China’s economy is a key problem for investors. The country’s economy has not fully recovered after the coronavirus pandemic, and the government’s attempts to provide stimulus have not yielded tangible results. China’s real estate sector, which was one of the key drivers of the country’s growth, shows no signs of improvement. The recent data indicates that China’s imports increased by just 0.5% year-over-year in August. Put simply, China’s demand remains under material pressure. Meanwhile, it looks like the U.S. labor market has started to cool down, which signals that the U.S. economy may be slowing down. Worries about weak China and the potential slowdown of the American economy have impacted investors’ risk appetite. Both cryptocurrencies and tech stocks are sensitive to fluctuations in market sentiment. When investors are ready to increase their risks in search of profits, they buy cryptocurrencies and tech stocks. In the opposite scenario, which implies rising demand for safe-haven assets, crypto and tech find themselves under pressure. The flight to quality has been going on in crypto markets for quite some time as investors increased their exposure to safe-haven assets. As a result, Bitcoin's dominance has been growing steadily since the start of the year and has already surpassed the 55% level. Jerome Powell’s comments may change the market sentiment and boost investors’ appetite for risk. Traditionally, markets pay close attention to the Fed Chair’s comments, so they will have a major impact on market dynamics. In case Powell signals that the Fed is ready to start cutting rates aggressively to provide additional support to the economy, investors will rush to buy riskier assets, which will be bullish for cryptocurrencies. Most likely, Bitcoin will be the biggest beneficiary of dovish Powell. In this scenario, investors’ demand for altcoins should also increase as they will search for bargains among coins that have suffered serious pullbacks in recent months. Next week will be extremely important for financial markets. On September 18, the Fed will announce its interest rate decision and start the new rate cut cycle. Markets expect that the most probable scenario is a 25 bps rate cut. It should be noted that traders do not rule out the possibility of a 50 bpd cut. From a big-picture point of view, the Fed is expected to cut rates by 75 - 100 bpd by the end of this year to provide support to the labor market, which has already started to show signs of weakness. expect expect Rate cuts improve the liquidity situation in the markets and make money cheaper, which usually serves as support for riskier assets. However, markets do not look too bullish right now. Crypto markets have settled well below highs that were seen back in March and remain under some pressure. The tech-heavy NASDAQ index has pulled back from this year’s highs by roughly 10%. Why are markets not rallying ahead of the start of the new rate-cut cycle? Markets are worried about the potential slowdown of the global economy. The situation in China’s economy is a key problem for investors. The country’s economy has not fully recovered after the coronavirus pandemic, and the government’s attempts to provide stimulus have not yielded tangible results. China’s real estate sector, which was one of the key drivers of the country’s growth, shows no signs of improvement. The recent data indicates that China’s imports increased by just 0.5% year-over-year in August. Put simply, China’s demand remains under material pressure. Meanwhile, it looks like the U.S. labor market has started to cool down, which signals that the U.S. economy may be slowing down. Worries about weak China and the potential slowdown of the American economy have impacted investors’ risk appetite. Both cryptocurrencies and tech stocks are sensitive to fluctuations in market sentiment. When investors are ready to increase their risks in search of profits, they buy cryptocurrencies and tech stocks. In the opposite scenario, which implies rising demand for safe-haven assets, crypto and tech find themselves under pressure. The flight to quality has been going on in crypto markets for quite some time as investors increased their exposure to safe-haven assets. As a result, Bitcoin's dominance has been growing steadily since the start of the year and has already surpassed the 55% level. Jerome Powell’s comments may change the market sentiment and boost investors’ appetite for risk. Traditionally, markets pay close attention to the Fed Chair’s comments, so they will have a major impact on market dynamics. In case Powell signals that the Fed is ready to start cutting rates aggressively to provide additional support to the economy, investors will rush to buy riskier assets, which will be bullish for cryptocurrencies. Most likely, Bitcoin will be the biggest beneficiary of dovish Powell. In this scenario, investors’ demand for altcoins should also increase as they will search for bargains among coins that have suffered serious pullbacks in recent months.

The Fed is Ready to Cut Rates: Where’s the Crypto Market Growth?

Transitioning from Hitachi Content Platform (HCP) to MinIO has never been easier, thanks to our HCP-to-MinIO tool. Developed to support our customers' evolving storage needs, this tool is freely available on GitHub and greatly simplifies the migration process. Many organizations are transitioning to leverage MinIO's modern, scalable, and high-performance object storage optimized for AI infrastructure. This tutorial provides a comprehensive step-by-step guide to ensure a smooth and efficient transition to MinIO. Why Transition to MinIO? Customers often migrate from HCP to MinIO for several compelling reasons, particularly when optimizing for AI-driven applications: Modern Application Requirements: MinIO is Kubernetes-native and built for the cloud operating model, which means that it inherently supports most modern application stacks for analytics, statistical analysis and AI/ML workloads.


Performance at Scale: MinIO efficiently handles mixed object sizes, from very small to very large, ensuring optimal performance for data-intensive AI operations.


Flexibility: Software-defined, S3-compatible object storage offers greater flexibility and cloud neutrality compared to dedicated storage appliances. HCP-to-MinIO Migration Process During migration, it's recommended to write exclusively to MinIO and not to HCP. This approach ensures you start as you plan to go on: using MinIO as your primary object store. Here’s the process: Initial Read Operations: While migrating, getobject calls are directed to MinIO. If an object isn't found, it's read from HCP. Once all objects are copied, this fallback can be removed.


Preserving Metadata: The tool retains the modified timestamps of objects during the migration. Step 1: Create a Temporary Directory After downloading the HCP-to-MinIO tool the first step is to create a temporary directory for the results of the list command in Step 2: $ mkdir /tmp/data Next, set the following environment variables: $ export MINIO_ACCESS_KEY=<Your-MinIO-Access-Key>
$ export MINIO_SECRET_KEY=<Your-MinIO-Secret-Key>
$ export MINIO_ENDPOINT=https://<Your-MinIO-IP-Address>:9000
$ export MINIO_BUCKET=newbucket  # optional, if unspecified HCP bucket name is used Step 2: List Objects in HCP The list command generates a list of objects stored in HCP and saves it to a local file. This file can be split into smaller lists and processed in parallel by multiple client machines to speed up the migration. Here is the command: hcp-to-minio list --auth-token --namespace-url --host-header --namespace-url --dir And an example of what it might look like: $ hcp-to-minio list --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "HOST:s3testbucket.tenant.hcp.example.com" --namespace-url "https://hcp-vip.example.com" --dir "/tmp/data" Step 3: Migrate Objects to MinIO The migrate command uses the list generated in the first step to transfer objects from HCP to MinIO. The command: hcp-to-minio migrate --namespace-url --auth-token --host-header --data-dir --bucket --input-file The example: $ hcp-to-minio migrate --namespace-url https://finance.europe.hcp.example.com --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "s3testbucket.sandbox.hcp.example.com" --data-dir /mnt/data --bucket s3testbucket --input-file /tmp/data/to-migrate.txt Get Started The HCP-to-MinIO tool is designed to help you move to a modern, scalable object storage solution. Download the tool from GitHub and start your migration today. For any questions or additional support, contact us at hello@min.io. Transitioning from Hitachi Content Platform (HCP) to MinIO has never been easier, thanks to our HCP-to-MinIO tool. Developed to support our customers' evolving storage needs, this tool is freely available on GitHub and greatly simplifies the migration process. Many organizations are transitioning to leverage MinIO's modern, scalable, and high-performance object storage optimized for AI infrastructure. This tutorial provides a comprehensive step-by-step guide to ensure a smooth and efficient transition to MinIO. GitHub GitHub Why Transition to MinIO? Customers often migrate from HCP to MinIO for several compelling reasons, particularly when optimizing for AI-driven applications: Modern Application Requirements: MinIO is Kubernetes-native and built for the cloud operating model, which means that it inherently supports most modern application stacks for analytics, statistical analysis and AI/ML workloads. Performance at Scale: MinIO efficiently handles mixed object sizes, from very small to very large, ensuring optimal performance for data-intensive AI operations. Flexibility: Software-defined, S3-compatible object storage offers greater flexibility and cloud neutrality compared to dedicated storage appliances. Modern Application Requirements: MinIO is Kubernetes-native and built for the cloud operating model, which means that it inherently supports most modern application stacks for analytics, statistical analysis and AI/ML workloads. Modern Application Requirements : MinIO is Kubernetes-native and built for the cloud operating model, which means that it inherently supports most modern application stacks for analytics, statistical analysis and AI/ML workloads. Modern Application Requirements Performance at Scale: MinIO efficiently handles mixed object sizes, from very small to very large, ensuring optimal performance for data-intensive AI operations. Performance at Scale : MinIO efficiently handles mixed object sizes, from very small to very large, ensuring optimal performance for data-intensive AI operations. Performance at Scale Flexibility: Software-defined, S3-compatible object storage offers greater flexibility and cloud neutrality compared to dedicated storage appliances. Flexibility : Software-defined, S3-compatible object storage offers greater flexibility and cloud neutrality compared to dedicated storage appliances. Flexibility HCP-to-MinIO Migration Process During migration, it's recommended to write exclusively to MinIO and not to HCP. This approach ensures you start as you plan to go on: using MinIO as your primary object store. Here’s the process: Initial Read Operations: While migrating, getobject calls are directed to MinIO. If an object isn't found, it's read from HCP. Once all objects are copied, this fallback can be removed. Preserving Metadata: The tool retains the modified timestamps of objects during the migration. Initial Read Operations: While migrating, getobject calls are directed to MinIO. If an object isn't found, it's read from HCP. Once all objects are copied, this fallback can be removed. Initial Read Operations : While migrating, getobject calls are directed to MinIO. If an object isn't found, it's read from HCP. Once all objects are copied, this fallback can be removed. Initial Read Operations getobject Preserving Metadata: The tool retains the modified timestamps of objects during the migration. Preserving Metadata : The tool retains the modified timestamps of objects during the migration. Preserving Metadata Step 1: Create a Temporary Directory After downloading the HCP-to-MinIO tool the first step is to create a temporary directory for the results of the list command in Step 2: $ mkdir /tmp/data $ mkdir /tmp/data Next, set the following environment variables: $ export MINIO_ACCESS_KEY=<Your-MinIO-Access-Key>
$ export MINIO_SECRET_KEY=<Your-MinIO-Secret-Key>
$ export MINIO_ENDPOINT=https://<Your-MinIO-IP-Address>:9000
$ export MINIO_BUCKET=newbucket  # optional, if unspecified HCP bucket name is used $ export MINIO_ACCESS_KEY=<Your-MinIO-Access-Key>
$ export MINIO_SECRET_KEY=<Your-MinIO-Secret-Key>
$ export MINIO_ENDPOINT=https://<Your-MinIO-IP-Address>:9000
$ export MINIO_BUCKET=newbucket  # optional, if unspecified HCP bucket name is used Step 2: List Objects in HCP The list command generates a list of objects stored in HCP and saves it to a local file. This file can be split into smaller lists and processed in parallel by multiple client machines to speed up the migration. list Here is the command: hcp-to-minio list --auth-token --namespace-url --host-header --namespace-url --dir hcp-to-minio list --auth-token --namespace-url --host-header --namespace-url --dir And an example of what it might look like: $ hcp-to-minio list --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "HOST:s3testbucket.tenant.hcp.example.com" --namespace-url "https://hcp-vip.example.com" --dir "/tmp/data" $ hcp-to-minio list --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "HOST:s3testbucket.tenant.hcp.example.com" --namespace-url "https://hcp-vip.example.com" --dir "/tmp/data" Step 3: Migrate Objects to MinIO The migrate command uses the list generated in the first step to transfer objects from HCP to MinIO. migrate The command: hcp-to-minio migrate --namespace-url --auth-token --host-header --data-dir --bucket --input-file hcp-to-minio migrate --namespace-url --auth-token --host-header --data-dir --bucket --input-file The example: $ hcp-to-minio migrate --namespace-url https://finance.europe.hcp.example.com --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "s3testbucket.sandbox.hcp.example.com" --data-dir /mnt/data --bucket s3testbucket --input-file /tmp/data/to-migrate.txt $ hcp-to-minio migrate --namespace-url https://finance.europe.hcp.example.com --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "s3testbucket.sandbox.hcp.example.com" --data-dir /mnt/data --bucket s3testbucket --input-file /tmp/data/to-migrate.txt Get Started The HCP-to-MinIO tool is designed to help you move to a modern, scalable object storage solution. Download the tool from GitHub and start your migration today. For any questions or additional support, contact us at hello@min.io. GitHub GitHub

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Is Bitcoin Ready for the Mainstream? How Rootstock is Paving the Way

SINGAPORE, Singapore, April 3rd, 2024/Chainwire/-- Bitlayer Labs, the first Bitcoin Layer 2 solution based on BitVM, announced a seed round of $5M led by Framework Ventures and ABCDE Capital, participation by StarkWare, OKX Ventures, Alliance DAO, UTXO Management, Asymmetric Capital and many more. The announcement comes as Bitlayer prepares the launch of its open-incentive program Ready Player One, inviting developers worldwide to augment, enhance, and build on the protocol. Bitlayer Labs, the developer of Bitcoin Layer 2 protocol , announced today the close of a $5 million seed funding round at an $80 million valuation, driving a vision to become the computation layer of Bitcoin. Bitlayer Investors & Ecosystem Partners Blockchain-based venture firms Framework Ventures and ABCDE Capital led the round, with participation from StarkWare, OKX Ventures, Alliance DAO, UTXO Management, Asymmetric Capital, Kenetic Capital, Kestrel, Global Coin Research, Pivot Global, and Web3Port. Many other incredible investors added value to the seed round. Prominent angel investors including Messari CEO Ryan Selkis, Messari co-founder Dan McArdle, Asymmetric Capital founder Dan Held, Hacken CEO Dyma, Sky Mavis CEO Trung and CTO Andy, and Kyber Network founder Loi Luu, among others, joined in capitalizing the round. Bitlayer’s seed round was delivered soaring value-add support from ecosystem partners including Hacken, AWS Cloud, Ankr, Polyhedra, Babylon, Particle Network, Meson, Nubit, BitSmiley, TokenPocket, Xverse, Flash Protocol, Umoja.xyz, RunesTerminal, and more. The Bitlayer Network Bitlayer is the first Bitcoin Layer 2 network based on BitVM, offering Bitcoin-equivalent security and Turing completeness. The protocol aims to build a more scalable and interconnected Bitcoin ecosystem. The securing of new capital pushes Bitlayer's lead in paving the way for more Bitcoin Layer 2 use cases, a race that will propel Bitcoin to prevail over other blockchains in size, scalability, and security. The fund will also enable Bitlayer to grow its team by hiring across business development and engineering faculties to support global expansion efforts. Investment firms are increasingly optimistic about the Bitcoin ecosystem as Bitlayer is rapidly gaining prominence, capturing the industry's attention. Investor Remarks Roy Learner, partner at Framework Ventures, spoke on the augmentation of Bitlayer with BitVM: “We are excited to back Bitlayer as they launch the first Bitcoin Layer 2 network based on BitVM. Looking at the trade-off space, BitVM struck us as the most practical approach to bring scalability and expressiveness into the BTC ecosystem without sacrificing security.With over $1T of global wealth looking to productively utilize their BTC, Bitlayer opens up the design space to introduce novel DeFi use cases and applications. Led by a team of OGs, we've been impressed by Kevin and Charlie's blend of deep technical background and rich crypto experience and are excited to partner with them as they deliver on their vision of making Bitcoin a more productive asset.” Qiao Wang, co-founder of Alliance DAO, emphasized the future of Bitlayer and Bitcoin L2s in the emerging era: “I spent the vast majority of my intellectual, social, and financial capital in the Ethereum ecosystem in the last decade, but deep down I've always been a Bitcoiner. The possibility to enhance Bitcoin's expressiveness and scalability via L2s such as Bitlayer is the most exciting moment in the history of Bitcoin. That is because there's over $1T of wealth stored on Bitcoin, and the holders would want to do something productive with it. Greater expressiveness and scalability enable this. Bitlayer is one of the most experienced, passionate, and technically capable teams to tackle this problem.” In the midst of building Bitlayer, co-founder Charlie Hu shared his thoughts on the protocol’s milestone: "This support signifies investors’ and builders’ trust in Bitlayer and the team, and demonstrates a robust backing for the Bitcoin ecosystem and the future of Bitcoin DeFi at large." Bitlayer ventures into the future of Bitcoin with newly supplied capital, anticipating a rush of development activity and investor interest in the Bitcoin Layer 2 space. "Bitlayer is dedicated to bringing super scalability into the Bitcoin ecosystem, with hundreds of millions of potential users as an addressable market," said Kevin He, co-founder of Bitlayer. "We’ve created the best, easiest-to-use scalability product in order to give users better fee rates and faster actions, all based on Bitcoin’s security foundation." With the approach of the Bitcoin halving event, the need for a robust Bitcoin ecosystem becomes more important than ever. Bitlayer’s exacting design and rigorous security auditing in preparation for public mainnet launch primes the protocol for a flourishing builder ecosystem. Mainnet launches on April 5th, with more milestones arriving soon on the project’s development pipeline. Ready Player One– Ecosystem Incentive Program. To accelerate ecosystem development and incentivize projects to deploy on the Bitlayer mainnet, Bitlayer is launching a series of ecosystem incentive programs– starting with the first event, Ready Player One. The campaign is an open incentive program designed to distribute token rewards valued at over $50M to protocols and teams that deploy to the Bitlayer mainnet and demonstrate exceptional performance. Bitlayer’s growth is accelerating, urging strong projects to join its ecosystem to grow the protocol in exponential ways. Bitlayer commits to providing comprehensive ecosystem support for all projects, offering the following resources to builders: Potential investment from the foundation and institutions Initial liquidity support Comprehensive product development resources Mentorship and pitch opportunities from top-tier incubators Support from the Bitcoin community and OGs Ecosystem collaboration and co-creation Users can find the full article for more information. here About Bitlayer is the first Bitcoin Layer 2 network based on BitVM, offering Bitcoin-equivalent security and Turing-completeness. Bitlayer is committed to becoming the computation layer for Bitcoin. It introduces ultra-scalability and inherits Bitcoin’s L1 security, providing users with high throughput and a low-cost transaction experience. Bitlayer Github Contact Digital Marketing Manager Sky Harris Bitlayer skyler@bitlayer.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Bitlayer, a Leading Bitcoin L2, Nets $5M In Funding, Unveils $50M 'Ready Player One' Program

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