Top NFT Marketplaces of 2025 and The Popularity of NFTs
NFT now mentioned, the first ever NFT was created by Quantum and minted by Kevin McCoy on Namecoin in 2014. After that, many NFT projects were launched but did not gain such traction like others. The major factor in the rise of NFT is NFT marketing and this gave a new concept to investment. Investors like to own a piece of art, music, film and other online items as non-fungible tokens. Even gamers use to own in-game assets as NFTs which they can later sell and trade on NFT marketplaces. According to the news, NFTs have grown to be worth more than $40 billion as their popularity has increased. In this blog, we will take a look at top NFT Marketplaces of 2025. In addition, we will explore the history of NFT marketplaces and the current market stats. From None To Trend – The Rise of NFT Marketplaces After Quantum, several other NFTs were launched in 2025 on pre-Ethereum blockchain including Spells of Genesis, It was the first-ever blockchain based game which had NFT game art and game design. In 2026, Rare Pepes was launched that kicked the crypto art market but failed to reach popularity. In addition, consumers say that NFT began to gain popularity in 2017. Before that, trading NFTs on blockchain and transferring ownership was difficult. But now, Ethereum network and its smart contracts functionality enabled token creation, programming, storage, and trading built directly into the blockchain itself. These new features eased the onboarding process and increased access. In addition, one of these earliest Ethereum projects was CryptoPunks, a collection launched by Larva Labs that has become synonymous with early NFT history. As a result, many of its individual pieces have sold for millions. Top NFT Marketplaces of 2025 NFT marketplaces brought many new opportunities for artists and brands. NFTs allow collectors to connect globally without intermediaries. It has backend technology called blockchain that enables secure transactions and clear ownership records. Moreover, users get full control over their assets as blockchain offers smart contracts. Lastly, best NFT marketplaces foster communities of similar minded entities of creators and investors. Here is the list of some top NFT marketplaces of 2025: 1. OpenSea Atallah and Devin Finzer. It gave a new concept to how people use to interact with digital assets. In addition, OpenSea is a platform that enables users to buy, sell, and trade NFTs. The stats say it all. OpenSea has reached over $20 billion in total sales. It offers a wide range of NFTs, including virtual worlds, music, photography, s]ports, and collectibles. It uses Ethereum’s smart contracts and focuses on trading on Ethereum. It also enables cross-blockchain trading of NFTs (Solana and Polygon). Typically, it is an ideal platform for regular NFT traders. OpenSea is supported by top companies like Coinbase and Trust Wallet. OpenSea users can create, get and trade a range of NFTs as digital assets, art, virtual world and in-game objects. Users can start by creating an account on OpenSea followed by creating a web3 wallet like Metamask to send compatible cryptos. Then, they can purchase NFTs by selecting one or multiple to make a purchase. 2. Axie Marketplace Axie Marketplace is a blockchain-based, top NFT marketplace for gamers. Players of the Axie Infinity game can raise and trade Axies, which are fantastical NFT objects and monsters, as well as collect, buy, and combat them. The game’s tokenized assets may be exchanged for cryptocurrency. It is ideal for gamers and NFT collectors. Here, you can sell, exchange your NFT Axies for cash. Axis are basically cartoon characters. The platform uses a token $AXS for voting, staking and play to win incentives. Moreover, users can visit Axie Marketplace to register. They can download a wallet like Metamask to store game tokens on NFT art marketplace and activate the Axie account wallet. Lastly, send cryptocurrency from another external wallet to the Ronin wallet. Ramp Network enables the purchase of Eth using money. After that, the user will have to own three Axies to play the game. 3. Rarible Rarible is an open-source NFT blockchain-based platform. In this platform, users can build, list, trade, and exchange NFTs. Most businesses and organizations prefer using Rarible than other NFT marketplaces. The reason behind this is that it shortens time to market by minting and securely distributing NFTs. Another reason is cross-blockchain transactions support including those on Solana, Flow, Tezos, Polygon, and Ethereum. Users can create, sell, bid, cancel, update, transfer, and burn ERC 721 and 1155 NFT orders as well as collect fees and royalties for sold NFTs. Users can register an account and purchase NFTs. Lastly, place a bid and follow the directions. 4. Decentraland Decentraland is a blockchain-based marketplace for virtual land. Users can make money from their apps. Owners of the land can develop settings and interactive applications, such as games and 3D scenarios. They can also include NFTs in spaces and parcels for this purpose. NFTs in Decentraland can be in the form of GIFs and pictures, but not in video and audio format. It has MANA platform tokens utilized as gas and for transaction payments. Users can create, sell and buy NFTs by joining the platform and linking the wallet. To sell, simply visit your profile and click the sell Nft button on the NFTs to add goods for sale. To buy Nfts, first decide on the use before purchasing any NFTs. 5. Binance NFT Binance is the biggest cryptocurrency exchange platform. They have their Binance NFT platform where users can trade and hold NFTs, and support all NFTs which are digital artwork and collectibles. Binance NFT also provides limited edition gaming NFTs. In this way, the market offers or lists NFTs so that projects may utilize the platform to list their gaming NFTs. To purchase NFTs on Binance NFT, users have to register for a standard cryptocurrency exchange account. Then, deposit crypto in the usual ways. After that, users should create an NFT from the Binance NFT marketplace list. It must
Kimi AI: China’s Another AI Drop To Redefine AI Reasoning
China is advancing AI at a breakneck pace. After Deepseek r1 headlines, another company named Moonshoot AI dropped Kimi AI 1.5. It is a model that is routing superior to Open AI GPT-4o and DeepSeek AI r1 model. The best part of Kimi AI is that it shows advancements in multimodal reasoning, long-context understanding, and real-time data processing, raising questions about the future of AI dominance. For the record, there’s a long-standing cliché: the U.S. innovates, China replicates, and Europe regulates. But we’re not here to dwell on geographic stereotypes. Instead, we’re looking beyond them to assess how Kimi AI k1.5 is disrupting the AI industry and what its rise means for the future of artificial intelligence: The Startup Behind Kimi AI – Moonshot AI Moonshot AI was founded in 2023 by the youngest CEO Yang Zhilin and is now one of the top AI companies. The company may be new but its rapid growth in AI is remarkable. According to stats, the company secured major funding from Alibaba, Tencent, and other investors, raising its valuation to $3 billion in just one year. What Is Kimi AI? Kimi AI is introduced by a company named Moonshot AI which is a Beijing-based startup. Kimi AI is a large language model (LLM) that understands and generates human-like text responses, particularly in Chinese. Amazingly, this AI tool can handle up to 2 million Chinese characters in a single prompt. It is a highly effective model to analyze lengthy documents and handle complex tasks. Moreover, Moonshot AI is positioning Kimi as a cost-effective yet powerful alternative to the frontier models. It can surpass models in performance like OpenAI’s GPT-4 and DeepSeek’s latest iterations. How Is It Different From Other Frontier AI Models? OpenAI is designed to solve complex problems by breaking them into small pieces. But Kimi k1.5 is better at handling math and coding problems while working with multiple types of data such as text, images and videos. It is setting new records in multiple areas like in advanced reasoning it scored 77.5% which means its surpassing other models. In complex mathematical problem solving it achieved an impressive 96.2 which is exceptional accuracy. Moreover, in visual understanding tests it scored 74.9% which means it has advanced abilities to process images and graphics. This means, Kimi k1.5 is faster and more versatile than any other. It can handle a variety of tasks, like math, coding, and processing text, images, and videos, more efficiently. Unlike DeepSeek-R1, which mainly focuses on text, Kimi k1.5 is more powerful and flexible. Moreover, there is another important fact that Kimi k1.5 costs less to develop than similar AI models in the U.S. The creators of Kimi believe it can compete directly with OpenAI’s O1, and its strong test results support this claim. What Sets Kimi AI 1.5 Apart? Kimi AI is not less than GPT like models. It has advanced AI model capabilities that are pushing the boundaries of reasoning, multimodal intelligence and real time data retrieval. Let’s see some of the features that sets Kimi from the competition in AI industry: Extended Context Memory: Kimi AI can handle 128k tokens at once. It makes it an ideal AI model for processing long-form documents and conversations without losing context. Existing models struggle with memory limitations so when you work with extensive research papers, tech documentations and in-depth research, Kimi AI k1.5 can be your go-to to get continuity and accuracy. Free and Unlimited Access: Existing AI tools come with subscription fees but Kimi AI is free and provides unlimited access to users which makes it an attractive option for users. However businesses and AI enthusiasts can use Kimi AI without any upfront costs. Real-Time Web Browsing: AI models rely on pre-trained data but Kimi AI 1.5 features real-time web browsing capabilities. It has the capability to scan over 1,000 websites instantly. It can pull up-to-date information to provide more accurate and relevant responses. Means that its prowess in financial analysis is already demonstrated by users. Kimi can assess stock trends and news in real time and this is something GPT-4 and DeepSeek currently struggle with. Multimodal Reasoning: Kimi is not text-based only but it can process multiple forms of data, including text, images, and charts. It has the ability to generate insights that consider multiple input sources. This feature makes it far more sophisticated than standard chatbots. AI Benchmark Performance: As mentioned earlier, Kimi AI 1.5 has outperformed GPT-4 and Claude 3.5 Sonnet in various technical benchmarks. This includes coding and mathematics. In the MATH 500, Kimi achieved an outstanding 96.2% accuracy rate proving that it is a high-level problem solver. The Future of AI: Rapid Expansion Moonshot AI’s Kimi model has surged from handling 200K Chinese characters in October 2023 to an astonishing 2 million by March 2024. This tenfold increase in just six months signifies a transformative shift in AI capabilities. This shows Kimi AI k1.5 is definitely showing a major shift in AI dominance. After deepseek AI launch and then kimi and qwen, China emerges itself as a serious contender in the race for artificial general intelligence (AGI). What This Means for AI’s Future and the Industry? Exponentially, AI models are becoming better at retaining and processing vast amounts of information within a single interaction. Kimi AI has revolutionized how AI handles long documents, research papers, coding tasks, and creative writing by enabling deeper comprehension and more nuanced responses. We don’t know about the future yet but since OpenAI, Google, and Anthropic are major players, Moonshot AI’s advancements suggest that China is positioning itself at the forefront of AI development. Sum and Substance – A New Wave of AI Development Competition After all the research and this article, we can say that Kimi AI stands out with its high reasoning power, long-context handling, and free unlimited access. It represents a significant leap in artificial intelligence reasoning, accessibility, and real-time processing. With backing from China’s biggest tech giants and a pricing model that undercuts its competitors,
DeepSeek / ChatGPT: Can China’s AI Disrupt U.S Giants?
The recent launch of DeepSeek AI R1 model has turned heads in the AI Industry. According to China, they have spent only $6 million per training run on their model, compared to the tens of millions required for U.S. competitors. This is amazing right, the social is full of the buzz Deepseek vs Chatgpt? Moreover, Its commercial pricing is also impressively low. According to DocsBots Website mentioned by Statistica, with 1 million tokens costing only 55 cents to upload. This rapid success raises important questions: can a Chinese AI model truly challenge the U.S. AI dominators without sacrificing quality and security? In this post, we’ll compare cost and performance between top U.S. and Chinese AI infrastructures, to find out best open-source LLM mainly focusing on DeepSeek vs ChatGpt and others like Qwen, Gemini and Llama. We will also explore if China’s AI disruptors can truly outperform their U.S. counterparts. Understanding AI Infrastructure and LLM Costs AI infrastructure is a combination of hardware, software, and cloud services required to train and deploy AI models. When developing cutting-edge AI models like ChatGPT, Gemini, or DeepSeek, they require massive computational power which often involves specialized chips, vast datasets, and advanced training techniques. Typically, training a large language model (LLM) involves millions of dollars in computational costs. According to analysis, running ChatGPT costs approximately $700,000 a day. That breaks down to 36 cents for each question. The US models also demand extensive datasets, advanced algorithms, and constant tuning to ensure they perform at the highest level. Technical Components LLMs Require: The Evolution of AI Training Costs (2017-2023) The evolution of AI training costs has seen an astonishing rise over the years. It reflects the growing sophistication and scale of large language models (LLMs). AI training costs have soared from modest beginnings to reach hundreds of millions today. This rise reflects the growing complexity of large language models (LLMs). Let’s examine how the increasing sophistication of AI models has led to this sharp escalation in development expenses. The above image presents a fascinating timeline of AI model training costs from 2017 to 2023. It shows a dramatic increase in investment over the years. If you see the visualization, it notes that these figures are adjusted for inflation and were calculated based on training duration, hardware requirements, and cloud computing costs, according to The AI Index 2024 Annual Report. US AI Models – The Pioneers The U.S. has long been the leader in artificial intelligence development. Here are several tech giants that are driving innovation in tech space: It was developed by OpenAI and has revolutionized as conversational AI. With iterations like GPT-3 and GPT-4, it remains one of the most advanced models on the market. Training a model like ChatGPT costs upwards of $78 million, reflecting its complexity and the computational power required. ChatGPT app development costs can range anywhere between $100,000 to $500,000. The factors that affect the cost are the dataset’s size, the chatbot’s end-use case, the services, the features required, etc. Claude AI is created by Anthropic. The ai model has emerged as a leading conversational agent as it provides an alternative to ChatGPT with a focus on safety and alignment. The development costs are significant but vary depending on deployment and specific business use cases. Meta’s Llama series is a key competitor in the open-source AI space. While the models are cheaper to access for businesses, developing applications using Llama models still incurs considerable costs mainly for larger-scale integrations. Google’s Gemini is the most expensive AI model in terms of training costs, requiring $191 million for development. It’s designed to handle more complex datasets, including multimedia formats. Despite its higher costs, Gemini is known for its reliability and performance across various tasks. China’s AI Models: A Low-Cost Revolution Recently, China has begun making waves with its innovative, cost-effective alternatives. Chinese companies are challenging the traditional AI ecosystem by introducing similar or better performance at a fraction of the price. Here are some of the newest models of AI: DeepSeek AI launch of its R1 model has sent shockwaves through the AI industry. With a development cost of just $6 million, DeepSeek has proven that cutting-edge AI can be achieved on a lean budget. Its pricing structure is also far more accessible, with 1 million tokens costing only 55 cents to upload. Despite the lower costs, DeepSeek’s model has earned strong performance reviews, often outperforming U.S. models in key benchmarks. Last night, Alibaba launched their AI offerings, including the Qwen series. It quickly gained traction as a viable alternative to expensive models like GPT-4. With a heavy focus on cloud-based AI solutions, Alibaba provides highly competitive pricing, ensuring that businesses can scale AI-powered applications affordably. Moonshot’s Kimi series is a rising star in China’s AI scene. But, it is a less-known AI architecture. However, the Kimi K1.5 has been praised for its efficiency and cost-effectiveness. As it is giving companies an affordable way to implement AI without compromising on quality. The Chinese AI model, ByteDance is known for revolutionizing social media through TikTok, ByteDance is also making strides in AI. Doubao 1.5 Pro is one of their leading LLMs, offering impressive capabilities at a significantly lower cost compared to its Western counterparts. Estimating AI Development Costs The cost of AI development varies greatly depending on the scale, complexity, and project requirements. From infrastructure to labor, software, and training, each component contributes to the overall cost. On average, businesses can expect to invest between $10,000 to $50,000 or more in AI projects. Key Cost Components: Cost Breakdown: Is DeepSeek-R1 Really a Threat? In particular, DeepSeek-R1 has been disruptive due to its low costs and strong performance. But longevity is controversial. However, that model only spends $6 million per training run, far less than models like ChatGPT or Google’s Gemini, which can cost tens of millions. Its commercial use pricing also reflects this, with 1 million tokens costing only 55 cents to upload and $2.19 to download, which is significantly cheaper than U.S.-based
Why DeepSeek AI Has Shaken the Tech World?
For years, Nvidia has been the undisputed king of AI hardware. It provides High-performance GPUs to power AI training and interference to many companies like OpenAI, Google DeepMind and Anthropic. These companies have invested billions in AI models that require massive computing power and they all rely on Nvidia’s hardware. However, on Monday, the tech market experienced a major shake-up after a disruptor arrived, DeepSeek AI. According to Forbes, Nvidia suffered an unprecedented $600 billion market value wipeout. It is the largest single-day loss in stock market history. This sudden meltdown left analysts scrambling for answers: What is DeepSeek AI, and why has it shaken up the tech world so dramatically? Understanding The Bull Case DeepSeek AI wiped out trillions of dollars from stock market valuations in just a matter of days. But, still DeepSeek’s future remains uncertain. However, the company’s recent technical paper offers a clear window into why it’s making such a huge impact and why it’s generating so much buzz. However, the R1 model came last week which showed that DeepSeek AI achieved something industry giants, despite spending billions, couldn’t. According to Wedbush Securities analyst Dan Ives, DeepSeek AI’s R1 model was built for just $6 million. It is a stunning contrast to Goldman Sachs’ report estimating U.S. tech giants will pour nearly $1 trillion into AI development. What is DeepSeek AI? DeepSeek released its V3 technical paper on December 27, it wasn’t until the unveiling of its R1 model just a week ago that the true scope of the disruption became clear. “DeepSeek AI is a reasoning-focused large language model that is designed to excel at logical problem-solving and structured reasoning tasks. Its goal is not just to generate responses, but to approach problems in a more analytical and efficient way. These features make DeepSeek AI stand out from other AI models, which often rely heavily on massive computational resources to train on vast datasets.” DeepSeek AI Technical Breakthrough The key innovation behind DeepSeek AI is its efficient training methods and scalable architecture. These advancements allow DeepSeek to build AI models that are smaller, more task-specific, and far more efficient in their use of computational resources compared to other AI systems on the market. Moreover: DeepSeek: A 45x Leap in Efficiency Traditional AI models, like those used by OpenAI, Google, and Nvidia, require massive infrastructure investments to perform at the cutting edge. But DeepSeek’s team has fewer resources than 200 engineers that developed a training method 45 times more efficient than traditional AI models. Furthermore, it is a revolutionary approach because; Efficient Training and Scalable Architecture The company’s R1 model, trained for just $6 million, is optimized for efficiency. A stark contrast to the $100 million or more required for models like GPT-4. DeepSeek’s approach focuses on achieving high performance with significantly lower computational demands. This includes: The Impact on the AI Tech Industry The Nasdaq has experienced significant milestones since the COVID crash in 2020, with major AI announcements driving market movements – from ChatGPT’s public launch in November 2022 to Google’s Gemini reveal in 2023. However, this upward trajectory faced a dramatic reversal on January 27, 2025, when DeepSeek’s announcement of a $6 million AI model sent shockwaves through the market, challenging the massive investments made by established tech giants in AI development. However, this shift in the cost-to-performance ratio has massive implications for investors, big tech and AI startups and developers alike. Investors: As DeepSeek has the ability to deliver top-tier performance with such minimal resource usage, it is a game-changer for the market. Investors are now rethinking their positions in the AI space, questioning whether the current billion-dollar investments in GPUs and infrastructure are still sustainable in the face of more efficient alternatives. The Big Players: Nvidia, traditionally, was the backbone of AI infrastructure. But after the arrival of DeepSeek, it faces unprecedented competition. Companies like OpenAI and Anthropic, which have spent vast sums on training and refining their models, could now face pressure to adapt to the significantly more cost-effective, and more efficient approach which DeepSeek has pioneered. AI Startups and Developers: DeepSeek has demonstrated that it does not require such astronomical investments in hardware or cloud computing resources, smaller startups and developers may be able to compete on a more even playing field, leading to possibly a more democratized AI landscape. The Turning Point DeepSeek is completely redefining the economics of AI while competing with tech AI giants. In short, their API services are reportedly 95% cheaper than OpenAI and Anthropic and this challenges the trillion-dollar AI infrastructure investments being made today. It is a defining moment for the AI industry. Everyone has questions like will companies continue investing billions in massive computing infrastructure, or pivot toward more efficient AI training methods? It is still unanswered and controversial. But, According to Giuseppe Sette, president of AI research firm Reflexivity: “DeepSeek AI has taken the market by storm by doing more with less. This shows that in AI, the biggest surprises are yet to come.” As dust settles, DeepSeek’s breakthroughs will unfold more but one thing is transparent that the AI industry is entering a new phase. Whether to leave expensive AI infrastructure or invest in cost-effective, scalable models like DeepSeek? As more companies will adopt this approach, the next few months will be critical as the market absorbs this disrup
The Shift From SaaS to Service as a Software
Traditionally, software was primarily delivered and deployed through a software licensing model. Businesses and individuals had to buy software licenses like CDs, and floppy disks or install the software in their local machines or servers. The major drawback of this process was that users had to pay a large amount for the software upfront rather than on a subscription. This model is known as on-premises software. Moreover, businesses had to maintain dedicated infrastructure (servers, networks, etc.) to support the software. This maintenance is costly and resource-intensive. There was a dire need for a SaaS (Service as a Software) model which could offer companies flexible and scalable alternatives. The potential benefits of this shift were massive. SaaS removed the need to install software on local machines and allowed users to access it through the cloud. The software providers took care of hosting, updates, and maintenance. This change made software more affordable, easier to use, and more accessible. In this blog, we will learn what is Service as a Software, what is software as a service, the differences between them and how this shift changed everything. What is Service as a Software (SaaS)? Service as Software shift is a concept of delivering traditional services (like customer support, consulting, or marketing) through a software platform. It often utilizes automation, AI, or machine learning to mimic human interaction. In this model, the focus shifts from providing a tool to offering a service through software. In this, technology is automated and service is improved. Thus, SaaS gives users a software tool to use, Service as Software which automates human services and delivers them as a digital experience. What is Software as a Service (SaaS)? SaaS (Software as a Service) refers to cloud-based software applications that are provided to users on a subscription basis. These apps include CRM tools, project management software and communication platforms. These tools are designed for users to perform specific tasks without worrying about installation, maintenance, or updates. According to Forbes, in software, companies provide tools like QuickBooks, but customers handle the outcomes. In the services business, however, the company takes responsibility for delivering results, such as AI-powered tax services. This shift creates a $4.6 trillion opportunity, as the global services market is much larger than the software market. Need For Service as a Software And Challenges of SaaS The main difference between Software as a Service (SaaS) and Service as a Software (SaaS) lies in what is being delivered to the user. SaaS provides cloud-based software tools to users that help them perform tasks. However, Service as software automates human services and delivers them through a software platform. This shift created more automated, and user-friendly experiences. Here are some challenges of software as a service that gave rise to service as a software: Challenges of Software as a Service (SaaS): Limited Responsibility for Outcomes: One of the key challenges of the SaaS model is that companies provide the tools, but users are responsible for achieving the desired results. For example, with Salesforce. Users need to learn the software and make strategic decisions based on the data. For non-tech-savvy users, this could pose challenges to utilizing the full potential of the tool which causes inefficiencies and missed opportunities. Customization and Complexity: Many SaaS solutions are a “one-size-fits-all” approach. Still, they do not address the unique needs of every user or organization. However, some platforms allow for integrations and customizations. These integrations are complex, costly, and time-consuming to implement. That is why companies need to hire additional experts or consultants to tailor the software to their business needs. Nevertheless, with advancements in SaaS solutions, it is more difficult to maintain compatibility with other systems and ensuring that updates don’t break critical workflows can also pose challenges. User Dependency and Learning Curve: SaaS tools are generally user-friendly, but still, they require some degree of learning and adaptation. In addition, businesses need to dedicate time and resources to training staff or onboarding new users. This creates a barrier for businesses as they become dependent on user expertise. Mainly small businesses are affected because they have limited IT resources. Moreover, there is a high chance of failure as without proper training users cannot operate which impacts productivity and return on investment (ROI). The Need for Service as a Software (SaaS) In the above section, you read all the limitations of traditional Service as a software. That is why, the software industry is increasingly moving toward Service as a Software. According to SNS Insider Research, The Software as a Service (SaaS) Market size is expected to reach USD 1057.8 billion by 2032, with a growing CAGR of 13.62% in 2024-2032. This shift has changed the role of the user in achieving outcomes. It does not only provide the tool for users to operate but it also automates services and integrates them into a software platform. This way, companies can also take the responsibility of delivering the desired outcome. Also, they do not need to rely on users to navigate and operate the tools. For example: QuickBooks can manage taxes. Instead of using this, the Service as a Software model provides fully automated tax services through an AI-powered accountant. This AI accountant can handle everything from tax preparation to filing. The user simply interacts with the software, and the system takes care of the rest. As a result, it ensures a seamless, hands-off experience. Therefore, this model shifts the focus from providing a tool to delivering a complete service, where the software handles the complexity of achieving the desired result. Benefits of Service As a Software Service as a Software automates services and cuts the need for users to manage and execute tasks. The software itself becomes responsible for ensuring that the right outcomes are achieved. Here are the benefits of Service as a Software over traditional SaaS models including : Efficiency and Convenience: Businesses and consumers can access fully automated services. It reduces the time and effort required to achieve specific outcomes. The backend technologies are
SurferMonkey and OptimusFox Strategic Partnership.
SurferMonkey and OptimusFox Forge Strategic Partnership to Revolutionize Blockchain Privacy and Compliance! Chicago, 21st Jan, 2025 SurferMonkey, a trailblazer in blockchain privacy and compliance, and OptimusFox, a leader in bespoke blockchain solutions, are thrilled to announce their strategic partnership aimed at setting new standards in the blockchain industry. SurferMonkey has pioneered the use of Zero-knowledge proof technology, essential for advancing privacy and security in blockchain networks. Their innovative API solutions enable businesses to uphold privacy without sacrificing compliance, ensuring a future where data integrity and user confidentiality are paramount. On the other side, OptimusFox brings its expertise in crafting custom blockchain applications that drive innovation, scalability, and operational excellence. Their commitment to integrating cutting-edge technologies ensures clients are always at the forefront of the blockchain evolution. Together, this partnership unites SurferMonkey’s cutting-edge privacy and compliance APIs with OptimusFox’s profound blockchain development knowledge. This collaboration is set to deliver transformative solutions that will lead the industry: We are already harnessing SurferMonkey’s APIs to explore new horizons in the blockchain sector,” stated @Mujab Ramzan, CEO of OptimusFox. “This partnership is not just about combining technologies; it’s about redefining what’s possible in blockchain for businesses.” Join us in this exciting journey as we make blockchain solutions that are private, compliant, and poised to lead the market. Together, we are shaping the future of digital trust and security. About SurferMonkey SurferMonkey is at the forefront of blockchain technology, specializing in privacy solutions that do not compromise on compliance. Their work with Zero-knowledge proofs ensures that businesses can operate securely in a digital-first world. About OptimusFox OptimusFox excels in providing tailored blockchain solutions that push the boundaries of technology. With a focus on innovation, they help #businesses harness the power of blockchain to achieve operational excellence and strategic growth.
How Does RPA Empower SMBs in 2024 with Affordable Automation?
he introduction of artificial intelligence (AI) has reshaped almost every size of business by complex task automation. This transformation gave rise to new sophisticated tools like Copilots, RPA, Low-code and No-code platforms. Traditionally, industries struggled with high costs, lack of decision-making, errors in processes, inflexibility in legacy systems, repetitive tasks and difficulties in scaling operations to meet consumer demands. Collectively, these drawbacks led to customer dissatisfaction and overall lost productivity. In addition, there was a need for a scalable solution like RPA that could streamline operations, enhance accuracy, and reduce costs. But how? Let’s find out. In this article, you will learn what is robotic process automation, how RPA works, and how RPA and AI are making a difference in SMBs by automating processes while staying within What is Robotic Process Automation? Robotic Process Automation (RPA) is software used to automate repetitive tasks in business and IT processes. It functions with sets of instructions called software scripts. These scripts mimic the way a person would interact with software. It includes actions like clicking buttons, entering data, or navigating through menus. Moreover, using RPA time-consuming tasks and manual effort get automated. It results in allowing users to set up these scripts using coding or through easy-to-use tools. These tools do not require programming skills. Lastly, when the scripts are done, they can run automatically across different systems which will free up time for employees and they can focus on more valuable work. RPA use is growing day by day, according to GlobeNewswire, the global robotic process automation market size was valued at USD 2.8 billion in 2023. Now, the market is projected to grow from USD 38.4 billion by 2032, exhibiting a CAGR of 33.8% during the forecast period. How RPA Works? Robotic Process Automation (RPA) functions by automating many manual tasks to eliminate repetitive errors, making business processes smoother and more efficient. RPA functionality includes Six key aspects. All these functions make RPA handle a range of tasks which makes employees less burdened and drained ultimately no human errors and more focus on other tasks. Here are the key aspects: RPA Benefits for SMBs RPA can provide numerous benefits to every size of business, including quick scalability, streamlining operations, saving costs, and allowing small teams to handle higher workloads with greater accuracy. Here are some key benefits of RPA that can help smaller businesses compete more effectively: 1. Boosts Efficiency: Robotic Process Automation for SMBs can automate manual and repetitive tasks that are time-consuming and prone to human errors including data entry, report generation, and inventory updates. When bots handle these processes 24/7, businesses get improved turnaround times. Their employees can focus on high-value activities to work more efficiently and for SMBs, there’s no need to hire additional staff. 2. Reduces Costs: SMBs usually have budget constraints when it comes to hiring more resources. However, RPA offers a cost-effective way to achieve more without hiring or outsourcing any resources. RPA and AI automate labour-intensive tasks which cut down on labor costs and minimizes the expenses related to human errors. As a result, it allows SMBs to reinvest the savings into growth areas like product development or customer acquisition. 3. Improves Accuracy and Reliability: RPA reduces human error in tasks including invoice processing, order entry, and payroll. These are areas where SMBs could cost more if there is any mistake. However, integration of RPA in business can provide only consistent and accurate results. reducing the need for rework and building customer trust by delivering reliable services. 4. Enables Scalability and Flexibility: RPA for small business is a scalable solution that can adapt to their growth. As business demands fluctuate, bots can be scaled up or down. It allow SMBs to meet seasonal or unexpected spikes in work without the tiredness of hiring temporary staff. In addition, the flexibility provides value to small businesses looking to grow sustainably. 5. Enhances Compliance and Security: Small businesses from industries like finance or healthcare(regulated industries) face strict compliance requirements. But, if RPA is integrated, it helps ensure that all tasks follow set rules and maintain accurate logs for audits. It can automate data handling and process tasks in no time. As a result, SMBs can thrive with more easily meet compliance standards. Also, there will be a reduced risk and a protected business reputation. Use Cases of RPA for Businesses RPA can go further from streamlining processes and addressing practical needs in real-time. It can boost operational efficiency across various industries. Here are RPA use cases with it’s additional practical applications: 1. RPA in Customer Service: Robotic Process Automation can make routine customer inquiries automated. It includes tasks like account updates, order tracking, and FAQs. Further, it can handle data entry and transfer between systems to enable agents to focus on more complex customer issues. In addition, RPA provides instant responses to customers through chatbots and automatically updates CRM systems with customer interaction details. Ultimately, ensuring a complete history for future service needs. 2. RPA in E-commerce: RPA in e-commerce automates order tracking to keep customers updated at each stage mentioned in the image above. This type of automation reduces the need for manual support. It provides timely notifications which keeps customers informed throughout the shipping process. The major benefit of RPA for e-commerce businesses is that it enhances satisfaction and reduces “Where is my order?” queries. These routine updates if automated, e-commerce companies can surely improve efficiency and focus on complex customer needs. 3. RPA in Accounting: RPA in fintech is utilized for the automation of invoice processing, accounts payable/receivable, financial reporting, and compliance checks. These complex tasks when done by humans repetitively can be prone to errors. That is why automating these tasks ensures timely financial management. Moreover, RPA reconciles bank statements with financial records and automatically flags discrepancies. As a result, it helps maintain accurate records without manual effort. 4. RPA in Banking: RPA in banking can be used to automate tasks like loan processing, customer onboarding, fraud detection, and compliance
A Transformative Journey from LLMs to Micro-LLMs
Introduction AI is a most discussed topic of today. Recently platforms like Medium, Reddit, and Quora had so many posts about “AI hype is dead” and “AI is a washed-up concept from yesterday”. Well, they’re half right because “AI is already everywhere now”, transforming businesses, disrupting enterprises, automating tasks, and making decisions like a boss. The potential is shown from developments in AI like NLP, deep learning and then Large Language Models (LLMs) like GPT-3 and GPT-4. These models are powerful and massive. They transform businesses by automating tasks and making intelligent decisions. But, with great power comes great resource demands which led to the rise of Small Language Models (SLMs) and Micro-LLMs. These models are more efficient and targeted for specific tasks. According to Lexalytics, micromodels offer precision with fewer resources. So, do smaller models make a bigger impact on businesses? Let’s find out which model is better for businesses and enterprise success! LLMs – The Powerhouse of AI For over a thousand years, humans have strived to develop spoken languages to communicate. The main purpose was to encourage development and collaboration through language. In the AI world, language models are creating a foundation for machines to communicate and generate new concepts. LLM refers to a large language model. A type of AI algorithm with the underlying technology of deep learning techniques and huge data sets to understand, summarize, generate and predict new content. GenAI or the term generative AI is also related to LLMs because they have been specifically architected to help generate text-based content. Furthermore, LLMs utilize transformer architectures. In 2017, a paper titled as “Attention is all you need” was published by Google to achieve tasks like content generation, translation, and summarization. Transformers use positional encoding and self-attention mechanisms. These aspects allow models to process large datasets efficiently and understand complex relationships between data points. Because of this, LLMs can handle vast information streams which makes them a powerful tool for generating and interpreting textual information. The image shows various transformer-based language models with different numbers of parameters. Different parameters reflect LLMs’ complexity and capabilities. The models in this category include GPT-4, GPT-3, Turing-NLG, GPT-NEO, GPT-2, and BERT. However, GPT-4 is the most advanced and has 1 trillion parameters. On the other hand, GPT-3 have 175 billion. These numbers make them the most powerful and widely used models. They can generate human-like text and can make complex decisions by learning context from large-scale datasets provided. For instance, GPT-4 can be used in: Significant Challenges of LLMs We know that large language model are known for their massive power. Apart from being massive, LLMs face significant challenges like: Latest Advancements in LLMs Despite the challenges, LLMs for enterprise AI solutions is revolutionizing by offering AI systems capable of learning and generating human-like content across numerous domains. Moreover, the complexity of LLMs gave rise to more advancements in models like encoder-only, decoder-only, and encoder-decoder models. Each model is best suited for different use cases such as classification, generation, or translation. Let’s understand each: Encoder-only models: Decoder-only models Encoder-decoder models Examples of Real-Life LLMs AI is evolving continuously and more and more developments are happening. These models are significant tools that are advancing open research and developing efficient AI applications. Here are some open-source large language models: For the designer: Add logos of each in one picture and add here. Small Language Models: The Solution to LLM’s Challenges While, LLM faces high computational costs, extensive data requirements, and significant infrastructure needs, Small Language Models (SLMs) provide a balanced solution with maintained strong performance and reduced resource burden. Within the vast domain of AI, Small Language Models (SLMs) stand as a subset of Natural Language Processing (NLP). These models have a compact architecture which costs less computational power. They are designed to perform specific language tasks, with a degree of efficiency and specificity that distinguishes them from their Large Language Model (LLM) counterparts. Furthermore, experts at IBM believes that Lightweight AI models for business optimization are best for data security, development and deployment. These features significantly enhance SLM appeal for enterprises, particularly in LLM evaluation results, accuracy, protecting sensitive information, and ensuring privacy. Focused Solutions With Small Language Models SLMs can target specific tasks, like customer service automation and real-time language processing. Being small in size, its more easy to deploy with low cost and fast processing time. Experts says that Low-resource AI models for business are ideal for businesses that need efficient, task-focused AI systems without the enormous computational footprint of LLMs. They also mitigate risks related to data privacy, as they can be deployed on-premises. As a result, they reduce the need for vast cloud infrastructure. Moreover, SLMs require less data which offers improved precision. This feature makes small language model more suitable for healthcare and finance sectors where privacy and efficiency is mandatory. Moreover, they excel at tasks like sentimental analysis, customer interaction and document summarization. These tasks usually require fast, accurate, and low-latency responses. In essence, SLMs provide businesses with the performance they need without the overwhelming demands of LLMs. SLMs For Industries Small Language Models (SLMs) are not only limited to their cost efficient quality but it has transformed many industries. The major benefit it offers is being efficient and task-specific AI solution that is why it is best for healthcare and customer support that needs quick deployment and precision. Lets see how: SLM in Healthcare: Domain-specific SLMs are fine-tuned. This make SLM handle medical terminologies, patient records, and research data. SLM in healthcare can provide benefits like: These aspects make SLM more efficient in healthcare by being helpful in diagnostic suggestions and summarizing records. SLM in Customer Service: SLM and Micro-LLM can similarly be deployed in customer service. They can automate responses based on past interactions, product details, and FAQs. They provide benefits in customer service like: These features make them a faster solutions to boost customer satisfaction and allow human agents to focus on complex issues. Phi-3: Redefining SLMs Microsoft developed a
How Internet Computer Protocol (ICP) is Powering and Redefining Blockchain
Introduction The Internet Computer, or ICP technology, has mixed perceptions online. Some people consider seeing only the fluctuations in its token price, while others are excited to know if it is a revolutionary technology to transform the internet. To clear up confusion, we will break down the concept of ICP into what is icp, how icp works, its features and how it redefines or powers blockchain. Before we start we need to clear up some things because the internet computer refers to three different things. First is a network, the internet computer is a decentralized cloud. Next is a token, ICP is a cryptocurrency and you can buy it on exchanges and trade it like any other token that has utility in the network. You can burn and stake it and you can earn rewards for it. The third one is the program, ICP stands for Internet Computer Protocol which is a program. It runs in the nodes that power the network. Moreover, the code is open source. Developers can find it online. The code is currently maintained by Dfinity foundation but other contributors are welcome to participate in maintaining the nodes. Let’s understand the Internet computers and why it was needed in detail: Smart Contracts and Their Limitations Traditional ecosystems like Ethereum use smart contracts. They are self-executing contracts that enable dApps to function. The major drawback was that they required intermediaries and wallets. They also charge transaction fees to users which introduce risks like censorship and centralized dependency. The Need For ICP ICP is a blockchain network that facilitates the development of Internet services. It uses a secure and decentralized protocol designed to surpass the limitations of traditional smart contracts. ICP protocol uses nodes to communicate over the Internet to create a cohesive, decentralized network called the Internet Computer. On top of the ICP network developers can create dApps called canisters. Canisters operate as a WebAssembly module that can be considered as an ICP solution to solve the limitations of traditional smart contracts. Picture: Do you know? Internet Computer (ICP) after Bitcoin and Ethereum is the third great innovation in blockchain. How ICP Works In simple words, The Internet Computer Protocol (ICP) provides a decentralized environment for hosting and running web applications. It enables a new generation of internet service including a network of nodes, a decentralized consensus protocol, and a distinctive approach to application hosting. Here’s a breakdown of how ICP works: 1. Decentralized Network and Node Structure The Internet Computer has nodes hosted in independent data centers worldwide. These nodes form a cohesive, decentralized network by communicating over the internet. ICP organizes these nodes into subnets. Each subnet manages multiple canisters(apps). Subnets can operate independently which allows the network to scale easily by adding new subnets as needed. 2. Canisters as Next-Gen Smart Contracts Traditional smart contracts have limitations which canisters solve. These modules enable developers to create internet-scale applications with advanced functionality. Users interact with these canisters by sending messages, which can trigger actions such as transferring tokens, posting on social media, or interacting with other decentralized applications. This module eliminates the need for centralized servers and reduces intermediary reliance for more seamless user experiences. 3. Reverse Gas Model The computation cost is covered by a canister itself in an ICP which is called a reverse gas model. This model allows users to interact with decentralized applications without paying transaction fees. As a result, it enhances accessibility and user experience. 4. Scalability and Consensus Protocol The Internet Computer have a unique threshold cryptography consensus protocol. Unlike traditional proof-of-work (PoW) or proof-of-stake (PoS) models, each subnet maintains a public key, and nodes work together to validate messages. It ensures network security even if some nodes act maliciously. This protocol supports secure and decentralized validation to make the network scale smoothly with each additional subnet. 5. Network Nervous System (NNS) ICP has its decentralized governance mechanism which is called Network Nervous System. NNS manages the public keys of all subnets which allow users to verify transactions without downloading the entire blockchain. In addition, NNS enables governance of the network that changes and upgrades itself to align with the community’s interests. Traditional Blockchain Challenges Blockchain technology has revolutionized itself across industries providing the finance industry with transparent transactions to secure data storage. With all this transformation, traditional blockchains like Ethereum and Bitcoin face significant limitations that are hindering scalability and user experience. Here are some of the challenges: 1. High Transaction Fees Let’s take an example of a traditional blockchain like Ethereum. Users have to pay transaction fees (gas) for each interaction with decentralized applications (dApps). Moreover, the cost fluctuates with network demand. Sometimes cost of transactions is high, sometimes low. This limitation of blockchain accessibility and fee structure creates a barrier for both users and developers. 2. Slow Processing Speeds Some blockchains struggle with the speed required for real-time applications. Do you know, that Bitcoin’s average transaction confirmation time is around 10 minutes, and Ethereum, while faster, typically processes transactions in 15-20 seconds? Some applications require immediate interactions and these delays impact usability and deter mass adoption. 3. Dependency on External Servers for Hosting dApps that are built on blockchains like Ethereum need centralized web servers to host their front-end interfaces. It creates a dependency on external cloud providers. This reliance can lead to censorship risks, increased operational costs, and decreased decentralization. 4. Limited Interoperability Traditional blockchains are siloed ecosystems that cannot interact directly with other blockchain networks. Users and developers face difficulties and have to rely on third-party bridges and mediators to transfer assets or data between networks. As a result, it is a challenge with increased security risks and complexity. 5. High Storage Costs Another common challenge of traditional blockchain is data storage being costly. Developers find it difficult to build data-intensive applications on-chain. Because storing large datasets like media files and transaction records is generally impractical due to high costs and limited on-chain capacity. 6. Energy-Intensive Consensus Mechanisms Proof-of-work (PoW) consensus algorithms like
Why Businesses Should Leverage Circle’s Cross-Chain Transfer Protocol?
Introduction Crypto users feel trapped while managing their assets on a single blockchain network. A single blockchain network has siloed environments. They operate independently with no direct interoperability with other networks. The main challenge is fragmented liquidity in which assets get isolated within one network and cannot easily move to another. As a result, users face high fees, slow transaction times, and increased risks when bridging assets across chains. Now, you know that each blockchain has its protocols and rules that make cross-chain asset management complex and limit the potential for a unified, interconnected blockchain ecosystem. So what’s the solution? Cross-chain transfer protocol is the key to blockchain interoperability that allows data and value to communicate between different networks seamlessly. It helps make Web3 more accessible by removing limitations. This article will expand your understanding of Circle’s cross-chain transfer protocol (CCTP) and its design, security, and trust assumptions. It will also demonstrate its key features and tradeoffs with a thorough analysis of its architecture. Overview of Circle’s Cross-Chain Transfer Protocol (CCTP) According to this report In 2018 – Circle, a financial technology company partnered with Coinbase to launch the USD Coin (USDC). It is a widely-used stablecoin tied to the American dollar. Five years later, in August 2023, Circle and Coinbase ended the Centre Consortium, leaving Circle as the only governing body of USDC. In April 2023 the same company launched CCTP in response to solve some major issues related to USDC. The main problem with USDC transfers across blockchains was the reliance on wrapped tokens and traditional bridges. These methods often created fragmented liquidity, increased security risks, and added complexity for users, as they involved maintaining multiple versions of USDC on different networks. Wrapped tokens also posed the risk of asset loss due to vulnerabilities in bridge contracts. Circle launched CCTP to address these issues, enabling direct, native USDC transfers through a secure burn-and-mint process, which simplifies multi-chain transactions and unifies liquidity. Circle’s Cross-chain transfer protocol initially supports USDC transfers between Ethereum and Avalanche. This enabled direct, secure cross-chain asset transfers via a burn-and-mint process. However, CCTP removes restrictions and allows for seamless movement of USDC across networks like Ethereum and Cosmos. As a result, USDC can now play a broader role in decentralized finance (DeFi), payments, and other blockchain protocols. The Origin of CCTP Users faced difficulties with managing liquidity on different blockchain networks. That is why, users and developers desired a consistent experience with one fungible USDC to work the same on different blockchains, like Ethereum and Solana. In the past, using USDC on multiple chains created many copies, called “wrapped tokens.” This was confusing and not very secure. For example, on Solana, there were 11 different types of USDC. Circle wanted to reduce risks tied to traditional bridges. To fix all the issues, according to Blockworks, Circle has now integrated its Cross-Chain Transfer Protocol (CCTP) with Solana for USDC transfers across Solana, Ethereum, and various EVM-compatible chains like Arbitrum and Polygon. CCTP is a permissionless on-chain protocol that enables native USDC transfers between blockchains. The key feature of CCTP is that it can help USDC move directly between chains without extra copies. It helps keep things simple, secure, and easy to use. As a result of the CCTP launch, USDC became useful across different apps and blockchains. Also, it minimizes security risks associated with traditional bridges. It also ensures quick, efficient transfers. The mission of Circle CCTP was to make USDC a widely accessible digital dollar. CCTP launch makes it integral to DeFi, payments, and Web3 applications. CCTP as an on-chain protocol opens up many opportunities like allowing native USDC transfers between blockchains, simplifying the process, preserving fungibility, and making cross-chain interactions seamless. Why CCTP Is Needed For USDC? According to Circle’s developer’s doc, Cross-Chain Transfer Protocol (CCTP) development aims to solve major issues of USDC users like inefficiencies in the multi-chain world. Let’s see: 1. Unified USDC Without Wrapped Tokens The major problem for USDC users was wrapped versions on different chains. These copies often make them confuse and fragment liquidity. With CCTP, there is a single, native USDC standard across all supported chains. Now, USDC is a clear choice for Defi Applications on any blockchain. Moreover, for users, it is a straightforward and clear way to know which version of USDC is native and widely accepted. 2. USDC Demand Across Multiple Chains and Strong Industry Support Wrapped versions of USDC show high demand. Users and developers demanded for consistent network for USDC. Circle gave priority to the industry players’ feedback like exchange – dYdX. They learnt how to make USDC effective and trusted across blockchains. These gathered insights helped them with the creation of a more robust solution, CCTP – a dependable cross-chain USDC solution. 3. Reduced Dependency on Third-Party Bridges Another drawback for users was to depend on third-party bridges to move USDC. These bridges had associated security risks like hacking or operational issues which could lead to loose user funds. CCTP help Circle directly manage transfers, and remove third-party dependency. Now, users can have control of safe transfers across chains without needing to rely on mediators. 4. Improved Efficiency Over Liquidity Pools Liquidity pool bridges require substantial capital to be locked. The major pitfall was fees and limiting transfer amounts to what was available in the pool. Cross-chain Transfer Protocol cuts these limitations and enables seamless transfers without liquidity pools. As a result, there is maximized capital efficiency and reduced costs. Ultimately, making USDC transfers faster and more economical. CCTP Benefits For Businesses: Cross Chain Transfer Protocol offers enhanced security and reliability to transfer USDC between blockchains. It also addresses issues that are common to traditional cross-chain bridges. It will be helpful for businesses as it is a versatile tool to support a more connected, efficient multi-chain environment for businesses. Let’s look into a breakdown version of its features: 1. Maximum Capital Efficiency: CCTP solves the issue that was previously faced: liquidity fragmentation and simplifies user experiences by allowing direct transfers
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