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Massive

massive

Massive is a decentralized ZK verification network service focused on achieving mass adoption for Web3. It aims to alleviate or even solve some of the key issues currently facing the industry. Here’s a breakdown: Mass Adoption: Massive could attract more users The sector is actively exploring ways to attract more traditional users and resources. Massive takes a novel approach by starting with mobile phones, a device most users are familiar with. By employing a “Verify to Earn” incentive mechanism and simplifying operations, Massive aims to draw more participants. This strategy helps newcomers gradually understand and join the Web3 industry, continuously injecting fresh blood into the sector. Web3 Highly Effective and Cheap: Massive could work as a settlement layer of blockchain Currently, all Rollup solutions based on ZK technology face delays in state confirmation on Layer1 (L1) due to cost and security concerns. This is typically addressed through proof aggregation technology and verification delays. introduces a solution that ensures Layer2 (L2)states can be decentralized and verified immediately after proof generation, significantly reducing the time for state transition confirmation. The verified state is then synchronized to L1, ensuring a swift and secure process. Massive Capital Efficiency: Massive could improve profits Nodes in the POS (Proof of Stake) network can join through staking and re-staking. If approach is reused, the POS consensus network can achieve Ethereum-level security. Massive supports staking and re-staking of various token types and offers flexible staking schemes tailored to different ecosystem types. Eigenlayer’s Modular Design: Massive could complete the modularity The emergence of Massive further advances the modular design of blockchain. It can be specifically divided into ZKRollup (off-chain computation), DA (Data Availability — off-chain transaction storage), ZKVaaS (Zero-Knowledge Verification as a Service — off-chain verification), and L1 (state updates and consensus). Keywords: Zero Knowledge, Mobile devices, Staking, Re-staking, Modularity, POS Introduction In essence, the blockchain industry is currently plagued by a crucial question: "How do we attract more traditional users to this sector?" Even though we witnessed a historical moment in January when a BTC spot ETF was approved, allowing some traditional investors to trade BTC in a compliant manner, the influx of new users and resources still falls far short of the industry's urgent need for fresh participants. Simply opening up investment opportunities can only bring in some investors; achieving mass adoption requires different strategies. Some believe gaming could be the key to reaching this goal, others see potential in social media, and still, others think AI could pave the way. Regardless of the method, it's clear that industry insiders are all striving for mass adoption of blockchain. A recent event that caught my attention was Starkware's STRK token airdrop, which highlighted a significant fact: some developers outside of the usual crypto circles began to explore and show interest in entering the industry. This interest was sparked because Starkware conducted an airdrop to contributors of certain GitHub repositories, developers who might not have known about blockchain previously but became interested due to this event. Another application that left a strong impression on me is StepN, an app that became popular in the last bull market for its "earn while you run" concept. Users simply needed to purchase a pair of virtual shoes and then could earn rewards by running a very straightforward proposition daily. This led to a surge in user numbers and effectively broke into new user demographics. These examples underscore applications or infrastructure projects capable of attracting new users to the industry. To draw new users, the following characteristics are necessary: Simple operation, easy for users to get started; Low hardware requirements; An incentive mechanism. Both examples satisfy the first criteria: GitHub is familiar to developers, and mobile phones are familiar to general users. They also meet the second criteria, requiring either a computer or a mobile phone. The third criteria is met as well, with one offering "contribute to earn" and the other "running to earn." However, to continuously attract new users, sustainable incentives are essential. For Starkware, continuous token airdrops may not be viable; likewise, for StepN, perpetual mining and selling are also unsustainable. Thus, for an application to sustainably attract new users and hold long-term value, it must inherently sustain its appeal. Massive serves as an infrastructure service aimed at sustainably attracting new users to achieve the goal of mass adoption. It can support all ZK chain services, whether ZK layer1, ZK layer2, or ZK layer3, introducing new users to these ZK chains, enhancing system security, reducing operational costs, and improving capital efficiency. Architecture Massive is a POS (Proof of Stake) consensus network that primarily utilizes mobile devices as its nodes. Users are required to download the official app on their mobile devices, which supports both iOS and Android, the two leading operating systems. The architectural framework is outlined as follows (Fig.1): To become a node, users can fall into one of the following two categories: New users without any cryptocurrency assets can initially join the network. Their accumulated rewards must reach a certain amount before they can start unlocking subsequent mining rewards. Users already possessing some cryptocurrency assets can stake these assets to become a node on the Massive network. Depending on the underlying L1 blockchain, users can stake different assets. For example, in a Massive network serving BTC L2/L3, users can stake BRC20 and BTC assets. For Ethereum-based L2/L3 services, users can stake ERC20 and ETH assets. The wallet will integrate several mainstream ZK technology stacks for verification, such as plonky2, plonky3, and boojum, all of which belong to the STARK family. Following market trends, support for mainstream algorithms from the SNARK family, such as Halo2 or Nova, will be iteratively introduced. Mobile nodes that have completed staking will form a POS network to provide Timely Settlement for all Layer 2s and Layer 3s. If there is an excessive number of participating users, sharding technology can be utilized to form multiple independent POS networks according to specific standards. Provide Timely Settlement As mentioned earlier, Massive is not solely focused on attracting users; it also aims to address some of the current issues in the industry. Among these, the confirmation time for ZK L2s/L3s is a frequently discussed issue. Before we delve into this, let's take a look at the main solutions for ZK L2s as illustrated in the following diagram (Fig.2): It's evident that in the design of most L2 solutions, there's a need to aggregate a sufficient number of proofs on L2 and then send them to Ethereum for verification. This approach is adopted to reduce costs. Even with this premise, the aggregated proofs sent to L1 must wait for a certain period before calling the Verify contract to alter the global state, a measure taken from a security standpoint, although this delay can be configured. Therefore, it can be concluded that a L2 transaction, to be finally confirmed, requires a waiting period of between 2 to 23 hours. Until then, the validity of the proofs is verified only by a centralized sequencer. The diagram below (Fig.3) illustrates the current situation where, after their generation, proofs will be verified. Therefore, the primary intention behind Massive's design is to ensure that, before a proof is verified on L1, it goes through a verification process by a decentralized network, so everyone can verify now. It's important to note that if the POS network used for verifying the proof is provided by Eigenlayer's AVS ( ) service, then this POS network possesses the same level of security as Ethereum. Ultimately, only the latest global state needs to be updated on Ethereum. This aspect also offers insights into the modular design evolution discussed in later chapters. Actively Validated Services After incorporating Massive, the entire process flow for L2s could be transformed as shown in the following diagram (Fig.4): By extracting the ZK verify function from L1 and placing trust in the security of the Massive network, then adding a challenge mechanism to reduce the probability of Massive acting maliciously, the entire system's security will depend on the security of the Massive network. If the ecosystem of these ZK L2s is more finance-oriented, Massive could be built on EigenLayer's AVS to ensure the cost of acting maliciously is sufficiently high. Conversely, if the ecosystem of these ZK L2s is related to gaming or social scenarios, then a basic Massive construction would suffice. Capital Efficiency Staking and re-staking have recently become very popular concepts, focusing on capital efficiency. Reutilizing staked assets to achieve compound interest is an attractive economic mechanism currently seen in platforms like Blast and Manta. Unlike these schemes, Massive offers a novel destination for native assets across various networks, as well as for staked assets, by allowing them to become nodes within the Massive network through staking and re-staking mechanisms. Since the Massive network can provide more users, lower costs, and improved security for ZK L2s/L3s, nodes within the Massive network will share in the network maintenance profits, embodying the "verify to earn" concept. Based on the actual circumstances of ZK L2s/L3s, different security levels of ZKVaaS can be chosen. The security level depends on the total amount staked and the number of nodes in the network. New Modular Design As mentioned earlier, Massive, from a certain perspective, further changes the process of blockchain modular design. Before Massive, modular design mainly consisted of execution modularization, with various Rollups solutions, and Data Availability (DA) modularization, with various DA solutions, leaving only verification, storage, and consensus functions on L1. After the introduction of Massive, modular design will evolve to include execution modularization, DA modularization, and verification modularization, with only storage and consensus remaining on L1. This is logical since verification itself is part of computation. Given that execution modularization has already moved computation off-chain, then only storage and consensus should remain on-chain. The specifics are illustrated in the following diagram (Fig.6): If verification remains on Layer 1 (L1), the specific consensus process involves each node executing, verifying proofs, and updating the state. However, if we extract the verification process and assign it to a separate network for consensus, the L1 consensus process would then only involve updates to storage. Under the latest modular design, existing L1 nodes will no longer undertake any computational tasks. They will only handle transactions related to the update of the global state and then execute the consensus process. There are two main reasons for this approach: As mentioned in previous sections, considering costs and security, the execution of verification can be significantly delayed. Therefore, extracting it and utilizing the Massive network for verification not only achieves rapid validation but also reduces the hardware requirements for verification nodes, making it easier for users to connect. The security of the separate Massive network is ensured by the amount of staked assets and the number of nodes. Compared to the original scheme, this might sacrifice a certain level of security (which depends on the value of the staked assets) but, in return, offers significant architectural advantages. The Ecosystem of Massive Leveraging the three core features of mobile phone mining, staking/re-staking, and Timely Settlement, Massive has the potential to become the largest network, providing safe, efficient, and low-cost services for a variety of ZK L2s/L3s. Additionally, due to its extremely low barrier to entry and profit effects, it will inject a significant number of real users into the industry. Massive's vision is to facilitate massive adoption within the industry. Mobile mining represents a highly exploratory practice. Through straightforward operations, lower thresholds, and continuous earnings, it aims to utilize the computational power of smartphones. According to statistics, the global sales of smartphones exceeded 1 billion units in 2023. Solana and Aptos have also successively launched their own Web3 phones. Through the Massive network, these devices will be leveraged. Reference Eigenlayer whitepaper: https://docs.eigenlayer.xyz/assets/files/EigenLayer_WhitePaper-88c47923ca0319870c611decd6e562ad.pdf Scroll explorer: https://scrollscan.com/batch/86321 Polygon explorer: https://zkevm.polygonscan.com/batches Zksync contract: https://etherscan.io/address/0xa0425d71cB1D6fb80E65a5361a04096E0672De03#readContract Starkware explorer: https://voyager.online/block/572773 Halo2: https://github.com/zcash/halo2 Nova: https://eprint.iacr.org/2021/370 Celestia: https://docs.celestia.org/learn/how-celestia-works/overview

Unveiling the Technical Vision Behind Massive: Decentralized, Cheap, Fair, and More

বিশালের পিছনে প্রযুক্তিগত দৃষ্টিভঙ্গি উন্মোচন করা: বিকেন্দ্রীভূত, সস্তা, ন্যায্য এবং আরও অনেক কিছু

Enthüllung der technischen Vision hinter Massive: Dezentralisiert, günstig, fair und mehr

Revelando la visión técnica detrás de Massive: descentralizado, barato, justo y más

Dévoiler la vision technique derrière Massive : décentralisé, bon marché, équitable et plus encore

बड़े पैमाने पर तकनीकी दृष्टि का अनावरण: विकेंद्रीकृत, सस्ता, निष्पक्ष और बहुत कुछ

Massive の背後にある技術的ビジョンを明らかにする: 分散化、安価、公正など

Massive의 기술적 비전 공개: 분산화, 저렴함, 공정함 등

Revelando a visão técnica por trás do Massive: descentralizado, barato, justo e muito mais

Раскрытие технического видения Massive: децентрализованное, дешевое, справедливое и многое другое

Massive'ın Arkasındaki Teknik Vizyonu Ortaya Çıkarıyoruz: Merkezi Olmayan, Ucuz, Adil ve Daha Fazlası

Tiết lộ tầm nhìn kỹ thuật đằng sau Massive: Phi tập trung, giá rẻ, công bằng và hơn thế nữa

揭示 Massive 背后的技术愿景：去中心化、廉价、公平等等

I know, it looks impossible. Javascript is not a very performant language, mostly because of its single-threaded model. So if I tell you we’re going to compare 1.8 million votes in a fictional city election, right into the browser, you’ll probably think I’m crazy. A browser would freeze and crash — every time. Guess what? You’d be right. Here is the result with a very popular diff library: Clearly, it’s not wow time. But with the right techniques — efficient memory management, a streaming architecture, web workers, and batch updates — you can do it. Here’s the proof: As you can see, even with 1.8 millions objects processed in real time, the UI is perfectly responsive. Most importantly, by injecting small data batches over time, we’ve turned a wait for it delay into a watch it happen experience. Alright, now let’s dive into the making process. Challenges We have three challenges to solve. Achieving top performance.


Handling different input formats.


Being easy for developers to use. Performance The first thing is to avoid blocking the main thread at all costs. So we need asynchronous code, a worker to handle the heavy lifting, and a linear complexity O(n), which means that the execution time grows proportionally with the input size. Additionally, efficient memory management is crucial. Efficient memory management requires freeing up memory as soon as some data is processed, and also progressively sending the data in chunks to minimize UI updates. For example, instead of sending one million object diffs in a row, we could send groups of a thousand. This would dramatically reduce the number of DOM updates. Input Format The second challenge is to handle different kind of input formats. We need to be versatile. We should accept arrays of objects, JSON files, and even data streams. So before starting the diff process, we need to convert everything into a single format: a readable stream. Developer Experience Finally, we need to provide a great developer experience. The function must be easy to use, easy to customize. Basically, the user will be able to give two lists to our function. We will compare them, and progressively send back the result. The simplest way to do it is to expose an event listener, with three kind of events: ondata, onerror, onfinish. Easy peasy. The ondata event will receive an array of object diffs, called a chunk. The diff should be clear, with a previous value, a current value, an index tracker, and a status — equal, updated, moved, or deleted. And because I know most of you love TypeScript, we’ll also have autocompletion. Icing on the cake, users will be able to specify options to refine the output. Let’s see how the algorithm works. Algorithm Walkthrough For simplicity’ sake, the code we’re going through comes from the streamListDiff function of the @donedeal0/superdiff library. Let’s take a look at the main function. It takes two lists to compare, a common key across all the objects, like an id for exemple, to match the objects between the two lists, and some options to refine the output. In the function body, we can see it returns an event listener before starting the diff. The trick is to trigger the logic block asynchronously. Basically, the event loop will execute all synchronous code first, and only then start the real work. Then, we convert our two lists to readable streams, using different methods for each input type (an array, a file, etc.). Once we have two valid streams, we iterate over both of them in parallel thanks to Promise.all(). At each iteration, we do two things: first, we check if the object is valid — if not, we emit an error message. Second, we check if an object with a similar reference property is already in the data buffer of the other list. What’s a data buffer? There are two buffers, one for each list. The idea is to store the unmatched objects in a hashmap so that the other list, which is being parsed at the same moment, can check in real time if there is a match for its current object. This avoids doing two full iterations, with no results and high memory consumption, before starting the real diff. We don’t lose time, and we’re efficient. We use a hashmap to store unmatched objects because it supports any kind of value as keys, is very performant and provides an iterator out of the box. Long story short, if there is a match in the buffer, we immediately remove it to free up memory and compare it to the current object. Once we have the object diff, we immediately send it to the user. If we can’t do the comparaison, we insert the object in the relevant buffer, waiting to be compared. Afterward, we simply iterate over each buffer and process the remaining objects in the same way. Now, you’re probably wondering why we don’t do a single iteration over the first list, and find its match in the second list. Well, if you have a million objects, a find() lookup will be highly inefficient, as it would lead to a huge number of iterations. But the data buffer method lets us retrieve the match with zero performance cost thanks to the has() method. Earlier, I said we immediately send each object diff to the user. This is partially true. Imagine the user receives a million objects in a row — it could overload the main thread. What we do instead, is store the object diffs in another buffer. Once this buffer reaches a certain size, say a thousand objects, we flush it and send a single batch of data to the user. To do it, we use a closure. Let’s take a look at the outputDiffChunk function. It has an array to store the diffs and returns two functions: handleDiffChunk and releaseLastChunks. handleDiffChunk receives an object diff and adds it to the buffer if it’s not full yet. If it’s full, it sends the batch to the user. Since we use a single instance of handleDiffChunk, the context of outputDiffChunk is preserved, which is why we can access the diff buffer each time we process a new object diff. Finally, releaseLastChunks is self-explanatory. Once all diffs are processed, it flushes the diff buffer one last time and send the remaining data to the user. Ultimately, we emit a finish event, and that’s all. One more thing The demo we saw earlier uses virtualized list to render massive amounts of items in the DOM, and also takes advantage of requestAnimationFrame to avoid updating it too often. As a result, everything runs really smooth. It seems like John Doe has been elected, congratulation to him! LINKS Repository | Documentation | Npm I know, it looks impossible. Javascript is not a very performant language, mostly because of its single-threaded model. So if I tell you we’re going to compare 1.8 million votes in a fictional city election, right into the browser, you’ll probably think I’m crazy. A browser would freeze and crash — every time. Guess what? You’d be right. Here is the result with a very popular diff library: Clearly, it’s not wow time. But with the right techniques — efficient memory management, a streaming architecture, web workers, and batch updates — you can do it. Here’s the proof: wow As you can see, even with 1.8 millions objects processed in real time, the UI is perfectly responsive. Most importantly, by injecting small data batches over time, we’ve turned a wait for it delay into a watch it happen experience. wait for it watch it happen Alright, now let’s dive into the making process. Challenges We have three challenges to solve. Achieving top performance. Handling different input formats. Being easy for developers to use. Achieving top performance. Achieving top performance. Handling different input formats. Handling different input formats. Being easy for developers to use. Being easy for developers to use. Performance The first thing is to avoid blocking the main thread at all costs. So we need asynchronous code, a worker to handle the heavy lifting, and a linear complexity O(n) , which means that the execution time grows proportionally with the input size. Additionally, efficient memory management is crucial. O(n) Efficient memory management requires freeing up memory as soon as some data is processed, and also progressively sending the data in chunks to minimize UI updates. For example, instead of sending one million object diffs in a row, we could send groups of a thousand. This would dramatically reduce the number of DOM updates. Input Format The second challenge is to handle different kind of input formats. We need to be versatile. We should accept arrays of objects, JSON files, and even data streams. So before starting the diff process, we need to convert everything into a single format: a readable stream. Developer Experience Finally, we need to provide a great developer experience. The function must be easy to use, easy to customize. Basically, the user will be able to give two lists to our function. We will compare them, and progressively send back the result. The simplest way to do it is to expose an event listener, with three kind of events: ondata , onerror , onfinish . Easy peasy. ondata onerror onfinish The ondata event will receive an array of object diffs, called a chunk . The diff should be clear, with a previous value, a current value, an index tracker, and a status — equal, updated, moved, or deleted. ondata chunk And because I know most of you love TypeScript, we’ll also have autocompletion. Icing on the cake, users will be able to specify options to refine the output. Let’s see how the algorithm works. Algorithm Walkthrough For simplicity’ sake, the code we’re going through comes from the streamListDiff function of the @donedeal0/superdiff library. streamListDiff @donedeal0/superdiff Let’s take a look at the main function. It takes two lists to compare, a common key across all the objects, like an id for exemple, to match the objects between the two lists, and some options to refine the output. id In the function body, we can see it returns an event listener before starting the diff. The trick is to trigger the logic block asynchronously. Basically, the event loop will execute all synchronous code first, and only then start the real work. Then, we convert our two lists to readable streams, using different methods for each input type (an array, a file, etc.). Once we have two valid streams, we iterate over both of them in parallel thanks to Promise.all() . At each iteration, we do two things: first, we check if the object is valid — if not, we emit an error message. Second, we check if an object with a similar reference property is already in the data buffer of the other list. Promise.all() What’s a data buffer? There are two buffers, one for each list. The idea is to store the unmatched objects in a hashmap so that the other list, which is being parsed at the same moment, can check in real time if there is a match for its current object. This avoids doing two full iterations, with no results and high memory consumption, before starting the real diff. We don’t lose time, and we’re efficient. We use a hashmap to store unmatched objects because it supports any kind of value as keys, is very performant and provides an iterator out of the box. Long story short, if there is a match in the buffer, we immediately remove it to free up memory and compare it to the current object. Once we have the object diff, we immediately send it to the user. If we can’t do the comparaison, we insert the object in the relevant buffer, waiting to be compared. Afterward, we simply iterate over each buffer and process the remaining objects in the same way. Now, you’re probably wondering why we don’t do a single iteration over the first list, and find its match in the second list. Well, if you have a million objects, a find() lookup will be highly inefficient, as it would lead to a huge number of iterations. But the data buffer method lets us retrieve the match with zero performance cost thanks to the has() method. find() has() Earlier, I said we immediately send each object diff to the user. This is partially true. Imagine the user receives a million objects in a row — it could overload the main thread. What we do instead, is store the object diffs in another buffer. Once this buffer reaches a certain size, say a thousand objects, we flush it and send a single batch of data to the user. To do it, we use a closure. Let’s take a look at the outputDiffChunk function. It has an array to store the diffs and returns two functions: handleDiffChunk and releaseLastChunks . handleDiffChunk receives an object diff and adds it to the buffer if it’s not full yet. If it’s full, it sends the batch to the user. Since we use a single instance of handleDiffChunk , the context of outputDiffChunk is preserved, which is why we can access the diff buffer each time we process a new object diff. outputDiffChunk handleDiffChunk releaseLastChunks handleDiffChunk handleDiffChunk outputDiffChunk Finally, releaseLastChunks is self-explanatory. Once all diffs are processed, it flushes the diff buffer one last time and send the remaining data to the user. releaseLastChunks Ultimately, we emit a finish event, and that’s all. finish One more thing The demo we saw earlier uses virtualized list to render massive amounts of items in the DOM, and also takes advantage of requestAnimationFrame to avoid updating it too often. requestAnimationFrame As a result, everything runs really smooth. It seems like John Doe has been elected, congratulation to him! LINKS Repository | Documentation | Npm Repository Documentation Npm

You Can't Compare Massive Data Streams In Javascript. Or Can You? 🤔

Authors:
(1) Jorge Francisco Garcia-Samartın, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain (jorge.gsamartin@upm.es);
(2) Adrian Rieker, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain;
(3) Antonio Barrientos, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain. Table of Links Abstract and 1 Introduction 2 Related Works 2.1 Pneumatic Actuation 2.2 Pneumatic Arms 2.3 Control of Soft Robots 3 PAUL: Design and Manufacturing 3.1 Robot Design 3.2 Material Selection 3.3 Manufacturing 3.4 Actuation Bank 4 Data Acquisition and Open-Loop Control 4.1 Hardware Setup 4.2 Vision Capture System 4.3 Dataset Generation: Table-Based Models 4.4 Open-Loop Control 5 Results 5.1 Final PAUL version 5.2 Workspace Analysis 5.3 Performance of the Table-Based Models 5.4 Bending Experiments 5.5 Weight Carrying Experiments 6 Conclusions Funding Information A. Conducted Experiments and References 4.3 Dataset Generation: Table-Based Models Due to the complexity of the robot, model-based methodologies, such as PCC or the ones based on Cosserat Rod Theory were discarded. Although the usage of FEM is an avenue that will not be closed in future work, the large number of parameters to be set experimentally for each segment (Young’s modulus, moment of inertia. . . ), given that the manufacturing process is so variable meant that, in this first phase, we opted to use some type of PAUL modelling based on data collection. The output of the system is taken as the position and orientation reached by the final end –thus, at this stage, all the positions of the intermediate segments are ignored– and as input, the inflation times of each of the bladders. As there were not enough pressure sensors available at the time of the construction of the robot, it was decided to take inflation time as an input variable. As the working pressure is limited by the pressure limiting valve and the flow rate into each bladder can be assumed to be constant, the time is equivalent to the volume of air introduced into each cavity. All the control options considered have in common the need for a large amount of empirical data, which leads to the need to develop an experimental design to systematise the collection of this data. Since the capture of this information is done in different phases and the datasets have to represent the behaviour of the robot in an objective way, the re-applicability of the experiment takes on special importance. The data stored in the datasets was the position of the robot tip and the set of inflation times that achieve this configuration. The aforementioned limitation that only two of the three bladders in the segment are inflated reduces redundancies. As previously stated, more than two segments lead to redundancies, which implies that the inverse kinematic model of the robot can have multiple solutions. The data collection process involves several sequential steps. Initially, a set number of samples is determined. For each sample, Matlab commands dispatch a random combination of nine inflation times, corresponding to each valve of PAUL, to the actuation bench. Times are generated below a maximum time limit Tmax, and ensuring that only two cavities per segment are inflated. Following this, the robot’s bladders are inflated based on the sent times. Subsequently, the vision system’s two cameras capture images to determine the position and orientation of the robot’s end. This entire procedure is repeated for the specified number of iterations, and upon completion, the collected data is stored in the dataset The information on swelling times is stored as a percentage, with a value of 0 corresponding to a zero swelling of that segment and 100 corresponding to Tmax, the swelling for the maximum number of milliseconds defined for this data collection session. This value Tmax is stored, together with the values, in the dataset, in order to be able to compare different datasets. The reason for this coding comes from the lack of information, a priori, on what is the maximum pressure that a PAUL bladder admits. Although it is true that it was experimentally determined that inflation times of more than 1500 ms in a row led to punctures, the application of lower times during a repeated number of cycles also generated leaks. On this basis, it was decided never to inflate any valve, either in one or several steps, more than 1000 ms. Along with the inflation times of each bladder, the position and orientation reached by the end tip is stored, based on the camera readings. In particular, the position of the green marker and the orientation of the trihedron are stored. The latter is expressed in Euler angles, as it is a much efficient form of storage than a rotation matrix. In addition, the dataset also contains metadata from the collection process that are believed to influence the results, such as the pneumatic line pressure or the ambient temperature. Some aspects in the pneumatic system merit attention. Initially, bladder inflation and deflation are not symmetrical processes. Geometric constraints in the pneumatic components result in a lower deflation rate compared to inflation. Consequently, when the PAUL receives a deflation time, it multiplies it by an empirically derived factor, approximately 1.45 for a 1.2 bar working pressure. This multiplier compensates for the discrepancy between inflation and deflation times of a singular group of bladders, ensuring that the deflation time aligns with the time required to reach the same inflation point. Similarly, although it is physically possible to inflate several valves at the same time, it has been shown that this parallel flow distribution means that the effective fillings of each valve are not the same as if they were inflated individually. To prevent this phenomenon, it was decided to inflate each bladder individually both during the data acquisition process and utterly, when PAUL was asked to reach certain positions. Finally, there are hysteresis phenomena in the silicone that cause the position reached by inflating for a time t to be different from the position reached by inflating first for a time t1 and then for a time t2 = t − t1. The strategy employed to tackle this problem was to capture the dataset bringing PAUL back to its zero position between each sample. Nevertheless, when controlling the robot in open-loop this is not possible, or, at least, not desirable, as one may wish to follow trajectories or travel through a sequence of points. Therefore, transitioning from position x1 to x2 requires an additional factor of 1.2, also derived experimentally, to account for hysteresis effects. 4.4 Open-Loop Control Once the dataset is generated, it can be used to model the behaviour of PAUL for open-loop control. It is foreseen, as a future line, to train a neural network for the direct kinematics and another one for the inverse kinematics. However, given the large amount of data that may be required (in [62] 24389 samples are used for a three-segment robot like this one), a table look-up method has been used for this work. The method for direct kinematics –which allows obtaining the position and orientation of the final end of the robot from the inflation times of the nine bladders– consists of searching, in the generated dataset in the previous step, the three inflation time values located at a shorter distance from the inflation time given as a reference. Obviously, if the set of inflation times sought were in the table, the value associated with these times would be returned as a result of the direct kinematic model. Otherwise, the average of the position and orientation values associated with the three closest inflation times, weighted by the distance (Euclidean norm) existing between each of them and the values of reference inflation times, is returned as the position and orientation value of the robot. with them, it is possible to calculate the position returned by the direct kinematic model using the expression: This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Jorge Francisco Garcia-Samartın, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain (jorge.gsamartin@upm.es); (2) Adrian Rieker, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain; (3) Antonio Barrientos, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain. Authors: Authors: (1) Jorge Francisco Garcia-Samartın, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain (jorge.gsamartin@upm.es); (2) Adrian Rieker, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain; (3) Antonio Barrientos, Centro de Automatica y Robotica (UPM-CSIC), Universidad Politecnica de Madrid — Consejo Superior de Investigaciones Cientıficas, Jose Gutierrez Abascal 2, 28006 Madrid, Spain. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Works 2.1 Pneumatic Actuation 2.1 Pneumatic Actuation 2.2 Pneumatic Arms 2.2 Pneumatic Arms 2.3 Control of Soft Robots 2.3 Control of Soft Robots 3 PAUL: Design and Manufacturing 3.1 Robot Design 3.1 Robot Design 3.2 Material Selection 3.2 Material Selection 3.3 Manufacturing 3.3 Manufacturing 3.4 Actuation Bank 3.4 Actuation Bank 4 Data Acquisition and Open-Loop Control 4.1 Hardware Setup 4.1 Hardware Setup 4.2 Vision Capture System 4.2 Vision Capture System 4.3 Dataset Generation: Table-Based Models 4.3 Dataset Generation: Table-Based Models 4.4 Open-Loop Control 4.4 Open-Loop Control 5 Results 5.1 Final PAUL version 5.1 Final PAUL version 5.2 Workspace Analysis 5.2 Workspace Analysis 5.3 Performance of the Table-Based Models 5.3 Performance of the Table-Based Models 5.4 Bending Experiments 5.4 Bending Experiments 5.5 Weight Carrying Experiments 5.5 Weight Carrying Experiments 6 Conclusions 6 Conclusions Funding Information Funding Information A. Conducted Experiments and References A. Conducted Experiments and References 4.3 Dataset Generation: Table-Based Models Due to the complexity of the robot, model-based methodologies, such as PCC or the ones based on Cosserat Rod Theory were discarded. Although the usage of FEM is an avenue that will not be closed in future work, the large number of parameters to be set experimentally for each segment (Young’s modulus, moment of inertia. . . ), given that the manufacturing process is so variable meant that, in this first phase, we opted to use some type of PAUL modelling based on data collection. The output of the system is taken as the position and orientation reached by the final end –thus, at this stage, all the positions of the intermediate segments are ignored– and as input, the inflation times of each of the bladders. As there were not enough pressure sensors available at the time of the construction of the robot, it was decided to take inflation time as an input variable. As the working pressure is limited by the pressure limiting valve and the flow rate into each bladder can be assumed to be constant, the time is equivalent to the volume of air introduced into each cavity. All the control options considered have in common the need for a large amount of empirical data, which leads to the need to develop an experimental design to systematise the collection of this data. Since the capture of this information is done in different phases and the datasets have to represent the behaviour of the robot in an objective way, the re-applicability of the experiment takes on special importance. The data stored in the datasets was the position of the robot tip and the set of inflation times that achieve this configuration. The aforementioned limitation that only two of the three bladders in the segment are inflated reduces redundancies. As previously stated, more than two segments lead to redundancies, which implies that the inverse kinematic model of the robot can have multiple solutions. The data collection process involves several sequential steps. Initially, a set number of samples is determined. For each sample, Matlab commands dispatch a random combination of nine inflation times, corresponding to each valve of PAUL, to the actuation bench. Times are generated below a maximum time limit Tmax, and ensuring that only two cavities per segment are inflated. Following this, the robot’s bladders are inflated based on the sent times. Subsequently, the vision system’s two cameras capture images to determine the position and orientation of the robot’s end. This entire procedure is repeated for the specified number of iterations, and upon completion, the collected data is stored in the dataset The information on swelling times is stored as a percentage, with a value of 0 corresponding to a zero swelling of that segment and 100 corresponding to Tmax, the swelling for the maximum number of milliseconds defined for this data collection session. This value Tmax is stored, together with the values, in the dataset, in order to be able to compare different datasets. The reason for this coding comes from the lack of information, a priori, on what is the maximum pressure that a PAUL bladder admits. Although it is true that it was experimentally determined that inflation times of more than 1500 ms in a row led to punctures, the application of lower times during a repeated number of cycles also generated leaks. On this basis, it was decided never to inflate any valve, either in one or several steps, more than 1000 ms. Along with the inflation times of each bladder, the position and orientation reached by the end tip is stored, based on the camera readings. In particular, the position of the green marker and the orientation of the trihedron are stored. The latter is expressed in Euler angles, as it is a much efficient form of storage than a rotation matrix. In addition, the dataset also contains metadata from the collection process that are believed to influence the results, such as the pneumatic line pressure or the ambient temperature. Some aspects in the pneumatic system merit attention. Initially, bladder inflation and deflation are not symmetrical processes. Geometric constraints in the pneumatic components result in a lower deflation rate compared to inflation. Consequently, when the PAUL receives a deflation time, it multiplies it by an empirically derived factor, approximately 1.45 for a 1.2 bar working pressure. This multiplier compensates for the discrepancy between inflation and deflation times of a singular group of bladders, ensuring that the deflation time aligns with the time required to reach the same inflation point. Similarly, although it is physically possible to inflate several valves at the same time, it has been shown that this parallel flow distribution means that the effective fillings of each valve are not the same as if they were inflated individually. To prevent this phenomenon, it was decided to inflate each bladder individually both during the data acquisition process and utterly, when PAUL was asked to reach certain positions. Finally, there are hysteresis phenomena in the silicone that cause the position reached by inflating for a time t to be different from the position reached by inflating first for a time t1 and then for a time t2 = t − t1. The strategy employed to tackle this problem was to capture the dataset bringing PAUL back to its zero position between each sample. Nevertheless, when controlling the robot in open-loop this is not possible, or, at least, not desirable, as one may wish to follow trajectories or travel through a sequence of points. Therefore, transitioning from position x1 to x2 requires an additional factor of 1.2, also derived experimentally, to account for hysteresis effects. 4.4 Open-Loop Control Once the dataset is generated, it can be used to model the behaviour of PAUL for open-loop control. It is foreseen, as a future line, to train a neural network for the direct kinematics and another one for the inverse kinematics. However, given the large amount of data that may be required (in [62] 24389 samples are used for a three-segment robot like this one), a table look-up method has been used for this work. The method for direct kinematics –which allows obtaining the position and orientation of the final end of the robot from the inflation times of the nine bladders– consists of searching, in the generated dataset in the previous step, the three inflation time values located at a shorter distance from the inflation time given as a reference. Obviously, if the set of inflation times sought were in the table, the value associated with these times would be returned as a result of the direct kinematic model. Otherwise, the average of the position and orientation values associated with the three closest inflation times, weighted by the distance (Euclidean norm) existing between each of them and the values of reference inflation times, is returned as the position and orientation value of the robot. with them, it is possible to calculate the position returned by the direct kinematic model using the expression: This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Why PAUL Needs a Massive Dataset to Improve Its Movements

Warum PAUL einen riesigen Datensatz benötigt, um seine Bewegungen zu verbessern

Por qué PAUL necesita un conjunto de datos masivo para mejorar sus movimientos

Zašto PAUL treba ogroman skup podataka za poboljšanje svojih kretanja

PAUL が運動を改善するために大規模なデータセットを必要とする理由

ហេតុអ្វីបានជា PAUL ត្រូវការសំណុំទិន្នន័យដ៏ធំ ដើម្បីកែលម្អចលនារបស់វា។

PAUL이 움직임을 개선하기 위해 방대한 데이터 세트가 필요한 이유

ເປັນຫຍັງ PAUL ຕ້ອງການຊຸດຂໍ້ມູນອັນໃຫຍ່ຫຼວງເພື່ອປັບປຸງການເຄື່ອນໄຫວຂອງມັນ

Kāpēc PAULam ir nepieciešama liela datu kopa, lai uzlabotu tās kustības

Nahoana i PAUL no mila angon-drakitra betsaka hanatsarana ny fihetsiny

De ce PAUL are nevoie de un set de date masiv pentru a-și îmbunătăți mișcările

PAUL හට එහි චලනයන් වැඩිදියුණු කිරීම සඳහා දැවැන්ත දත්ත කට්ටලයක් අවශ්‍ය වන්නේ ඇයි?

کیوں PAUL کو اپنی نقل و حرکت کو بہتر بنانے کے لیے بڑے پیمانے پر ڈیٹا سیٹ کی ضرورت ہے۔

Sentient has completed a large-scale ownership mint with 650,000 participants, setting a new benchmark in crypto and AI history. The campaign distributes NFTs tied to Dobby, the first decentralized Loyal AI model. Holders can later claim ownership and unique model fingerprints. The scale of participation reflects growing interest in decentralized AI. Sentient’s mint follows the rise of DeepSeek, an AI model rivaling OpenAI’s capabilities while operating on a limited budget. DeepSeek’s efficiency and open-source framework have fueled shifts in AI markets, affecting stock prices and token valuations. Sentient introduces a new approach to AI ownership with its fingerprinting technology. These digital signatures embed query-response pairs directly into an AI model, ensuring a consistent output for verification. The mechanism helps communities confirm model authenticity and prevent unauthorized use. The campaign aligns with Sentient’s goal of creating AI that is decentralized and community-controlled. Other AI initiatives focus on developing intelligent agents, but Sentient prioritizes decentralization at the model level. By embedding loyalty mechanisms into the AI core, the organization aims to build trust and transparency. The fingerprinting campaign reinforces this vision, allowing a distributed community to hold shared ownership of Dobby. Participants will use NFTs to verify legitimate use and enforce control over the model. Sandeep Nailwal, Polygon founder and Sentient core contributor, said, “When AGI or ASI emerges, it should be community-controlled and community-owned to guarantee its loyalty. Sentient’s fingerprinting mechanism allows communities to enforce ownership, control, and alignment of open-source AI models. This incentivizes communities to come together and build high-quality open-source models. In the long run, the goal is to make sure that instead of some large institution having exclusive control over a closed-source AGI, AGI is open-source and owned by everyone individually and collectively.” To claim an NFT, participants must pass a test proving both their humanity and intelligence. This process, which drew over 650,000 users, represents one of the largest NFT campaigns by engagement. Beyond ownership, these NFTs grant direct access to the fingerprinted model, reinforcing the link between the AI and its community. Sentient’s mission extends beyond this campaign. The organization challenges centralized AI development, advocating for a transparent, community-driven alternative. With AGI development accelerating, the question of AI ownership remains open. Sentient’s approach suggests that the answer may lie in decentralization. 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 Sentient has completed a large-scale ownership mint with 650,000 participants, setting a new benchmark in crypto and AI history. The campaign distributes NFTs tied to Dobby, the first decentralized Loyal AI model. Holders can later claim ownership and unique model fingerprints. Sentient The scale of participation reflects growing interest in decentralized AI. Sentient’s mint follows the rise of DeepSeek, an AI model rivaling OpenAI’s capabilities while operating on a limited budget. DeepSeek’s efficiency and open-source framework have fueled shifts in AI markets, affecting stock prices and token valuations. Sentient introduces a new approach to AI ownership with its fingerprinting technology. These digital signatures embed query-response pairs directly into an AI model, ensuring a consistent output for verification. The mechanism helps communities confirm model authenticity and prevent unauthorized use. The campaign aligns with Sentient’s goal of creating AI that is decentralized and community-controlled. Other AI initiatives focus on developing intelligent agents, but Sentient prioritizes decentralization at the model level. By embedding loyalty mechanisms into the AI core, the organization aims to build trust and transparency. The fingerprinting campaign reinforces this vision, allowing a distributed community to hold shared ownership of Dobby. Participants will use NFTs to verify legitimate use and enforce control over the model. Sandeep Nailwal, Polygon founder and Sentient core contributor, said, “When AGI or ASI emerges, it should be community-controlled and community-owned to guarantee its loyalty. Sentient’s fingerprinting mechanism allows communities to enforce ownership, control, and alignment of open-source AI models. This incentivizes communities to come together and build high-quality open-source models. In the long run, the goal is to make sure that instead of some large institution having exclusive control over a closed-source AGI, AGI is open-source and owned by everyone individually and collectively.” “When AGI or ASI emerges, it should be community-controlled and community-owned to guarantee its loyalty. Sentient’s fingerprinting mechanism allows communities to enforce ownership, control, and alignment of open-source AI models. This incentivizes communities to come together and build high-quality open-source models. In the long run, the goal is to make sure that instead of some large institution having exclusive control over a closed-source AGI, AGI is open-source and owned by everyone individually and collectively.” To claim an NFT, participants must pass a test proving both their humanity and intelligence. This process, which drew over 650,000 users, represents one of the largest NFT campaigns by engagement. Beyond ownership, these NFTs grant direct access to the fingerprinted model, reinforcing the link between the AI and its community. Sentient’s mission extends beyond this campaign. The organization challenges centralized AI development, advocating for a transparent, community-driven alternative. With AGI development accelerating, the question of AI ownership remains open. Sentient’s approach suggests that the answer may lie in decentralization. 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

Who Owns AI? Sentient’s Massive NFT Mint Redefines AI Ownership

Wie besit KI? Sentient se massiewe NFT-munt herdefinieer KI-eienaarskap

¿Quién es el propietario de la IA? La gigantesca NFT Mint de Sentient redefine la propiedad de la IA

Noren jabe da AI? Sentient-en Massive NFT Mint-ek AIren jabetza birdefinitzen du

Kuka omistaa tekoälyn? Sentientin massiivinen NFT Mint määrittelee tekoälyn omistajuuden uudelleen

Quen é o propietario da IA? Massive NFT Mint de Sentient redefine a propiedade da IA

AI का मालिक कौन है? सेंटिएंट के विशाल NFT मिंट ने AI स्वामित्व को फिर से परिभाषित किया

Ո՞ւմ է պատկանում AI-ն: Sentient's Massive NFT Mint-ը վերասահմանում է AI-ի սեփականությունը

AI を所有するのは誰か? Sentient の大規模な NFT ミントが AI の所有権を再定義

Cine deține AI? Sentient’s Massive NFT Mint redefinește proprietatea AI

Кӣ соҳиби AI аст? Mint азими NFT Sentient моликияти AI-ро аз нав муайян мекунад

Sino ang May-ari ng AI? Ang Napakalaking NFT Mint ng Sentient ay Muling Tinutukoy ang Pagmamay-ari ng AI

I Mani N’wini wa AI? Sentient’s Massive NFT Mint Yi Hlamusela Hi vuntshwa Vun’wini bya AI

MIAMI, Florida, January 29th, 2025/Chainwire/Floki has announced a strategic three-month marketing campaign in collaboration with CoinGecko, the world’s largest independent cryptocurrency data aggregator. Set to launch on Jan. 31, the campaign aims to showcase the Floki Trading Bot to millions of crypto users globally. CoinGecko’s extensive ecosystem will serve as the campaign’s platform, leveraging over 167 million monthly pageviews, 111 million monthly users, and 9.9 million total app downloads. The collaboration will also tap into CoinGecko’s 3 million+ social media followers and its coverage of over 16,000 crypto assets across 1,000+ exchanges. The campaign will include premium placements like Top Leaderboard Banners (projected 3.03 million impressions), Row Banners (3.33 million impressions), and in-app programmatic banners (6.5 million impressions). It will also feature dedicated video content for CoinGecko’s 222,000 YouTube subscribers, mobile push notifications, and social media promotions across Facebook, Instagram, and X. Altogether, the campaign is expected to generate approximately 12.9 million impressions, boosting the visibility of the Floki Trading Bot. What the Floki Trading Bot is The Floki Trading Bot is a multichain trading tool designed to simplify cryptocurrency trading via Telegram. It offers an intuitive interface, allowing users to trade cryptocurrencies seamlessly across multiple blockchains. The bot charges a 1% fee per trade, with half of the fee allocated to buying and burning the FLOKI token, supporting its deflationary mechanism. The other half contributes to the Floki Treasury to fuel ecosystem growth. With a community of over 480,000 onchain holders, the Floki Trading Bot has already gained significant traction. It boasts over 60,000 users, a total trade volume exceeding $139 million, and nearly 70,000 trades executed. https://www.youtube.com/embed/XEoXPzLXfbw https://youtu.be/XEoXPzLXfbw?si=ps9MI0e-Ffajv3WT About Floki Floki is the people’s cryptocurrency and utility token of the Floki Ecosystem. Focused on utility, community, philanthropy, and strategic marketing, Floki is working toward becoming the world’s most recognized and used cryptocurrency. With over 490,000 holders globally, Floki has already established a strong brand presence. Learn more at floki.com. Website: https://www.floki.com/ Youtube: https://www.youtube.com/@FlokiOfficial Telegram: https://t.co/vBZd3lbFQK Instagram: https://www.instagram.com/floki_crypto/ Tik Tok: https://www.tiktok.com/@floki_crypto Discord: https://discord.com/invite/floki Facebook: https://www.facebook.com/groups/290420602797109/?ref=share Reddit: https://www.reddit.com/r/Floki/ Twitch: https://www.twitch.tv/officialfloki Valhalla: https://valhalla.game Contact Community Relations Officer Pedro Vidal Floki Marketing@floki.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here MIAMI, Florida, January 29th, 2025/Chainwire/Floki has announced a strategic three-month marketing campaign in collaboration with CoinGecko, the world’s largest independent cryptocurrency data aggregator. Set to launch on Jan. 31, the campaign aims to showcase the Floki Trading Bot to millions of crypto users globally. CoinGecko’s extensive ecosystem will serve as the campaign’s platform, leveraging over 167 million monthly pageviews, 111 million monthly users, and 9.9 million total app downloads. The collaboration will also tap into CoinGecko’s 3 million+ social media followers and its coverage of over 16,000 crypto assets across 1,000+ exchanges. The campaign will include premium placements like Top Leaderboard Banners (projected 3.03 million impressions), Row Banners (3.33 million impressions), and in-app programmatic banners (6.5 million impressions). It will also feature dedicated video content for CoinGecko’s 222,000 YouTube subscribers, mobile push notifications, and social media promotions across Facebook, Instagram, and X. Altogether, the campaign is expected to generate approximately 12.9 million impressions, boosting the visibility of the Floki Trading Bot. What the Floki Trading Bot is The Floki Trading Bot is a multichain trading tool designed to simplify cryptocurrency trading via Telegram. It offers an intuitive interface, allowing users to trade cryptocurrencies seamlessly across multiple blockchains. The bot charges a 1% fee per trade, with half of the fee allocated to buying and burning the FLOKI token, supporting its deflationary mechanism. The other half contributes to the Floki Treasury to fuel ecosystem growth. With a community of over 480,000 onchain holders, the Floki Trading Bot has already gained significant traction. It boasts over 60,000 users, a total trade volume exceeding $139 million, and nearly 70,000 trades executed. https://www.youtube.com/embed/XEoXPzLXfbw https://www.youtube.com/embed/XEoXPzLXfbw https://www.youtube.com/embed/XEoXPzLXfbw https://youtu.be/XEoXPzLXfbw?si=ps9MI0e-Ffajv3WT https://youtu.be/XEoXPzLXfbw?si=ps9MI0e-Ffajv3WT https://youtu.be/XEoXPzLXfbw?si=ps9MI0e-Ffajv3WT About Floki Floki is the people’s cryptocurrency and utility token of the Floki Ecosystem. Focused on utility, community, philanthropy, and strategic marketing, Floki is working toward becoming the world’s most recognized and used cryptocurrency. Floki Floki With over 490,000 holders globally, Floki has already established a strong brand presence. Learn more at floki.com. Website: https://www.floki.com/ https://www.floki.com/ https://www.floki.com/ Youtube: https://www.youtube.com/@FlokiOfficial https://www.youtube.com/@FlokiOfficial https://www.youtube.com/@FlokiOfficial Telegram: https://t.co/vBZd3lbFQK https://t.co/vBZd3lbFQK https://t.co/vBZd3lbFQK Instagram: https://www.instagram.com/floki_crypto/ https://www.instagram.com/floki_crypto/ https://www.instagram.com/floki_crypto/ Tik Tok: https://www.tiktok.com/@floki_crypto https://www.tiktok.com/@floki_crypto https://www.tiktok.com/@floki_crypto Discord: https://discord.com/invite/floki https://discord.com/invite/floki https://discord.com/invite/floki Facebook: https://www.facebook.com/groups/290420602797109/?ref=share https://www.facebook.com/groups/290420602797109/?ref=share https://www.facebook.com/groups/290420602797109/?ref=share Reddit: https://www.reddit.com/r/Floki/ https://www.reddit.com/r/Floki/ https://www.reddit.com/r/Floki/ Twitch: https://www.twitch.tv/officialfloki https://www.twitch.tv/officialfloki https://www.twitch.tv/officialfloki Valhalla: https://valhalla.game https://valhalla.game https://valhalla.game Contact Community Relations Officer Pedro Vidal Floki Marketing@floki.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

Floki Launches Massive Marketing Campaign With CoinGecko To Promote Floki Trading Bot

Floki تطلق حملة تسويقية ضخمة مع CoinGecko للترويج لروبوت التداول Floki

Floki lancerer massiv markedsføringskampagne med CoinGecko for at fremme Floki Trading Bot

Floki lanza una campaña de marketing masiva con CoinGecko para promocionar el robot comercial Floki

Floki Luncurkan Kampanye Pemasaran Besar-besaran Bersama CoinGecko untuk Mempromosikan Bot Perdagangan Floki

Floki は CoinGecko と連携して Floki トレーディング ボットの宣伝に大規模なマーケティング キャンペーンを開始

Floki uzsāk masveida mārketinga kampaņu ar CoinGecko, lai reklamētu Floki tirdzniecības robotu

Floki yatangije ubukangurambaga bukomeye bwo kwamamaza hamwe na CoinGecko Gutezimbere Ubucuruzi bwa Floki

Флоки покреће огромну маркетиншку кампању са ЦоинГецко како би промовисао Флоки трговачког бота

Floki lanserar massiv marknadsföringskampanj med CoinGecko för att främja Floki Trading Bot

ஃப்ளோக்கி ட்ரேடிங் போட்டை ஊக்குவிக்க CoinGecko உடன் பாரிய சந்தைப்படுத்தல் பிரச்சாரத்தை Floki துவக்குகிறது

Floki เปิดตัวแคมเปญการตลาดครั้งใหญ่ร่วมกับ CoinGecko เพื่อส่งเสริม Floki Trading Bot

Floki, Floki söwda botyny öňe sürmek üçin CoinGecko bilen köpçülikleýin marketing kampaniýasyna başlady

These 5 Crypto Projects Are Solid Bets For Massive Growth In 2025

Гэтыя 5 крыптапраектаў з'яўляюцца надзейнымі стаўкамі на вялікі рост у 2025 годзе

Тези 5 крипто проекта са солидни залози за огромен растеж през 2025 г

Αυτά τα 5 έργα κρυπτογράφησης είναι σταθερά στοιχήματα για τεράστια ανάπτυξη το 2025

Estos 5 proyectos criptográficos son apuestas sólidas para un crecimiento masivo en 2025

5 פרויקטי הקריפטו הללו הם הימורים מוצקים לצמיחה מסיבית בשנת 2025

これら 5 つの暗号プロジェクトは、2025 年に大幅な成長が見込まれる有力な候補です

Бұл 5 криптожоба 2025 жылы жаппай өсу үшін сенімді ставкалар болып табылады

Эти 5 криптопроектов имеют надежные шансы на массовый рост в 2025 году

Dessa 5 kryptoprojekt är solida satsningar för massiv tillväxt 2025

Bu 5 kripto taslamasy, 2025-nji ýylda uly ösüş üçin gaty jedeller

یہ 5 کرپٹو پروجیکٹس 2025 میں بڑے پیمانے پر ترقی کے لیے ٹھوس شرطیں ہیں۔

5 dự án tiền điện tử này là sự đặt cược chắc chắn cho sự tăng trưởng mạnh mẽ vào năm 2025

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 8 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 5. Data We constructed the OLAT (One Light at a Time) dataset using a light stage [10, 49] equipped with 137 programmable LED lights and 7 frontal-viewing cameras. Our dataset comprises images of 287 subjects, with each subject being captured in up to 15 different poses, resulting in a total of 29,705 OLAT sequences. We sourced HDRI dataset from several publicly available archives. Specifically, we acquired 559 HDRI maps from Polyhaven, 76 from Noah Witchell, 364 from HDRMAPS, 129 from iHDRI, and 34 from eisklotz. In addition, we incorporated synthetic HDRIs created using the method proposed in [31]. During training, HDRIs are randomly selected with equal probability from either real-world or synthetic collections. We produced training pairs by projecting the sampled source and target lighting maps onto the reflectance fields of the OLAT images [10]. To derive the ground truth intrinsics, we applied the photometric stereo method [51] and obtained normal and albedo maps. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 8 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 8 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 8 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 8 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 5. Data We constructed the OLAT (One Light at a Time) dataset using a light stage [10, 49] equipped with 137 programmable LED lights and 7 frontal-viewing cameras. Our dataset comprises images of 287 subjects, with each subject being captured in up to 15 different poses, resulting in a total of 29,705 OLAT sequences. We sourced HDRI dataset from several publicly available archives. Specifically, we acquired 559 HDRI maps from Polyhaven, 76 from Noah Witchell, 364 from HDRMAPS, 129 from iHDRI, and 34 from eisklotz. In addition, we incorporated synthetic HDRIs created using the method proposed in [31]. During training, HDRIs are randomly selected with equal probability from either real-world or synthetic collections. We produced training pairs by projecting the sampled source and target lighting maps onto the reflectance fields of the OLAT images [10]. To derive the ground truth intrinsics, we applied the photometric stereo method [51] and obtained normal and albedo maps. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Researchers Build Massive AI Training Dataset to Perfect Lighting on Faces

AUSTIN, TX, September 18th, 2024/CyberNewsWire/--Research indicates that an infostealer malware infection is often a precursor to a ransomware attack SpyCloud, the leader in Cybercrime Analytics, today announced new cybersecurity research highlighting the growing and alarming threat of infostealers – a type of malware designed to exfiltrate digital identity data, login credentials, and session cookies from infected devices. SpyCloud’s latest findings reveal the staggering scale of identity exposure caused by infostealers, the influence this type of malware has had on the surge in ransomware incidents, and the profound implications for businesses worldwide. Massive Scale of Identity Exposure Creates New Risks According to SpyCloud, 61% of all data breaches in the past year were malware-related, with infostealers responsible for the theft of 343.78 million credentials. These stolen credentials are then sold in criminal communities for use in further attacks. The research also found that one in five individuals has been a victim of an infostealer infection. Each infection, on average, exposes 10-25 third-party business application credentials, creating fertile ground for further access and exploitation, particularly by ransomware operators. “Our latest findings reveal a critical shift in the cybersecurity landscape,” said Damon Fleury, chief product officer at SpyCloud. “Infostealers have become the go-to tool for cybercriminals, with their ability to exfiltrate valuable data in a matter of seconds, creating a runway for cyberattacks like ransomware off the vast amounts of stolen access to SSO, VPN, admin panels, and other critical applications.” Infostealers: The Precursor To Ransomware Attacks The link between infostealers and ransomware is becoming increasingly evident. Through deep analysis of recaptured infostealer logs, SpyCloud discovered a worrying trend: companies with employees and contractors who are infected with infostealer malware are significantly more likely to experience a ransomware attack. In fact, nearly one-third of companies that suffered a ransomware attack last year had previously experienced an infostealer infection. According to the report, this is based on publicly known incidents and confirmed ransomware events. The true exposure is potentially even higher as not all ransomware incidents are made publicly available. “The correlation between infostealer infections and subsequent ransomware attacks is a wake-up call for businesses,” said Trevor Hilligoss, vice president of SpyCloud Labs, SpyCloud. “However, this field is incredibly complex and fast-moving. This year, we’re seeing new infostealers families that make use of expanded capabilities such as advanced encryption to stay stealthy or the ability to restore expired authentication cookies for more persistent access.” The Rise of Malware-as-a-Service And Account Takeover Attacks The infostealer threat is further exacerbated by the rise of Malware-as-a-Service (MaaS). This off-the-shelf model allows even low-skilled cybercriminals to purchase and deploy sophisticated malware, including infostealers, with ease. Through MaaS, these criminals can acquire fresh and accurate identity data in bulk, fueling the cycle of cybercrime. SpyCloud’s findings also shed light on the evolution of account takeover (ATO) attacks, powered by infostealers. Unlike traditional ATO, which relies on stolen credentials (username and password combinations), next-generation ATO leverages stolen session cookies to sidestep traditional authentication methods in what is known as session hijacking. By taking over these already-authenticated sessions, cybercriminals can mimic legitimate users and infiltrate networks undetected. This method significantly increases the success rate of attacks and poses a severe threat to organizational security. “The sheer volume of credentials and session cookies being siphoned by infostealers is staggering,” said Hilligoss. “In the last 90 days alone, SpyCloud has recaptured over 5.4 billion stolen cookie records – with an average of nearly 2,000 exposed records per infected device. This vast trove of data is increasingly used by ransomware operators and initial access brokers to facilitate their attacks, highlighting the need for advanced defense strategies.” Antivirus, MFA And Traditional Defenses Are No Longer Enough At least 54% of devices infected with infostealers in the first half of 2024 had antivirus or endpoint detection and response (EDR) solutions installed, underscoring the limitations of traditional cybersecurity measures in combating the techniques used by modern cybercriminals. Furthermore, infostealers and session hijacking attacks render multi-factor authentication (MFA) and passwordless authentication methods like passkeys ineffective. By hijacking already-authenticated sessions, cybercriminals can impersonate legitimate users and side-step even the most robust authentication methods. The call for next-generation cybersecurity The findings from SpyCloud make it clear: traditional malware mitigation is no longer sufficient and ignoring the problem only exacerbates the impact on businesses. Organizations must move beyond merely removing infections and focus on remediating the long-term risks posed by exposed data. This includes resetting compromised application credentials and invalidating session cookies siphoned by infostealers. By understanding the risks posed by infostealers and working to mitigate the data that has been exfiltrated, organizations are able to limit the likelihood of devastating cyberattacks such as ransomware that stem from this stolen data. SpyCloud remains committed to helping organizations navigate these challenges and safeguard their digital assets. Readers can download the full 2024 Malware and Ransomware Defense Report. To learn more about how SpyCloud helps organizations defend against ransomware, readers can visit https://spycloud.com/use-case/ransomware-prevention/. About the SpyCloud 2024 Malware and Ransomware Defense Report For this fourth annual report, SpyCloud surveyed 510 individuals in active cybersecurity roles within organizations in the US and the UK with at least 500 employees. The report examines the top concerns and real-life impacts of ransomware, including popular entry points, ransom payments, and the cumulative costs of these attacks to the business. It also highlights key cyber threat prevention strategies and future security priorities identified by these experts. About SpyCloud SpyCloud transforms recaptured darknet data to disrupt cybercrime. Its automated identity threat protection solutions leverage advanced analytics to proactively prevent ransomware and account takeover, safeguard employee and consumer accounts, and accelerate cybercrime investigations. SpyCloud's data from breaches, malware-infected devices, and successful phishes also powers many popular dark web monitoring and identity theft protection offerings. Customers include more than half of the Fortune 10, along with hundreds of global enterprises, mid-sized companies, and government agencies worldwide. Headquartered in Austin, TX, SpyCloud is home to more than 200 cybersecurity experts whose mission is to protect businesses and consumers from the stolen identity data criminals are using to target them now. To learn more and see insights on their company’s exposed data, readers can visit spycloud.com Contact EVP, Public Relations Katie Hanusik REQ on behalf of SpyCloud spycloud@req.co This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here AUSTIN, TX, September 18th, 2024/CyberNewsWire/--Research indicates that an infostealer malware infection is often a precursor to a ransomware attack SpyCloud, the leader in Cybercrime Analytics, today announced new cybersecurity research highlighting the growing and alarming threat of infostealers – a type of malware designed to exfiltrate digital identity data, login credentials, and session cookies from infected devices. SpyCloud’s latest findings reveal the staggering scale of identity exposure caused by infostealers, the influence this type of malware has had on the surge in ransomware incidents, and the profound implications for businesses worldwide. Massive Scale of Identity Exposure Creates New Risks According to SpyCloud, 61% of all data breaches in the past year were malware-related, with infostealers responsible for the theft of 343.78 million credentials. These stolen credentials are then sold in criminal communities for use in further attacks. The research also found that one in five individuals has been a victim of an infostealer infection. Each infection, on average, exposes 10-25 third-party business application credentials, creating fertile ground for further access and exploitation, particularly by ransomware operators. “Our latest findings reveal a critical shift in the cybersecurity landscape,” said Damon Fleury, chief product officer at SpyCloud. “Our latest findings reveal a critical shift in the cybersecurity landscape,” said Damon Fleury, chief product officer at SpyCloud. “Infostealers have become the go-to tool for cybercriminals, with their ability to exfiltrate valuable data in a matter of seconds, creating a runway for cyberattacks like ransomware off the vast amounts of stolen access to SSO, VPN, admin panels, and other critical applications.” “Infostealers have become the go-to tool for cybercriminals, with their ability to exfiltrate valuable data in a matter of seconds, creating a runway for cyberattacks like ransomware off the vast amounts of stolen access to SSO, VPN, admin panels, and other critical applications.” Infostealers: The Precursor To Ransomware Attacks The link between infostealers and ransomware is becoming increasingly evident. Through deep analysis of recaptured infostealer logs, SpyCloud discovered a worrying trend: companies with employees and contractors who are infected with infostealer malware are significantly more likely to experience a ransomware attack. In fact, nearly one-third of companies that suffered a ransomware attack last year had previously experienced an infostealer infection. According to the report, this is based on publicly known incidents and confirmed ransomware events. The true exposure is potentially even higher as not all ransomware incidents are made publicly available. “The correlation between infostealer infections and subsequent ransomware attacks is a wake-up call for businesses,” said Trevor Hilligoss, vice president of SpyCloud Labs, SpyCloud. “The correlation between infostealer infections and subsequent ransomware attacks is a wake-up call for businesses,” said Trevor Hilligoss, vice president of SpyCloud Labs, SpyCloud. “However, this field is incredibly complex and fast-moving. This year, we’re seeing new infostealers families that make use of expanded capabilities such as advanced encryption to stay stealthy or the ability to restore expired authentication cookies for more persistent access.” “However, this field is incredibly complex and fast-moving. This year, we’re seeing new infostealers families that make use of expanded capabilities such as advanced encryption to stay stealthy or the ability to restore expired authentication cookies for more persistent access.” The Rise of Malware-as-a-Service And Account Takeover Attacks The infostealer threat is further exacerbated by the rise of Malware-as-a-Service (MaaS). This off-the-shelf model allows even low-skilled cybercriminals to purchase and deploy sophisticated malware, including infostealers, with ease. Through MaaS, these criminals can acquire fresh and accurate identity data in bulk, fueling the cycle of cybercrime. SpyCloud’s findings also shed light on the evolution of account takeover (ATO) attacks, powered by infostealers. Unlike traditional ATO, which relies on stolen credentials (username and password combinations), next-generation ATO leverages stolen session cookies to sidestep traditional authentication methods in what is known as session hijacking. By taking over these already-authenticated sessions, cybercriminals can mimic legitimate users and infiltrate networks undetected. This method significantly increases the success rate of attacks and poses a severe threat to organizational security. “The sheer volume of credentials and session cookies being siphoned by infostealers is staggering,” said Hilligoss. “The sheer volume of credentials and session cookies being siphoned by infostealers is staggering,” said Hilligoss. “In the last 90 days alone, SpyCloud has recaptured over 5.4 billion stolen cookie records – with an average of nearly 2,000 exposed records per infected device. This vast trove of data is increasingly used by ransomware operators and initial access brokers to facilitate their attacks, highlighting the need for advanced defense strategies.” “In the last 90 days alone, SpyCloud has recaptured over 5.4 billion stolen cookie records – with an average of nearly 2,000 exposed records per infected device. This vast trove of data is increasingly used by ransomware operators and initial access brokers to facilitate their attacks, highlighting the need for advanced defense strategies.” Antivirus, MFA And Traditional Defenses Are No Longer Enough At least 54% of devices infected with infostealers in the first half of 2024 had antivirus or endpoint detection and response (EDR) solutions installed, underscoring the limitations of traditional cybersecurity measures in combating the techniques used by modern cybercriminals. Furthermore, infostealers and session hijacking attacks render multi-factor authentication (MFA) and passwordless authentication methods like passkeys ineffective. By hijacking already-authenticated sessions, cybercriminals can impersonate legitimate users and side-step even the most robust authentication methods. The call for next-generation cybersecurity The findings from SpyCloud make it clear: traditional malware mitigation is no longer sufficient and ignoring the problem only exacerbates the impact on businesses. Organizations must move beyond merely removing infections and focus on remediating the long-term risks posed by exposed data. This includes resetting compromised application credentials and invalidating session cookies siphoned by infostealers. By understanding the risks posed by infostealers and working to mitigate the data that has been exfiltrated, organizations are able to limit the likelihood of devastating cyberattacks such as ransomware that stem from this stolen data. SpyCloud remains committed to helping organizations navigate these challenges and safeguard their digital assets. Readers can download the full 2024 Malware and Ransomware Defense Report . 2024 Malware and Ransomware Defense Report 2024 Malware and Ransomware Defense Report To learn more about how SpyCloud helps organizations defend against ransomware, readers can visit https://spycloud.com/use-case/ransomware-prevention/ . https://spycloud.com/use-case/ransomware-prevention/ https://spycloud.com/use-case/ransomware-prevention/ About the SpyCloud 2024 Malware and Ransomware Defense Report For this fourth annual report , SpyCloud surveyed 510 individuals in active cybersecurity roles within organizations in the US and the UK with at least 500 employees. fourth annual report fourth annual report The report examines the top concerns and real-life impacts of ransomware, including popular entry points, ransom payments, and the cumulative costs of these attacks to the business. It also highlights key cyber threat prevention strategies and future security priorities identified by these experts. About SpyCloud SpyCloud transforms recaptured darknet data to disrupt cybercrime. Its automated identity threat protection solutions leverage advanced analytics to proactively prevent ransomware and account takeover, safeguard employee and consumer accounts, and accelerate cybercrime investigations. SpyCloud SpyCloud SpyCloud's data from breaches, malware-infected devices, and successful phishes also powers many popular dark web monitoring and identity theft protection offerings. Customers include more than half of the Fortune 10, along with hundreds of global enterprises, mid-sized companies, and government agencies worldwide. Headquartered in Austin, TX, SpyCloud is home to more than 200 cybersecurity experts whose mission is to protect businesses and consumers from the stolen identity data criminals are using to target them now. To learn more and see insights on their company’s exposed data, readers can visit spycloud.com spycloud.com spycloud.com Contact EVP, Public Relations Katie Hanusik REQ on behalf of SpyCloud spycloud@req.co This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cyberwire under HackerNoon’s Business Blogging Program. Learn more about the program here here

SpyCloud Unveils Massive Scale Of Identity Exposure, Highlighting Need For Advanced Security Measure

SpyCloud تكشف عن نطاق هائل من الكشف عن الهوية، مما يسلط الضوء على الحاجة إلى تدابير أمنية متقدمة

SpyCloud ukax Jach’a Escala de Exposición de Identidad ukar uñacht’ayi, Medida de Seguridad Avanzada ukan munatapat uñacht’ayi

SpyCloud revela una escala masiva de exposición de identidad, lo que pone de relieve la necesidad de medidas de seguridad avanzadas

SpyCloud مقیاس عظیمی از قرار گرفتن در معرض هویت را معرفی می کند، که نیاز به اقدامات امنیتی پیشرفته را برجسته می کند

स्पाईक्लाउड ने पहचान उजागर करने के बड़े पैमाने का खुलासा किया, उन्नत सुरक्षा उपाय की आवश्यकता पर प्रकाश डाला

SpyCloud が大規模な個人情報漏洩を発表、高度なセキュリティ対策の必要性を浮き彫りに

SpyCloud გამოაქვეყნებს პირადობის ექსპოზიციის მასიურ მასშტაბებს, ხაზს უსვამს უსაფრთხოების გაფართოებული ზომების საჭიროებას

SpyCloud විසින් අනන්‍යතා නිරාවරණයේ දැවැන්ත පරිමාණයක් එළිදක්වයි, උසස් ආරක්ෂක පියවරක අවශ්‍යතාවය ඉස්මතු කරයි

СпиЦлоуд открива огромну скалу откривања идентитета, наглашавајући потребу за напредном мјером сигурности

SpyCloud Inafichua Kiwango Kikubwa cha Ufichuaji wa Utambulisho, Ikiangazia Haja ya Kipimo cha Kina Usalama

ஸ்பைக்ளவுட் அடையாள வெளிப்பாட்டின் பாரிய அளவை வெளிப்படுத்துகிறது, மேம்பட்ட பாதுகாப்பு நடவடிக்கையின் தேவையை எடுத்துக்காட்டுகிறது

SpyCloud tiết lộ quy mô lớn về việc tiết lộ danh tính, làm nổi bật nhu cầu về biện pháp bảo mật tiên tiến

Just when you thought Google couldn’t get any more insufferable, a federal judge throws down a ruling that could rock the very foundation of the tech giant's empire! On Monday, US District Judge Amit Mehta smacked Google with a colossal antitrust defeat, declaring that the company’s search business isn’t just dominant—it’s downright monopolistic. This isn’t just a slap on the wrist; it’s a full-blown smackdown aimed at the heart of Google’s search engine hegemony. Judge Mehta didn’t mince words. He laid it out straight: Google’s not just playing the search game; it’s rigging it to keep everyone else out. Through a web of exclusive contracts and under-the-table deals with major players like Apple, Google has cemented its stranglehold on search—squeezing out competitors like Microsoft’s Bing and DuckDuckGo. And let’s be honest, it’s not like Google’s been subtle about it. They’ve spent billions on these deals, effectively ensuring that when you need to find something online, Google’s your only choice. The court ruling paints a grim picture of Google’s power trip, citing anticompetitive practices and outrageous search ad pricing as key problems. The judge isn’t just wagging a finger; he’s laying the groundwork for potentially groundbreaking remedies. Think “choice screens” and other measures that could force Google to give rivals a fighting chance. And don’t think this is just about search ads—oh no. This decision is setting the stage for a new era of tech antitrust battles. Remember Microsoft’s antitrust debacle back in the day? This case is being compared to that. It’s a landmark moment, echoing historic rulings like the breakups of AT&T and Standard Oil. Attorney General Merrick Garland and the White House are dancing in the streets, calling this a triumph for the American people and a win for fair competition. But don’t expect Google to take this lying down. The company’s already gearing up for an appeal, insisting it’s still the best search engine out there. Classic Google, right? Meanwhile, the ruling is stirring the pot for future tech antitrust cases. With other giants like Apple and Amazon facing their own legal heat, this Google decision might just be the tip of the iceberg. It could also make waves in the Justice Department’s case against Ticketmaster. So, buckle up—Google’s legal rollercoaster is far from over, and the reverberations are going to be felt across the tech world for years to come. Just when you thought Google couldn’t get any more insufferable, a federal judge throws down a ruling that could rock the very foundation of the tech giant's empire! On Monday, US District Judge Amit Mehta smacked Google with a colossal antitrust defeat, declaring that the company’s search business isn’t just dominant—it’s downright monopolistic. This isn’t just a slap on the wrist; it’s a full-blown smackdown aimed at the heart of Google’s search engine hegemony. Judge Mehta didn’t mince words. He laid it out straight: Google’s not just playing the search game; it’s rigging it to keep everyone else out. Through a web of exclusive contracts and under-the-table deals with major players like Apple, Google has cemented its stranglehold on search—squeezing out competitors like Microsoft’s Bing and DuckDuckGo. And let’s be honest, it’s not like Google’s been subtle about it. They’ve spent billions on these deals, effectively ensuring that when you need to find something online, Google’s your only choice. The court ruling paints a grim picture of Google’s power trip, citing anticompetitive practices and outrageous search ad pricing as key problems. The judge isn’t just wagging a finger; he’s laying the groundwork for potentially groundbreaking remedies. Think “choice screens” and other measures that could force Google to give rivals a fighting chance. And don’t think this is just about search ads—oh no. This decision is setting the stage for a new era of tech antitrust battles. Remember Microsoft’s antitrust debacle back in the day? This case is being compared to that. It’s a landmark moment, echoing historic rulings like the breakups of AT&T and Standard Oil. Attorney General Merrick Garland and the White House are dancing in the streets, calling this a triumph for the American people and a win for fair competition. But don’t expect Google to take this lying down. The company’s already gearing up for an appeal, insisting it’s still the best search engine out there. Classic Google, right? Meanwhile, the ruling is stirring the pot for future tech antitrust cases. With other giants like Apple and Amazon facing their own legal heat, this Google decision might just be the tip of the iceberg. It could also make waves in the Justice Department’s case against Ticketmaster. So, buckle up—Google’s legal rollercoaster is far from over, and the reverberations are going to be felt across the tech world for years to come.

Google Slammed with Massive Antitrust Defeat: Monopoly Power Under Fire

LOS ANGELES, United States, July 30th, 2024/Chainwire/--DSCVR, the largest web3 social media and community-building platform, announces the launch of Canvas, a groundbreaking framework that enables developers to effortlessly build and integrate custom applications directly into DSCVR’s social feeds. With just a few tweaks any web application can be made into a Canvas application. Combined with DSCVR’s API, Canvas provides an easy-to-build framework to launch and distribute embedded apps on the DSCVR platform. Canvas apps can have rich interfaces previously unseen in Web3 social that extend beyond the existing frame standards of buttons and textboxes to support games, interactive media, and more. Developers can find viral distribution for Canvases in feed, making them instantly accessible to the community and scaling them across hundreds of thousands of users. With Canvas, building, launching, and managing embedded apps has never been easier or more innovative, offering end users dynamic and rewarding experiences. A New Era of Web3 Embedded App Development DSCVR Canvas introduces a framework that makes app creation accessible to a breadth of developers, from seasoned engineers to students just learning Javascript. The platform offers a user-friendly interface that simplifies the development process, allowing anyone to build dynamic, interactive applications without advanced technical skills. Canvas is also built to support existing Frames and Solana Blinks so developers in these ecosystems can easily share their apps on DSCVR. “We’re excited to introduce Canvas to our developer community,” said Rick Porter, CEO of DSCVR. “Our goal is to empower developers with the framework they need to create and scale apps within the DSCVR ecosystem for the benefit of our users. Canvas is a significant step towards realizing that vision.” Key Features of DSCVR Canvas: Transactions: Canvas applications can interact directly with user wallets which are already paired with DSCVR accounts, creating new utility for NFTs and fungible totokens. Seamless Integration: Apps built on Canvas integrate effortlessly with the DSCVR platform, leveraging robust APIs and the speed of the platform for smooth performance. Enhanced Performance: Designed for scalability, Canvas supports everything from simple ways to earn crypto, tipping, to games and other complex applications. Rich Media Support: Users can incorporate images, videos, and interactive elements to create engaging and immersive apps. Driving Innovation in Web3 Social MeMedia With more canvases being built on DSCVR, users will enjoy a wide range of fun and engaging experiences directly within their social feed. From interactive games and trading platforms to token minting, DSCVR Canvas Embedded Apps will enrich the user experience, making social interactions more dynamic and rewarding. These apps will allow users to easily monetize their activities, participate in immersive gaming and trade digital assets, transforming the way they interact on the platform. At the same time, this is an opportunity for developers to gain distribution for their applications to an audience of hundreds of thousands of users. Launch Event and Community Engagement To celebrate the launch, DSCVR will be organizing a global hackathon on August 5th hosted by Dorahacks to encourage experimentation and creativity among its developer community. Sponsors include Metaplex, Matrica, Helius, Spiderswap and Tweed. Notable judges include Austen Federa, Jon Wong, and Pedro Miranda from the Solana Foundation as well as Josh Finer from Solana Ventures. “We’re committed to supporting our developer community and providing them with the support and tools they need to succeed,” added Rick Porter. “DSCVR Canvas is a testament to our dedication to innovation in the Web3 social ecosystem.” About DSCVR With over 500k monthly unique visitors, DSCVR (pronounced “Discover”) is a Web3 social network combining the usability of a Web2-style social app with crypto technology that unlocks ownership, monetization and distribution for users, creators, and projects. Through its native crypto functionality, DSCVR rewards users and supercharges community-building, while its API allows developers and partners to build applications that target through the social graph, distribute through the feed, and monetize in posts. DSCVR’s seed investors include Polychain Capital, Upfront Ventures, BDMI, Shima Capital, Tomahawk VC, and Fyrfly Venture Partners. Users can follow DSCVR on Twitter (X) for more updates. Contact Press Inquiries Cher Park DSCVR press@dscvr.one This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. LOS ANGELES, United States, July 30th, 2024/Chainwire/--DSCVR, the largest web3 social media and community-building platform, announces the launch of Canvas, a groundbreaking framework that enables developers to effortlessly build and integrate custom applications directly into DSCVR’s social feeds. With just a few tweaks any web application can be made into a Canvas application. Combined with DSCVR’s API, Canvas provides an easy-to-build framework to launch and distribute embedded apps on the DSCVR platform. Canvas apps can have rich interfaces previously unseen in Web3 social that extend beyond the existing frame standards of buttons and textboxes to support games, interactive media, and more. Developers can find viral distribution for Canvases in feed, making them instantly accessible to the community and scaling them across hundreds of thousands of users. With Canvas, building, launching, and managing embedded apps has never been easier or more innovative, offering end users dynamic and rewarding experiences. A New Era of Web3 Embedded App Development DSCVR Canvas introduces a framework that makes app creation accessible to a breadth of developers, from seasoned engineers to students just learning Javascript. The platform offers a user-friendly interface that simplifies the development process, allowing anyone to build dynamic, interactive applications without advanced technical skills. Canvas is also built to support existing Frames and Solana Blinks so developers in these ecosystems can easily share their apps on DSCVR. “We’re excited to introduce Canvas to our developer community,” said Rick Porter, CEO of DSCVR. “Our goal is to empower developers with the framework they need to create and scale apps within the DSCVR ecosystem for the benefit of our users. Canvas is a significant step towards realizing that vision.” “We’re excited to introduce Canvas to our developer community,” said Rick Porter, CEO of DSCVR. “Our goal is to empower developers with the framework they need to create and scale apps within the DSCVR ecosystem for the benefit of our users. Canvas is a significant step towards realizing that vision.” Key Features of DSCVR Canvas: Transactions: Canvas applications can interact directly with user wallets which are already paired with DSCVR accounts, creating new utility for NFTs and fungible totokens. Transactions: Seamless Integration: Apps built on Canvas integrate effortlessly with the DSCVR platform, leveraging robust APIs and the speed of the platform for smooth performance. Enhanced Performance: Designed for scalability, Canvas supports everything from simple ways to earn crypto, tipping, to games and other complex applications. Rich Media Support: Users can incorporate images, videos, and interactive elements to create engaging and immersive apps. Driving Innovation in Web3 Social MeMedia With more canvases being built on DSCVR, users will enjoy a wide range of fun and engaging experiences directly within their social feed. From interactive games and trading platforms to token minting, DSCVR Canvas Embedded Apps will enrich the user experience, making social interactions more dynamic and rewarding. These apps will allow users to easily monetize their activities, participate in immersive gaming and trade digital assets, transforming the way they interact on the platform. At the same time, this is an opportunity for developers to gain distribution for their applications to an audience of hundreds of thousands of users. Launch Event and Community Engagement To celebrate the launch, DSCVR will be organizing a global hackathon on August 5th hosted by Dorahacks to encourage experimentation and creativity among its developer community. Sponsors include Metaplex, Matrica, Helius, Spiderswap and Tweed. Notable judges include Austen Federa, Jon Wong, and Pedro Miranda from the Solana Foundation as well as Josh Finer from Solana Ventures. “We’re committed to supporting our developer community and providing them with the support and tools they need to succeed,” added Rick Porter. “DSCVR Canvas is a testament to our dedication to innovation in the Web3 social ecosystem.” “We’re committed to supporting our developer community and providing them with the support and tools they need to succeed,” added Rick Porter. “DSCVR Canvas is a testament to our dedication to innovation in the Web3 social ecosystem.” About DSCVR With over 500k monthly unique visitors, DSCVR (pronounced “Discover”) is a Web3 social network combining the usability of a Web2-style social app with crypto technology that unlocks ownership, monetization and distribution for users, creators, and projects. DSCVR Through its native crypto functionality, DSCVR rewards users and supercharges community-building, while its API allows developers and partners to build applications that target through the social graph, distribute through the feed, and monetize in posts. DSCVR’s seed investors include Polychain Capital, Upfront Ventures, BDMI, Shima Capital, Tomahawk VC, and Fyrfly Venture Partners. Users can follow DSCVR on Twitter (X) for more updates. Twitter Contact Press Inquiries Cher Park DSCVR press@dscvr.one 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

DSCVR Launches Canvas: A Massive Leap For Web3 Social Embedded Apps

DSCVR ক্যানভাস চালু করেছে: Web3 সোশ্যাল এমবেডেড অ্যাপের জন্য একটি বিশাল ঝাঁপ

DSCVR bringt Canvas auf den Markt: Ein gewaltiger Sprung für eingebettete soziale Web3-Apps

DSCVR lanza Canvas: un gran salto para las aplicaciones sociales integradas en Web3

DSCVR lance Canvas : un grand pas en avant pour les applications sociales Web3 intégrées

डीएससीवीआर ने कैनवास लॉन्च किया: वेब3 सोशल एम्बेडेड ऐप्स के लिए एक बड़ी छलांग

DSCVR が Canvas をリリース: Web3 ソーシャル埋め込みアプリの大きな飛躍

DSCVR, 캔버스 출시: Web3 소셜 임베디드 앱의 대대적인 도약

DSCVR lança Canvas: um grande salto para aplicativos sociais incorporados na Web3

DSCVR запускает Canvas: огромный скачок для встраиваемых приложений Web3 Social

DSCVR Canvas'ı Piyasaya Sürüyor: Web3 Sosyal Gömülü Uygulamalar İçin Büyük Bir Atılım

DSCVR ra mắt Canvas: Bước nhảy vọt lớn cho các ứng dụng nhúng trên mạng xã hội Web3

DSCVR 推出 Canvas：Web3 社交嵌入式应用的一次巨大飞跃

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