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Nvidia Corp. rose the most in about nine months after the chipmaker delivered another eye-popping sales forecast, adding fresh momentum to a stock rally that already made it the world’s most valuable chipmaker.

Nvidia Surges After AI Boom Shows No Signs of Letting Up

Turning AI Into Better Thinkers With Pointer-Based Memory

Authors:
(1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia;
(2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia;
(3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia;
(4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia;
(5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia. Table of Links Abstract & Introduction Methods Methods Part 2 Experimental Results Experimental Results Part 2 Related Works, Discussion, & References Appendix A, B, & C Appendix D 3.2 Dyck Language Recognition 3.3 Compositional Learning SCAN In this task , one needs to map an input sentence into an output sequence of commands [Lake and Baroni, 2018]. The sequences are compositional, consisting of reusable parts. For example, in one case, “jump twice” should be mapped to “JUMP JUMP” and in another, “walk twice” becomes “WALK WALK”. We focus on the “length split” datasets where the training sequences are shorter than the test ones with 11 length modes L = 22, 24, .., 40 [Newman et al., 2020]. We adopt the benchmark, training procedure and baselines prepared by Csord´as et al. [2021], which achieves strong results under standard s2s learning. Here, our aim is not to break SOTA, which can be achieve by hybrid-symbolic architectures [Chen et al., 2020, Shaw et al., 2021]. Instead, we focus on improving Transformer generalization in this task, hence the baselines are chosen as several variants of Transformers (TRM) targeted to sequence extrapolation, including those using Relative Positional Encoding (RPE [Dai et al., 2019]) and Universal Transformer (U. TRM [Dehghani et al., 2018]), which is an advanced Transformer variant that recurrently processes each token, and can dynamically adjust the number of processing steps. Following Csord´as et al. [2021], each baseline is trained 5 times for 50K steps and the resulting model after training is used for evaluation (no validation). Here, we use Transformer as the Encoder, which is the same as the TRM, and stack the Controller to another Transform decoder (see details in Appendix D.3). Hence, the only difference is the decoding where PANM leverages pointer manipulation. Table 2 shows that PANM outperforms other baselines in the hardest settings when the training length is up-to 22, 24, and 25. For 22 and 24 cases, general models like PANM cannot show perfect generalization because some testing compositions is entirely discarded from the train set. In easier settings, PANM shares the perfect median accuracy with the sophisticated U. TRM + RPE although it does not use RPE. Remarkably, despite sharing the same encoder, TRM performs much worse than PANM and even fails to learn in easy modes (33, 36, 40), indicating the importance of pointer handling in this testbed. One problem for other baselines is the EOS decision (when to generate ending token), which requires length tracking [Newman et al., 2020]. As they do not have content free sequence iteration mechanisms, it is extremely hard to trace the length without overfitting to the training data. On the other hand, PANM can hypothetically generate pointer incrementally and capture the difference between the last and the first pointers, i.e. the input length, and infer the output sequence length based on that information. Mathematical Problems We test our model on mathematics [Saxton et al., 2018] where the input/output are questions and answers about math and each token is a character. For example, What is − 5 − 110911? → −110916 (add or sub) and What is the hundreds digit of 31253? → 2 (place value). The task requires not only math reasoning, but also natural language understanding. We follow the training from Csord´as et al. [2021] to conduct experiments on 2 subsets: add or sub (a.s) and place value (p.v), and compare our method with Transformer-based baselines. Here, we focus on the extrapolating test set involving larger numbers, more numbers, more compositions, and thus longer input sequences than the training. We use TRM + RPE as the Encoder and the Controller is added to a normal TRM decoder. As shown in Table 2, on place value, PANM does not suffer from performance crash as TRM + RPE (0% test accuracy, as admitted in the paper [Csord´as et al., 2021] even though it uses the same encoder). PANM achieves similar results as U. TRM+ RPE on add or sub while outperforming it by 11% on place value. We also examine PANM with the original Transformer and report additional results in Appendix D.3. 3.4 Other NLP Tasks Question Answering Our objective is to explore the PANM’s generalization beyond obviously compositional data by applying it in a more practical setting of question answering. For this purpose, we utilize two datasets, namely bAbI [Weston et al., 2015] and SQUAD 1.1 [Rajpurkar et al., 2016] where the input sequence is a context paragraph and a question, and the output is the answer. To add complexity to the task, we ensure the length of test sequence is greater than that of the training by sorting the context paragraph by length and splitting the sorted data into 0.8/0.2 and 0.5/0.5 ratio. Details of the data/task are in Appendix D.4. In bAbI, we configure the PANM similarly to the one described in § 3.3 using Transformer backbone, and test the models after 100-epoch training. The models predict the answer tokens given the context and question tokens. As shown in Table 3 and Appendix Fig. 5 (right), PANM helps Transformer generalize better, consistently improving around 6% and 5% using 0.8/0.2 and 0.5/0.5 splits, respectively. Notably, PANM’s testing loss is not diverged quickly as Transformer’s, indicating PANM’s capability of reducing overfitting. In SQUAD, we use BERT as the backbone to predict the start and the end of the answer as in Kenton and Toutanova [2019]. PANM-assisted model outperforms the baselines by 1% and 2% exact match accuracy, respectively (Table 4). The improvement is significant as BERT is a big foundation model already pretrained with big data and robust against novel test data. Machine Translation Here, we want to verify the PANM in machine translation and show that PANM can work with different number layers of Transformer. The results are presented in Fig. 3 (b) where we report the model perplexity on Multi30K (en-de) dataset. The 30K-sample dataset is sorted by input length and split into training and testing s.t. testing sequences are longer, similar to QA task. The results demonstrate PANM can consistently improve the generalization performance of Transformer across different split ratios and the number of encoder/decoder layers. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia; (2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia; (3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia; (4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia; (5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia. Authors: Authors: (1) Hung Le, Applied AI Institute, Deakin University, Geelong, Australia; (2) Dung Nguyen, Applied AI Institute, Deakin University, Geelong, Australia; (3) Kien Do, Applied AI Institute, Deakin University, Geelong, Australia; (4) Svetha Venkatesh, Applied AI Institute, Deakin University, Geelong, Australia; (5) Truyen Tran, Applied AI Institute, Deakin University, Geelong, Australia. Table of Links Abstract & Introduction Abstract & Introduction Methods Methods Methods Part 2 Methods Part 2 Experimental Results Experimental Results Experimental Results Part 2 Experimental Results Part 2 Related Works, Discussion, & References Related Works, Discussion, & References Appendix A, B, & C Appendix A, B, & C Appendix D Appendix D 3.2 Dyck Language Recognition 3.3 Compositional Learning SCAN In this task , one needs to map an input sentence into an output sequence of commands [Lake and Baroni, 2018]. The sequences are compositional, consisting of reusable parts. For example, in one case, “jump twice” should be mapped to “JUMP JUMP” and in another, “walk twice” becomes “WALK WALK”. We focus on the “length split” datasets where the training sequences are shorter than the test ones with 11 length modes L = 22, 24, .., 40 [Newman et al., 2020]. We adopt the benchmark, training procedure and baselines prepared by Csord´as et al. [2021], which achieves strong results under standard s2s learning. Here, our aim is not to break SOTA, which can be achieve by hybrid-symbolic architectures [Chen et al., 2020, Shaw et al., 2021]. Instead, we focus on improving Transformer generalization in this task, hence the baselines are chosen as several variants of Transformers (TRM) targeted to sequence extrapolation, including those using Relative Positional Encoding (RPE [Dai et al., 2019]) and Universal Transformer (U. TRM [Dehghani et al., 2018]), which is an advanced Transformer variant that recurrently processes each token, and can dynamically adjust the number of processing steps. Following Csord´as et al. [2021], each baseline is trained 5 times for 50K steps and the resulting model after training is used for evaluation (no validation). Here, we use Transformer as the Encoder, which is the same as the TRM, and stack the Controller to another Transform decoder (see details in Appendix D.3). Hence, the only difference is the decoding where PANM leverages pointer manipulation. SCAN Table 2 shows that PANM outperforms other baselines in the hardest settings when the training length is up-to 22, 24, and 25. For 22 and 24 cases, general models like PANM cannot show perfect generalization because some testing compositions is entirely discarded from the train set. In easier settings, PANM shares the perfect median accuracy with the sophisticated U. TRM + RPE although it does not use RPE. Remarkably, despite sharing the same encoder, TRM performs much worse than PANM and even fails to learn in easy modes (33, 36, 40), indicating the importance of pointer handling in this testbed. One problem for other baselines is the EOS decision (when to generate ending token), which requires length tracking [Newman et al., 2020]. As they do not have content free sequence iteration mechanisms, it is extremely hard to trace the length without overfitting to the training data. On the other hand, PANM can hypothetically generate pointer incrementally and capture the difference between the last and the first pointers, i.e. the input length, and infer the output sequence length based on that information. Mathematical Problems We test our model on mathematics [Saxton et al., 2018] where the input/output are questions and answers about math and each token is a character. For example, What is − 5 − 110911? → −110916 (add or sub) and What is the hundreds digit of 31253? → 2 (place value). The task requires not only math reasoning, but also natural language understanding. We follow the training from Csord´as et al. [2021] to conduct experiments on 2 subsets: add or sub (a.s) and place value (p.v), and compare our method with Transformer-based baselines. Here, we focus on the extrapolating test set involving larger numbers, more numbers, more compositions, and thus longer input sequences than the training. We use TRM + RPE as the Encoder and the Controller is added to a normal TRM decoder. As shown in Table 2, on place value, PANM does not suffer from performance crash as TRM + RPE (0% test accuracy, as admitted in the paper [Csord´as et al., 2021] even though it uses the same encoder). PANM achieves similar results as U. TRM+ RPE on add or sub while outperforming it by 11% on place value. We also examine PANM with the original Transformer and report additional results in Appendix D.3. Mathematical Problems 3.4 Other NLP Tasks Question Answering Our objective is to explore the PANM’s generalization beyond obviously compositional data by applying it in a more practical setting of question answering. For this purpose, we utilize two datasets, namely bAbI [Weston et al., 2015] and SQUAD 1.1 [Rajpurkar et al., 2016] where the input sequence is a context paragraph and a question, and the output is the answer. To add complexity to the task, we ensure the length of test sequence is greater than that of the training by sorting the context paragraph by length and splitting the sorted data into 0.8/0.2 and 0.5/0.5 ratio. Details of the data/task are in Appendix D.4. In bAbI , we configure the PANM similarly to the one described in § 3.3 using Transformer backbone, and test the models after 100-epoch training. The models predict the answer tokens given the context and question tokens. As shown in Table 3 and Appendix Fig. 5 (right), PANM helps Transformer generalize better, consistently improving around 6% and 5% using 0.8/0.2 and 0.5/0.5 splits, respectively. Notably, PANM’s testing loss is not diverged quickly as Transformer’s, indicating PANM’s capability of reducing overfitting. In SQUAD , we use BERT as the backbone to predict the start and the end of the answer as in Kenton and Toutanova [2019]. PANM-assisted model outperforms the baselines by 1% and 2% exact match accuracy, respectively (Table 4). The improvement is significant as BERT is a big foundation model already pretrained with big data and robust against novel test data. Question Answering bAbI SQUAD Machine Translation Here, we want to verify the PANM in machine translation and show that PANM can work with different number layers of Transformer. The results are presented in Fig. 3 (b) where we report the model perplexity on Multi30K (en-de) dataset. The 30K-sample dataset is sorted by input length and split into training and testing s.t. testing sequences are longer, similar to QA task. The results demonstrate PANM can consistently improve the generalization performance of Transformer across different split ratios and the number of encoder/decoder layers. Machine Translation This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Researchers Built a Pointer System That Helps AI Generalize Better

What You Have to Know About the New Features in Go 1.24: Cleanups and Weak Pointers

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Methodology 2 Methodology 3 Hardware 3 Hardware 4 Software 4 Software 5 Network 5 Network 6 Consensus 6 Consensus 7 Cryptocurrency Economics 7 Cryptocurrency Economics 8 Client API 8 Client API 9 Governance 9 Governance 10 Geography 10 Geography 11 Case Studies 11 Case Studies 12 Discussion and References 12 Discussion and References A. Decentralization and Policymaking A. Decentralization and Policymaking B. Software Testing B. Software Testing C. Brief Evaluations per Layer C. Brief Evaluations per Layer D. Measuring decentralization D. Measuring decentralization E. Fault Tolerance and Decentralization E. Fault Tolerance and Decentralization 4 Software Software development is another dimension of distributed ledgers that has been associated with potential centralization [44,147,11]. Diverse software development and usage is a core element of stability and safety of distributed ledgers, as it increases resilience to catastrophic bugs in a product’s code. A vulnerability in one implementation may jeopardize a part of the system, but if the system is sufficiently decentralized, such vulnerabilities would not escalate to systemic threats. Following, we discuss the development of core blockchain software components, namely transaction validation and PoW mining (via full nodes) and management of keys and digital assets (via wallets).[7] Protocol Participation. The principal type of software in blockchain systems is the full node. Full nodes implement the ledger protocol by: i) keeping a local chain; ii) validating new transactions; iii) extending the local chain with new blocks; iv) participating in the consensus mechanism to incorporate new blocks. To analyze decentralization, we identify two resources of interest: 1) (number of) full nodes; 2) participating power (e.g., computational or stake) that is hosted on full nodes. The relevant parties are: 1) full node software implementations; 2) full node software developers. Protocol Participation. Protocol Participation. Relying on a handful of full node implementations introduces safety, liveness, and stability hazards. A bug that fails to validate correct transactions would hurt the system’s liveness, whereas accepting incorrect transactions could hurt the system’s safety and stability, e.g., via a network split or token forgery. Such bugs have been observed in Bitcoin Core and could have resulted in Denial-of-Service (DoS) attacks [75] and token forgery [48]. Implementation bugs could also threaten privacy, if nodes reveal information about message origin (e.g., IP addresses). Similar threats arise when code gets reused across different projects. Often, a new blockchain is only a “derivative”, that is a project that started by forking an existing codebase, including copyrighted information [147]. Such projects often remain unpatched, so bugs in the initial implementation tend to spill over [44]. Vulnerabilities may also arise when adapting an existing implementation in a new setting, e.g., copying Bitcoin’s code and replacing PoW with PoS [96]. Thus, widespread usage of multiple node implementations, developed by different teams, is a hallmark of a secure and reliant ecosystem. Asset Management. Distributed ledgers are mostly used for bookkeeping of digital asset transactions, so securely managing and transferring assets is a core necessity. Digital assets are typically managed by private keys and represented via addresses. The software responsible for managing keys and addresses is the wallet [100] and its principal functionalities are: i) store the user’s keys; ii) prove ownership of the assets (managed by the keys); iii) issue transactions that transfer assets to other accounts; iv) retrieve the user’s (keys’) balance and history information. Therefore, the resource of interest is the set of all assets managed via the ledger and the relevant parties are, accordingly: i) wallet implementations; ii) developers of wallet software. Asset Management. Asset Management. The wallet is a major point of security consideration, so multiple properties rely on it. A bug which e.g., corrupts the user’s keys could not only prohibit a user from transacting with their assets (liveness hazard), but forever lose access to them (stability hazard). This was demonstrated in 2017, when the “Parity” Ethereum wallet saw a vulnerability that allowed a user to take ownership of multiple assets and then lock them [89]; as a result, 300m worth of Ethereum tokens were forever lost. In another example, some Bitcoin wallets possibly displayed incorrect balance, effectively enabling a double spending attack [116]. Consequently, if a few implementations are predominantly used, a vulnerability could result in assets being unusable or stolen. Such vulnerability could also turn into a systemic point of failure, with the whole ledger becoming unusable.[8] Authors:
(1) Christina Ovezik, University of Edinburgh (c.ovezik@ed.ac.uk);
(2) Dimitris Karakostas, University of Edinburgh (dkarakos@ed.ac.uk);
(3) Aggelos Kiayias, University of Edinburgh and IOG (akiayias@ed.ac.uk). Authors: Authors: (1) Christina Ovezik, University of Edinburgh (c.ovezik@ed.ac.uk); (2) Dimitris Karakostas, University of Edinburgh (dkarakos@ed.ac.uk); (3) Aggelos Kiayias, University of Edinburgh and IOG (akiayias@ed.ac.uk). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv [7] Appendix B also explores software testing. [8] A prime such example was “The DAO”, which in 2016 attracted nearly 14% of all Ethereum tokens and, when hacked, instigated a change in Ethereum’s consensus layer and a hard fork which split the network [169].

Blockchain’s Single Point of Failure? The Software Running It

I remember reading some years ago an L1’s proposal to migrate to an L2. That's where I came across the term decentralized sequencers. At the time, it seemed like an interesting but somewhat distant concept—an optimization rather than a necessity. But today, as rollups solidify their role as the dominant scaling solution for Ethereum and beyond, the importance of decentralized sequencers has grown considerably. decentralized sequencers For all the innovation that rollups bring, they still carry a significant centralization risk: most of them rely on a single, privileged sequencer. While this approach helps improve transaction speed and cost efficiency, it reintroduces some of the trust assumptions that blockchains were designed to eliminate. The debate around decentralized sequencers is not just about removing centralization for the sake of ideology; it’s about solving real bottlenecks in performance, censorship resistance, and fair transaction ordering. In this deep dive, we'll explore why sequencers matter, how decentralized sequencer networks work, the emerging designs in this space, and what challenges remain before they see widespread adoption. What is a sequencer and why does it matter? What is a sequencer and why does it matter? Sequencers play a critical role in rollup architectures. In simple terms, a sequencer is responsible for: Ordering transactions before they are posted on L1.


Providing rapid confirmations to users, even before final settlement on Ethereum.


Mitigating MEV (Maximal Extractable Value) by controlling how transactions are arranged. Ordering transactions before they are posted on L1. Ordering transactions before they are posted on L1. Providing rapid confirmations to users, even before final settlement on Ethereum. Providing rapid confirmations to users, even before final settlement on Ethereum. Mitigating MEV (Maximal Extractable Value) by controlling how transactions are arranged. Mitigating MEV (Maximal Extractable Value) by controlling how transactions are arranged. Without sequencers, rollups would function much like L1 blockchains—transactions would have to wait for finalization before they can be considered confirmed, resulting in slower user experiences. A sequencer brings the benefit of near-instant finality, making rollups feel as fast as traditional Web2 applications. Okay, but why do we need decentralized sequencers? Well, decentralized sequencers implies the opposite exists too. What's interesting is that centralized sequencers are more scalable and even faster. But its a double edged sword, its advantages in terms of scaling, comes with significant risk. Censorship – A centralized sequencer can choose which transactions to include or exclude, leading to concerns about neutrality, fair access and defeats the purpose of decentralized technology.
Single point of failure – If the sequencer goes offline, transactions cannot be processed until it comes back. While some rollups have fallback mechanisms, they are inefficient and not ideal for mainstream adoption. Censorship – A centralized sequencer can choose which transactions to include or exclude, leading to concerns about neutrality, fair access and defeats the purpose of decentralized technology. Single point of failure – If the sequencer goes offline, transactions cannot be processed until it comes back. While some rollups have fallback mechanisms, they are inefficient and not ideal for mainstream adoption. This might also seem like an unlikely scenario but it has in fact happened before. Arbitrum’s sequencer once stalled, making the network unusable for over an hour. MEV exploitation – A centralized sequencer can extract value from users through unfair transaction ordering, front-running, or sandwich attacks. MEV exploitation – A centralized sequencer can extract value from users through unfair transaction ordering, front-running, or sandwich attacks. These risks make it pertinent that L2s move towards a decentralized sequencer architecture.While a number of chains are still using centralized sequencers like Arbitrum one, optimism and base, we have seen new players like Morph adopting the decentralized sequencer model. How do decentralized sequencers work? A decentralized sequencer network, distributes the sequencing function across multiple entities, removing the reliance on a single entity. Its essentially the opposite of centralized sequencers. Its more time consuming and more expensive (in comparison with centralized sequencers) but for what it lacks, it makes up for it with: Censorship resistance – No single entity can exclude transactions.
High availability – Even if one sequencer node goes down, others can take over. This eliminates the risk of network downtime.
Fair transaction ordering – Preventing MEV abuse and ensuring a level playing field. Censorship resistance – No single entity can exclude transactions. High availability – Even if one sequencer node goes down, others can take over. This eliminates the risk of network downtime. Fair transaction ordering – Preventing MEV abuse and ensuring a level playing field. Shared Sequencing In the shared sequencing approach, multiple sequencers collaborate to process transactions in a shared mempool. The sequencers collectively determine the order of transactions and propose blocks to the L1 chain based on a consensus mechanism. Advantages: Ensures consistent transaction ordering across all sequencers
Reduces the risk of MEV exploitation through a fair ordering mechanism
Enables seamless failover in case of individual sequencer failures\ Ensures consistent transaction ordering across all sequencers Reduces the risk of MEV exploitation through a fair ordering mechanism Enables seamless failover in case of individual sequencer failures\ Challenges: Requires a robust consensus mechanism to ensure fair participation and prevent attacks
Potential for lower throughput compared to centralized sequencing due to the coordination overhead
Increased communication complexity among sequencers Requires a robust consensus mechanism to ensure fair participation and prevent attacks Potential for lower throughput compared to centralized sequencing due to the coordination overhead Increased communication complexity among sequencers Example: The Morph rollup uses a shared sequencing model based on a proof-of-stake consensus among sequencers. Example The Future of Decentralized Sequencing As the Ethereum ecosystem continues to scale and mature, the need for decentralized sequencing solutions will only grow. While the current approaches offer significant improvements over centralized sequencers, there is still room for innovation and optimization. One area of active research is the development of hybrid sequencing models that combine the benefits of different approaches. For example, a system could use shared sequencing for high-value transactions that require strong censorship resistance, while employing a more centralized approach for low-value transactions to maximize throughput. Another promising direction is the integration of privacy-preserving technologies like zero-knowledge proofs into decentralized sequencers. This could enable users to submit transactions without revealing their identities or transaction details to the sequencers, further enhancing the censorship resistance and neutrality of the system. As the technology evolves, we can expect to see more rollups and L2 solutions adopting decentralized sequencing architectures. This will not only improve the security and fairness of these systems but also bring us closer to the vision of a fully decentralized and scalable blockchain ecosystem. Conclusion Decentralized sequencers are a critical component of the Ethereum scaling puzzle. By removing the reliance on a single, privileged entity, they mitigate the risks of censorship, single points of failure, and MEV exploitation. While the current approaches have their tradeoffs and challenges, they represent a significant step towards a more resilient and trustless blockchain infrastructure. As the space continues to innovate and mature, we can expect to see more advanced and efficient decentralized sequencing solutions emerge. These developments will not only benefit Ethereum but also have far-reaching implications for the broader blockchain ecosystem, enabling new use cases and unlocking the full potential of decentralized technologies.

Rollups Are the Future—But Their Biggest Bottleneck Might Be a Single Point of Failure

The news cycle is exceptionally choppy right now. Brands need to make critical decisions on where they stand and the course ahead. And if they don’t take a stand and choose where they’re headed, how long can they stay up and stay above the waters? When boating, or doing other things where a fall can be dangerous, there’s something called the three points of contact rule. The idea is your body should have at least three points of contact with something stable at all times. Anything less than that and a wave can come along and throw you. That’s how marketing should be done right now. Sales One of those points of contact is almost always going to be the deck of the boat. At least one of your feet is going to be planted there. In marketing or public relations that should be sales. Even if you’re a non-profit, it’s going to be where the money comes from. You can have points of contact elsewhere, but if revenue or sponsorship is not coming from somewhere, you don’t have a going concern. While that seems obvious, many marketing and most public relations teams don’t do this. Sales people can be a pain in the rear end. If they win business, they can say it was because of their efforts and marketing and sales didn’t really help. When sales aren’t coming in, well marketing isn’t doing its job and what in the world is public relations doing apart from absorbing budget? And the concreteness of sales’ metrics… either money is coming into the bank account or not, is scary if you don’t have a direct role in bringing in those dollars, but are rather supporting it indirectly. Too frequently marketing and public relations functions then try to set up metrics that they can control, and are at a safe distance from sales. Moving leads and prospects through a marketing funnel, impressions, clicks, etc. And there’s correlation between that and sales, but it sure is tenuous. Sales are sales are sales. And if you don’t have at least one of your points of contact on the deck of the boat formed by sales, it really doesn’t matter where the other points of contact are. Integration Beyond sales, the other points of contact can be any number of things that work for sales. It could be media relations, social media, email campaigns, collateral, your site, etc. But whatever it is it had better be connected to sales. And if you’re moving you’d better through a changing market and media environment, make sure those other points of contact are also connected to each other in addition to sales. This seems obvious, but if you have a newsletter, how many of its articles have been selected or written with prospects in your funnel in mind? Does your sales team know when the newsletter is going out and has it been written in a fashion where it organically can be forwarded to leads and prospects? Can a sales person send a message saying, “hey you know how we talked about Hackernoon the other day and how great it is as a resource? Here’s an article I found there that’s useful to marketers and public relations professionals and shows how brands should pay attention to places like Hackernoon to understand how they can become more proficient in today’s crazy environment.” Why now? And let’s come back to today’s crazy environment. Before Donald Trump's inauguration, what society seemed to respect in a brand, and what society expects now is in volatile flux. What’s the constant message and brand running through those three points of contact? Here's three constants we heard from Dominic-Madori Davis of TechCrunch and Alyse Killeen of Stillmark as they were interviewed by Meredith Beuchaw and Ed Zimmerman of Lowenstein Sandler LLP at VentureCrush recently: Your personal story and choices you've made anchor brand and the sale
Your primary, consistent, and differentiated value prop through those choices sharpens brand and backs the sale
Non-textual signals amplify brand and facilitate the sale In times like these, you are what you have chosen to do. And what you choose now will be what your brand is tomorrow and beyond. Davis said a personal story is part of what draws something to her journalistic attention. She also looks for stories she and readers would not have found on their own. Davis also went on to say today a journalist has a brand that transcends the news brand they work for. For her personal story, she shared how her professional journey started with screenwriting at University of Southern California. She was pulled into copy editing her student paper. The Varsity Blues scandals put a news spotlight on the campus (Melissa Korn's journalism is also great here for context). Does her initial choice of screenwriting tell us something about her investment in personal stories and having the space to explore the unexpected? Killeen shared the influence of working for her grandfather, and hearing people talk about the respect they had for his integrity, the choices he made. At Stillmark she said she focuses their brand on a primary value proposition. And she pointed out that in working with startup founders, what they think and want their primary value proposition to be, may change when they see why customers are coming to them. Davis highlighted how fashion choices, are an example of showing differentiation and a brand, rather than telling. You can watch the full panel here: https://www.youtube.com/watch?v=_LBEZl5dRGI&embedable=true Bringing together brand and sales Here’s the thing, sales ensure you are a going concern. Brand ensures your going concern will endure based on the memory of the choices you’ve made in the course of winning sales. If your sales don’t have integrity, if you sacrifice integrity for sales, the dollars are good now, but they’ll be cheaper later as your brand declines. For those three points of contact, make sure the point of contact you have with sales, is anchored in the right kind of sales, for the right reasons, backed with the kind of demonstrated integrity that will sustain your business and grow it dependably. The news cycle is exceptionally choppy right now. Brands need to make critical decisions on where they stand and the course ahead. And if they don’t take a stand and choose where they’re headed, how long can they stay up and stay above the waters? When boating, or doing other things where a fall can be dangerous, there’s something called the three points of contact rule. The idea is your body should have at least three points of contact with something stable at all times. Anything less than that and a wave can come along and throw you. That’s how marketing should be done right now. Sales Sales One of those points of contact is almost always going to be the deck of the boat. At least one of your feet is going to be planted there. In marketing or public relations that should be sales. Even if you’re a non-profit, it’s going to be where the money comes from. You can have points of contact elsewhere, but if revenue or sponsorship is not coming from somewhere, you don’t have a going concern. While that seems obvious, many marketing and most public relations teams don’t do this. Sales people can be a pain in the rear end. If they win business, they can say it was because of their efforts and marketing and sales didn’t really help. When sales aren’t coming in, well marketing isn’t doing its job and what in the world is public relations doing apart from absorbing budget? And the concreteness of sales’ metrics… either money is coming into the bank account or not, is scary if you don’t have a direct role in bringing in those dollars, but are rather supporting it indirectly. Too frequently marketing and public relations functions then try to set up metrics that they can control, and are at a safe distance from sales. Moving leads and prospects through a marketing funnel, impressions, clicks, etc. And there’s correlation between that and sales, but it sure is tenuous. Sales are sales are sales. And if you don’t have at least one of your points of contact on the deck of the boat formed by sales, it really doesn’t matter where the other points of contact are. Integration Integration Beyond sales, the other points of contact can be any number of things that work for sales. It could be media relations, social media, email campaigns, collateral, your site, etc. But whatever it is it had better be connected to sales. And if you’re moving you’d better through a changing market and media environment, make sure those other points of contact are also connected to each other in addition to sales. This seems obvious, but if you have a newsletter, how many of its articles have been selected or written with prospects in your funnel in mind? Does your sales team know when the newsletter is going out and has it been written in a fashion where it organically can be forwarded to leads and prospects? Can a sales person send a message saying, “hey you know how we talked about Hackernoon the other day and how great it is as a resource? Here’s an article I found there that’s useful to marketers and public relations professionals and shows how brands should pay attention to places like Hackernoon to understand how they can become more proficient in today’s crazy environment.” Why now? Why now? And let’s come back to today’s crazy environment. Before Donald Trump's inauguration, what society seemed to respect in a brand, and what society expects now is in volatile flux. What’s the constant message and brand running through those three points of contact? Here's three constants we heard from Dominic-Madori Davis of TechCrunch and Alyse Killeen of Stillmark as they were interviewed by Meredith Beuchaw and Ed Zimmerman of Lowenstein Sandler LLP at VentureCrush recently: Dominic-Madori Davis Dominic-Madori Davis TechCrunch TechCrunch Alyse Killeen Alyse Killeen Stillmark Stillmark Meredith Beuchaw Meredith Beuchaw Ed Zimmerman Ed Zimmerman Lowenstein Sandler LLP Lowenstein Sandler LLP VentureCrush VentureCrush Your personal story and choices you've made anchor brand and the sale Your primary, consistent, and differentiated value prop through those choices sharpens brand and backs the sale Non-textual signals amplify brand and facilitate the sale Your personal story and choices you've made anchor brand and the sale Your primary, consistent, and differentiated value prop through those choices sharpens brand and backs the sale Non-textual signals amplify brand and facilitate the sale In times like these, you are what you have chosen to do. And what you choose now will be what your brand is tomorrow and beyond. Davis said a personal story is part of what draws something to her journalistic attention. She also looks for stories she and readers would not have found on their own. Davis also went on to say today a journalist has a brand that transcends the news brand they work for. For her personal story, she shared how her professional journey started with screenwriting at University of Southern California. She was pulled into copy editing her student paper. The Varsity Blues scandals put a news spotlight on the campus ( Melissa Korn 's journalism is also great here for context). Melissa Korn Melissa Korn Does her initial choice of screenwriting tell us something about her investment in personal stories and having the space to explore the unexpected? Killeen shared the influence of working for her grandfather, and hearing people talk about the respect they had for his integrity, the choices he made. At Stillmark she said she focuses their brand on a primary value proposition. And she pointed out that in working with startup founders, what they think and want their primary value proposition to be, may change when they see why customers are coming to them. Davis highlighted how fashion choices, are an example of showing differentiation and a brand, rather than telling. You can watch the full panel here: https://www.youtube.com/watch?v=_LBEZl5dRGI&embedable=true https://www.youtube.com/watch?v=_LBEZl5dRGI&embedable=true Bringing together brand and sales Bringing together brand and sales Here’s the thing, sales ensure you are a going concern. Brand ensures your going concern will endure based on the memory of the choices you’ve made in the course of winning sales. If your sales don’t have integrity, if you sacrifice integrity for sales, the dollars are good now, but they’ll be cheaper later as your brand declines. For those three points of contact, make sure the point of contact you have with sales, is anchored in the right kind of sales, for the right reasons, backed with the kind of demonstrated integrity that will sustain your business and grow it dependably.

Your Brand Is Sinking Without These Three Points of Contact

Table of Links Abstract and 1. Introduction Clock in Orbit
2.1 Coordinate Time
2.2 Local Frame for the Moon


Clock Rate Differences Between Earth and Moon


Clocks at Earth-Moon Lagrance Points
4.1 Clock at Lagrange point L1
4.2. Clock at Lagrange point L2
4.3. Clock at Lagrange point L4 or L5


Conclusions Appendix 1: Fermi Coordinates with Origin at the Center of the Moon Appendix 2: Construction of Freely Falling Center of Mass Frame Appendix 3: Equations of Motion of Earth and Moon Appendix 4: Comparing Results in Rotating and Non-Rotating Coordinate Systems Acknowledgments and References 4. CLOCKS AT EARTH-MOON LAGRANGE POINTS The Lagrange points offer a cost-effective and low-noise environment for stationing spacecraft with clocks because there is no net acceleration. As a result, the orbits of clocks at these points with respect to the Earth-Moon barycenter are well-determined and the corresponding frequency offsets are precisely determined. Therefore, the proper times of clocks at Lagrange points can be related to the proper times of clocks on the Earth and Moon with high precision. This information is crucial for synchronizing remote clocks in cislunar space. Both L4 and L5 are stable points, but L1 and L2 are only metastable locations. 4.1 Clock at Lagrange point L1 In this section, we consider a clock at L1 and compare its rate to a clock on Earth’s surface. It is most convenient to use a freely falling inertial frame centered at the center of mass of the Earth-Moon system for this calculation. First, we need the precise position of L1; most treatments assume circular motion, but this is inadequate for our purposes. We assume Keplerian elliptical orbits for the Earth and Moon about the center of mass, for which the Earth-Moon distance is given by Eq. (19). The radial velocity is then and the radial acceleration of the Moon relative to Earth is Assume that L1 is between the Earth and Moon and at a distance x1D from the Moon, the net gravitational force towards the Moon supplies the radial acceleration of L1, diminished by the centripetal acceleration due to rotation of the Earth-Moon line. This gives the condition Solving Eq. (39) for x1 gives x1 = 0.15093428(1). The metric, neglecting solar tides, is The motion is so slow that the potential terms in the last line can be neglected. The transverse and radial velocities of the clock due to the rotation of the Earth-Moon line are So, the total velocity squared is 4.2. Clock at Lagrange point L2 4.3. Clock at Lagrange point L4 or L5 The clock is equidistant from Earth and the Moon. The total velocity squared is the sum of the radial velocity squared and the transverse velocity squared: Authors:
(1) Neil Ashby, National Institute of Standards and Technology, Boulder, CO 80305 (Neil.Ashby@Colorado.Edu);
(2) Bijunath R. Patla, National Institute of Standards and Technology, Boulder, CO 80305 (bijunath.patla@nist.gov). This paper is available on arxiv under CC0 1.0 license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Clock in Orbit
2.1 Coordinate Time
2.2 Local Frame for the Moon Clock Rate Differences Between Earth and Moon Clocks at Earth-Moon Lagrance Points
4.1 Clock at Lagrange point L1
4.2. Clock at Lagrange point L2
4.3. Clock at Lagrange point L4 or L5 Conclusions Clock in Orbit 2.1 Coordinate Time 2.2 Local Frame for the Moon Clock in Orbit Clock in Orbit 2.1 Coordinate Time 2.1 Coordinate Time 2.2 Local Frame for the Moon 2.2 Local Frame for the Moon Clock Rate Differences Between Earth and Moon Clock Rate Differences Between Earth and Moon Clock Rate Differences Between Earth and Moon Clocks at Earth-Moon Lagrance Points 4.1 Clock at Lagrange point L1 4.2. Clock at Lagrange point L2 4.3. Clock at Lagrange point L4 or L5 Clocks at Earth-Moon Lagrance Points 4.1 Clock at Lagrange point L1 4.1 Clock at Lagrange point L1 4.2. Clock at Lagrange point L2 4.2. Clock at Lagrange point L2 4.3. Clock at Lagrange point L4 or L5 4.3. Clock at Lagrange point L4 or L5 Conclusions Conclusions Conclusions Appendix 1: Fermi Coordinates with Origin at the Center of the Moon Appendix 1: Fermi Coordinates with Origin at the Center of the Moon Appendix 2: Construction of Freely Falling Center of Mass Frame Appendix 2: Construction of Freely Falling Center of Mass Frame Appendix 3: Equations of Motion of Earth and Moon Appendix 3: Equations of Motion of Earth and Moon Appendix 4: Comparing Results in Rotating and Non-Rotating Coordinate Systems Appendix 4: Comparing Results in Rotating and Non-Rotating Coordinate Systems Acknowledgments and References Acknowledgments and References 4. CLOCKS AT EARTH-MOON LAGRANGE POINTS The Lagrange points offer a cost-effective and low-noise environment for stationing spacecraft with clocks because there is no net acceleration. As a result, the orbits of clocks at these points with respect to the Earth-Moon barycenter are well-determined and the corresponding frequency offsets are precisely determined. Therefore, the proper times of clocks at Lagrange points can be related to the proper times of clocks on the Earth and Moon with high precision. This information is crucial for synchronizing remote clocks in cislunar space. Both L4 and L5 are stable points, but L1 and L2 are only metastable locations. 4.1 Clock at Lagrange point L1 In this section, we consider a clock at L1 and compare its rate to a clock on Earth’s surface. It is most convenient to use a freely falling inertial frame centered at the center of mass of the Earth-Moon system for this calculation. First, we need the precise position of L1; most treatments assume circular motion, but this is inadequate for our purposes. We assume Keplerian elliptical orbits for the Earth and Moon about the center of mass, for which the Earth-Moon distance is given by Eq. (19). The radial velocity is then and the radial acceleration of the Moon relative to Earth is Assume that L1 is between the Earth and Moon and at a distance x1D from the Moon, the net gravitational force towards the Moon supplies the radial acceleration of L1, diminished by the centripetal acceleration due to rotation of the Earth-Moon line. This gives the condition Solving Eq. (39) for x1 gives x1 = 0.15093428(1). The metric, neglecting solar tides, is The motion is so slow that the potential terms in the last line can be neglected. The transverse and radial velocities of the clock due to the rotation of the Earth-Moon line are So, the total velocity squared is 4.2. Clock at Lagrange point L2 4.3. Clock at Lagrange point L4 or L5 The clock is equidistant from Earth and the Moon. The total velocity squared is the sum of the radial velocity squared and the transverse velocity squared: Authors: (1) Neil Ashby, National Institute of Standards and Technology, Boulder, CO 80305 (Neil.Ashby@Colorado.Edu); (2) Bijunath R. Patla, National Institute of Standards and Technology, Boulder, CO 80305 (bijunath.patla@nist.gov). Authors: Authors: (1) Neil Ashby, National Institute of Standards and Technology, Boulder, CO 80305 ( Neil.Ashby@Colorado.Edu ); Neil.Ashby@Colorado.Edu (2) Bijunath R. Patla, National Institute of Standards and Technology, Boulder, CO 80305 ( bijunath.patla@nist.gov ). bijunath.patla@nist.gov This paper is available on arxiv under CC0 1.0 license. This paper is available on arxiv under CC0 1.0 license. available on arxiv available on arxiv

The Case for Lagrange Points As Ideal Locations for Space Clocks

Deep Learning Runs on Floating-Point Math. What If That’s a Mistake?

Will 2025 Be The Turning Point For Passwords: What’s Next For The Passwordless Future?

A Single Point of Access or Chaos — Unlocking Non-Blocking API Gateways

2025: A New Chapter in DeAI Begins The Open ecosystem has dropped a groundbreaking initiative: the $OPEN Point System, tagged with the bold and ambitious slogan, #WEBisOpen. This program transforms every interaction with Open’s ecosystem into tangible rewards—$OPEN points—designed to fuel engagement, incentivize innovation, and supercharge the journey into the decentralized AI future. 🌐✨ Turn Engagement into Value 💡 Whether you’re deploying AI agents, participating in DeSci breakthroughs, or gaming in the metaverse, your on-chain interactions now generate $OPEN points. For OGs who were part of the viral @owlonmusk_ campaign, your rewards are getting an upgrade—automatically converted into $OPEN points. Utilities and Formula: Details about point calculations and their jaw-dropping applications are under wraps for now but are set to be revealed soon. Keep your eyes locked on webisopen.com for the full scoop. 👀 Exploring the Open Ecosystem: Where Innovation Thrives 🌟 The Open ecosystem is home to trailblazing projects that redefine what’s possible in Web3. The first batch of applications joining the $OPEN points system are: AiSweat.Shop (ASS): Deploy AI-driven agents with DeFi superpowers. Tokenize and trade decentralized AI tools in a way never seen before.
Open DeSci Platform: Decentralized science takes center stage with tools to democratize computational research and create verifiable scientific breakthroughs.
Overlord.bot: Automate operations with AI bots enhanced by scalable compute and blockchain transparency.
Gameboi.ai: Rethink Web3 airdrop systems, simplify engagement, filter bots, all while keeping things fun, interactive and decentralized. OVM: The Wormhole for Decentralized AI & DeSci 🌀 At the core of Open’s ecosystem is the Open Virtual Machine (OVM), a blockchain marvel that merges decentralized high performance compute power with on-chain immutability and verifiability. From AI breakthroughs to scientific advancements, OVM is the decentralized engine powering Web3’s high-performance future. 🔗 AI on OVM: The Brain Meets the Chain 🤖 On-Chain AI & ML Applications\AI and machine learning are no longer confined to centralized, opaque systems. With OVM, resource-heavy AI computations like training models, predictive analytics, and even autonomous agents run entirely on-chain—transparent, reproducible, and secure. Use Case Highlights:


Autonomous trader AI agents optimizing DeFi strategies in real-time.


Sentiment analysis bots for decentralized content curation.


Predictive models enhancing precision for decentralized health or finance.




Tokenizing AI Power: AiSweat.ShopAiSweat.Shop (ASS) is a flagship project enabling users to deploy and monetize AI agents. These agents perform various DeFi tasks like yield farming, liquidity management, and automated trading. It’s a decentralized AI factory, built with Open’s scalable, immutable compute wormhole infrastructure, bridging high performance decentralized compute to immutable blockchains. 🔬 DeSci Powered by OVM: Science Decentralized and Verified The Open DeSci Platform is a shining example of how OVM brings decentralized science into the spotlight. Genomic Comparisons on Blockchain:In a recent showcase, researchers compared the genomes ofSARS-CoV-2 (2019) and SARS-CoV (2003)—entirely on-chain—using Open’s OVM within a Trusted Execution Environment (TEE). This experiment highlighted:


Verifiability: Every step of the analysis is immutable and tamper-proof.


Democratization: Cutting-edge computation, without reliance on exclusive hardware or centralized labs.


Transparency: Results are open for public scrutiny and reproducibility.




Revolutionizing Scientific Research:\OVM enables on-chain peer review, ensuring results are reproducible, transparent, and immune to tampering. By integrating decentralized compute with blockchain technology, OVM democratizes access to the tools required for groundbreaking discoveries. \ Potential DeSci Applications**: Climate modeling and environmental simulations.
AI-driven drug discovery, leveraging open data sets and decentralized computation.
Real-time epidemiological research with global collaboration. OVM: A Compute Lego for Endless Possibilities 🔧 Modularity Meets Scalability: Think of OVM as the Lego blocks of decentralized compute. Developers can stack and integrate OVM between immutable blockchains to high performance decentralized compute to create a wide range of applications, all on-chain: AI Supercomputing: Seamlessly train and deploy advanced AI models.
DeSci Simulations: Power real-time, on-chain decentralized scientific research experiences with DeSci economies and asset security. The Future of Open Ecosystem: Innovation Unleashed 🚀 From AI to DeSci, finance to gaming, the Open ecosystem is redefining the boundaries of blockchain innovation. The #WEBisOpen Point System is just the beginning—a portal into a decentralized revolution where participation is rewarded, science is democratized, and AI is verifiable. Be a part of this future. Earn $OPEN points. Unlock possibilities. 👉 Learn more and stay updated at webisopen.com. About Open (aka webisOpen on X) Open is a series of endeavors aimed at propelling the Open Web forward. Collectively, Open is the Data and Compute Wormhole for innovations like verifiable and transparent AI and DeSci. The Open ecosystem includes @RSS3_ @_RSSHub & @openinfo_. webisOpen. 2025: A New Chapter in DeAI Begins 2025: A New Chapter in DeAI Begins The Open ecosystem has dropped a groundbreaking initiative: the $OPEN Point System , tagged with the bold and ambitious slogan, #WEBisOpen . This program transforms every interaction with Open’s ecosystem into tangible rewards—$OPEN points—designed to fuel engagement, incentivize innovation, and supercharge the journey into the decentralized AI future. 🌐✨ The Open ecosystem has dropped a groundbreaking initiative: the $OPEN Point System #WEBisOpen Turn Engagement into Value 💡 Turn Engagement into Value 💡 Whether you’re deploying AI agents, participating in DeSci breakthroughs, or gaming in the metaverse, your on-chain interactions now generate $OPEN points . For OGs who were part of the viral @owlonmusk_ campaign , your rewards are getting an upgrade—automatically converted into $OPEN points. $OPEN points @owlonmusk_ campaign Utilities and Formula : Details about point calculations and their jaw-dropping applications are under wraps for now but are set to be revealed soon. Keep your eyes locked on webisopen.com for the full scoop. 👀 Utilities and Formula webisopen.com webisopen.com Exploring the Open Ecosystem: Where Innovation Thrives 🌟 Exploring the Open Ecosystem: Where Innovation Thrives 🌟 The Open ecosystem is home to trailblazing projects that redefine what’s possible in Web3. The first batch of applications joining the $OPEN points system are: trailblazing projects AiSweat.Shop (ASS): Deploy AI-driven agents with DeFi superpowers. Tokenize and trade decentralized AI tools in a way never seen before. Open DeSci Platform: Decentralized science takes center stage with tools to democratize computational research and create verifiable scientific breakthroughs. Overlord.bot: Automate operations with AI bots enhanced by scalable compute and blockchain transparency. Gameboi.ai: Rethink Web3 airdrop systems, simplify engagement, filter bots, all while keeping things fun, interactive and decentralized. AiSweat.Shop (ASS): Deploy AI-driven agents with DeFi superpowers. Tokenize and trade decentralized AI tools in a way never seen before. AiSweat.Shop (ASS): Open DeSci Platform: Decentralized science takes center stage with tools to democratize computational research and create verifiable scientific breakthroughs. Open DeSci Platform: Overlord.bot: Automate operations with AI bots enhanced by scalable compute and blockchain transparency. Overlord.bot: Gameboi.ai: Rethink Web3 airdrop systems, simplify engagement, filter bots, all while keeping things fun, interactive and decentralized. Gameboi.ai: OVM: The Wormhole for Decentralized AI & DeSci 🌀 OVM: The Wormhole for Decentralized AI & DeSci 🌀 At the core of Open’s ecosystem is the Open Virtual Machine (OVM) , a blockchain marvel that merges decentralized high performance compute power with on-chain immutability and verifiability. From AI breakthroughs to scientific advancements , OVM is the decentralized engine powering Web3’s high-performance future. Open Virtual Machine (OVM) blockchain marvel AI breakthroughs to scientific advancements 🔗 AI on OVM: The Brain Meets the Chain 🤖 🔗 AI on OVM: The Brain Meets the Chain 🤖 On-Chain AI & ML Applications\AI and machine learning are no longer confined to centralized, opaque systems. With OVM, resource-heavy AI computations like training models, predictive analytics, and even autonomous agents run entirely on-chain—transparent, reproducible, and secure. Use Case Highlights:


Autonomous trader AI agents optimizing DeFi strategies in real-time.


Sentiment analysis bots for decentralized content curation.


Predictive models enhancing precision for decentralized health or finance. Tokenizing AI Power: AiSweat.ShopAiSweat.Shop (ASS) is a flagship project enabling users to deploy and monetize AI agents. These agents perform various DeFi tasks like yield farming, liquidity management, and automated trading. It’s a decentralized AI factory, built with Open’s scalable, immutable compute wormhole infrastructure, bridging high performance decentralized compute to immutable blockchains. On-Chain AI & ML Applications\AI and machine learning are no longer confined to centralized, opaque systems. With OVM, resource-heavy AI computations like training models, predictive analytics, and even autonomous agents run entirely on-chain—transparent, reproducible, and secure. Use Case Highlights: Autonomous trader AI agents optimizing DeFi strategies in real-time.


Sentiment analysis bots for decentralized content curation.


Predictive models enhancing precision for decentralized health or finance. On-Chain AI & ML Applications\AI and machine learning are no longer confined to centralized, opaque systems. With OVM, resource-heavy AI computations like training models, predictive analytics, and even autonomous agents run entirely on-chain—transparent, reproducible, and secure. Use Case Highlights: On-Chain AI & ML Applications\AI and machine learning are no longer confined to centralized, opaque systems. With OVM, resource-heavy AI computations like training models, predictive analytics, and even autonomous agents Autonomous trader AI agents optimizing DeFi strategies in real-time. Sentiment analysis bots for decentralized content curation. Predictive models enhancing precision for decentralized health or finance. Autonomous trader AI agents optimizing DeFi strategies in real-time. Autonomous trader AI agents optimizing DeFi strategies in real-time. Sentiment analysis bots for decentralized content curation. Sentiment analysis bots for decentralized content curation. Predictive models enhancing precision for decentralized health or finance. Predictive models enhancing precision for decentralized health or finance. Tokenizing AI Power: AiSweat.ShopAiSweat.Shop (ASS) is a flagship project enabling users to deploy and monetize AI agents. These agents perform various DeFi tasks like yield farming, liquidity management, and automated trading. It’s a decentralized AI factory, built with Open’s scalable, immutable compute wormhole infrastructure, bridging high performance decentralized compute to immutable blockchains. Tokenizing AI Power: AiSweat.ShopAiSweat.Shop (ASS) is a flagship project enabling users to deploy and monetize AI agents. These agents perform various DeFi tasks like yield farming, liquidity management, and automated trading. It’s a decentralized AI factory, built with Open’s scalable, immutable compute wormhole infrastructure, bridging high performance decentralized compute to immutable blockchains. Tokenizing AI Power: AiSweat.Shop AiSweat.Shop (ASS) 🔬 DeSci Powered by OVM: Science Decentralized and Verified 🔬 DeSci Powered by OVM: Science Decentralized and Verified The Open DeSci Platform is a shining example of how OVM brings decentralized science into the spotlight. Open DeSci Platform Genomic Comparisons on Blockchain:In a recent showcase, researchers compared the genomes ofSARS-CoV-2 (2019) and SARS-CoV (2003)—entirely on-chain—using Open’s OVM within a Trusted Execution Environment (TEE). This experiment highlighted:


Verifiability: Every step of the analysis is immutable and tamper-proof.


Democratization: Cutting-edge computation, without reliance on exclusive hardware or centralized labs.


Transparency: Results are open for public scrutiny and reproducibility. Revolutionizing Scientific Research:\OVM enables on-chain peer review, ensuring results are reproducible, transparent, and immune to tampering. By integrating decentralized compute with blockchain technology, OVM democratizes access to the tools required for groundbreaking discoveries. Genomic Comparisons on Blockchain:In a recent showcase, researchers compared the genomes ofSARS-CoV-2 (2019) and SARS-CoV (2003)—entirely on-chain—using Open’s OVM within a Trusted Execution Environment (TEE). This experiment highlighted: Verifiability: Every step of the analysis is immutable and tamper-proof.


Democratization: Cutting-edge computation, without reliance on exclusive hardware or centralized labs.


Transparency: Results are open for public scrutiny and reproducibility. Genomic Comparisons on Blockchain : In a recent showcase, researchers compared the genomes of SARS-CoV-2 (2019) and SARS-CoV (2003)—entirely on-chain—using Open’s OVM within a Trusted Execution Environment (TEE). This experiment highlighted: Genomic Comparisons on Blockchain SARS-CoV-2 SARS-CoV Verifiability: Every step of the analysis is immutable and tamper-proof. Democratization: Cutting-edge computation, without reliance on exclusive hardware or centralized labs. Transparency: Results are open for public scrutiny and reproducibility. Verifiability: Every step of the analysis is immutable and tamper-proof. Verifiability : Every step of the analysis is immutable and tamper-proof. Verifiability Democratization: Cutting-edge computation, without reliance on exclusive hardware or centralized labs. Democratization : Cutting-edge computation, without reliance on exclusive hardware or centralized labs. Democratization Transparency: Results are open for public scrutiny and reproducibility. Transparency : Results are open for public scrutiny and reproducibility. Transparency Revolutionizing Scientific Research:\OVM enables on-chain peer review, ensuring results are reproducible, transparent, and immune to tampering. By integrating decentralized compute with blockchain technology, OVM democratizes access to the tools required for groundbreaking discoveries. Revolutionizing Scientific Research:\OVM enables on-chain peer review, ensuring results are reproducible, transparent, and immune to tampering. By integrating decentralized compute with blockchain technology, OVM democratizes access to the tools required for groundbreaking discoveries. Revolutionizing Scientific Research:\OVM enables on-chain peer \ Potential DeSci Applications**: Climate modeling and environmental simulations. AI-driven drug discovery, leveraging open data sets and decentralized computation. Real-time epidemiological research with global collaboration. Climate modeling and environmental simulations. AI-driven drug discovery, leveraging open data sets and decentralized computation. Real-time epidemiological research with global collaboration. OVM: A Compute Lego for Endless Possibilities 🔧 OVM: A Compute Lego for Endless Possibilities 🔧 Modularity Meets Scalability: Think of OVM as the Lego blocks of decentralized compute. Developers can stack and integrate OVM between immutable blockchains to high performance decentralized compute to create a wide range of applications, all on-chain: Modularity Meets Scalability: Lego blocks AI Supercomputing: Seamlessly train and deploy advanced AI models. DeSci Simulations: Power real-time, on-chain decentralized scientific research experiences with DeSci economies and asset security. AI Supercomputing: Seamlessly train and deploy advanced AI models. AI Supercomputing: DeSci Simulations: Power real-time, on-chain decentralized scientific research experiences with DeSci economies and asset security. DeSci Simulations: The Future of Open Ecosystem: Innovation Unleashed 🚀 The Future of Open Ecosystem: Innovation Unleashed 🚀 From AI to DeSci, finance to gaming, the Open ecosystem is redefining the boundaries of blockchain innovation. The #WEBisOpen Point System is just the beginning—a portal into a decentralized revolution where participation is rewarded, science is democratized, and AI is verifiable. #WEBisOpen Be a part of this future. Earn $OPEN points. Unlock possibilities. Be a part of this future. Earn $OPEN points. Unlock possibilities. 👉 Learn more and stay updated at webisopen.com. 👉 Learn more and stay updated at webisopen.com . webisopen.com webisopen.com About Open (aka webisOpen on X) About Open (aka webisOpen on X) Open is a series of endeavors aimed at propelling the Open Web forward. Collectively, Open is the Data and Compute Wormhole for innovations like verifiable and transparent AI and DeSci. The Open ecosystem includes @RSS3_ @_RSSHub & @openinfo_ . webisOpen. @RSS3_ @RSS3_ @_RSSHub @_RSSHub @openinfo_ @openinfo_

To Catalyze AI Infrastructure Collision: Open Launches #WEBisOpen Point System 🚀

Infraestructura AI ukan ch’axwawip catalizañataki: Abierto ukax #WEBisOpen Point System 🚀 ukan uñstayatawa

Um den Zusammenstoß von KI-Infrastrukturen zu katalysieren: Open führt das Punktesystem #WEBisOpen ein 🚀

Para catalizar la colisión de infraestructuras de IA: Open lanza el sistema de puntos #WEBisOpen 🚀

כדי לזרז התנגשות תשתיות בינה מלאכותית: Open משיקה #WEBisOpen Point System 🚀

एआई इंफ्रास्ट्रक्चर टकराव को उत्प्रेरित करने के लिए: ओपन ने #WEBisOpen पॉइंट सिस्टम लॉन्च किया 🚀

Az AI-infrastruktúra ütközésének katalizálása: Az Open elindítja a #WEBisOpen Point System rendszert 🚀

AI インフラの衝突を促進: Open が #WEBisOpen ポイント システムを開始 🚀

AI Infraestructura T'uqyayta Catalizanapaq: Kichasqa #WEBisOpen Punto Sistema 🚀 qallarichin

Yapay Zeka Altyapısı Çatışmasını Tetiklemek İçin: Open #WEBisOpen Nokta Sistemini Başlattı 🚀

AI infratuzilmasi to'qnashuvini katalizlash uchun: #WEBisOpen Point tizimini ishga tushiradi 🚀

Để thúc đẩy sự va chạm của cơ sở hạ tầng AI: Open ra mắt hệ thống điểm #WEBisOpen 🚀

George Town, Cayman Islands, January 14th, 2025, Chainwire Sonic Labs announced the launch of its points program today, an innovative incentive framework designed to reward users and stimulate the growth of DeFi on Sonic. Through a multi-layered approach that combines passive asset holding, active liquidity deployment, and developer-focused “Sonic Gems”, the program will distribute around 200 million S tokens as part of the S airdrop. This initiative positions Sonic as the premier hub for DeFi enthusiasts and newcomers, offering an ecosystem where users can enhance the utility and opportunities of their assets. Three Types of Points The Sonic Points program offers users multiple opportunities to accumulate points: Passive Points Holding whitelisted assets in a Web3 wallet, such as Rabby or MetaMask, or a hardware wallet allows users to accumulate passive points, while assets stored on centralized exchanges are excluded.
Activity Points If users choose to deploy whitelisted assets as liquidity on participating apps, such as DEXs, lending protocols, or other apps, they can gain activity points in addition to their passive points. This approach incentivizes users who actively contribute to the network’s liquidity. App Points (Sonic Gems) In addition to the user-focused portion of the airdrop, Sonic Labs has designed a developer-focused portion where participating apps compete for an allocation known as Sonic Gems. Apps can redeem these Gems for S tokens, which they may distribute to their users as rewards through independent points programs. By controlling token distribution, apps can design tailored points programs by incorporating weighting factors based on the total liquidity a user provides or the duration that capital is deployed. The competitive aspect of Sonic Gems allows users to strategically choose apps that demonstrate strong Gems earning potential and offer well-structured points programs, optimizing opportunities for capital growth within the ecosystem. Whitelisted Assets To get points, users must select from Sonic’s initial list of whitelisted assets, which ranges from scUSD, USDC.e, and scETH, to other select tokens. Notably, the assets have boosted multipliers in the initial phases of the points program to foster rapid adoption. Additional assets may be whitelisted in the future, ensuring the continued evolution of the ecosystem. Rings: Infusing Apps on Sonic With Liquidity An important element in Sonic’s liquidity strategy is Rings, a yield-bearing stablecoin protocol. By depositing stablecoins or ETH-based tokens, users can mint scUSD or scETH (both whitelisted assets). While these tokens are deployed on Sonic, the collateral used to mint them is sent to vaults on Ethereum operated by Veda, where they generate yield. The yield generated is periodically converted into scUSD and bridged back to Sonic, where it’s dispersed among DeFi apps as liquidity infusions. This cyclical flow not only amplifies earning potential for users but also supplies DeFi apps on Sonic with a stable inflow of liquidity, which ensures deep liquidity pools, more robust lending markets, and a healthier DeFi landscape overall. Rings is a third-party project and not affiliated with Sonic Labs. Users can learn more about Rings here. Airdrop Dashboard: All-in-One Hub The Sonic points dashboard is a comprehensive platform on which users can: Check their earned points
Check the list of participating apps
Get whitelisted assets through a simple interface
Generate a referral code and share it with friends to get extra points
View a leaderboard that displays the points and Gems earned by users and apps A New Approach to DeFi The Sonic Points program underscores Sonic Labs's commitment to empowering users and developers with a dynamic range of opportunities. Through passive holding, active liquidity provision, and Sonic Gems, participants can access innovative reward mechanisms while helping to drive deeper liquidity and broader DeFi adoption on the platform. Overall, Sonic’s multi-layered incentive system and high-performance infrastructure set a new standard and position the chain as a premier DeFi hub. About Sonic Sonic is a high-performance, EVM L1 platform that offers developers attractive incentives and powerful infrastructure for DeFi. The chain provides 10,000 TPS, sub-second confirmation times, and a secure gateway to Ethereum for enhanced liquidity and asset security. Contact Sonic Labs press@soniclabs.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here George Town, Cayman Islands, January 14th, 2025, Chainwire Sonic Labs announced the launch of its points program today, an innovative incentive framework designed to reward users and stimulate the growth of DeFi on Sonic. Through a multi-layered approach that combines passive asset holding, active liquidity deployment, and developer-focused “Sonic Gems”, the program will distribute around 200 million S tokens as part of the S airdrop. This initiative positions Sonic as the premier hub for DeFi enthusiasts and newcomers, offering an ecosystem where users can enhance the utility and opportunities of their assets. Three Types of Points The Sonic Points program offers users multiple opportunities to accumulate points: Passive Points Holding whitelisted assets in a Web3 wallet, such as Rabby or MetaMask, or a hardware wallet allows users to accumulate passive points, while assets stored on centralized exchanges are excluded. Activity Points If users choose to deploy whitelisted assets as liquidity on participating apps, such as DEXs, lending protocols, or other apps, they can gain activity points in addition to their passive points. This approach incentivizes users who actively contribute to the network’s liquidity. Passive Points Holding whitelisted assets in a Web3 wallet, such as Rabby or MetaMask, or a hardware wallet allows users to accumulate passive points, while assets stored on centralized exchanges are excluded. Activity Points If users choose to deploy whitelisted assets as liquidity on participating apps, such as DEXs, lending protocols, or other apps, they can gain activity points in addition to their passive points. This approach incentivizes users who actively contribute to the network’s liquidity. App Points (Sonic Gems) In addition to the user-focused portion of the airdrop, Sonic Labs has designed a developer-focused portion where participating apps compete for an allocation known as Sonic Gems. Apps can redeem these Gems for S tokens, which they may distribute to their users as rewards through independent points programs. By controlling token distribution, apps can design tailored points programs by incorporating weighting factors based on the total liquidity a user provides or the duration that capital is deployed. The competitive aspect of Sonic Gems allows users to strategically choose apps that demonstrate strong Gems earning potential and offer well-structured points programs, optimizing opportunities for capital growth within the ecosystem. Whitelisted Assets To get points, users must select from Sonic’s initial list of whitelisted assets, which ranges from scUSD, USDC.e, and scETH, to other select tokens. Notably, the assets have boosted multipliers in the initial phases of the points program to foster rapid adoption. Additional assets may be whitelisted in the future, ensuring the continued evolution of the ecosystem. Rings: Infusing Apps on Sonic With Liquidity An important element in Sonic’s liquidity strategy is Rings , a yield-bearing stablecoin protocol. By depositing stablecoins or ETH-based tokens, users can mint scUSD or scETH (both whitelisted assets). Rings Rings While these tokens are deployed on Sonic, the collateral used to mint them is sent to vaults on Ethereum operated by Veda , where they generate yield. The yield generated is periodically converted into scUSD and bridged back to Sonic, where it’s dispersed among DeFi apps as liquidity infusions. Veda Veda This cyclical flow not only amplifies earning potential for users but also supplies DeFi apps on Sonic with a stable inflow of liquidity, which ensures deep liquidity pools, more robust lending markets, and a healthier DeFi landscape overall. Rings is a third-party project and not affiliated with Sonic Labs. Users can learn more about Rings here . here here Airdrop Dashboard: All-in-One Hub The Sonic points dashboard is a comprehensive platform on which users can: Sonic points dashboard Sonic points dashboard Check their earned points Check the list of participating apps Get whitelisted assets through a simple interface Generate a referral code and share it with friends to get extra points View a leaderboard that displays the points and Gems earned by users and apps Check their earned points Check the list of participating apps Get whitelisted assets through a simple interface Generate a referral code and share it with friends to get extra points View a leaderboard that displays the points and Gems earned by users and apps A New Approach to DeFi The Sonic Points program underscores Sonic Labs's commitment to empowering users and developers with a dynamic range of opportunities. Through passive holding, active liquidity provision, and Sonic Gems, participants can access innovative reward mechanisms while helping to drive deeper liquidity and broader DeFi adoption on the platform. Overall, Sonic’s multi-layered incentive system and high-performance infrastructure set a new standard and position the chain as a premier DeFi hub. About Sonic Sonic is a high-performance, EVM L1 platform that offers developers attractive incentives and powerful infrastructure for DeFi. The chain provides 10,000 TPS, sub-second confirmation times, and a secure gateway to Ethereum for enhanced liquidity and asset security. Sonic Sonic Contact Sonic Labs press@soniclabs.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

Sonic Labs Introduces Innovative Points Program To Drive DeFi Growth And User Rewards

Sonic Labs تقدم برنامج نقاط مبتكر لتعزيز نمو DeFi ومكافآت المستخدمين

Sonic Labs прадстаўляе інавацыйную праграму балаў для стымулявання росту DeFi і ўзнагароджання карыстальнікаў

Sonic Labs presenta un innovador programa de puntos para impulsar el crecimiento de DeFi y las recompensas para los usuarios

Sonic Labs מציגה תוכנית נקודות חדשנית כדי להניע את צמיחת ה-DeFi ולתגמולים למשתמשים

Sonic Labs、DeFiの成長とユーザー報酬を促進する革新的なポイントプログラムを導入

Sonic Labs ណែនាំកម្មវិធីពិន្ទុច្នៃប្រឌិត ដើម្បីជំរុញកំណើន DeFi និងរង្វាន់អ្នកប្រើប្រាស់

Sonic Labs, DeFi 성장과 사용자 보상을 촉진하기 위한 혁신적인 포인트 프로그램 출시

Sonic Labs DeFi өсүшүнө жана колдонуучунун сыйлыктарына ээ болуу үчүн инновациялык упайлар программасын киргизет

„Sonic Labs“ pristato novatorišką taškų programą, skatinančią „DeFi“ augimą ir naudotojų naudą

Ny Sonic Labs dia manolotra fandaharan'asa momba ny teboka vaovao mba handrisika ny fitomboan'ny DeFi sy ny valisoa ho an'ny mpampiasa

Sonic Labs Musuq Puntokuna Programa Riqsichin DeFi Wiñay chaymanta Usuario Premios kaqmanta purichinapaq

Sonic Labs prezanton program inovativ të pikëve për të nxitur rritjen e DeFi dhe shpërblimet e përdoruesve

SAN FRANCISCO, California, January 7th 2025/Chainwire/--On-chain interaction with selected Open ecosystem projects will now grant users $OPEN points. The list of the first batch of eligible projects that can earn users $OPEN points includes AISweat.Shop, Overlord.bot, Gameboi.ai, OpenDeSci Platform, with more incoming to be disclosed at https://webisopen.com/ The points from the previous @owlonmusk_ campaign will be automatically bridged/converted to $OPEN points. Check the previous campaign here: https://x.com/Owlonmusk_/status/1856245362870411344 The exact calculation of $OPEN points and utilities are yet to be disclosed. Details should soon be available at https://webisopen.com/ In addition, all Open ecosystem projects such as AiSweat.Shop (ASS), Open DeSci Platform, Overlord.bot, Follow and more are expected to generate long term incentives for Open stakeholders. What is Open? The core technology of Open is the Open Virtual Machine (OVM). OVM powers blockchains with limitless, scalable, decentralized, and verifiable compute power, giving rise to possibilities of compute-intensive applications such as AI training, scientific simulations (DeSci) and complex financial algorithm executions, all done on-chain, in an immutable, transparent and verifiable manner. OVM as the Wormhole for Complex Compute OVM serves as a bridge between blockchain’s on-chain immutability and the scalability of decentralized compute, enabling blockchain networks to run sophisticated applications like decentralized AI and scientific simulations (DeSci). By leveraging OVM, blockchains can now support advanced computations, unlocking new possibilities for high-performance, decentralized ecosystems. This architecture supports a broad range of use cases, from AI-driven decentralized apps to complex financial models, all while preserving the immutability, verifiability, scalability, high performance computability, and decentralization of on-chain operations. OVM - Verifiable AI Infrastructure On-Chain AI and Machine Learning: OVM enables resource-intensive AI and ML computations on-chain, supporting advanced applications and agents like autonomous trader AI agents, sentient sentiment agents, predictive models and personalized execution assistants while maintaining verifiability. For example, AiSweat.Shop: AiSweat.Shop (A.S.S.) is built with the AI infrastructure of Open (also known as webisOpen on X, collective includes RSS3). It allows Easy deployment of autonomous AI Agents with various decentralized financial abilities (DeFi) and the tokenization of DeAI-Fi Agents. Verifiable DeSci The Open Virtual Machine (OVM) offers a high-performance, decentralized, and verifiable compute layer, designed to democratize access to advanced computational resources. Its implementation begins with a demonstration and experimental applications on-chain analysis of genetic similarities between two viruses: SARS-CoV-2 (sequenced in Wuhan, 2019) and SARS-CoV (sequenced in Toronto, 2003) on the Open DeSci Platform, showcasing its capabilities in bridging decentralized computation with the immutability of blockchain technology. By bridging powerful decentralized computation with blockchain immutability, OVM is a Data Wormhole for innovations like verifiable and transparent AI and DeSci. This analysis is performed fully on-chain using Open’s Open Virtual Machine (OVM) within a Trusted Execution Environment (TEE) (together known as Open’s Compute Wormhole) utilizing and bridging decentralized compute from Hyperbolic Labs, to ensure integrity, immutability, determinism, and resistance to tampering. Additionally, OVM guarantees verifiability through on-chain transactions, promoting transparency and trust in the results. This demo and experiment of Open Desci Platform Alpha and its record on-chain (on Open Chain) has also been released to the public to showcase the power of OVM to the public. Users can check out this link for more information. Reproducibility Guaranteed: Every computation is verifiable and reproducible, a critical feature for peer-reviewed scientific research.
Secure Data Processing: A built-in Trusted Execution Environment (TEE) safeguards sensitive data, ensuring integrity and tamper resistance throughout the computational process.
Effortless Genomic Analysis: Researchers can now compare viral genomes, such as [SARS-CoV-2] and [SARS-Tor-2], in a decentralized and democratized manner, without permission and access to exclusive and expensive hardware. These capabilities can highlight the expanding potential of OVM in decentralized science (DeSci), paving the way for further innovations in transparent and democratized research. OVM - Compute Lego OVM extends blockchain’s functionality by enabling verifiable decentralized compute, allowing blockchains to securely harness limitless high performance computing power while preserving blockchain immutability and verifiability. Through its modular and scalable design, OVM enables different blockchains to be combined and integrated with decentralized compute like Lego blocks, whether for AI models or decentralized science (DeSci). OVM - Institutional DeFi Infrastructure Complex Financial Modeling: Ideal for DeFi applications, OVM allows for on-chain risk assessments, yield optimizations, and real-time derivatives pricing with verifiable and scalable compute, bringing institutional-grade modeling to on-chain decentralized finance. OVM - Gaming & Metavere Simulations OVM provides the sufficient decentralized and verifiable computational power for real-time simulations, NFT generation, and complex in-game economies on-chain, enhancing the trust and security of assets in decentralized gaming and virtual worlds. Open Ecosystem Apart from the experiments and initiatives mentioned, the Open Ecosystem has projects ranging from AI, information, socials, infrastructure, middleware, finance, data sources, creatives and media. Together Open accelerates the course of the open web. About Open (aka webisOpen on X) Open is a series of endeavors aimed at propelling the Open Web forward. Collectively, Open is the Data and Compute Wormhole for innovations like verifiable and transparent AI and DeSci. The Open ecosystem includes @RSS3_ @_RSSHub & @openinfo_. webisOpen. Contact Marketing Lead KM KM Open Collective km@open.network This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here SAN FRANCISCO, California, January 7th 2025/Chainwire/--On-chain interaction with selected Open ecosystem projects will now grant users $OPEN points. The list of the first batch of eligible projects that can earn users $OPEN points includes AISweat.Shop, Overlord.bot, Gameboi.ai, OpenDeSci Platform, with more incoming to be disclosed at https://webisopen.com/ https://webisopen.com/ https://webisopen.com/ The points from the previous @owlonmusk_ campaign will be automatically bridged/converted to $OPEN points. Check the previous campaign here: https://x.com/Owlonmusk_/status/1856245362870411344 @owlonmusk_ @owlonmusk_ The exact calculation of $OPEN points and utilities are yet to be disclosed. Details should soon be available at https://webisopen.com/ https://webisopen.com/ https://webisopen.com/ In addition, all Open ecosystem projects such as AiSweat.Shop (ASS), Open DeSci Platform, Overlord.bot, Follow and more are expected to generate long term incentives for Open stakeholders. What is Open? The core technology of Open is the Open Virtual Machine (OVM). OVM powers blockchains with limitless, scalable, decentralized, and verifiable compute power, giving rise to possibilities of compute-intensive applications such as AI training, scientific simulations (DeSci) and complex financial algorithm executions, all done on-chain, in an immutable, transparent and verifiable manner. OVM as the Wormhole for Complex Compute OVM serves as a bridge between blockchain’s on-chain immutability and the scalability of decentralized compute, enabling blockchain networks to run sophisticated applications like decentralized AI and scientific simulations (DeSci). By leveraging OVM, blockchains can now support advanced computations, unlocking new possibilities for high-performance, decentralized ecosystems. This architecture supports a broad range of use cases, from AI-driven decentralized apps to complex financial models, all while preserving the immutability, verifiability, scalability, high performance computability, and decentralization of on-chain operations. OVM - Verifiable AI Infrastructure On-Chain AI and Machine Learning: OVM enables resource-intensive AI and ML computations on-chain, supporting advanced applications and agents like autonomous trader AI agents, sentient sentiment agents, predictive models and personalized execution assistants while maintaining verifiability. For example, AiSweat.Shop: AiSweat.Shop (A.S.S.) is built with the AI infrastructure of Open (also known as webisOpen on X, collective includes RSS3). It allows Easy deployment of autonomous AI Agents with various decentralized financial abilities (DeFi) and the tokenization of DeAI-Fi Agents. Verifiable DeSci The Open Virtual Machine (OVM) offers a high-performance, decentralized, and verifiable compute layer, designed to democratize access to advanced computational resources. Its implementation begins with a demonstration and experimental applications on-chain analysis of genetic similarities between two viruses: SARS-CoV-2 (sequenced in Wuhan, 2019) and SARS-CoV (sequenced in Toronto, 2003) on the Open DeSci Platform, showcasing its capabilities in bridging decentralized computation with the immutability of blockchain technology. By bridging powerful decentralized computation with blockchain immutability, OVM is a Data Wormhole for innovations like verifiable and transparent AI and DeSci. This analysis is performed fully on-chain using Open’s Open Virtual Machine (OVM) within a Trusted Execution Environment (TEE) (together known as Open’s Compute Wormhole) utilizing and bridging decentralized compute from Hyperbolic Labs, to ensure integrity, immutability, determinism, and resistance to tampering. Additionally, OVM guarantees verifiability through on-chain transactions, promoting transparency and trust in the results. This demo and experiment of Open Desci Platform Alpha and its record on-chain (on Open Chain) has also been released to the public to showcase the power of OVM to the public. Users can check out this link for more information. Reproducibility Guaranteed: Every computation is verifiable and reproducible, a critical feature for peer-reviewed scientific research. Secure Data Processing: A built-in Trusted Execution Environment (TEE) safeguards sensitive data, ensuring integrity and tamper resistance throughout the computational process. Effortless Genomic Analysis: Researchers can now compare viral genomes, such as [SARS-CoV-2] and [SARS-Tor-2], in a decentralized and democratized manner, without permission and access to exclusive and expensive hardware. Reproducibility Guaranteed: Every computation is verifiable and reproducible, a critical feature for peer-reviewed scientific research. Secure Data Processing: A built-in Trusted Execution Environment (TEE) safeguards sensitive data, ensuring integrity and tamper resistance throughout the computational process. Effortless Genomic Analysis: Researchers can now compare viral genomes, such as [SARS-CoV-2] and [SARS-Tor-2], in a decentralized and democratized manner, without permission and access to exclusive and expensive hardware. These capabilities can highlight the expanding potential of OVM in decentralized science (DeSci), paving the way for further innovations in transparent and democratized research. OVM - Compute Lego OVM extends blockchain’s functionality by enabling verifiable decentralized compute, allowing blockchains to securely harness limitless high performance computing power while preserving blockchain immutability and verifiability. Through its modular and scalable design, OVM enables different blockchains to be combined and integrated with decentralized compute like Lego blocks, whether for AI models or decentralized science (DeSci). OVM - Institutional DeFi Infrastructure Complex Financial Modeling: Ideal for DeFi applications, OVM allows for on-chain risk assessments, yield optimizations, and real-time derivatives pricing with verifiable and scalable compute, bringing institutional-grade modeling to on-chain decentralized finance. OVM - Gaming & Metavere Simulations OVM provides the sufficient decentralized and verifiable computational power for real-time simulations, NFT generation, and complex in-game economies on-chain, enhancing the trust and security of assets in decentralized gaming and virtual worlds. Open Ecosystem Apart from the experiments and initiatives mentioned, the Open Ecosystem has projects ranging from AI, information, socials, infrastructure, middleware, finance, data sources, creatives and media. Together Open accelerates the course of the open web. About Open (aka webisOpen on X) Open is a series of endeavors aimed at propelling the Open Web forward. Collectively, Open is the Data and Compute Wormhole for innovations like verifiable and transparent AI and DeSci. The Open ecosystem includes @RSS3_ @_RSSHub & @openinfo_ . webisOpen. @RSS3_ @RSS3_ @_RSSHub @_RSSHub @openinfo_ @openinfo_ Contact Marketing Lead KM KM Open Collective km@open.network 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

To Catalyze AI Infrastructure - Open Launches WEBisOpen Point System

To Catalyze AI Infrastructure - Open Spouští WEBisOpen Point System

Para catalizar la infraestructura de IA, Open lanza el sistema de puntos WEBisOpen

Za kataliziranje AI infrastrukture - Open pokreće WEBisOpen Point System

Pou katalize AI enfrastrikti - Open Lanse WEBisOpen Point System

כדי לזרז תשתית AI - Open משיקה מערכת WEBisOpen Point System

AIインフラの促進 - OpenがWEBisOpenポイントシステムを開始

AI дэд бүтцийг идэвхжүүлэхийн тулд - WEBisOpen Point системийг нээнэ

Untuk Memangkin Infrastruktur AI - Terbuka Melancarkan Sistem WEBisOpen Point

AI Infraestructura Catalizanapaq - Kichasqa WEBisOpen Punto Sistemata qallarichin

AI உள்கட்டமைப்பை ஊக்குவிக்க - WEBisOpen Point அமைப்பைத் திறக்கவும்

Yapay Zeka Altyapısını Hızlandırmak İçin - Open, WEBisOpen Puan Sistemini Başlattı

Ukwenza Ingqalasizinda ye-AI - Vula i-WEBisOpen Point System

Analog, a blockchain protocol focusing on cross-chain communication, launched its network today, enabling connections between different blockchain platforms. The initial release operates on a Proof-of-Authority mechanism, with plans to transition to Nominated Proof of Stake by the time of its token release. The network launch follows testing phases that processed 1.9 million cross-chain data transfers among 345,000 participating accounts. This testing period allowed developers to examine the protocol's capabilities and provide technical feedback before the main release. "Cross-chain communication remains a central challenge in blockchain development," says Dr. Elena Martinez, blockchain researcher at MIT's Digital Currency Institute. "Solutions that enable different chains to interact could reduce fragmentation in the blockchain space." The protocol's architecture includes components for processing data between chains, executing smart contracts across networks, and maintaining consistent information across systems. These features aim to address current limitations in blockchain interoperability, where applications often operate in isolation on single networks. Several blockchain projects have begun implementing the protocol. Frax Finance, which operates a decentralized stablecoin system, plans to use the network for transmitting price data across different blockchain platforms. This implementation could help maintain consistent asset pricing across multiple networks. XYO Network, which operates location-tracking systems on blockchain, intends to use the protocol to make geographical data accessible across different chains. This application demonstrates potential uses beyond financial transactions, extending to real-world data integration. The protocol also enables governance activities across chains. Token holders of various projects can participate in voting and decision-making regardless of which blockchain holds their assets, potentially increasing participation in project governance. Technical metrics from the testing period indicate sustained usage, with users listing 290,000 smart contracts across multiple chains. The protocol processed these contracts while maintaining data consistency, suggesting capability for sustained operation at scale. Security measures include identity verification systems, with 42,000 users completing verification processes before participating. These measures aim to maintain network integrity while enabling broad participation. Looking forward, the development team plans additional features, including the consensus mechanism transition and expanded cross-chain messaging capabilities. These updates aim to increase the network's capability for handling complex multi-chain operations. The launch represents progress in blockchain interoperability research, potentially contributing to increased communication between previously isolated networks. As blockchain systems continue operating on separate networks, protocols enabling cross-chain interaction may become increasingly relevant for developers building distributed applications. Developers interested in examining the protocol's capabilities can access technical documentation through official channels, with the network now processing live transactions and cross-chain communications. 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 Analog , a blockchain protocol focusing on cross-chain communication, launched its network today, enabling connections between different blockchain platforms. The initial release operates on a Proof-of-Authority mechanism, with plans to transition to Nominated Proof of Stake by the time of its token release. Analog The network launch follows testing phases that processed 1.9 million cross-chain data transfers among 345,000 participating accounts. This testing period allowed developers to examine the protocol's capabilities and provide technical feedback before the main release. "Cross-chain communication remains a central challenge in blockchain development," says Dr. Elena Martinez, blockchain researcher at MIT's Digital Currency Institute. "Solutions that enable different chains to interact could reduce fragmentation in the blockchain space." The protocol's architecture includes components for processing data between chains, executing smart contracts across networks, and maintaining consistent information across systems. These features aim to address current limitations in blockchain interoperability, where applications often operate in isolation on single networks. Several blockchain projects have begun implementing the protocol. Frax Finance , which operates a decentralized stablecoin system, plans to use the network for transmitting price data across different blockchain platforms. This implementation could help maintain consistent asset pricing across multiple networks. Frax Finance XYO Network, which operates location-tracking systems on blockchain, intends to use the protocol to make geographical data accessible across different chains. This application demonstrates potential uses beyond financial transactions, extending to real-world data integration. The protocol also enables governance activities across chains. Token holders of various projects can participate in voting and decision-making regardless of which blockchain holds their assets, potentially increasing participation in project governance. Technical metrics from the testing period indicate sustained usage, with users listing 290,000 smart contracts across multiple chains. The protocol processed these contracts while maintaining data consistency, suggesting capability for sustained operation at scale. Security measures include identity verification systems, with 42,000 users completing verification processes before participating. These measures aim to maintain network integrity while enabling broad participation. Looking forward, the development team plans additional features, including the consensus mechanism transition and expanded cross-chain messaging capabilities. These updates aim to increase the network's capability for handling complex multi-chain operations. The launch represents progress in blockchain interoperability research, potentially contributing to increased communication between previously isolated networks. As blockchain systems continue operating on separate networks, protocols enabling cross-chain interaction may become increasingly relevant for developers building distributed applications. Developers interested in examining the protocol's capabilities can access technical documentation through official channels, with the network now processing live transactions and cross-chain communications. 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

Analog's Cross-Chain System Processes 1.9M Data Points Before Launch

Analoog se kruiskettingstelsel verwerk 1,9 miljoen datapunte voor bekendstelling

يقوم نظام Cross-Chain من Analog بمعالجة 1.9 مليون نقطة بيانات قبل الإطلاق

Analog ukax Sistema de Cadena Cruzada ukax 1.9M Puntos de Datos Janïr Lanzamiento ukaruw puri

Analoqun Çarpaz Zəncirli Sistemi işə salınmazdan əvvəl 1,9 milyon məlumat nöqtəsini emal edir

Analogov cross-chain sistem obrađuje 1,9 miliona tačaka podataka prije lansiranja

El sistema de cadena cruzada de Analog procesa 1,9 millones de puntos de datos antes del lanzamiento

מערכת חוצת השרשרת של Analog מעבדת 1.9 מיליון נקודות נתונים לפני ההשקה

アナログのクロスチェーンシステムは発売前に190万のデータポイントを処理

Analogā starpķēžu sistēma pirms palaišanas apstrādā 1,9 miljonus datu punktu

Analógový cross-chain systém pred spustením spracuje 1,9 milióna dátových bodov

Sistemi ndër-zinxhiror i Analog përpunon 1,9 milion pikë të dhënash përpara nisjes

Analogs tvärkedjesystem behandlar 1,9 miljoner datapunkter före lansering

Home Assistant is a massive beast. It can be overwhelming for a newcomer; it was for me. In this post, I want to describe the underlying model of Home Assistant, which is a good entry point for your home automation journey. The biggest issue in describing the Home Assistant is the number of conflicting sources for this model: The helpers package of the GitHub repository. The database; disclaimer: I didn't find the schema generation in the code, and I wasn't bold enough to check the deployed SQLite database. The documentation. The web interface. As far as I understand it, here's the conceptual model. Integrations Integrations are pieces of software that allow Home Assistant to connect to other software and platforms. For example, a product by Philips called Hue would use the Philips Hue integration and allow Home Assistant to talk to the hardware controller Hue Bridge. Any Home Assistant compatible devices connected to the Hue Bridge would appear in Home Assistant as devices.
-- Home Assistant documentation When you switch on Home Assistant for the first time, it's a blank slate. You must make it communicate with your different devices. That's the role of Integration; thus, I think it's a critical entity. You can add an Integration on the integration dashboard: there's an ADD INTEGRATION in the lower left corner. Alternatively, you can add it from its documentation page, via a ADD INTEGRATION TO MY HOME ASSISTANT button. In this case, clicking on the latter opens a new page. You can change it; the URL is stored on the browser. Some integrations require additional configuration, e.g., authentication. Devices Devices are a logical grouping for one or more entities. A device may represent a physical device, which can have one or more sensors. The sensors appear as entities associated with the device. For example, a motion sensor is represented as a device. It may provide motion detection, temperature, and light levels as entities. Entities have states such as detected when motion is detected and clear when there is no motion.
-- Home Assistant documentation Once you've added the integration, you can add Device: Some categorize as Service when they need a Cloud connection, e.g., Google Cloud SDK or GitHub.



Some as Device when they need one, e.g., Philips Hue. Entities Entities are the basic building blocks to hold data in Home Assistant. An entity represents a sensor, actor, or function in Home Assistant. Entities are used to monitor physical properties or to control other entities. An entity is usually part of a device or a service. Entities have states.
-- Home Assistant documentation For example, the Philips Hue motion sensor device has three different available sensors: Illuminance in Lux
Motion as a boolean
Temperature in Celsius Unexpectedly, a motion sensor provides a temperature sensor. Check your devices for such sensors; you might be in for a good surprise. Other Objects Areas:
An Area represents a location. You can put a physical  Device in an Area.

Automations:
An Automation is pretty self-explanatory. Each consists of two main components: a trigger and an action. Note that you can add an optional condition.For example, let's imagine a Welcome Home Automation:
The trigger is when the user enters a geofenced perimeter around one's home.
The action is to switch on the lights
Yet, switching on the lights in daylight doesn't make sense.

The condition would be "only during the night."

Scripts:
As per the documentation, a Script is akin to an Automation but with no trigger; it's a named action. It lets you define the action once and reuse it across several Automation objects. Think DRY.

Add-ons:
While an Integration allows interfacing with an external device or service, an Add-On provides a service. In other words, an Add-On extends the platform itself. For example, I installed the Let's Encrypt add-on to my Home Assistant installation to automatically manage a TLS certificate.

Scenes:
A Scene for a Device is akin to a profile for your phone. It's a named set of configurations that you save and reuse. I must admit that I don't use profiles on my phone; I don't have any Scene objects at the moment, and I have to understand their usage beyond lighting. Conclusion In this post, I described the model objects available in Home Assistant. In the next post, I'll migrate from the proprietary Philips Hue automation to Home Assistant. To go further: Home Assistant
Concepts and terminology Originally published at A Java Geek on December 2nd, 2024 Home Assistant is a massive beast. It can be overwhelming for a newcomer; it was for me. In this post, I want to describe the underlying model of Home Assistant, which is a good entry point for your home automation journey. The biggest issue in describing the Home Assistant is the number of conflicting sources for this model: The helpers package of the GitHub repository. The helpers package of the GitHub repository . helpers GitHub repository The database; disclaimer: I didn't find the schema generation in the code, and I wasn't bold enough to check the deployed SQLite database. The database; disclaimer: I didn't find the schema generation in the code, and I wasn't bold enough to check the deployed SQLite database. The documentation. The documentation . documentation The web interface. The web interface. As far as I understand it, here's the conceptual model. Integrations Integrations are pieces of software that allow Home Assistant to connect to other software and platforms. For example, a product by Philips called Hue would use the Philips Hue integration and allow Home Assistant to talk to the hardware controller Hue Bridge. Any Home Assistant compatible devices connected to the Hue Bridge would appear in Home Assistant as devices. -- Home Assistant documentation Integrations are pieces of software that allow Home Assistant to connect to other software and platforms. For example, a product by Philips called Hue would use the Philips Hue integration and allow Home Assistant to talk to the hardware controller Hue Bridge. Any Home Assistant compatible devices connected to the Hue Bridge would appear in Home Assistant as devices. -- Home Assistant documentation Home Assistant documentation When you switch on Home Assistant for the first time, it's a blank slate. You must make it communicate with your different devices. That's the role of Integration ; thus, I think it's a critical entity. Integration You can add an Integration on the integration dashboard : there's an ADD INTEGRATION in the lower left corner. Alternatively, you can add it from its documentation page, via a ADD INTEGRATION TO MY HOME ASSISTANT button. In this case, clicking on the latter opens a new page. Integration integration dashboard ADD INTEGRATION ADD INTEGRATION TO MY HOME ASSISTANT You can change it; the URL is stored on the browser. Some integrations require additional configuration, e.g. , authentication. e.g. Devices Devices are a logical grouping for one or more entities. A device may represent a physical device, which can have one or more sensors. The sensors appear as entities associated with the device. For example, a motion sensor is represented as a device. It may provide motion detection, temperature, and light levels as entities. Entities have states such as detected when motion is detected and clear when there is no motion. -- Home Assistant documentation Devices are a logical grouping for one or more entities. A device may represent a physical device, which can have one or more sensors. The sensors appear as entities associated with the device. For example, a motion sensor is represented as a device. It may provide motion detection, temperature, and light levels as entities. Entities have states such as detected when motion is detected and clear when there is no motion. -- Home Assistant documentation Home Assistant documentation Once you've added the integration, you can add Device : Device Some categorize as Service when they need a Cloud connection, e.g., Google Cloud SDK or GitHub. Some as Device when they need one, e.g., Philips Hue. Some categorize as Service when they need a Cloud connection, e.g., Google Cloud SDK or GitHub. Some categorize as Service when they need a Cloud connection, e.g. , Google Cloud SDK or GitHub . Service e.g. Google Cloud SDK GitHub Some as Device when they need one, e.g., Philips Hue. Some as Device when they need one, e.g. , Philips Hue . Device e.g. Philips Hue Entities Entities are the basic building blocks to hold data in Home Assistant. An entity represents a sensor, actor, or function in Home Assistant. Entities are used to monitor physical properties or to control other entities. An entity is usually part of a device or a service. Entities have states. -- Home Assistant documentation Entities are the basic building blocks to hold data in Home Assistant. An entity represents a sensor, actor, or function in Home Assistant. Entities are used to monitor physical properties or to control other entities. An entity is usually part of a device or a service. Entities have states. -- Home Assistant documentation Home Assistant documentation For example, the Philips Hue motion sensor device has three different available sensors: Illuminance in Lux Motion as a boolean Temperature in Celsius Illuminance in Lux Motion as a boolean Temperature in Celsius Unexpectedly, a motion sensor provides a temperature sensor. Check your devices for such sensors; you might be in for a good surprise. Other Objects Areas:
An Area represents a location. You can put a physical  Device in an Area. Automations:
An Automation is pretty self-explanatory. Each consists of two main components: a trigger and an action. Note that you can add an optional condition.For example, let's imagine a Welcome Home Automation:
The trigger is when the user enters a geofenced perimeter around one's home.
The action is to switch on the lights
Yet, switching on the lights in daylight doesn't make sense.

The condition would be "only during the night." Scripts:
As per the documentation, a Script is akin to an Automation but with no trigger; it's a named action. It lets you define the action once and reuse it across several Automation objects. Think DRY. Add-ons:
While an Integration allows interfacing with an external device or service, an Add-On provides a service. In other words, an Add-On extends the platform itself. For example, I installed the Let's Encrypt add-on to my Home Assistant installation to automatically manage a TLS certificate. Scenes:
A Scene for a Device is akin to a profile for your phone. It's a named set of configurations that you save and reuse. I must admit that I don't use profiles on my phone; I don't have any Scene objects at the moment, and I have to understand their usage beyond lighting. Areas: An Area represents a location. You can put a physical  Device in an Area. Areas: An Area represents a location. You can put a physical Device in an Area . Area physical Device Area Automations: An Automation is pretty self-explanatory. Each consists of two main components: a trigger and an action. Note that you can add an optional condition. For example, let's imagine a Welcome Home Automation: The trigger is when the user enters a geofenced perimeter around one's home.
The action is to switch on the lights
Yet, switching on the lights in daylight doesn't make sense. The condition would be "only during the night." Automations: An Automation is pretty self-explanatory. Each consists of two main components: a trigger and an action. Note that you can add an optional condition. Automation For example, let's imagine a Welcome Home Automation : Automation The trigger is when the user enters a geofenced perimeter around one's home. The action is to switch on the lights Yet, switching on the lights in daylight doesn't make sense. The trigger is when the user enters a geofenced perimeter around one's home. The action is to switch on the lights Yet, switching on the lights in daylight doesn't make sense. The condition would be "only during the night." Scripts: As per the documentation, a Script is akin to an Automation but with no trigger; it's a named action. It lets you define the action once and reuse it across several Automation objects. Think DRY. Scripts: As per the documentation, a Script is akin to an Automation but with no trigger; it's a named action. It lets you define the action once and reuse it across several Automation objects. Think DRY. Script Automation Automation Add-ons: While an Integration allows interfacing with an external device or service, an Add-On provides a service. In other words, an Add-On extends the platform itself. For example, I installed the Let's Encrypt add-on to my Home Assistant installation to automatically manage a TLS certificate. Add-ons: While an Integration allows interfacing with an external device or service, an Add-On provides a service. In other words, an Add-On extends the platform itself. For example, I installed the Let's Encrypt add-on to my Home Assistant installation to automatically manage a TLS certificate. Integration Add-On Add-On Let's Encrypt Scenes: A Scene for a Device is akin to a profile for your phone. It's a named set of configurations that you save and reuse. I must admit that I don't use profiles on my phone; I don't have any Scene objects at the moment, and I have to understand their usage beyond lighting. Scenes: A Scene for a Device is akin to a profile for your phone. It's a named set of configurations that you save and reuse. I must admit that I don't use profiles on my phone; I don't have any Scene objects at the moment, and I have to understand their usage beyond lighting. Scene Device profile Scene Conclusion In this post, I described the model objects available in Home Assistant. In the next post, I'll migrate from the proprietary Philips Hue automation to Home Assistant. To go further: To go further: Home Assistant Concepts and terminology Home Assistant Home Assistant Concepts and terminology Concepts and terminology Originally published at A Java Geek on December 2nd, 2024 Originally published at A Java Geek on December 2nd, 2024 A Java Geek

The Home Assistant Model: A Good Entry Point

A Pentester Took Apart a Website’s Code to Prove It Was Totally Pointless

ፍፁም ትርጉም የለሽ መሆኑን ለማረጋገጥ አንድ ጴንጤ የድረ-ገጽ ኮድ ለየ

একজন পেন্টেস্টার একটি ওয়েবসাইটের কোডটি সম্পূর্ণরূপে অর্থহীন প্রমাণ করার জন্য নিয়েছিলেন

Pentester rozebral kód webové stránky, aby dokázal, že je naprosto nesmyslný

En Pentester adskilte en hjemmesides kode for at bevise, at den var fuldstændig meningsløs

Un pentester desmanteló el código de un sitio web para demostrar que era totalmente inútil

פנטסטר פירק קוד של אתר כדי להוכיח שהוא חסר טעם לחלוטין

ペンテスターがウェブサイトのコードを解析し、全く意味がないことを証明した

Pentester කෙනෙක් වෙබ් අඩවියේ කේතයක් වෙන්කර ගත්තේ එය සම්පූර්ණයෙන්ම තේරුමක් නැති බව ඔප්පු කිරීමටයි.

ผู้ทดสอบการเจาะระบบแยกรหัสเว็บไซต์เพื่อพิสูจน์ว่ามันไร้ประโยชน์โดยสิ้นเชิง

Bir Pentester, Tamamen Anlamsız Olduğunu Kanıtlamak İçin Bir Web Sitesinin Kodunu Parçaladı

I-Pentester Yahlulahlula iKhowudi yeWebhusayithi ukungqina ukuba yayingenanjongo kwaphela

I-Pentester Yahlukanisa Ikhodi Yewebhusayithi Ukufakazela Ukuthi Yayingenanjongo Ngokuphelele

SeaTunnel-Powered Data Integration: How 58 Group Handles Over 500 Billion+ Data Points Daily

The rise of artificial intelligence (AI) has sparked one of the most hotly debated topics in the tech industry: Will AI take away our jobs? For developers, engineers, and data scientists, the concern hits close to home. After all, if AI can code, test, and even debug, where does that leave us? But here’s the truth: AI isn’t here to steal your job it’s here to change how we work, enhance what we do, and help us be better at it. Let’s dive into why AI isn’t something to fear, and how it’s more likely to complement your work than replace it. 1. AI Enhances Productivity Instead of Replacing Humans Let’s start with the basics. AI isn’t about replacing people it’s about giving us the tools to do more and do it faster. Tools like GitHub Copilot, for example, are there to help developers by suggesting code snippets. It doesn’t take the place of a developer it allows developers to skip past repetitive, mundane tasks and focus on solving real problems. In software testing, AI-powered tools identify bugs much quicker than manual testing ever could. This doesn’t mean quality assurance engineers are obsolete; it means they can spend less time on tedious tasks and more time improving the product and tackling higher-level issues. By handling the small stuff, AI frees up your brainpower for the big ideas. 2. Technological Advancements Create New Job Opportunities If we look back through history, one thing is clear: every new wave of technology creates new roles. The internet gave rise to web developers, cybersecurity analysts, and digital marketers. In much the same way, AI is creating entirely new job categories like Machine Learning Engineers, AI Trainers, and AI Ethicists. The World Economic Forum reports that by 2025, AI and automation will create 97 million new roles globally. These new jobs won’t just be about coding—they’ll also involve things like overseeing AI systems, ensuring fairness in algorithms, and managing the collaboration between humans and machines. 3. AI Requires Human Oversight and Ethical Direction One thing that AI cannot do is make moral or ethical decisions. AI is only as good as the data it’s trained on and even then, it can’t understand the deeper context or nuances that humans bring. For example, AI tools used for hiring can sometimes reflect biases from the data they’re trained on, which means we need human intervention to identify and correct these biases. That’s where you, as a techie, come in. AI needs your guidance to ensure it’s applied ethically. Whether it’s curating data or fixing unintended outcomes, your role in overseeing AI systems is crucial. Machines might be able to process data at lightning speed, but they’ll always need humans to guide them in the right direction. 4. Upskilling: The Key to Thriving Alongside AI AI doesn’t spell the end of jobs it’s a call to evolve. Upskilling is the bridge that connects you to the future of work. If you stay ahead of the curve, you can adapt and thrive in an AI-driven world. It’s not just about learning how to use AI tools it’s about mastering new skills. Skills like cloud computing, AI ethics, and data analytics are becoming increasingly valuable. A USA Today report estimates that around 73 million jobs in the U.S. could be at risk of automation by 2030, which highlights how essential reskilling is. Fortunately, platforms like Coursera, edX, and Udemy make it easy to dive into new fields, from AI and machine learning to natural language processing and ethical AI. By investing in your learning today, you’re not just securing your future—you’re ensuring that you’re indispensable in a world where AI only enhances what you do. 5. Creativity and Empathy: Human Traits AI Can’t Replace AI is great at data, patterns, and tasks that can be boiled down to algorithms. But creativity? Empathy? Critical thinking? Those are still very much human strengths. AI can’t replace the imaginative spark that leads to groundbreaking software architecture, or the empathy required for a top-tier UX design. In fact, as AI takes over routine tasks, it gives tech professionals more space to tap into their creative and emotional intelligence. Think about it: with AI handling the repetitive parts of the work, you have more freedom to focus on the design, the vision, and the strategy that truly drives innovation. 6. AI as a Tool, Not a Threat At the end of the day, AI is a tool that’s designed to make your life easier, not harder. Think of it like your new assistant: tireless, efficient, and always ready to help you get more done. Take DevOps professionals, for example. They’re using AI-powered platforms to monitor systems, predict maintenance needs, and optimize processes in real time. AI isn’t replacing them it’s empowering them to deliver faster and more efficiently. And the same goes for most other fields in tech: AI enhances the work we do, giving us more time to focus on high-value tasks and creative problem-solving. 7. AI Adoption in the Tech Industry: Real-World Examples We’ve seen how AI is empowering professionals rather than replacing them. Look at these real-world examples: GitHub Copilot: It assists developers by suggesting code snippets, allowing them to write code faster and focus on the more creative aspects of software development.
AIOps (AI in IT Operations): Platforms like Splunk and Moogsoft are used by IT teams to troubleshoot and monitor systems, improving uptime without cutting jobs.
Data Analytics: AI tools help data scientists analyze massive datasets, but the human touch is still needed to interpret those insights and apply them meaningfully. In all of these cases, AI isn’t a replacement for human expertise; it’s an enhancement that helps professionals do their jobs more effectively. 8. Challenges and How to Overcome Them Of course, there are challenges when it comes to AI adoption. A lot of the fear about AI replacing jobs stems from misconceptions or a lack of understanding. But businesses and professionals can tackle these issues by: Educating Employees: Organizations should prioritize training programs that teach employees about AI and how it can be leveraged.
Promoting Adaptability: Encouraging a mindset of continuous learning will ensure workers stay adaptable and ready for any changes.
Focusing on Collaboration: The best outcomes come when humans and AI collaborate, not compete. Fostering this mindset will ensure a smooth transition to an AI-enhanced workplace. By addressing these challenges head-on, we can build a future where AI and humans work together seamlessly. 9. The Future of Jobs in an AI-Driven World The future of work isn’t about humans versus machines it’s about humans and machines working together. As AI continues to evolve, it will redefine roles, but it won’t eliminate them. For tech professionals, this means shifting focus from routine tasks to more strategic, creative, and high-level responsibilities. For example: Software developers will focus on system design and integration, not just writing code.
Data scientists will oversee data quality and ethical AI practices rather than simply crunching numbers.
IT managers will guide AI-driven processes while ensuring alignment with business objectives. The change may seem intimidating, but it opens up new, more meaningful career opportunities that blend technology with creativity and problem-solving. 10. Proven Points to Remember Here’s a quick recap of why AI isn’t going to steal your job: AI boosts productivity by automating repetitive tasks.
Technological advancements create new roles and opportunities.
AI requires human oversight and ethical direction.
Creativity, critical thinking, and empathy remain irreplaceable.
Upskilling and adaptability are key to staying relevant.
AI is a tool that complements human expertise. By understanding these points, you can shift your mindset and see AI as an ally rather than a threat. Final Thoughts: Embracing the Change AI isn’t here to take away your job it’s here to transform it. As tech professionals, the key to thriving in an AI-powered world lies in adaptability, continuous learning, and leveraging AI to amplify your abilities. The most successful careers will belong to those who view AI as a partner in innovation, not a competitor. In the broader context of Digital Transformation in the Age of AI, AI is reshaping industries, but it’s not about replacing the workforce. It’s about creating new roles, improving efficiencies, and empowering workers to do more and do it better. Instead of fearing change, let’s embrace it and see how humans and machines can work together to build a brighter future. The rise of artificial intelligence (AI) has sparked one of the most hotly debated topics in the tech industry: Will AI take away our jobs? For developers, engineers, and data scientists, the concern hits close to home. After all, if AI can code, test, and even debug, where does that leave us? But here’s the truth: AI isn’t here to steal your job it’s here to change how we work, enhance what we do, and help us be better at it. Let’s dive into why AI isn’t something to fear, and how it’s more likely to complement your work than replace it. 1. AI Enhances Productivity Instead of Replacing Humans Let’s start with the basics. AI isn’t about replacing people it’s about giving us the tools to do more and do it faster. Tools like GitHub Copilot, for example, are there to help developers by suggesting code snippets. It doesn’t take the place of a developer it allows developers to skip past repetitive, mundane tasks and focus on solving real problems. In software testing, AI-powered tools identify bugs much quicker than manual testing ever could. This doesn’t mean quality assurance engineers are obsolete; it means they can spend less time on tedious tasks and more time improving the product and tackling higher-level issues. By handling the small stuff, AI frees up your brainpower for the big ideas. AI-powered tools 2. Technological Advancements Create New Job Opportunities If we look back through history, one thing is clear: every new wave of technology creates new roles. The internet gave rise to web developers, cybersecurity analysts, and digital marketers. In much the same way, AI is creating entirely new job categories like Machine Learning Engineers, AI Trainers, and AI Ethicists. The World Economic Forum reports that by 2025, AI and automation will create 97 million new roles globally. These new jobs won’t just be about coding—they’ll also involve things like overseeing AI systems, ensuring fairness in algorithms, and managing the collaboration between humans and machines. World Economic Forum reports World Economic Forum reports 3. AI Requires Human Oversight and Ethical Direction One thing that AI cannot do is make moral or ethical decisions. AI is only as good as the data it’s trained on and even then, it can’t understand the deeper context or nuances that humans bring. For example, AI tools used for hiring can sometimes reflect biases from the data they’re trained on, which means we need human intervention to identify and correct these biases. That’s where you, as a techie, come in. AI needs your guidance to ensure it’s applied ethically. Whether it’s curating data or fixing unintended outcomes, your role in overseeing AI systems is crucial. Machines might be able to process data at lightning speed, but they’ll always need humans to guide them in the right direction. 4. Upskilling: The Key to Thriving Alongside AI AI doesn’t spell the end of jobs it’s a call to evolve. Upskilling is the bridge that connects you to the future of work. If you stay ahead of the curve, you can adapt and thrive in an AI-driven world. It’s not just about learning how to use AI tools it’s about mastering new skills. Skills like cloud computing, AI ethics, and data analytics are becoming increasingly valuable. A USA Today report estimates that around 73 million jobs in the U.S. could be at risk of automation by 2030, which highlights how essential reskilling is. USA Today report USA Today report Fortunately, platforms like Coursera, edX, and Udemy make it easy to dive into new fields, from AI and machine learning to natural language processing and ethical AI. By investing in your learning today, you’re not just securing your future—you’re ensuring that you’re indispensable in a world where AI only enhances what you do. 5. Creativity and Empathy: Human Traits AI Can’t Replace AI is great at data, patterns, and tasks that can be boiled down to algorithms. But creativity? Empathy? Critical thinking? Those are still very much human strengths. AI can’t replace the imaginative spark that leads to groundbreaking software architecture, or the empathy required for a top-tier UX design. In fact, as AI takes over routine tasks, it gives tech professionals more space to tap into their creative and emotional intelligence. Think about it: with AI handling the repetitive parts of the work, you have more freedom to focus on the design, the vision, and the strategy that truly drives innovation. 6. AI as a Tool, Not a Threat At the end of the day, AI is a tool that’s designed to make your life easier, not harder. Think of it like your new assistant: tireless, efficient, and always ready to help you get more done. Take DevOps professionals, for example. They’re using AI-powered platforms to monitor systems, predict maintenance needs, and optimize processes in real time. AI isn’t replacing them it’s empowering them to deliver faster and more efficiently. And the same goes for most other fields in tech: AI enhances the work we do, giving us more time to focus on high-value tasks and creative problem-solving. 7. AI Adoption in the Tech Industry: Real-World Examples We’ve seen how AI is empowering professionals rather than replacing them. Look at these real-world examples: GitHub Copilot: It assists developers by suggesting code snippets, allowing them to write code faster and focus on the more creative aspects of software development. AIOps (AI in IT Operations): Platforms like Splunk and Moogsoft are used by IT teams to troubleshoot and monitor systems, improving uptime without cutting jobs. Data Analytics: AI tools help data scientists analyze massive datasets, but the human touch is still needed to interpret those insights and apply them meaningfully. GitHub Copilot: It assists developers by suggesting code snippets, allowing them to write code faster and focus on the more creative aspects of software development. AIOps (AI in IT Operations): Platforms like Splunk and Moogsoft are used by IT teams to troubleshoot and monitor systems, improving uptime without cutting jobs. Data Analytics: AI tools help data scientists analyze massive datasets, but the human touch is still needed to interpret those insights and apply them meaningfully. In all of these cases, AI isn’t a replacement for human expertise; it’s an enhancement that helps professionals do their jobs more effectively. 8. Challenges and How to Overcome Them Of course, there are challenges when it comes to AI adoption. A lot of the fear about AI replacing jobs stems from misconceptions or a lack of understanding. But businesses and professionals can tackle these issues by: Educating Employees: Organizations should prioritize training programs that teach employees about AI and how it can be leveraged. Promoting Adaptability: Encouraging a mindset of continuous learning will ensure workers stay adaptable and ready for any changes. Focusing on Collaboration: The best outcomes come when humans and AI collaborate, not compete. Fostering this mindset will ensure a smooth transition to an AI-enhanced workplace. Educating Employees: Organizations should prioritize training programs that teach employees about AI and how it can be leveraged. Promoting Adaptability: Encouraging a mindset of continuous learning will ensure workers stay adaptable and ready for any changes. Focusing on Collaboration: The best outcomes come when humans and AI collaborate, not compete. Fostering this mindset will ensure a smooth transition to an AI-enhanced workplace. By addressing these challenges head-on, we can build a future where AI and humans work together seamlessly. 9. The Future of Jobs in an AI-Driven World The future of work isn’t about humans versus machines it’s about humans and machines working together. As AI continues to evolve, it will redefine roles, but it won’t eliminate them. For tech professionals, this means shifting focus from routine tasks to more strategic, creative, and high-level responsibilities. For example: Software developers will focus on system design and integration, not just writing code. Data scientists will oversee data quality and ethical AI practices rather than simply crunching numbers. IT managers will guide AI-driven processes while ensuring alignment with business objectives. Software developers will focus on system design and integration, not just writing code. Data scientists will oversee data quality and ethical AI practices rather than simply crunching numbers. IT managers will guide AI-driven processes while ensuring alignment with business objectives. The change may seem intimidating, but it opens up new, more meaningful career opportunities that blend technology with creativity and problem-solving. 10. Proven Points to Remember Here’s a quick recap of why AI isn’t going to steal your job: AI boosts productivity by automating repetitive tasks. Technological advancements create new roles and opportunities. AI requires human oversight and ethical direction. Creativity, critical thinking, and empathy remain irreplaceable. Upskilling and adaptability are key to staying relevant. AI is a tool that complements human expertise. AI boosts productivity by automating repetitive tasks. Technological advancements create new roles and opportunities. AI requires human oversight and ethical direction. Creativity, critical thinking, and empathy remain irreplaceable. Upskilling and adaptability are key to staying relevant. AI is a tool that complements human expertise. By understanding these points, you can shift your mindset and see AI as an ally rather than a threat. Final Thoughts: Embracing the Change AI isn’t here to take away your job it’s here to transform it. As tech professionals, the key to thriving in an AI-powered world lies in adaptability, continuous learning, and leveraging AI to amplify your abilities. The most successful careers will belong to those who view AI as a partner in innovation, not a competitor. In the broader context of Digital Transformation in the Age of AI, AI is reshaping industries, but it’s not about replacing the workforce. It’s about creating new roles, improving efficiencies, and empowering workers to do more and do it better. Instead of fearing change, let’s embrace it and see how humans and machines can work together to build a brighter future.

AI Isn't Here to Steal Your Job, Techies—Proven Points to Understand

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