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Repsense

repsense

Authors:
(1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier;
(2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier;
(3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier;
(4) Stefano Ermon, CZ Biohub;
(5) Christopher D. Manning, Stanford University;
(6) Chelsea Finn, Stanford University. Table of Links Abstract and 1. Introduction 2 Related Work 3 Preliminaries 4 Direct Preference Optimization 5 Theoretical Analysis of DPO 6 Experiments 7 Discussion, Acknowledgements, and References Author Contributions A Mathematical Derivations A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.6 Proof of Theorem 1 B DPO Implementation Details and Hyperparameters C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C.2 GPT-4 prompts for computing summarization and dialogue win rates C.3 Unlikelihood baseline D Additional Empirical Results D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.3 Human study details Additional Empirical Results D.1 Performance of Best of N baseline for Various N We find that the Best of N baseline is a strong (although computationally expensive, requiring sampling many times) baseline in our experiments. We include an evaluation of the Best of N baseline for various N for the Anthropic-HH dialogue and TL;DR summarization; the results are shown in Figure 4. D.2 Sample Responses and GPT-4 Judgments In this section, we present examples of comparisons between DPO and the baseline (PPO temp 0. for summarization, and the ground truth chosen response for dialogue). See Tables 4-6 for summarization examples, and Tables 7-10 for dialogue examples. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier; (2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier; (3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier; (4) Stefano Ermon, CZ Biohub; (5) Christopher D. Manning, Stanford University; (6) Chelsea Finn, Stanford University. Authors: Authors: (1) Rafael Rafailo, Stanford University and Equal contribution; more junior authors listed earlier; (2) Archit Sharma, Stanford University and Equal contribution; more junior authors listed earlier; (3) Eric Mitchel, Stanford University and Equal contribution; more junior authors listed earlier; (4) Stefano Ermon, CZ Biohub; (5) Christopher D. Manning, Stanford University; (6) Chelsea Finn, Stanford University. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Preliminaries 3 Preliminaries 4 Direct Preference Optimization 4 Direct Preference Optimization 5 Theoretical Analysis of DPO 5 Theoretical Analysis of DPO 6 Experiments 6 Experiments 7 Discussion, Acknowledgements, and References 7 Discussion, Acknowledgements, and References Author Contributions Author Contributions A Mathematical Derivations A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.2 Deriving the DPO Objective Under the Bradley-Terry Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.3 Deriving the DPO Objective Under the Plackett-Luce Model A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.4 Deriving the Gradient of the DPO Objective and A.5 Proof of Lemma 1 and 2 A.6 Proof of Theorem 1 A.6 Proof of Theorem 1 B DPO Implementation Details and Hyperparameters B DPO Implementation Details and Hyperparameters C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C Further Details on the Experimental Set-Up and C.1 IMDb Sentiment Experiment and Baseline Details C.2 GPT-4 prompts for computing summarization and dialogue win rates C.2 GPT-4 prompts for computing summarization and dialogue win rates C.3 Unlikelihood baseline C.3 Unlikelihood baseline D Additional Empirical Results D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.1 Performance of Best of N baseline for Various N and D.2 Sample Responses and GPT-4 Judgments D.3 Human study details D.3 Human study details Additional Empirical Results D.1 Performance of Best of N baseline for Various N We find that the Best of N baseline is a strong (although computationally expensive, requiring sampling many times) baseline in our experiments. We include an evaluation of the Best of N baseline for various N for the Anthropic-HH dialogue and TL;DR summarization; the results are shown in Figure 4. D.2 Sample Responses and GPT-4 Judgments In this section, we present examples of comparisons between DPO and the baseline (PPO temp 0. for summarization, and the ground truth chosen response for dialogue). See Tables 4-6 for summarization examples, and Tables 7-10 for dialogue examples. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Performance of Best of N Baseline for Various N and Sample Responses and GPT-4 Judgments

Data is the new gold. Every organisation and industry is strongly steeped in data, from market research all the way to grassroots implementation, data plays a significant part in everything. As a result of this, security has become all the more important. Data breaches can cost millions in lost patents, penalties, law suits, databases, and also the loss of potential profits. In a recent report by IBM, it is estimated that an average of $4.88 million is lost for every data breach and the head of IBM Security, Kevin Skapinetz was quoted as saying that, “Businesses are caught in a continuous cycle of breaches, containment and fallout response. This cycle now often includes investments in strengthening security defences and passing breach expenses on to consumers – making security the new cost of doing business.” Aside from the costs associated with data breaches, clients would also be less likely to engage in the services of a company that doesn’t have a stellar track record in terms of privacy protection, which leads to the loss of potential sales. Harvard reports a 7.5% loss in stock values following data breaches. The loss of confidence is also a hefty price to pay for these security breaches. Hackers are also known to use these breaches as a means to extort money from corporations that require their databases by encrypting it. These are some of the risks associated with data breaches, and one of the many cybersecurity threats that linger in the world wide web, especially in today’s landscape whereby data is largely stored in the cloud. Fortunately, consumer protection in AI LLMs is steadily increasing. The Application of AI-driven LLMs in the Interest of Consumers AI LLMs, or Large Language Models, might sound extremely technical to many but the fact is that many more are utilising these technologies without realising. For instance, one of the most prominent AI LLMs at the moment is ChatGPT, a generative tool. It is used by college students to complete their homework assignments, or by your everyday Joe to research on a particular matter by using it as a search engine. The key feature of the tool is that it is able to consolidate information in a comprehensible manner while taking into consideration the vast amount of data available at its disposal. But how can a generative tool be used in cybersecurity? Being able to analyse vast amounts of data is key. LLMs have the capacity to process network traffic, emails, system logs, and other data to detect any anomalies that may indicate a security breach. Aside from that, it can also formulate a response based on the threat by drawing on its predefined response strategies. This means that the software will be able to either isolate the event, alert security teams, or block the attack altogether. Understanding the context of a data breach is heavily reliant on the company’s ability to feed the tool a specific dataset, as generative tools are only able to draw on what data they have access to. Much like the human brain that stores information, LLMs are not able to generate information that they do not “know”. Therefore, it is important for these tools to be so sophisticated that it is able to distinguish between genuine threats or false alarms. Unlike a human brain, software can process huge amounts of information in very little time, and it is not prone to human error. By analysing the available history and emerging threat patterns, AI LLMs are able to provide predictive insights into potential future attacks as well as neutralising current threats. The positive implications of using AI LLMs Imagine how a security team might work to protect consumers in real time - they would need to work around the clock, without taking a break in between as they rotate shifts. The price to keep eyes on all digital fronts would be exorbitant. Fortunately, AI is able to offer a feasible and cost-effective solution. The Ponemon Institute's Cost of Data Breach Report 2023 indicated that organisations and corporations that are utilising AI LLMs save up to $1.2 million per breach, a comparable improvement especially when considering the amount of money needed to fund a manual process running. AI-driven security solutions are not only much easier on the wallet for corporations, it is also much more efficient. According to IBM, these contemporary methods are 27% quicker in pinpointing breaches as compared to traditional methods. But simply identifying these breaches is not enough, the accuracy of detection is also very much higher, with false positives reduced by 40% when using AI LLMs. This is crucial to prevent burnout of security teams which will have to review each individual breach to ensure that they have all been properly dealt with. Identifying and responding to cybersecurity threats in real time to protect consumers with AI LLMs is the future Rapid analysis capabilities afforded by AI LLMs will push forward the boundaries of cybersecurity, mitigating threats before they cause any significant damage as well as providing cost saving solutions that require less manpower, maintenance, yet having more powerful output and systems. In the future, all organisations will be outfitted with their own AI LLMs directly into their existing cybersecurity mainframe such as Security Information and Event Management (SIEM) systems and other threat managing platforms. There will also be new positions available for tech crews who are dedicated to ‘teaching’ these LLMs with the latest trends and most relevant histories - as mentioned before, LLMs operate on the database that it is outfitted with, which means that when dealing with the ever-evolving landscape of something like security, the datasets require frequent updates and fine-tuning in order to stay current, much like the antiviruses that we run on our computers or the constant updates that we download onto our phones, all of which include the latest security updates. But this also means that AI isn’t here to replace a manual workforce, but rather enhance it overall as these platforms will be able to deliver faster results, while giving the human workforce the ability to focus solely on the most crucial component: ensuring that the appropriate threat response is administered. Data is the new gold. Every organisation and industry is strongly steeped in data, from market research all the way to grassroots implementation, data plays a significant part in everything. As a result of this, security has become all the more important. Data breaches can cost millions in lost patents, penalties, law suits, databases, and also the loss of potential profits. In a recent report by IBM, it is estimated that an average of $4.88 million is lost for every data breach and the head of IBM Security, Kevin Skapinetz was quoted as saying that, “Businesses are caught in a continuous cycle of breaches, containment and fallout response. This cycle now often includes investments in strengthening security defences and passing breach expenses on to consumers – making security the new cost of doing business.” average of $4.88 million average of $4.88 million Aside from the costs associated with data breaches, clients would also be less likely to engage in the services of a company that doesn’t have a stellar track record in terms of privacy protection, which leads to the loss of potential sales. Harvard reports a 7.5% loss in stock values following data breaches. The loss of confidence is also a hefty price to pay for these security breaches. 7.5% loss 7.5% loss Hackers are also known to use these breaches as a means to extort money from corporations that require their databases by encrypting it. These are some of the risks associated with data breaches, and one of the many cybersecurity threats that linger in the world wide web, especially in today’s landscape whereby data is largely stored in the cloud. Fortunately, consumer protection in AI LLMs is steadily increasing. consumer protection in AI LLMs consumer protection in AI LLMs The Application of AI-driven LLMs in the Interest of Consumers AI LLMs, or Large Language Models, might sound extremely technical to many but the fact is that many more are utilising these technologies without realising. For instance, one of the most prominent AI LLMs at the moment is ChatGPT, a generative tool. It is used by college students to complete their homework assignments, or by your everyday Joe to research on a particular matter by using it as a search engine. The key feature of the tool is that it is able to consolidate information in a comprehensible manner while taking into consideration the vast amount of data available at its disposal. But how can a generative tool be used in cybersecurity? Being able to analyse vast amounts of data is key. LLMs have the capacity to process network traffic, emails, system logs, and other data to detect any anomalies that may indicate a security breach. Aside from that, it can also formulate a response based on the threat by drawing on its predefined response strategies. This means that the software will be able to either isolate the event, alert security teams, or block the attack altogether. Understanding the context of a data breach is heavily reliant on the company’s ability to feed the tool a specific dataset, as generative tools are only able to draw on what data they have access to. Much like the human brain that stores information, LLMs are not able to generate information that they do not “know”. Therefore, it is important for these tools to be so sophisticated that it is able to distinguish between genuine threats or false alarms. Unlike a human brain, software can process huge amounts of information in very little time, and it is not prone to human error. By analysing the available history and emerging threat patterns, AI LLMs are able to provide predictive insights into potential future attacks as well as neutralising current threats. The positive implications of using AI LLMs Imagine how a security team might work to protect consumers in real time - they would need to work around the clock, without taking a break in between as they rotate shifts. The price to keep eyes on all digital fronts would be exorbitant. Fortunately, AI is able to offer a feasible and cost-effective solution. The Ponemon Institute's Cost of Data Breach Report 2023 indicated that organisations and corporations that are utilising AI LLMs save up to $1.2 million per breach, a comparable improvement especially when considering the amount of money needed to fund a manual process running. AI-driven security solutions are not only much easier on the wallet for corporations, it is also much more efficient. According to IBM, these contemporary methods are 27% quicker in pinpointing breaches as compared to traditional methods. pinpointing breaches pinpointing breaches But simply identifying these breaches is not enough, the accuracy of detection is also very much higher, with false positives reduced by 40% when using AI LLMs. This is crucial to prevent burnout of security teams which will have to review each individual breach to ensure that they have all been properly dealt with. Identifying and responding to cybersecurity threats in real time to protect consumers with AI LLMs is the future Rapid analysis capabilities afforded by AI LLMs will push forward the boundaries of cybersecurity, mitigating threats before they cause any significant damage as well as providing cost saving solutions that require less manpower, maintenance, yet having more powerful output and systems. In the future, all organisations will be outfitted with their own AI LLMs directly into their existing cybersecurity mainframe such as Security Information and Event Management (SIEM) systems and other threat managing platforms. There will also be new positions available for tech crews who are dedicated to ‘teaching’ these LLMs with the latest trends and most relevant histories - as mentioned before, LLMs operate on the database that it is outfitted with, which means that when dealing with the ever-evolving landscape of something like security, the datasets require frequent updates and fine-tuning in order to stay current, much like the antiviruses that we run on our computers or the constant updates that we download onto our phones, all of which include the latest security updates. But this also means that AI isn’t here to replace a manual workforce, but rather enhance it overall as these platforms will be able to deliver faster results, while giving the human workforce the ability to focus solely on the most crucial component: ensuring that the appropriate threat response is administered.

Leveraging AI LLMs for Real-Time Threat Detection and Response

I Asked the Mixtral LLM a Question About AGI. This was the Shocking Response.

How to Deploy a SPA on AWS With S3 and CloudFront: The Right Way to Do It

Why Smart Design Decisions in Payment Systems Can Save You Millions

With the U.S. elections now behind us, we’re left to assess what the candidates stood for—and one surprising theme has been the push for Bitcoin and blockchain technology. Imagine a future where campaign promises aren’t just words but commitments you can actually track. This article explores how Bitcoin could be more than a campaign buzzword, giving voters a way to hold elected leaders accountable by offering a transparent look into fulfilling their promises. System for Registering Political Proposals Based on Bitcoin In the current landscape of blockchain technology, most systems for political proposals and voting have been developed on second and third-generation platforms that offer advanced features and flexibility. This trend has fostered the perception that Bitcoin’s network, with its steadfast focus on security and decentralization, is unsuitable for such applications. However, despite the deactivation of OP_CAT, there remains significant potential in Bitcoin’s base layer. By leveraging the OP_RETURN field, we can immutably record political proposals, enabling the creation of a system that not only promotes transparency and accountability but also highlights Bitcoin's disruptive role in the political realm. This approach challenges the notion that only newer blockchains can tackle issues like political proposals, revealing a unique opportunity to innovate within Bitcoin's ecosystem. To date, no formal system exists that registers proposals from politicians directly on Bitcoin (using the OP_RETURN field), although several attempts have been made to apply blockchain technology to improve transparency and accountability in politics. Projects like BitCongress aimed to use blockchain for voting and proposal registration, but they did not succeed. Others have explored the idea of making political campaigns more transparent or implementing voting systems on blockchain, but these efforts remain experimental. This leaves an open space for innovation, allowing for the creation of a system that immutably records political promises on Bitcoin. Below, we introduce the Political Proposal Recording System Based on Bitcoin (PPR-BTC), an approach that enables the recording and auditing of both campaign promises and expenses. Objective To record campaign promises and the expenses associated with campaign contributions on the Bitcoin blockchain, ensuring transparency and accountability from politicians. Key Components of the System: Proposal Registration in OP_RETURN: Political proposals are stored directly on the blockchain using the OP_RETURN field.
A summary of the proposal or a hash (SHA-256) of the complete proposal is registered if it is too lengthy.
Campaign Contributions: Each politician will have a specific Bitcoin address to receive campaign contributions.
Contributions are recorded on the blockchain via transactions with OP_RETURN, detailing the amount received (in the currency specified at the time of the transaction) and the purpose of the expenditure. This allows for clear tracking of how campaigns are financed.
Expense Registration: Every time a campaign-related expense is made, a new OP_RETURN transaction is created that describes the expenditure, the date, and the amount, as well as a receipt that can be a hash of the digital receipt.
This ensures that citizens can audit how the money raised for the campaign is being utilized.
Additionally, the UTXO system can be leveraged to accurately identify the coherence between specific contributions and expenditures. In this regard, the system has a clear advantage over the account or balance systems used by other blockchains like Ethereum.
Symbolic Voting with Bitcoin Addresses (optional): Two Bitcoin addresses are generated:
Address A: To vote that the politician has fulfilled the promise.
Address B: To vote that the politician has not fulfilled the promise.
Citizens can express their opinion by sending small amounts of Bitcoin (satoshis) to the address representing their stance.
Proposal Modifications (optional): The spirit of the system should not contemplate the possibility of modifications. In fact, Bitcoin does not allow it, even if desired. If a politician fails to fulfill a proposal, they simply do not fulfill it.
However, if an alternative is sought and a politician wishes to update their proposal, a new transaction can be registered referencing the original in the OP_RETURN. This creates an immutable chain of promises and modifications, ensuring that the history of proposals is clear and accessible. Advantages of the System: Total Transparency: Promises and expenses are recorded on the blockchain, providing an unprecedented level of trust and auditability.
Accountability: Citizens can see how campaign funds are being used.
Citizen Participation: Citizens can express their opinions on the fulfillment of promises through a simple voting system on the blockchain. Disadvantages of the System: Proposal Size: The 80-byte limitation in OP_RETURN restricts the amount of information that can be recorded directly.
Transaction Costs: Each transaction incurs a cost on the Bitcoin network, which could increase with multiple modifications or expenses. Technical Considerations: Access Control: Implement a verification system so that only verified accounts can register proposals.
User-Friendly Interfaces: Create a platform that translates transactions on the blockchain into an accessible format for citizens, allowing for easy verification of proposals and expenses.
BIP-420 or Similar: If the option OP_CAT were to be re-enabled, we could concatenate strings and significantly expand the possibilities of PPR-BTC, including the ability to introduce basic smart contracts. Conclusion The possibility of integrating Bitcoin into the political sphere, especially for recording and verifying electoral promises, represents a profound transformation in the way citizens interact with their leaders. By utilizing the immutability and transparency offered by the blockchain, a system is created in which political promises can no longer fade into thin air. Bitcoin not only acts as a crypto asset, but its robustness serves as a tool for accountability. The Bitcoin-Based Political Proposal Registration System ensures that voters can track every word from politicians and provides an auditable and decentralized mechanism to guarantee that campaign funds are used transparently. The immutability of transactions on Bitcoin, combined with precise tracking of contributions and campaign expenses, sets a new standard for transparency and accountability in politics. Although the OP_RETURN field currently limits the amount of information that can be recorded, the reactivation of OP_CAT—a feature that would allow data concatenation in Bitcoin—would further facilitate the management of more complex information, opening the door to smart contracts and expanding the scope of political proposal registration. This would enhance the system's ability to incorporate even more details and make political promises clearer and more traceable. Bitcoin not only ensures economic value; it guarantees promises. What is recorded on its blockchain remains forever, forcing politicians to fulfill what they promise or face the reality that their commitments will not be forgotten or erased. This new era of transparency could be the necessary impetus to restore trust in the democratic process, especially for those candidates who use Bitcoin in their campaigns merely as a marketing hook, seeking to attract new niches of enthusiastic voters but then fail to deliver on their promises. Now more than ever: Don’t trust, verify. With the U.S. elections now behind us, we’re left to assess what the candidates stood for—and one surprising theme has been the push for Bitcoin and blockchain technology. Imagine a future where campaign promises aren’t just words but commitments you can actually track. This article explores how Bitcoin could be more than a campaign buzzword, giving voters a way to hold elected leaders accountable by offering a transparent look into fulfilling their promises. System for Registering Political Proposals Based on Bitcoin In the current landscape of blockchain technology, most systems for political proposals and voting have been developed on second and third-generation platforms that offer advanced features and flexibility. This trend has fostered the perception that Bitcoin’s network, with its steadfast focus on security and decentralization, is unsuitable for such applications. However, despite the deactivation of OP_CAT, there remains significant potential in Bitcoin’s base layer. By leveraging the OP_RETURN field, we can immutably record political proposals, enabling the creation of a system that not only promotes transparency and accountability but also highlights Bitcoin's disruptive role in the political realm. This approach challenges the notion that only newer blockchains can tackle issues like political proposals, revealing a unique opportunity to innovate within Bitcoin's ecosystem. To date, no formal system exists that registers proposals from politicians directly on Bitcoin (using the OP_RETURN field), although several attempts have been made to apply blockchain technology to improve transparency and accountability in politics. Projects like BitCongress aimed to use blockchain for voting and proposal registration, but they did not succeed. Others have explored the idea of making political campaigns more transparent or implementing voting systems on blockchain, but these efforts remain experimental. This leaves an open space for innovation, allowing for the creation of a system that immutably records political promises on Bitcoin. Below, we introduce the Political Proposal Recording System Based on Bitcoin (PPR-BTC) , an approach that enables the recording and auditing of both campaign promises and expenses. Political Proposal Recording System Based on Bitcoin (PPR-BTC) Objective Objective To record campaign promises and the expenses associated with campaign contributions on the Bitcoin blockchain, ensuring transparency and accountability from politicians. Key Components of the System: Key Components of the System: Proposal Registration in OP_RETURN: Political proposals are stored directly on the blockchain using the OP_RETURN field. A summary of the proposal or a hash (SHA-256) of the complete proposal is registered if it is too lengthy. Campaign Contributions: Each politician will have a specific Bitcoin address to receive campaign contributions. Contributions are recorded on the blockchain via transactions with OP_RETURN, detailing the amount received (in the currency specified at the time of the transaction) and the purpose of the expenditure. This allows for clear tracking of how campaigns are financed. Expense Registration: Every time a campaign-related expense is made, a new OP_RETURN transaction is created that describes the expenditure, the date, and the amount, as well as a receipt that can be a hash of the digital receipt. This ensures that citizens can audit how the money raised for the campaign is being utilized. Additionally, the UTXO system can be leveraged to accurately identify the coherence between specific contributions and expenditures. In this regard, the system has a clear advantage over the account or balance systems used by other blockchains like Ethereum. Symbolic Voting with Bitcoin Addresses (optional): Two Bitcoin addresses are generated: Address A: To vote that the politician has fulfilled the promise. Address B: To vote that the politician has not fulfilled the promise. Citizens can express their opinion by sending small amounts of Bitcoin (satoshis) to the address representing their stance. Proposal Modifications (optional): The spirit of the system should not contemplate the possibility of modifications. In fact, Bitcoin does not allow it, even if desired. If a politician fails to fulfill a proposal, they simply do not fulfill it. However, if an alternative is sought and a politician wishes to update their proposal, a new transaction can be registered referencing the original in the OP_RETURN. This creates an immutable chain of promises and modifications, ensuring that the history of proposals is clear and accessible. Proposal Registration in OP_RETURN: Political proposals are stored directly on the blockchain using the OP_RETURN field. A summary of the proposal or a hash (SHA-256) of the complete proposal is registered if it is too lengthy. Campaign Contributions: Each politician will have a specific Bitcoin address to receive campaign contributions. Contributions are recorded on the blockchain via transactions with OP_RETURN, detailing the amount received (in the currency specified at the time of the transaction) and the purpose of the expenditure. This allows for clear tracking of how campaigns are financed. Expense Registration: Every time a campaign-related expense is made, a new OP_RETURN transaction is created that describes the expenditure, the date, and the amount, as well as a receipt that can be a hash of the digital receipt. This ensures that citizens can audit how the money raised for the campaign is being utilized. Additionally, the UTXO system can be leveraged to accurately identify the coherence between specific contributions and expenditures. In this regard, the system has a clear advantage over the account or balance systems used by other blockchains like Ethereum. Symbolic Voting with Bitcoin Addresses (optional): Two Bitcoin addresses are generated: Address A: To vote that the politician has fulfilled the promise. Address B: To vote that the politician has not fulfilled the promise. Citizens can express their opinion by sending small amounts of Bitcoin (satoshis) to the address representing their stance. Proposal Modifications (optional): The spirit of the system should not contemplate the possibility of modifications. In fact, Bitcoin does not allow it, even if desired. If a politician fails to fulfill a proposal, they simply do not fulfill it. However, if an alternative is sought and a politician wishes to update their proposal, a new transaction can be registered referencing the original in the OP_RETURN. This creates an immutable chain of promises and modifications, ensuring that the history of proposals is clear and accessible. Advantages of the System: Advantages of the System: Total Transparency: Promises and expenses are recorded on the blockchain, providing an unprecedented level of trust and auditability. Accountability: Citizens can see how campaign funds are being used. Citizen Participation: Citizens can express their opinions on the fulfillment of promises through a simple voting system on the blockchain. Total Transparency: Promises and expenses are recorded on the blockchain, providing an unprecedented level of trust and auditability. Total Transparency: Accountability: Citizens can see how campaign funds are being used. Accountability: Citizen Participation: Citizens can express their opinions on the fulfillment of promises through a simple voting system on the blockchain. Citizen Participation: Disadvantages of the System: Disadvantages of the System: Proposal Size: The 80-byte limitation in OP_RETURN restricts the amount of information that can be recorded directly. Transaction Costs: Each transaction incurs a cost on the Bitcoin network, which could increase with multiple modifications or expenses. Proposal Size: The 80-byte limitation in OP_RETURN restricts the amount of information that can be recorded directly. Proposal Size: Transaction Costs: Each transaction incurs a cost on the Bitcoin network, which could increase with multiple modifications or expenses. Transaction Costs: Technical Considerations: Technical Considerations: Access Control: Implement a verification system so that only verified accounts can register proposals. User-Friendly Interfaces: Create a platform that translates transactions on the blockchain into an accessible format for citizens, allowing for easy verification of proposals and expenses. BIP-420 or Similar: If the option OP_CAT were to be re-enabled, we could concatenate strings and significantly expand the possibilities of PPR-BTC, including the ability to introduce basic smart contracts. Access Control: Implement a verification system so that only verified accounts can register proposals. Access Control: User-Friendly Interfaces: Create a platform that translates transactions on the blockchain into an accessible format for citizens, allowing for easy verification of proposals and expenses. User-Friendly Interfaces: BIP-420 or Similar: If the option OP_CAT were to be re-enabled, we could concatenate strings and significantly expand the possibilities of PPR-BTC, including the ability to introduce basic smart contracts. BIP-420 or Similar: Conclusion Conclusion The possibility of integrating Bitcoin into the political sphere, especially for recording and verifying electoral promises, represents a profound transformation in the way citizens interact with their leaders. By utilizing the immutability and transparency offered by the blockchain, a system is created in which political promises can no longer fade into thin air. Bitcoin not only acts as a crypto asset, but its robustness serves as a tool for accountability. The Bitcoin-Based Political Proposal Registration System ensures that voters can track every word from politicians and provides an auditable and decentralized mechanism to guarantee that campaign funds are used transparently. The immutability of transactions on Bitcoin, combined with precise tracking of contributions and campaign expenses, sets a new standard for transparency and accountability in politics. Although the OP_RETURN field currently limits the amount of information that can be recorded, the reactivation of OP_CAT—a feature that would allow data concatenation in Bitcoin—would further facilitate the management of more complex information, opening the door to smart contracts and expanding the scope of political proposal registration. This would enhance the system's ability to incorporate even more details and make political promises clearer and more traceable. Bitcoin not only ensures economic value; it guarantees promises. What is recorded on its blockchain remains forever, forcing politicians to fulfill what they promise or face the reality that their commitments will not be forgotten or erased. This new era of transparency could be the necessary impetus to restore trust in the democratic process, especially for those candidates who use Bitcoin in their campaigns merely as a marketing hook, seeking to attract new niches of enthusiastic voters but then fail to deliver on their promises. Now more than ever: Don’t trust, verify. Don’t trust, verify.

Can Bitcoin Hold Politicians to Their Promises?

WILMINGTON, Delaware, November 7th, 2024/Chainwire/--Rekt Brands Inc. (Rekt), the parent company behind the Rektguy NFT project, Rekt Drinks, and the Rekt brand intellectual property, is excited to announce the completion of a $1.5 million seed funding round. Funded exclusively by angel investors and the community—without institutional venture capital—this achievement highlights the support Rekt received from its loyal and growing network. This funding announcement follows the debut of Rekt Drinks, Rekt’s first consumer product: a lime-flavored sparkling water with zero caffeine and zero alcohol, featuring the Rekt branding. Rekt Drinks made its debut last week with 222,456 units available across 32 countries. The entire stock sold out in under 48 hours, with demand in the U.S. alone resulting in a complete sell-out within just 4 hours and 20 minutes. This launch represents one of the largest real-world product sales by a Web3 brand, reinforcing Rekt’s potential as a cultural force in both digital and traditional markets. Rekt was founded from the success of the Rektguy NFT collection, created by artist Ovie Faruq (OSF) in May 2022. Since then, the Rektguy project has expanded into Rekt Brands, which oversees the Rekt IP and pioneered a unique industry model by offering equity in the parent company directly to its NFT holders, utilizing a Reg CF exemption under SEC guidelines in the U.S. “Rekt has had a die-hard, cult-like community for the past 2 years, and our ambition is to fulfill its potential as one of the largest Web3 brands,” said OSF. “With the sell-out of our drinks last week, that’s 222,456 times someone is going to pick up a can with the Rekt logo and a Rektguy on it. It’s a magnitude higher for us in terms of brand awareness.” With this new funding, Rekt is poised to expand its product lineup, strengthen its brand awareness in and outside of Web3, while further bridging digital culture with physical consumer products. The Rekt community remains at the heart of the brand’s mission, with upcoming projects that will continue to empower, engage, and reward its dedicated supporters. About Rekt Rekt Brands Inc. is the parent company that owns the Rektguy NFT project, Rekt Drinks, and the Rekt brand intellectual property. Established from the success of the Rektguy NFT collection by artist Ovie Faruq (OSF), the brand has expanded into consumer goods and groundbreaking community equity models. Rekt aims to redefine brand ownership and engagement through its Web3 foundation, innovative products, and viral global community. Contact CEO Ovie Faruq Rekt Brands Inc. ovie@rektdrinks.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here WILMINGTON, Delaware, November 7th, 2024/Chainwire/--Rekt Brands Inc. (Rekt), the parent company behind the Rektguy NFT project, Rekt Drinks, and the Rekt brand intellectual property, is excited to announce the completion of a $1.5 million seed funding round. Funded exclusively by angel investors and the community—without institutional venture capital—this achievement highlights the support Rekt received from its loyal and growing network. This funding announcement follows the debut of Rekt Drinks , Rekt’s first consumer product: a lime-flavored sparkling water with zero caffeine and zero alcohol, featuring the Rekt branding. Rekt Drinks made its debut last week with 222,456 units available across 32 countries. Rekt Drinks Rekt Drinks The entire stock sold out in under 48 hours, with demand in the U.S. alone resulting in a complete sell-out within just 4 hours and 20 minutes. This launch represents one of the largest real-world product sales by a Web3 brand, reinforcing Rekt’s potential as a cultural force in both digital and traditional markets. Rekt was founded from the success of the Rektguy NFT collection, created by artist Ovie Faruq (OSF) in May 2022. Since then, the Rektguy project has expanded into Rekt Brands, which oversees the Rekt IP and pioneered a unique industry model by offering equity in the parent company directly to its NFT holders, utilizing a Reg CF exemption under SEC guidelines in the U.S. Rektguy Rektguy Ovie Faruq (OSF) Ovie Faruq (OSF) “Rekt has had a die-hard, cult-like community for the past 2 years, and our ambition is to fulfill its potential as one of the largest Web3 brands,” said OSF. “With the sell-out of our drinks last week, that’s 222,456 times someone is going to pick up a can with the Rekt logo and a Rektguy on it. It’s a magnitude higher for us in terms of brand awareness.” “Rekt has had a die-hard, cult-like community for the past 2 years, and our ambition is to fulfill its potential as one of the largest Web3 brands,” said OSF. “With the sell-out of our drinks last week, that’s 222,456 times someone is going to pick up a can with the Rekt logo and a Rektguy on it. It’s a magnitude higher for us in terms of brand awareness.” With this new funding, Rekt is poised to expand its product lineup, strengthen its brand awareness in and outside of Web3, while further bridging digital culture with physical consumer products. The Rekt community remains at the heart of the brand’s mission, with upcoming projects that will continue to empower, engage, and reward its dedicated supporters. About Rekt Rekt Brands Inc. is the parent company that owns the Rektguy NFT project, Rekt Drinks , and the Rekt brand intellectual property. Established from the success of the Rektguy NFT collection by artist Ovie Faruq (OSF) , the brand has expanded into consumer goods and groundbreaking community equity models. Rekt aims to redefine brand ownership and engagement through its Web3 foundation, innovative products, and viral global community. Rekt Brands Inc. Rekt Brands Inc. Rektguy Rektguy Rekt Drinks Rekt Drinks Rektguy Rektguy Ovie Faruq (OSF) Ovie Faruq (OSF) Contact CEO Ovie Faruq Rekt Brands Inc. ovie@rektdrinks.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

Rekt Raises $1.5M Seed Round Backed By Angels and Community, Following Sell-Out Of Rekt Drinks

Rekt samel $1,5 miljoen saadronde in, gerugsteun deur engele en gemeenskap, na die uitverkoop van Rekt-drankies

Rekt набира 1,5 милиона долара начален кръг, подкрепен от ангели и общност, след разпродаването на Rekt напитки

Η Rekt συγκεντρώνει $1,5 εκατομμύρια σπόρους με την υποστήριξη των αγγέλων και της κοινότητας, μετά το ξεπούλημα των ποτών Rekt

Rekt recauda 1,5 millones de dólares en una ronda de capital semilla respaldada por inversores ángeles y la comunidad, tras la venta de Rekt Drinks

רקט מגייס 1.5 מיליון דולר סבב זרעים בגיבוי מלאכים וקהילה, בעקבות מכירת משקאות רקט

Rekt prikupio 1,5 milijuna dolara početni krug uz podršku anđela i zajednice, nakon rasprodaje Rekt pića

Rekt Mengumpulkan Dana Awal Sebesar $1,5 Juta yang Didukung oleh Angel dan Komunitas, Setelah Penjualan Habis dari Rekt Drinks

Rekt は Rekt ドリンクの完売を受けて、エンジェル投資家とコミュニティの支援を受けて 150 万ドルのシードラウンドを調達

Рэкт ундаануудаа зарсны дараа Тэнгэр элч нар болон олон нийтийн дэмжлэгтэйгээр 1.5 сая долларын үрийг цуглуулж байна.

Rekt haalt $ 1,5 miljoen op in een startkapitaalronde, gesteund door investeerders en de gemeenschap, na de uitverkoop van Rekt Drinks

Rekt ระดมทุนรอบ Seed Round ได้ 1.5 ล้านเหรียญสหรัฐ โดยได้รับการสนับสนุนจาก Angels และชุมชน หลังจากการขายเครื่องดื่ม Rekt หมดเกลี้ยง

Rekt, Rekt içgileriniň satylmagyndan soň, perişdeler we jemgyýet tarapyndan goldanýan 1.5 million dollarlyk tohum tegelek ýygnaýar

Have you ever wondered how social media apps like Instagram manage to keep us scrolling, almost as if the content is endless? I know I have! It goes beyond just great content — it taps into a continuous flow that keeps us hooked, waiting for what’s next. Unlike traditional pagination, where users have to click "Next" to load more, infinite scrolling creates a seamless experience. Just as we reach the end of the visible content, new items load in, sparking curiosity and anticipation. This is the magic of infinite scroll: keeping the journey going without any interruptions. Let’s dive right into it In this guide, we’ll create a simple React app that uses infinite scroll to display GitHub users information. We'll be using: React: For creating and managing the component-based structure of our app.
react-infinite-scroll-component: A popular package to manage the infinite scroll behavior.
Tailwind CSS: For quick and clean styling.
Axios: For making HTTP requests to the GitHub API. 1.) Set Up the Project npx create-react-app infinite-scroll-app
cd infinite-scroll-app
npm start Install axios, react-infinite-scroll-component and tailwindcss npm install axios react-infinite-scroll-component tailwindcss 2.) Generate a Github Personal Token Since we’ll be using the Github Apis,  it’s important to get Access Token from GitHub To avoid rate limitations, Here’s how to create a GitHub Personal Access Token and use it in your project: Go to your GitHub Settings.
Click on "Generate new token".
Select the scopes you need (for read-only public data, you typically just need the public_repo scope).
Generate and copy the token somewhere secure; you’ll need it in your app
Create an .env file in the root directory, include the github API url and the Access Token you just copied REACT_APP_API_URL=https://api.github.com
REACT_APP_API_KEY=your_access_token 3.) Fetch the data from the Github Api const fetchUsers = async () => {
    try {
      const response = await axios.get(`${apiUrl}/users`, {
        headers: {
          Authorization: `Bearer ${apiKey}`,
        },
        params: {
          per_page: 20, // Adjust the number of users per page
          since: lastUserId, //The user id it should begin listing
        },
      });
      const newUsers = response.data;
      setUsers((prevUsers) => [...prevUsers, ...newUsers]);
      setLastUserId(newUsers[newUsers.length - 1]?.id); // update the last user ID
      setHasMore(newUsers.length > 0);
    } catch (error) {
      console.error("Error fetching users from GitHub API:", error);
      return [];
    }
  }; It’s important to note that Github uses since and not page just as is in the code above 4.) Setting Up The Infinite Scroll <InfiniteScroll
          dataLength={users.length}
          next={fetchUsers}
          hasMore={hasMore}
          loader={
            <h4 className="font-semibold mt-4 text-center">Loading...</h4>
          }
          endMessage={<p>No more users to display</p>}
        >
          <div className="flex flex-wrap gap-4 justify-between items-center">
            {users.map((user, key) => (
              <div
                key={key}
                className="border h-[8rem] w-[45%] sm:w-[30%] md:w-[20%] flex flex-col gap-2 items-center p-2"
              >
                <img
                  src={user.avatar_url}
                  alt={`Avatar image of ${user.login}`}
                  width="80px"
                  height="80px"
                  className="rounded-full"
                />
                <p>{user.login}</p>
              </div>
            ))}
          </div>
        </InfiniteScroll> 5. )Putting it all together import axios from "axios";
import React, { useEffect, useState } from "react";
import InfiniteScroll from "react-infinite-scroll-component";

function App() {
  const [users, setUsers] = useState([]);
  const [lastUserId, setLastUserId] = useState(null);
  const [hasMore, setHasMore] = useState(true);

  const apiKey = process.env.REACT_APP_API_KEY;
  const apiUrl = process.env.REACT_APP_API_URL;

  const fetchUsers = async () => {
    try {
      const response = await axios.get(`${apiUrl}/users`, {
        headers: {
          Authorization: `Bearer ${apiKey}`,
        },
        params: {
          per_page: 20, // Adjust the number of users per page
          since: lastUserId, //The user id it should begin listing
        },
      });
      const newUsers = response.data;
      setUsers((prevUsers) => [...prevUsers, ...newUsers]);
      setLastUserId(newUsers[newUsers.length - 1]?.id); // update the last user ID
      setHasMore(newUsers.length > 0);
    } catch (error) {
      console.error("Error fetching users from GitHub API:", error);
      return [];
    }
  };

  useEffect(() => {
    fetchUsers();
  }, []);

  return (
    <div className="container py-8">
      <div className="mx-auto">
        <h1 className="text-4xl font-bold text-center mb-8">
          React Infinite Scroll Demo
        </h1>
        <InfiniteScroll
          dataLength={users.length}
          next={fetchUsers}
          hasMore={hasMore}
          loader={
            <h4 className="font-semibold mt-4 text-center">Loading...</h4>
          }
          endMessage={<p>No more users to display</p>}
        >
          <div className="flex flex-wrap gap-4 justify-between items-center">
            {users.map((user, key) => (
              <div
                key={key}
                className="border h-[8rem] w-[45%] sm:w-[30%] md:w-[20%] flex flex-col gap-2 items-center p-2"
              >
                <img
                  src={user.avatar_url}
                  alt={`Avatar image of ${user.login}`}
                  width="80px"
                  height="80px"
                  className="rounded-full"
                />
                <p>{user.login}</p>
              </div>
            ))}
          </div>
        </InfiniteScroll>
      </div>
    </div>
  );
}

export default App; Conclusion Infinite scrolling is a way to keep users engaged by continuously loading new content without interruption, and by now, you’ve experienced how easy it is to add to a React app. Using react-infinite-scroll-component, axios for API calls, and tailwindcss for styling, we’ve created a user-friendly experience that mimics popular social platforms. Find the source code on github. Check the app here. Have you ever wondered how social media apps like Instagram manage to keep us scrolling, almost as if the content is endless? I know I have! It goes beyond just great content — it taps into a continuous flow that keeps us hooked, waiting for what’s next. Unlike traditional pagination, where users have to click "Next" to load more, infinite scrolling creates a seamless experience. Just as we reach the end of the visible content, new items load in, sparking curiosity and anticipation. This is the magic of infinite scroll : keeping the journey going without any interruptions. infinite scroll Let’s dive right into it In this guide, we’ll create a simple React app that uses infinite scroll to display GitHub users information. We'll be using: React: For creating and managing the component-based structure of our app. react-infinite-scroll-component: A popular package to manage the infinite scroll behavior. Tailwind CSS: For quick and clean styling. Axios: For making HTTP requests to the GitHub API. React : For creating and managing the component-based structure of our app. React react-infinite-scroll-component : A popular package to manage the infinite scroll behavior. react-infinite-scroll-component Tailwind CSS : For quick and clean styling. Tailwind CSS Axios : For making HTTP requests to the GitHub API. Axios 1.) Set Up the Project npx create-react-app infinite-scroll-app
cd infinite-scroll-app
npm start npx create-react-app infinite-scroll-app
cd infinite-scroll-app
npm start Install axios , react-infinite-scroll-component and tailwindcss axios react-infinite-scroll-component tailwindcss npm install axios react-infinite-scroll-component tailwindcss npm install axios react-infinite-scroll-component tailwindcss 2.) Generate a Github Personal Token Since we’ll be using the Github Apis,  it’s important to get Access Token from GitHub To avoid rate limitations, Here’s how to create a GitHub Personal Access Token and use it in your project: Go to your GitHub Settings. Click on "Generate new token". Select the scopes you need (for read-only public data, you typically just need the public_repo scope). Generate and copy the token somewhere secure; you’ll need it in your app Create an .env file in the root directory, include the github API url and the Access Token you just copied Go to your GitHub Settings . GitHub Settings Click on "Generate new token" . "Generate new token" Select the scopes you need (for read-only public data, you typically just need the public_repo scope). public_repo Generate and copy the token somewhere secure; you’ll need it in your app Create an .env file in the root directory, include the github API url and the Access Token you just copied REACT_APP_API_URL=https://api.github.com
REACT_APP_API_KEY=your_access_token REACT_APP_API_URL=https://api.github.com
REACT_APP_API_KEY=your_access_token 3.) Fetch the data from the Github Api const fetchUsers = async () => {
    try {
      const response = await axios.get(`${apiUrl}/users`, {
        headers: {
          Authorization: `Bearer ${apiKey}`,
        },
        params: {
          per_page: 20, // Adjust the number of users per page
          since: lastUserId, //The user id it should begin listing
        },
      });
      const newUsers = response.data;
      setUsers((prevUsers) => [...prevUsers, ...newUsers]);
      setLastUserId(newUsers[newUsers.length - 1]?.id); // update the last user ID
      setHasMore(newUsers.length > 0);
    } catch (error) {
      console.error("Error fetching users from GitHub API:", error);
      return [];
    }
  }; const fetchUsers = async () => {
    try {
      const response = await axios.get(`${apiUrl}/users`, {
        headers: {
          Authorization: `Bearer ${apiKey}`,
        },
        params: {
          per_page: 20, // Adjust the number of users per page
          since: lastUserId, //The user id it should begin listing
        },
      });
      const newUsers = response.data;
      setUsers((prevUsers) => [...prevUsers, ...newUsers]);
      setLastUserId(newUsers[newUsers.length - 1]?.id); // update the last user ID
      setHasMore(newUsers.length > 0);
    } catch (error) {
      console.error("Error fetching users from GitHub API:", error);
      return [];
    }
  }; It’s important to note that Github uses since and not page just as is in the code above since page 4.) Setting Up The Infinite Scroll <InfiniteScroll
          dataLength={users.length}
          next={fetchUsers}
          hasMore={hasMore}
          loader={
            <h4 className="font-semibold mt-4 text-center">Loading...</h4>
          }
          endMessage={<p>No more users to display</p>}
        >
          <div className="flex flex-wrap gap-4 justify-between items-center">
            {users.map((user, key) => (
              <div
                key={key}
                className="border h-[8rem] w-[45%] sm:w-[30%] md:w-[20%] flex flex-col gap-2 items-center p-2"
              >
                <img
                  src={user.avatar_url}
                  alt={`Avatar image of ${user.login}`}
                  width="80px"
                  height="80px"
                  className="rounded-full"
                />
                <p>{user.login}</p>
              </div>
            ))}
          </div>
        </InfiniteScroll> <InfiniteScroll
          dataLength={users.length}
          next={fetchUsers}
          hasMore={hasMore}
          loader={
            <h4 className="font-semibold mt-4 text-center">Loading...</h4>
          }
          endMessage={<p>No more users to display</p>}
        >
          <div className="flex flex-wrap gap-4 justify-between items-center">
            {users.map((user, key) => (
              <div
                key={key}
                className="border h-[8rem] w-[45%] sm:w-[30%] md:w-[20%] flex flex-col gap-2 items-center p-2"
              >
                <img
                  src={user.avatar_url}
                  alt={`Avatar image of ${user.login}`}
                  width="80px"
                  height="80px"
                  className="rounded-full"
                />
                <p>{user.login}</p>
              </div>
            ))}
          </div>
        </InfiniteScroll> 5. )Putting it all together import axios from "axios";
import React, { useEffect, useState } from "react";
import InfiniteScroll from "react-infinite-scroll-component";

function App() {
  const [users, setUsers] = useState([]);
  const [lastUserId, setLastUserId] = useState(null);
  const [hasMore, setHasMore] = useState(true);

  const apiKey = process.env.REACT_APP_API_KEY;
  const apiUrl = process.env.REACT_APP_API_URL;

  const fetchUsers = async () => {
    try {
      const response = await axios.get(`${apiUrl}/users`, {
        headers: {
          Authorization: `Bearer ${apiKey}`,
        },
        params: {
          per_page: 20, // Adjust the number of users per page
          since: lastUserId, //The user id it should begin listing
        },
      });
      const newUsers = response.data;
      setUsers((prevUsers) => [...prevUsers, ...newUsers]);
      setLastUserId(newUsers[newUsers.length - 1]?.id); // update the last user ID
      setHasMore(newUsers.length > 0);
    } catch (error) {
      console.error("Error fetching users from GitHub API:", error);
      return [];
    }
  };

  useEffect(() => {
    fetchUsers();
  }, []);

  return (
    <div className="container py-8">
      <div className="mx-auto">
        <h1 className="text-4xl font-bold text-center mb-8">
          React Infinite Scroll Demo
        </h1>
        <InfiniteScroll
          dataLength={users.length}
          next={fetchUsers}
          hasMore={hasMore}
          loader={
            <h4 className="font-semibold mt-4 text-center">Loading...</h4>
          }
          endMessage={<p>No more users to display</p>}
        >
          <div className="flex flex-wrap gap-4 justify-between items-center">
            {users.map((user, key) => (
              <div
                key={key}
                className="border h-[8rem] w-[45%] sm:w-[30%] md:w-[20%] flex flex-col gap-2 items-center p-2"
              >
                <img
                  src={user.avatar_url}
                  alt={`Avatar image of ${user.login}`}
                  width="80px"
                  height="80px"
                  className="rounded-full"
                />
                <p>{user.login}</p>
              </div>
            ))}
          </div>
        </InfiniteScroll>
      </div>
    </div>
  );
}

export default App; import axios from "axios";
import React, { useEffect, useState } from "react";
import InfiniteScroll from "react-infinite-scroll-component";

function App() {
  const [users, setUsers] = useState([]);
  const [lastUserId, setLastUserId] = useState(null);
  const [hasMore, setHasMore] = useState(true);

  const apiKey = process.env.REACT_APP_API_KEY;
  const apiUrl = process.env.REACT_APP_API_URL;

  const fetchUsers = async () => {
    try {
      const response = await axios.get(`${apiUrl}/users`, {
        headers: {
          Authorization: `Bearer ${apiKey}`,
        },
        params: {
          per_page: 20, // Adjust the number of users per page
          since: lastUserId, //The user id it should begin listing
        },
      });
      const newUsers = response.data;
      setUsers((prevUsers) => [...prevUsers, ...newUsers]);
      setLastUserId(newUsers[newUsers.length - 1]?.id); // update the last user ID
      setHasMore(newUsers.length > 0);
    } catch (error) {
      console.error("Error fetching users from GitHub API:", error);
      return [];
    }
  };

  useEffect(() => {
    fetchUsers();
  }, []);

  return (
    <div className="container py-8">
      <div className="mx-auto">
        <h1 className="text-4xl font-bold text-center mb-8">
          React Infinite Scroll Demo
        </h1>
        <InfiniteScroll
          dataLength={users.length}
          next={fetchUsers}
          hasMore={hasMore}
          loader={
            <h4 className="font-semibold mt-4 text-center">Loading...</h4>
          }
          endMessage={<p>No more users to display</p>}
        >
          <div className="flex flex-wrap gap-4 justify-between items-center">
            {users.map((user, key) => (
              <div
                key={key}
                className="border h-[8rem] w-[45%] sm:w-[30%] md:w-[20%] flex flex-col gap-2 items-center p-2"
              >
                <img
                  src={user.avatar_url}
                  alt={`Avatar image of ${user.login}`}
                  width="80px"
                  height="80px"
                  className="rounded-full"
                />
                <p>{user.login}</p>
              </div>
            ))}
          </div>
        </InfiniteScroll>
      </div>
    </div>
  );
}

export default App; Conclusion Infinite scrolling is a way to keep users engaged by continuously loading new content without interruption, and by now, you’ve experienced how easy it is to add to a React app. Using react-infinite-scroll-component , axios for API calls, and tailwindcss for styling, we’ve created a user-friendly experience that mimics popular social platforms. react-infinite-scroll-component axios tailwindcss Find the source code on github . github Check the app here . here

How to Build a Simple and User-Friendly Infinite Scroll in React

On the eve of January 9th, 2009, Satoshi Nakamoto published the version 0.1 code for Bitcoin software on Sourceforge and launched the network by defining the genesis block of Bitcoin (block 0). I’d like to think that anyone who understood this technology early on would have bought into the hype train pretty quickly. I mean, who wouldn’t be intrigued by the idea of digital money? By promising to empower true peer-to-peer interactions—no middlemen, no banks, no “big brother”—Satoshi’s open-source design welcomed everyone to be part of a network that no single person or entity could own or control. When we view this technology over the long run, it has lived up to its seemingly lofty promises. At the risk of sounding too passionate, it is restorative, reconstructive, and even revolutionary. This technology has led to countless innovations. From the smart contracts and the proof-of-stake functionality of Ethereum to the proof-of-stake mechanism on Solana, to newer blockchains in the Cosmos ecosystem (including Injective, Atom, Celestia, etc.) all growing rapidly and becoming more interconnected. Built on these different blockchains are several products, including games, DeFi (Decentralized Finance), lending and borrowing protocols, NFTs (Non-Fungible Tokens), RWAs (Real World Assets), and many more. One of the latest protocols to launch on the Bitcoin ecosystem, and one that has quickly made waves across the entire crypto space, is the Rune Protocol. The foundation for Rune’s development is closely tied to the rise of the Ordinals protocol. Since Ordinals was introduced, the DeFi ecosystem on Bitcoin has experienced marked growth, paving the way for fungible tokens. What are Runes? Casey Rodarmor, driven by his passion for innovation, was the force behind the creation of the Ordinals protocol. In September 2023, he took his vision further by developing Rune. His goal was to revolutionize the way fungible tokens were issued and controlled directly on the Bitcoin blockchain. In an interview on the Bankless YouTube channel, Casey highlighted that establishing a robust fungible token protocol for Bitcoin could greatly expand its user base, attract more developers, and boost transaction fee revenue. DeFi revolves around fungible tokens. Fungible tokens are digital assets that can be exchanged for other digital assets. They are used to facilitate lending, borrowing, and decentralized exchanges. Although Bitcoin provides a safe, decentralized base for the development of DeFi applications, Ethereum leads in the DeFi department due to its flexibility in the creation of tokens. For fungible tokens, however, the core architecture of Bitcoin poses a problem (taking Omni Layer as an example where the complicated structure of the system is increased by the system’s reliance on extra layers constructed on top of Bitcoin). With Runes, on the other hand, there’s a significant shift as they enhance direct token operations by leveraging the Bitcoin blockchain itself. The Runes protocol is built on a UTXO-based (Unspent Transaction Outputs) foundation that facilitates a more minute transaction management and is consistent with Bitcoin’s original design. How does the Rune Protocol work? Runes are the basic units representing fungible tokens at the center of the protocol. In solid contrast to Ethereum’s ERC-20 standard, the Bitcoin UTXO system comes with Runes built in. Each Rune can be uniquely identified by its Rune ID, which also contains data regarding its value and current output. Three (3) concepts establish the framework of the Runes protocol, namely; 1. UTXOs 2. Runestones 3. OP_RETURN The protocol functions using a mechanism in which each transaction output represents unspent Bitcoin that can be used as input for a new transaction. The protocol utilizes this model to embed data about token creation and transfers directly within Bitcoin transactions. It is totally unlike Ethereum’s account-based system; that’s where the UTXO model comes in. The OP_RETURN function is a Bitcoin opcode that makes it possible to include data in a transaction without compromising its legitimacy. This data, which is just 80 bytes long, contains details regarding the etching and transfer of tokens as well as other features. In contrast to standard Bitcoin transactions, which involve sending currency, OP_RETURN is intended to attach additional data. It is not possible to spend the coins within this output normally. The use case for the OP_RETURN function is wide and cannot be streamlined to runes protocol alone, developers use it to; - Timestamp documents - Store small bits of game data - Embed messages, and of course - Create tokens on Bitcoin Although the main objective of Bitcoin is safe financial transactions, OP_RETURN adds a little flexibility to the platform as it enables the permanent recording of minute amounts of data on the blockchain without obstructing its primary function. The messages that are encoded within transactions using OP_RETURN are called Runestones. These encoded messages include information on the precise operations being carried out with a Rune token, including; adding new units, moving current ones, or destroying them. There are three types of these encoded messages (Runestones) namely; Etching: This describes the origin of a new Rune token. It includes specifications such as the initial supply, divisibility, token name, and symbol.
Transfer:  This type of Runestone carries the Rune ID, output number, and the amount to be transferred.
Burn: This type of runestone deliberately destroys a certain number of Runes. It is very helpful in controlling the supply of the token. Runestones are key components in the Rune lifecycle—from its creation to validation. Etching is described as a process of creating a fungible token using the Runes protocol. This major step involves designing a Bitcoin transaction with a special message (encoded using the OP_RETURN opcode). This Runestone serves as an origin for the new token, outlining its salient attributes that include; - A unique name - A recognizable Symbol - Divisibility - Initial Supply After miners have validated and processed a transaction containing etched Runestone, a new Rune token is created and stored in the transaction’s allocated output. Also, another thing to note about the etching process is its permission and compatibility for pre-mining process in which a certain number of the Runes are allocated to the creator’s wallet before they are minted. The transfer of a freshly etched Rune token is carried out by a specialized Runestone known as the transfer Runestone. The message stored in the transfer Runestone for a new Bitcoin transaction highlights the specifics of the move. The attributes that are stored in transfer Runestone include; - The Rune's identity - It helps to identify the exact Rune token being transferred by including its unique ID. - The Current location on the Bitcoin blockchain where the Runes currently reside - The New destination for the Rune token (usually a destination Bitcoin address) - The amount of Rune tokens being transferred The specified quantity of Runes travels to the destination Bitcoin address automatically as soon as miners approve this transaction. The Runes protocol prides itself in its security and this makes it beautiful, and like any working system, the protocol has inbuilt check and balance system to ensure everything runs smoothly. The gatekeepers in the Bitcoin network are the miners and the nodes, who verify every transaction including Runestones. They guarantee that; - Runes aren’t duplicated - Runes aren’t unexpectedly destroyed - Runes follow their designated path In the case of any mishap of a Runestone, for example, an invalid encoding or a missed output, the transaction would get flagged as invalid. To sort this out, Cenotaphs are required. Cenotaphs basically burn/destroy Runes. They act as a deterrent for sloppy UTXO management. This mechanism encourages the conscious and meticulous use of Runes. In conclusion, the introduction of the Runes protocol has upgraded DeFi in the Bitcoin ecosystem and has significantly reduced the entire blockchain bloat. Unlike other token solutions that create tiny, unspendable outputs, Runes focuses on integration with Bitcoin’s UTXO system, and by the reduction of these unspendable outputs, it keeps the blockchain leaner. The Runes protocol represents a major shift in the interaction possibilities of fungible tokens and the Bitcoin network. It also represents how far we have come from Satoshi’s first line of code- Which will undoubtedly bring a smile to his face. On the eve of January 9th, 2009, Satoshi Nakamoto published the version 0.1 code for Bitcoin software on Sourceforge and launched the network by defining the genesis block of Bitcoin (block 0). I’d like to think that anyone who understood this technology early on would have bought into the hype train pretty quickly. I mean, who wouldn’t be intrigued by the idea of digital money? By promising to empower true peer-to-peer interactions—no middlemen, no banks, no “big brother”—Satoshi’s open-source design welcomed everyone to be part of a network that no single person or entity could own or control. When we view this technology over the long run, it has lived up to its seemingly lofty promises. At the risk of sounding too passionate, it is restorative, reconstructive, and even revolutionary. This technology has led to countless innovations. From the smart contracts and the proof-of-stake functionality of Ethereum to the proof-of-stake mechanism on Solana, to newer blockchains in the Cosmos ecosystem (including Injective, Atom, Celestia, etc.) all growing rapidly and becoming more interconnected. Built on these different blockchains are several products, including games, DeFi (Decentralized Finance), lending and borrowing protocols, NFTs (Non-Fungible Tokens), RWAs (Real World Assets), and many more. One of the latest protocols to launch on the Bitcoin ecosystem, and one that has quickly made waves across the entire crypto space, is the Rune Protocol. The foundation for Rune’s development is closely tied to the rise of the Ordinals protocol. Since Ordinals was introduced, the DeFi ecosystem on Bitcoin has experienced marked growth, paving the way for fungible tokens. What are Runes? What are Runes? Casey Rodarmor, driven by his passion for innovation, was the force behind the creation of the Ordinals protocol. In September 2023, he took his vision further by developing Rune. His goal was to revolutionize the way fungible tokens were issued and controlled directly on the Bitcoin blockchain. In an interview on the Bankless YouTube channel, Casey highlighted that establishing a robust fungible token protocol for Bitcoin could greatly expand its user base, attract more developers, and boost transaction fee revenue. DeFi revolves around fungible tokens. Fungible tokens are digital assets that can be exchanged for other digital assets. They are used to facilitate lending, borrowing, and decentralized exchanges. Although Bitcoin provides a safe, decentralized base for the development of DeFi applications, Ethereum leads in the DeFi department due to its flexibility in the creation of tokens. For fungible tokens, however, the core architecture of Bitcoin poses a problem (taking Omni Layer as an example where the complicated structure of the system is increased by the system’s reliance on extra layers constructed on top of Bitcoin). With Runes, on the other hand, there’s a significant shift as they enhance direct token operations by leveraging the Bitcoin blockchain itself. The Runes protocol is built on a UTXO-based (Unspent Transaction Outputs) foundation that facilitates a more minute transaction management and is consistent with Bitcoin’s original design. How does the Rune Protocol work? How does the Rune Protocol work? Runes are the basic units representing fungible tokens at the center of the protocol. In solid contrast to Ethereum’s ERC-20 standard, the Bitcoin UTXO system comes with Runes built in. Each Rune can be uniquely identified by its Rune ID, which also contains data regarding its value and current output. Three (3) concepts establish the framework of the Runes protocol, namely; 1. UTXOs 2. Runestones 3. OP_RETURN The protocol functions using a mechanism in which each transaction output represents unspent Bitcoin that can be used as input for a new transaction. The protocol utilizes this model to embed data about token creation and transfers directly within Bitcoin transactions. It is totally unlike Ethereum’s account-based system; that’s where the UTXO model comes in. The OP_RETURN function is a Bitcoin opcode that makes it possible to include data in a transaction without compromising its legitimacy. This data, which is just 80 bytes long, contains details regarding the etching and transfer of tokens as well as other features. In contrast to standard Bitcoin transactions, which involve sending currency, OP_RETURN is intended to attach additional data. It is not possible to spend the coins within this output normally. The use case for the OP_RETURN function is wide and cannot be streamlined to runes protocol alone, developers use it to; - Timestamp documents - Store small bits of game data - Embed messages, and of course - Create tokens on Bitcoin Although the main objective of Bitcoin is safe financial transactions, OP_RETURN adds a little flexibility to the platform as it enables the permanent recording of minute amounts of data on the blockchain without obstructing its primary function. The messages that are encoded within transactions using OP_RETURN are called Runestones. These encoded messages include information on the precise operations being carried out with a Rune token, including; adding new units, moving current ones, or destroying them. There are three types of these encoded messages (Runestones) namely; Etching: This describes the origin of a new Rune token. It includes specifications such as the initial supply, divisibility, token name, and symbol. Transfer:  This type of Runestone carries the Rune ID, output number, and the amount to be transferred. Burn: This type of runestone deliberately destroys a certain number of Runes. It is very helpful in controlling the supply of the token. Etching : This describes the origin of a new Rune token. It includes specifications such as the initial supply, divisibility, token name, and symbol. Etching Transfer :  This type of Runestone carries the Rune ID, output number, and the amount to be transferred. Transfer Burn : This type of runestone deliberately destroys a certain number of Runes. It is very helpful in controlling the supply of the token. Burn Runestones are key components in the Rune lifecycle—from its creation to validation. Etching is described as a process of creating a fungible token using the Runes protocol. This major step involves designing a Bitcoin transaction with a special message (encoded using the OP_RETURN opcode). This Runestone serves as an origin for the new token, outlining its salient attributes that include; Etching - A unique name - A recognizable Symbol - Divisibility - Initial Supply After miners have validated and processed a transaction containing etched Runestone, a new Rune token is created and stored in the transaction’s allocated output. Also, another thing to note about the etching process is its permission and compatibility for pre-mining process in which a certain number of the Runes are allocated to the creator’s wallet before they are minted. The transfer of a freshly etched Rune token is carried out by a specialized Runestone known as the transfer Runestone . The message stored in the transfer Runestone for a new Bitcoin transaction highlights the specifics of the move. The attributes that are stored in transfer Runestone include; transfer Runestone - The Rune's identity - It helps to identify the exact Rune token being transferred by including its unique ID. - The Current location on the Bitcoin blockchain where the Runes currently reside - The New destination for the Rune token (usually a destination Bitcoin address) - The amount of Rune tokens being transferred The specified quantity of Runes travels to the destination Bitcoin address automatically as soon as miners approve this transaction. The Runes protocol prides itself in its security and this makes it beautiful, and like any working system, the protocol has inbuilt check and balance system to ensure everything runs smoothly. The gatekeepers in the Bitcoin network are the miners and the nodes, who verify every transaction including Runestones. They guarantee that; - Runes aren’t duplicated - Runes aren’t unexpectedly destroyed - Runes follow their designated path In the case of any mishap of a Runestone, for example, an invalid encoding or a missed output, the transaction would get flagged as invalid. To sort this out, Cenotaphs are required. Cenotaphs basically burn/destroy Runes. They act as a deterrent for sloppy UTXO management. This mechanism encourages the conscious and meticulous use of Runes. In conclusion, the introduction of the Runes protocol has upgraded DeFi in the Bitcoin ecosystem and has significantly reduced the entire blockchain bloat. Unlike other token solutions that create tiny, unspendable outputs, Runes focuses on integration with Bitcoin’s UTXO system, and by the reduction of these unspendable outputs, it keeps the blockchain leaner. The Runes protocol represents a major shift in the interaction possibilities of fungible tokens and the Bitcoin network. It also represents how far we have come from Satoshi’s first line of code- Which will undoubtedly bring a smile to his face.

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