2025-05-10 01:46:37
\ 2025 is heralded as the year of AI agents by venture capitalists, tech giants, and even Time Magazine.
\ Unlike chatbots, AI agents can autonomously complete tasks for you and identify when they’re needed. They use a combination of prompted instructions, reasoning capabilities, and real-time data from the apps and services that you use every day.
\ Imagine you’re preparing for a meeting. With Ninja AI’s Deep Research, for example, you could have it automatically gather your company’s competitor news and generate an in-depth report on key insights and actionable recommendations. You could even enhance this with industry analysis and expert opinions drawn from diverse sources, including files, video, and audio.
\ Sequoia Capital predicted that this year we can expect to see swarms of agents augmenting—rather than cutting out—professionals in these ways. They’re already seeing instances where agents working alongside sales have tripled performance compared to when team members weren’t involved in the process.
\ YC believes that agents will replace SaaS, just as SaaS replaced on-premise software. They see demand for agents following a similar pattern—where individual use for general purposes, like chat, will drive more businesses to adopt them too.
\ With any innovation in its early stages, it can often take time for users to experience practical, everyday benefits. In contrast, applications like Ninja’s Deep Research are already proving they can deliver immediate value across a wide range of needs.
Generally, Deep Research creates and executes a plan to search, analyze, and synthesize hundreds of online sources to answer a question. What would take a human hours can instead be completed in tens of minutes.
\ There’s only a handful of options on the market from Ninja, Open AI, Google, Perplexity, Anthropic, and Monica. Each varies when it comes to usage, reliability, and cost. Some cap queries at different amounts, range between $20-$200/mo, or take 30 minutes or more to return an answer. Others can miss information easily found online, have high hallucination rates, and be inconsistent with citations—or do not provide them at all.
\ How Ninja is addressing limitations in the market is by consistently improving its speed, performance, and affordability. Powered by custom, next-generation chips, our Deep Research can handle unlimited queries, delivers instant, expert-level results, and offers a flat-rate membership at $15/mo. It also sets a new standard in the industry with its superior capabilities:
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Validate Insights with Confidence: Deep Research makes it easy to verify information with citations and direct links to sources. The platform also shows its reasoning for every step of its plan, whereas a new study found that other models fail to do so 80% of the time.
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Proven to Surpass Leading AI Models: For every request, Deep Research generates code to precisely locate information,significantly improving accuracy and reducing hallucinations. In fact, it outperformed leading models in the field at minimizing hallucinations, achieving 91.2% accuracy on the SimpleQA test—one of the best proxies for assessing hallucination levels.
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Curious about how millions of people are leveraging our Deep Research? Here are just some of the many examples of its diverse use cases:
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Technical: Analyzes system specifications, external documentation, and industry best practices to recommend solutions for software integration, API development, and security protocol enhancements.
\ Try it in Ninja: Evaluate the system specifications and external documentation for [insert software/product], and recommend best practices for integrating [insert software/technology].
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Marketing: Assesses market trends, competitor strategies, key search terms, and content gaps to inform and optimize campaigns.
\ Try it in Ninja: Analyze market trends for [insert product/industry], assess competitor strategies, and identify key search terms. Highlight content gaps and suggest improvements to optimize a campaign targeting [insert target audience].
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Product: Synthesizes user interviews, surveys, focus groups, and analytics to create reports on customer demographics, behaviors, motivations, and challenges.
\ Try it in Ninja: Combine insights from user interviews, surveys, and focus groups to create a report on customer [insert characteristics]. Suggest product improvements for [insert product] based on feedback.
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Sales: Identifies high-potential leads, prospects' pain points and needs, and competitor solutions and pricing to improve outreach and close more deals.
\ Try it in Ninja: Analyze the [insert industry/sector], identifying high-potential leads and key company pain points and needs. Assess competitor solutions and pricing, then recommend strategies for initiating outreach to engage prospects for [insert product/service].
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Skill Advancement: Improves in specific programming languages, frameworks, or technologies by generating in-depth tutorials, coding exercises, and summaries.
\ Try it in Ninja: Create a detailed learning path for advancing in [insert programming language/framework/technology]. Provide clear tutorials, practical coding exercises, and concise summaries to reinforce key concepts and skills.
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Product Comparison: Compares features, specifications, performance, and pricing to receive hyper-personalized recommendations.
\ Try it in Ninja: Compare the features, specifications, performance, and pricing of [insert product 1] and [insert product 2]. Provide a recommendation based on [insert specific needs or criteria], such as [insert feature preference or use case].
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Traveling: Generate itineraries for destinations, accommodations, attractions, activities, or visa requirements based on timeframe, budget, and preferences.
\ Try it in Ninja: Suggest the best travel dates for a trip to [insert destination] in [insert month], along with four-star accommodation options within a budget range of [insert price range]. Identify visa requirements for [insert nationality] and provide a list of recommended attractions and activities.
So far, there have already been many developments supporting the idea that 2025 will be the year of AI agents.
\ This month, Google announced the Agent2Agent (A2A) protocol, a new system that allows agents to communicate with each other, securely exchange information, and coordinate actions across enterprise platforms and services. The initiative is backed by over 50 major tech companies, including Salesforce, PayPal, Workday, and Cohere.
\ How could this benefit Deep Research use cases? A developer could leverage it to build and optimize applications using collaborative AI agents. One agent could serve as a coding assistant, analyzing and pulling relevant code patterns, while another could access Salesforce’s APIs to automatically sync customer data, update records, or trigger workflows. A third agent could review the implementation, debug the code for errors, and suggest specific fixes.
\ As for whether or not agents will replace SaaS, that question remains. What we do know is that they’re already transforming how we access, learn about, and share information. According to McKinsey, agents can produce high-quality content that reduces review cycle times by 20 to 60%. And unlike previous waves of innovation, you no longer have to wait or pay high prices to take advantage of the tools that power them through Ninja.
\ Beyond Ninja providing access to the world’s best models, we’ve just released APIs for Deep Research and our other SuperAgents. They offer a flexible and cost-effective way to join us in building powerful products with the best performance at the lowest cost. Join Ninja today and experience how it can transform your day-to-day work and applications.
2025-05-10 01:26:27
Launching Summer 2025, T-Rex’s built-in distribution engine introduces a radically simple idea: rewarding people seamlessly for doing what they already love online.
\ T-Rex, a purpose-built blockchain for entertainment, content, and cultural virality, today announced a $17 million fundraise and Incubation Fund to revolutionize online content publishing by seamlessly rewarding consumers for everyday interactions on social platforms such as YouTube, TikTok, or X (formerly Twitter) and reshape how consumer-facing decentralized applications (dApps) are discovered, published, distributed and scaled across Web3.
\ Backed by strategic investors including Portal Ventures, North Island Ventures, Framework Ventures, Arbitrum Gaming Ventures, ArkStream Capital, Mindfulness Capital, Hypersphere, SNZ, and Arche Fund, and developed by EVG, Asia’s prominent product builder and publisher, known for driving mass adoption, T-Rex directly addresses the critical “ghost town” and “mercenary users” challenges in blockchain.
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"Arbitrum Gaming Ventures recognizes the transformative potential of platforms like T-Rex that are expanding Web3 beyond DeFi. EVG's strong regional influence and deep understanding of the Southeast Asian market, projected to reach $7 billion by 2028, provide a strategic gateway for us to significantly scale our footprint within this high-growth region. Combining our robust scaling capabilities with EVG's extensive ecosystem expertise, we are strategically positioned to penetrate the entertainment industry and broader consumer market in a pivotal and rapidly expanding part of the world," said Dan Peng, Partner at Arbitrum Gaming Ventures.
T-Rex leverages Arbitrum’s powerful Layer-2 scaling solution, delivering ultra-fast, secure, and low-cost transactions essential for consumer-scale adoption. Arbitrum Gaming Ventures has specifically identified T-Rex as key to its regional growth strategy.
UI screenshot: A user earning rewards while browsing YouTube with T-Rex’s browser extension.)
\ While Web3 holds the promise of revolutionary change, the current landscape is fragmented, user-unfriendly and often empty with underutilized dApps. Despite all its innovation, most chains lack real users, real culture, and real reasons to stay. T-Rex flips the model with its simple first principle: stop trying to reinvent user habits and instead enhance them invisibly.
\ Its core innovation, Proof-of-Engagement (PoE) mechanism, automatically rewards users on apps they already use daily through a privacy-preserving browser extension, turning everyday online actions like watching videos, liking posts, and sharing memes into tangible, valuable digital ownership and offline perks.
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“Instead of building another blockchain pipe, we’re delivering actual water: engaging content, thriving communities, real-world rewards”, said Allen Ng, Co-Founder of T-Rex and EVG.
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“Users shouldn’t have to think about crypto until they want to. Our blockchain stays invisible, letting them effortlessly turn their online fun into meaningful rewards. Our vision is to let attention becomes equity, community becomes power and participation becomes currency.”
(UI screenshot: Home Discovery pushing relevant content and dApps to user.) At launch, users simply install T-Rex’s browser extension and continue browsing popular social media platforms. Every interaction is quietly, securely recorded via advanced zero-knowledge tech (zkTLS), and seamlessly turned into rewards, with no wallet or tech jargon required.
\ Creators and projects benefit from instant audience discovery and viral distribution, while end users earn rewards effortlessly, redeemable both digitally and offline, such as cash back at favorite restaurants or exclusive local deals.
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“The next wave of breakout applications will be defined by distribution, UI / UX, and product differentiation. The team at T-Rex gets this. They're building a protocol that seamlessly onboards social media users and allows them to monetize their data through network rewards. We're excited to back T-Rex as they merge protocol-native network design with clean consumer product expertise," said Evan Fisher, Founder and General Partner of Portal Ventures.
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“Web3 infrastructure has matured, but it still lacks consumer momentum because only a few use cases resonate with the masses. T-Rex is a strong contender to bridge this gap. By prioritizing culture and abstracting complexity, it offers a path to broad adoption,” said Travis Scher Managing Partner and Co-founder of North Island Ventures.
The $8 million Incubation Fund backed by EVG with support from the Arbitrum Gaming Ventures will accelerate ecosystem growth, offering comprehensive developer support beyond capital, including engineering resources, media partnerships, community activations, premium events, Key Opinion Leader (KOL) networks, and strategic Intellectual Property (IP) applications.
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“We provide real firepower, not just capital”, Joyce Yim, Co-founder of T-Rex emphasised. “Developers gain immediate user traffic, creators grow loyal audiences effortlessly, and users become not just spectators, but stakeholders in the success of viral content.”
\ Set to launch in Summer 2025, T-Rex’s distribution layer and browser extension create the railroads linking popular online consumer applications such as gaming, music, interactive media, and Web3.
\ From creators rewarding top users with digital assets, to new projects gaining instant discovery, to active users earning a stake, T-Rex’s ecosystem is designed for intuitive use, cultural resonance, and economic empowerment at scale.
T-Rex is a new blockchain ecosystem purpose-built for consumer-facing applications, designed to transform the Web3 attention layer through its innovative Proof of Engagement (PoE) mechanism.
\ Developed by EVG and backed by leading investors including Arbitrum Gaming Ventures, Framework Ventures, North Island Ventures, and Portal Ventures, T-Rex offers creators and developers a UX-first infrastructure with built-in discovery, rewards, and distribution, enabling the next billion users to engage, earn, and build on-chain.
\ Website: https://trex.xyz/
Twitter: https://x.com/TREX_chain
Founded in 2018, EVG is one of APAC’s largest and most active Web3 product builders and publishers.
\ With 200+ engineers and 10+ millions of users, EVG has built a complex of innovative products across consumer infrastructures (e.g Edgen AI, T-Rex), entertainment / culture dApps (e.g Deek, Last Odyssey, LiveArt), and fintech platforms (e.g Aspen Digital).
\ EVG’s corporate venture arm also owns strategic stakes across 100+ Web3 companies including the likes of Celestia, Wormhole, Berachain, Pudgy Penguins, Abstract, Infinex, Dapper Labs, Animoca Brands, The Sandbox, Stacks, Immutable, Kraken and Dunamu.
\ Website: https://www.evg.co/
Twitter: https://twitter.com/EVGHQ/
Media Contact: [email protected]
:::tip This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program.
:::
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2025-05-10 00:04:35
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2025-05-09 23:00:08
CHAPTER 1: INTRODUCTION
CHAPTER 2: BACKGROUND
2.1 Blockchains & smart contracts
2.2 Transaction prioritization norms
2.3 Transaction prioritization and contention transparency
2.5 Blockchain Scalability with Layer 2.0 Solutions
CHAPTER 3. TRANSACTION PRIORITIZATION NORMS
CHAPTER 4. TRANSACTION PRIORITIZATION AND CONTENTION TRANSPARENCY
Transaction Prioritization and Contention Transparency
4.2 On contention transparency
CHAPTER 5. DECENTRALIZED GOVERNANCE
CHAPTER 6. RELATED WORK
6.1 Transaction prioritization norms
6.2 Transaction prioritization and contention transparency
CHAPTER 7. DISCUSSION, LIMITATIONS & FUTURE WORK
7.3 Voting power distribution to amend smart contracts
\ Appendices
APPENDIX A: Additional Analysis of Transactions Prioritization Norms
APPENDIX B: Additional analysis of transactions prioritization and contention transparency
APPENDIX C: Additional Analysis of Distribution of Voting Power
We gathered data from Messari (Messari, 2023) to determine the categories, subcategories, and the level of importance associated with each Compound proposal. Figure C.1 shows the distribution of 101 executed Compound proposals across different categories and subcategories. We show the degree of importance for each proposal according to Messari divided into “low”, “medium”, “high”, and “very high”. As a result, a few proposals categorized as “Parameter Change” and “Security” demonstrate a high level of importance. Furthermore, proposals with the highest level of importance are found within the “Security” category, specifically within the “Mining and Validation” subcategory. This refers to the proposal 64 that was created to fix a bug introduced by proposal #62 (Loewen, 2021a,b).
\ The majority of the proposals (61 proposals, accounting for 60.4%) are related to “Parameter Change” followed by “Team and Operations” and “Token Supply” accounting for 10 (9.9%) each, and “Governance” with 7 (6.93%) proposals. According to the level of importance reported by Messari, out of the total of 101 executed proposals, 51 proposals (50.5%) are classified as low importance, 46 proposals (45.54%) as medium importance, 3 proposals as high importance, and 1 proposal as very high importance.
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\ This section describes the details required to filter and collect transactions data that triggered events of interest from any smart contract on the Ethereum blockchain. Before creating a filter, we need the address of our target contract and its Application Binary Interface (ABI). The ABI is a JSON file that specifies the functions available in the contract, their signatures, and the available events. We can retrieve this information by calling the Etherscan API (Etherscan, 2023a). Once we have the contract address and ABI, we can create a filter to track the contract’s activity on the Ethereum blockchain using an important Python library for interacting with Ethereum nodes called Web3.py (web3.py team, 2022) to facilitate the communication with our node’s API.
\ The Web3.py library provides a filtering function called createFilter. This function can be used to filter transactions that triggered events of interest from a specific contract within a range of block numbers. We use this function to efficiently collect all transactions that triggered these events from the Compound (Leshner and Hayes, 2019) smart contract.
We aim to identify the ownership of public wallet addresses on the Ethereum blockchain. Due to the inherent anonymity of blockchain addresses, this proves to be a challenging task as we can only know the owners of an address if the owner chooses to disclose it. However, popular blockchain explorers such as Etherscan (Etherscan, 2023b) often provide information on the top holders of specific cryptocurrencies, which allows us to partially overcome this obstacle.
\ Then, we first obtained the lists of the top 10,000 Ether holders from which there are 290 (2.9%) identified addresses and the top 1000 COMP holders from which there are 82 (8.2%) identified addresses from Etherscan. By comparing these lists to our data set, we were able to identify most of the top COMP holder addresses in our sample. However, this method did not work for the top delegated accounts, as most of them were not included in the list of top COMP holders on Etherscan. This means that most of the delegated accounts does not hold many tokens. Further, we also used the list of top 100 delegated Compound addresses by voting weight available on the Compound website (Compound Labs, Inc., 2022b) from which there are 66 identified addresses.
\ Furthermore, to extend the available identified addresses in our analysis, we obtained the addresses of 2783 identified users from the Sybil-List (Sybil, 2023b), a project maintained by Uniswap that uses cryptographic proofs to verify wallet addresses ownership. By combining the identified addresses from both sources, we were able to obtain the ownership of 3191 inferred public wallet addresses to use in our analysis. We were able to infer 114 (3.41%) out of the 3341 unique addresses in our data set. Considering the top 10 most powerful voters for each proposal (refer to Figure C.3 in §C.5), we were able to infer 67 (50.37%) of the 133 unique addresses. Overall, our methodology allowed us to partially overcome the anonymity of public wallet addresses on the Ethereum blockchain and shed light on the ownership of these addresses in our data set. Finally, as an entity can control more than one address, we grouped the addresses we identified belonging to the same entity together to conduct our analysis.
There are various smart contract applications that utilize decentralized governance protocols for decision-making, including those for lending, decentralized exchanges (DEXes), and stablecoins, among others. An example of such protocols can be found on the Ethereum blockchain, where a number of these applications are available. We have selected some of the most protocols that use decentralized governance for decisionmaking. Table C.1 presents 8 protocols, including Maker Executive and Maker Pooling, which are part of the MakerDAO (MakerDAO, 2023) stablecoin protocol responsible for the DAI token. These protocols use decentralized governance mechanisms, and we characterize them based on whether their votes are cast on- or off-chain, the delegation methods they use, how they aggregate the votes, and how the proposal outcome take effect.
This section examines how each of the top-10 voters of Compound and Uniswap cast their votes. Some proposals may not have received any votes if they were cancelled before the voting period began. See §5.3.2 for details. Figure C.3 shows how each of the top-10 voters cast their votes in each of the 126 (94.74%) out of 133 Compound proposals.
\ Figure C.4 shows the all votes cast in chronological order per proposal. On average, voters took 1.4 days (with a standard deviation of 0.95 and a median of 1.34 days) to cast their votes after the voting period began.
For a proposal to pass, it must receive a majority of in favor votes and at least 400,000 (4%) votes in favor from the total supply of Compound tokens. This minimum number of in favor votes is referred to as the quorum and is defined by the Compound Governor Bravo contract.
\ We analyzed how long it takes for these proposals to reach the required quorum. Figure C.2 shows the number of days it took each of the evaluated Compound proposals to reach the quorum. On average, it takes 1.64 days with a standard deviation of 0.72 days for the proposals to reach the quorum. The cumulative distribution function of our results, where 32% take more than 2 days to reach the quorum. The shortest and longest time it took was 0.11 and 3 days, respectively.
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\
:::info Author:
(1) Johnnatan Messias Peixoto Afonso
:::
:::info This paper is available on arxiv under CC BY 4.0 DEED license.
:::
\
2025-05-09 22:00:08
CHAPTER 1: INTRODUCTION
CHAPTER 2: BACKGROUND
2.1 Blockchains & smart contracts
2.2 Transaction prioritization norms
2.3 Transaction prioritization and contention transparency
2.5 Blockchain Scalability with Layer 2.0 Solutions
CHAPTER 3. TRANSACTION PRIORITIZATION NORMS
CHAPTER 4. TRANSACTION PRIORITIZATION AND CONTENTION TRANSPARENCY
Transaction Prioritization and Contention Transparency
4.2 On contention transparency
CHAPTER 5. DECENTRALIZED GOVERNANCE
CHAPTER 6. RELATED WORK
6.1 Transaction prioritization norms
6.2 Transaction prioritization and contention transparency
CHAPTER 7. DISCUSSION, LIMITATIONS & FUTURE WORK
7.3 Voting power distribution to amend smart contracts
\ Appendices
APPENDIX A: Additional Analysis of Transactions Prioritization Norms
APPENDIX B: Additional analysis of transactions prioritization and contention transparency
APPENDIX C: Additional Analysis of Distribution of Voting Power
We conducted 4 active experiments where we issued 8 Ethereum transactions; half issued publicly and the other half privately through a private-channel network known as Taichi Network (SparkPool, 2021). Table B.1 summarizes the transactions in our experiment. Spark Pool and Babel Pool included all private transactions (2 transactions each) sent directly to these miners through Taichi Network.
In AAVE, of 1154 bundles, 994 (86.14%) include one Chainlink oracle update followed by a liquidation. There are 52 (4.51%) with two oracle updates followed by liquidations. Out of 1301 oracle updates bundled with liquidations, 282 (21.68%) are USDC-ETH, 203 (15.60%) are USDT-ETH, 169 (12.99%) are DAI-ETH, 70 (5.38%) are SUSD-ETH, and 60 (4.61%) are LINK-ETH. In Compound, of 641 bundles, 548 (85.49%) contain one Chainlink oracle update followed by one liquidation, while 39 (6.08%) include two oracle updates followed by liquidations. Out of 751 oracle updates bundled with liquidations, 311 (41.41%) are ETH-USD, 128 (17.04%) are BTC-USD, and 53 (7.06%) are UNI-USD.
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Per Figure B.1, the hash rates of Bitcoin mining pools such as BTC.com, F2Pool, and AntPool alone accounted for almost half the total hash rate of the network around May 2018, and roughly a year later, i.e., from March 2019, together with Poolin the four mining pools alone represent more than 50% of the total network hash rate. At the end of 2020, new MPOs, e.g., Lubian.com and Binance Pool, started mining Bitcoin, which help improve the decentralization of Bitcoin. However, BTC.com, F2Pool, AntPool, and Poolin still account for almost half of the hash rates showing that a few mining pools control a considerable portion of the Bitcoin hash rate.
\ Hash rates of Ethereum mining pools, in contrast to Bitcoin, do not show a high variance (see Figure B.2). We also observed that Spark Pool, the second-largest Ethereum mining pool, suspended their mining services on September 30, 2021, due to regulatory requirements in response to Chinese authorities (Helen Partz, 2021).
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We ran an active Bitcoin transaction acceleration experiment where we paid 205 EUR to ViaBTC (ViaBTC, 2022) to accelerated 10 transactions from 10 different snapshots of our Mempool. To select these transactions, we checked whether the Mempool was congested (i.e., having more transactions waiting for inclusion than the next block would be able to include), with its size being at least 8 MB. Then, we considered only transactions with low fee rates—less than or equal to 2 sat-per-byte—to ensure that these transactions would be highly unlikely to be included soon in a subsequent block. Next, we sorted the remaining transactions by size to limit the experiment cost as the acceleration-service costs grow proportional to the transaction size. Finally, we select the transaction with the smallest size in bytes for our active experiment.
\ Most of these 10 accelerated transactions were included nearly in the next block, demonstrating the acceleration efficiency. Also, these transactions were wrongly positioned in the block: They appeared, for instance, at the top of the block, i.e., higher
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\ than the non-accelerated transactions, showing that miners indeed prioritized them (see Table 4.3). Further, we observed that although we had only accelerated transactions via ViaBTC, other top mining pools were also involved in confirming the accelerated transactions.
\ Table B.2 shows the transactions used in our experiments. At the time we conducted our experiments, if we rank the miners whose included these transactions based on their daily hash-rate power as (D) and weekly hash-rate power as (W), we would have Huobi (D: 8.1%; W: 9.3%), Binance (D: 9.6%; W: 10.3%), F2Pool (D: 19.9%; W: 18.7%), AntPool (D: 12.5%; W: 10.6%), ViaBTC (D: 5.1%; W: 7.1%). Together these mining pools corresponds to a hash-rate power of (D: 55.2%; W: 56%). Figures B.3 and B.4 show the hash-rate of mining pools in the active experiment and considering the passive experiment (inferred to be accelerated by BTC.com API), respectively.
\ Furthermore, BTC.com (BTC.com, 2022), one of the leading Bitcoin mining pools, provides transaction acceleration services and allows users to verify if transactions have been accelerated through their platform or partner services. From our dataset, we selected those with a SPPE greater than or equal to 1% (12,983,282 transactions in total) and checked if they were said to be accelerated by BTC.com’s API. Of these transactions, 14,104 were found to have been accelerated. Our findings also show that transaction acceleration services are becoming quite common among Bitcoin mining pools (as shown in Figure B.5). Between 2018 and April 2019, only BTC.com and F2Pool alone accounted for most of the accelerated transactions. However, as of December 2020, we see that BTC.com accounts for a very small fraction of accelerated transactions, with AntPool, Huobi, and F2Pool accounting for most of the accelerated transactions.
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:::info Author:
(1) Johnnatan Messias Peixoto Afonso
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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2025-05-09 21:00:02
CHAPTER 1: INTRODUCTION
CHAPTER 2: BACKGROUND
2.1 Blockchains & smart contracts
2.2 Transaction prioritization norms
2.3 Transaction prioritization and contention transparency
2.5 Blockchain Scalability with Layer 2.0 Solutions
CHAPTER 3. TRANSACTION PRIORITIZATION NORMS
CHAPTER 4. TRANSACTION PRIORITIZATION AND CONTENTION TRANSPARENCY
Transaction Prioritization and Contention Transparency
4.2 On contention transparency
CHAPTER 5. DECENTRALIZED GOVERNANCE
CHAPTER 6. RELATED WORK
6.1 Transaction prioritization norms
6.2 Transaction prioritization and contention transparency
CHAPTER 7. DISCUSSION, LIMITATIONS & FUTURE WORK
7.3 Voting power distribution to amend smart contracts
\ Appendices
APPENDIX A: Additional Analysis of Transactions Prioritization Norms
APPENDIX B: Additional analysis of transactions prioritization and contention transparency
APPENDIX C: Additional Analysis of Distribution of Voting Power
In this thesis, we adopted a data-driven approach to examine fairness within blockchain contexts, focusing on three key aspects: (i) Fairness in ordering; (ii) Fairness in transparency; and (iii) Fairness in voting power to amend smart contract applications.
\ Our findings reveal a discrepancy between assumed prioritization norms and actual practices within the blockchain community. In particular, miners often deviate from these norms by prioritizing transactions that serve their own interests or friendly miners. This contradicts the principle of exclusively fee-based prioritization.
\ Through active experiments, we have uncovered instances of miner collusion involving dark-fee transactions. These transactions provide miners with off-chain incentives in a non-transparent manner, contributing to a lack of transparency in the ecosystem. These fees are kept private between the miner and the issuer of a particular transaction, even after the transaction is confirmed on the blockchain. This exacerbates the challenge of accurately estimating fees. As a result, transaction issuers struggle to determine appropriate fees because they do not have a complete view of all transaction fees being offered.
\ In addition, blockchain applications, or smart contracts, are often amended by governance protocols. These protocols aim to distribute decision-making power among participants. However, we show that the concentration of voting power based on token ownership skews the dynamics of decision-making. A small subset of participants with a significant token stake wields disproportionate influence, allowing them to shape proposals and votes in line with their self-interest. This practice undermines the true decentralization of decision-making power in the blockchain ecosystem.
\ We believe that our findings provide valuable insights for designing new and more fair blockchains. Additionally, to ensure the reproducibility of our results, we have made the code and data sets used in this thesis publicly available (Messias, 2023a,b).
Congestion in Mempool is typical not only in A (as discussed in §3.2.1), but also in B. Indeed, Figure A.1 reveals a huge variance in Mempool congestion, much higher than that observed in A. Mempool size fluctuations in B are, for instance, approximately three times higher than that in A (with the size of unconfirmed transactions at one point in time exceeding almost 50 times the maximum block size). Around June 22nd, there was a surge in Bitcoin price following the announcements of Facebook’s Libra[21] and another surge around June 25th after the news of US dollar depreciation (Paul R. La Monica, 2019). These price surges significantly increased the number of transaction issued, which in turn introduced delays. As a consequence, at times, Mempool in B takes much longer duration than in A to be drained of all transactions.
Table A.1 shows the contribution of transaction fees towards miners’ revenue across all blocks mined from 2016 to 2020. In 2018, fees accounted for an average of 3.19% of miners’ total revenue per block; in 2019 and 2020 were 2.75% and 6.29%, respectively. However, if we consider only blocks mined from May 2020 (i.e., blocks with a mining reward of 6.25 BTC), the fees account for, on average, 8.90% with an std. of 6.54% in total. Therefore, revenue from transaction fees is increasing (Easley et al., 2017), and it tends to continue.
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In Figure A.3, we show the fee rates of transactions observed in 4 different bins or congestion levels in data set B. Each bin in the plot corresponds to a specific level of congestion identified by the Mempool size: lower than 1 MB (no congestion), in (1, 2] MB (lowest congestion), in (2, 4] MB, and higher than 4 MB (highest congestion). Fee rates at high congestion levels are strictly higher (in distribution, and hence also on average)
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\ than those at low congestion levels. Users, therefore, increase transaction fees to mitigate the delays incurred during congestion.
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To examine the miners’ behavior during the Twitter scam attack from July 14th to August 9 th, 2020, we selected all blocks mined (3697 in total, containing 8,318,621 issued transactions) during this time period from our data set C. If we rank the MPOs responsible for these blocks by the number of blocks (B) mined (or, essentially, the approximate hashing capacity h), the top five MPOs (refer Figure A.5) turn out to be Poolin (B: 565; h: 15.28%), F2Pool (B: 536; h: 14.5%), BTC.com (B: 424; h: 11.47%), AntPool (B: 404; h: 10.93%), and Huobi (B: 353; h: 9.55%)
In this experiment, we compare the transaction-acceleration fee with the typical transaction fees in Bitcoin. To this end, we retrieved a snapshot containing 26,332 unconfirmed transactions from our node’s Mempool on November 24th 2020 at 10:08:41 UTC. Then, for each transaction, we searched its respective transaction accelerator price (or acceleration fee) via the acceleration service provided by BTC.com (BTC.com, 2022). We inferred the acceleration fees for 23,341 (88.64%) out of the 26,332 unconfirmed transactions. Figure A.6 shows the CDF of both the Bitcoin transaction fees and the acceleration fees provided by BTC.com. Acceleration fee is on average 566.3 times higher (4734.67 of std.) and on median 116.64 times higher than the Bitcoin transaction fees. At the time of this experiment, 1 BTC was worth 18,875.10 USD.
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:::info Author:
(1) Johnnatan Messias Peixoto Afonso
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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[21] On June 18th, Facebook announced its cryptocurrency, Libra, which was later renamed to Diem. https://www.diem.com
