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Blockchain in Healthcare: Real-World Case Studies and Implementations – Web3oclock

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Blockchain in Healthcare: Real-World Case Studies and Implementations – Web3oclock


Role of Blockchain in Healthcare

Benefits of Using Blockchain in Healthcare

Case Studies & Implementations of Blockchain in Healthcare

Challenges & Limitations of Using Blockchain in Healthcare

10. Public Health Surveillance:

1. MedRec: Blockchain for Electronic Medical Records (EMR)

2. MedicalChain: Blockchain for Secure Health Record Sharing

3. Guardtime: Blockchain for Healthcare Data Integrity in Estonia

4. Pharmaceutical Supply Chain: IBM and Walmart’s Blockchain Initiative

5. SimplyVital Health: Blockchain for Healthcare Payments and Data Sharing

6. Solve. Care: Blockchain for Healthcare Administration

7. BurstIQ: Blockchain for Precision Medicine and Data Sharing



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Solving post-TGE fundraising challenges: How Vellos is transforming token markets

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Solving post-TGE fundraising challenges: How Vellos is transforming token markets


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The journey for blockchain protocols doesn’t end after their Token Generation Event (TGE); in many ways, it’s just the beginning. While pre-TGE projects often bask in the glow of investor hype and support, post-TGE protocols frequently face significant hurdles when attempting to raise additional funds by selling discounted tokens from their treasury.

Fragmented fundraising environments and liquidity issues plague these projects, diverting their focus from development to fundraising. Enter Vellos—a groundbreaking platform designed to address these challenges head-on.

The Fragmented Fundraising Environment

For pre-TGE projects, the fundraising landscape is bustling with venture capitalists, brokers, and launchpads eager to invest in the next big thing. However, this enthusiasm often wanes once the TGE is complete. Post-TGE protocols find themselves in a fragmented environment where:

Limited Access to Investors: VCs and funds that specialize in pre-TGE investments may not extend their services to post-TGE projects, leaving a gap in support.Focus on New Hype: Many investors chase after the newest projects generating buzz, sidelining established protocols that still require capital to grow.Time-Consuming Funding Hunts: Protocol founders are forced to spend valuable time seeking funding instead of advancing their platforms.

The lack of a centralized marketplace for post-TGE investments means there’s no streamlined way for these projects to connect with suitable investors, impeding their progress and innovation.

The Liquidity Conundrum

Liquidity poses another significant challenge:

Difficult Offloading: Existing players in the discount token market deal primarily with pre-existing SAFTs or token warrants laden with conditions, making them hard to offload.Large Ticket Sizes: Big-ticket discount token deals are tough to sell, limiting the pool of potential buyers.Concentration of Tokens: When large amounts of a protocol’s tokens are held by a single entity, it creates selling pressure as tokens vest, potentially destabilizing the token’s value.

These issues contribute to a less dynamic market, where both buyers and sellers face obstacles that hinder the ecosystem’s overall health.

Introducing Vellos: A New Opportunity for Post-TGE Protocols

Vellos emerges as a solution to these persistent problems. It is a discount token marketplace specifically tailored for post-TGE projects, aiming to democratize access to discounted tokens and streamline the fundraising process.

What is Vellos?

Vellos is a platform where projects can offer their treasury tokens at a discount directly to their loyal community and a broad spectrum of other users. By breaking down large raises into smaller, manageable ticket sizes, Vellos enables individual users to participate in funding rounds traditionally reserved for big players.

How Vellos Addresses the Challenges

Unified Marketplace: Vellos creates a centralized environment where post-TGE projects and interested investors can connect effortlessly.Enhanced Liquidity: By dealing with new investment vehicles rather than pre-existing ones, Vellos simplifies the process of buying and selling discounted tokens.Dynamic NFTs: Vellos employs proprietary dynamic NFTs that act as keys to the underlying tokens. As tokens vest, NFT owners can claim them at will, adding a layer of flexibility and security.

Key Features of Vellos

Automated Onboarding: The entire process is permissionless and automated, including wallet and token checks, reducing administrative burdens.NFT Resale Marketplace: To introduce liquidity for buyers, NFTs can be traded at any time, even before vesting begins, providing an exit strategy and market dynamism.Revenue Sharing: Projects receive a portion of the revenue from secondary NFT trades, creating an ongoing revenue stream.

The Benefits for Protocols

By leveraging Vellos, protocols can unlock several advantages:

Direct Funding from the Community

Protocols can raise funds by offering discounted tokens directly to the community that has supported them from the start. This inclusivity strengthens community bonds and diversifies the investor base.

Creation of ‘Micro-KOLs’

Each user who purchases discounted tokens becomes a ‘micro-Key Opinion Leader.’ With skin in the game at a favorable price, these users are more likely to advocate for the project, amplifying its reach and influence.

Controlled Selling Pressure

Protocols can set custom cliff and vesting terms proportional to the discount offered, allowing them to manage the selling pressure and maintain token stability.

Consistent Revenue Stream

By earning a share of the revenue from secondary NFT trades, protocols establish an additional, consistent income source that can fund ongoing development and operations.

Focus on Core Development

With fundraising streamlined through Vellos, protocol founders can dedicate more time and resources to building and enhancing their products, driving innovation and value creation.

Vellos Telegram App: Bootstrapping Community Growth

To expand and engage its community, Vellos recently launched a Telegram app that has quickly gained traction. Within the first two weeks since its launch, the app amassed 240,000 active users, signaling strong interest and participation.

The app allows users to interact with several engaging features, earning points along the way:

Doge To Earn: Players can participate in the “Doge To Earn” game, where they dodge enemies to accumulate points. The more successful they are at avoiding obstacles, the more points they can earn. Players can also use these points to upgrade their in-game features, enabling them to boost their point-earning potential over time.

Invite: Users can invite their friends to join the app, earning significant points for each referral. Additionally, they benefit from passive point-earning opportunities, receiving 16% of the points earned by their direct friends and 8% from indirect friends.

Task: Players can complete tasks, such as following Vellos on various social networks (SNS), to earn extra points and rewards.

Looking beyond just point farming, Vellos aims to utilize the Telegram app as a platform for education and community-building, providing valuable content and resources that enhance user experience and engagement over time.

Conclusion

Vellos stands poised to disrupt the post-TGE fundraising landscape by addressing the fragmentation and liquidity issues that have long hindered protocols.

By creating a unified, automated, and liquid marketplace for discounted tokens, Vellos empowers projects to connect with a broader investor base, engage their communities, and focus on what truly matters—building exceptional products in the web3 space.

To learn more:

Disclaimer: CryptoSlate is a strategic advisor for Vellos.

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Blockchain Whitepaper – A Comprehensive Overview for Investors and Enthusiasts – Web3oclock

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Blockchain Whitepaper – A Comprehensive Overview for Investors and Enthusiasts – Web3oclock


Importance of whitepapers in blockchain projects

Standard Whitepaper Components

How to read and analyze a whitepaper

Importance of whitepapers in blockchain projects: 

1. Clear Project Vision:

2. Technical Blueprint:

3. Problem-Solution Explanation:

4. Transparency and Trust:

5. Investor Attraction:

6. Guidance for Developers:

7. Legal and Regulatory Clarity:

8. Marketing Tool:

9. Community Engagement:

10. Roadmap and Development Timeline:

The Components of Blockchain Whitepaper:

1. Introduction:

2. Market Overview:

3. Problem Statement:

4. Solution:

5. Technology and Architecture:

6. Tokenomics (Token Economy):

7. Roadmap:

8. Team:

9. Legal and Regulatory Information:

10. Conclusion:

How to Read and Analyze a Blockchain Whitepaper: 5 Simple Steps

1. Start with the Problem and Solution:

2. Understand the Technology:

3. Check the Tokenomics:

4. Evaluate the Team and Roadmap:

5. Look for Legal and Regulatory Compliance:

Top Blockchain Whitepapers: 

1. Bitcoin Whitepaper (2008):

2. Ethereum Whitepaper (2013):

3. Polkadot Whitepaper (2016):

4. Cardano Whitepapers (2017):

5. Libra (Diem) Whitepaper (2019):

6. Filecoin Whitepaper (2017):

7. Solana Whitepaper (2017):



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Tron Blockchain – Everything You Need to Know About Its Features, Tokens, and Future – Web3oclock

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Tron Blockchain – Everything You Need to Know About Its Features, Tokens, and Future – Web3oclock


History of Tron blockchain

Tron Token in Tron Blockchain 

Features and use cases of Tron Blockchain

How Tron Blockchain Works

Benefits of Tron Blockchain

Tron Blockchain’s ecosystem and dApps

Future of Tron Blockchain

Tron Blockchain is a decentralized platform, focused on building a free, global digital content entertainment system with distributed storage technology, enabling easy and cost-effective sharing of digital content. Tron Blockchain tries to eliminate intermediaries, allowing creators to interact with consumers. It has become a major player in the Blockchain space. 

History of Tron Blockchain:

What is TRX in Tron Blockchain? 

How Does Tron Blockchain Work? 

Step 1: Understanding Bandwidth Points

Step 3: Earning and Spending TRX

Step 4: Using Smart Contracts

Step 5: The Broker Ratio and Reward Distribution

Example in Action

1. High Transaction Speed:  

2. Low Transaction Costs:

3. Scalability:

4. Delegated Proof of Stake (DPoS):

5. Support for dApps:

6. Rewards: 

7. Staking and Passive Income:

1. TRX (Tron’s Native Token) in Tron Blockchain:

Examples of Popular dApps in Tron’s Ecosystem:

JustLend:

Sun.io: 

WinkLink: 

BitTorrent (BTT): 

WINk: 

JUST Network:

3. TronLink Wallet:

4. BitTorrent Chain (BTTC):

5. Tron DeFi (Decentralized Finance):

6. Entertainment on Tron:



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SocialFi super app Phaver launches SOCIAL token airdrop rewarding Lens, Farcaster users

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SocialFi super app Phaver launches SOCIAL token airdrop rewarding Lens, Farcaster users


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SocialFi app Phaver launched its Phavercoin (SOCIAL) token today, initiating the Phairdrop event that marks the platform’s transition to a token-powered decentralized social ecosystem. The token generation event signifies Phaver’s move from the “DeSoc” era to the “SocialFi” era, integrating the SOCIAL token into its point economy and in-app functionalities.

As of press time, SOCIAL is trading around $0.0148 with a market cap of $146 million.

The SOCIAL token has a total supply of 10 billion tokens. While the initial circulating supply has not been specified, the token distribution involves several methods. Users who opted in on Cyber will receive tokens directly in their Phaver Primary Wallet today. Others can claim tokens on the Phaver website using their Primary Wallet, which requires Base ETH for gas fees. Eligibility criteria include possessing a Lens profile, a Farcaster profile, or at least one Cred item connected on Phaver. The Season 1 snapshot has already determined user allocations based on these criteria.

Trading of SOCIAL commenced today on multiple exchanges. Bybit has confirmed the listing, making the token accessible to its user base. The SOCIAL/USDT trading pair began trading on Sept. 24 at 10:00 UTC. Deposits are already open on MEXC, and withdrawals will be enabled on Sept. 25 at 10:00 UTC. The token is issued on the Base network, with the contract address ‘0xD3C68968137317a57a9bAbeacC7707Ec433548B4‘ provided for verification.

Phavercoin utility in decentralized social media apps

The utilities of the SOCIAL token within the Phaver ecosystem are multifaceted. Users can earn SOCIAL by redeeming Phaver Points in unique campaigns, with higher Cred levels affording better point-to-token conversion ratios. Holding SOCIAL tokens enhances a user’s Cred score and accumulates more Points, which are instrumental in the platform’s reward system. Higher Cred levels unlock benefits such as increased monthly withdrawal quotas, VIP support, preferential visibility, and early access to new features and whitelists.

Per Phaver’s whitepaper, the token can be utilized for various platform features, including advertising, boosting posts, and collaboration tools. Additionally, SOCIAL tokens can be used to purchase Points within the app, offering a more cost-effective option than other in-app payment methods.

Tokenomics details reveal that 300 million tokens (3% of the total supply) are allocated for user airdrops. Season 2 redemptions are set at 200 million tokens (2% of the total supply), scheduled to occur one month after the token generation event. Eligibility for Season 2 redemptions depends on user levels, with Level 1 users being ineligible. The conversion ratio for redemptions is influenced by the user’s Cred level and average SOCIAL holdings over 30 days before redemption, potentially offering up to a 60x multiplier.

Phaver’s platform integrates with Lens Protocol and Farcaster Protocol, enabling cross-posting capabilities that enhance user experience across decentralized social networks. The Phaver Point system incentivizes community participation, while the Cred credibility score system is designed to prevent bot abuse and improve the utility of NFTs within the platform.

Holding SOCIAL tokens may provide access to future opportunities, including whitelists, airdrops, and benefits from Phaver’s partners, which include notable companies like Animoca, Pudgy Penguins, and Rakuten Group. By encouraging users to hold tokens, Phaver aims to foster a sustainable ecosystem that rewards long-term engagement and supports the token’s value over time.

As Phaver embarks on this new phase with the launch of the SOCIAL token, users and investors are advised to stay informed through official channels for updates on token distribution, exchange listings, and platform developments and to stay vigilant of scams. Links to token claiming are available through Phaver’s iOS and Android apps via the Phairdrop website.

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0G Labs: A Comprehensive Overview

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0G Labs: A Comprehensive Overview



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0G is a transformative blockchain infrastructure project that facilitates high-performance applications such as on-chain AI, gaming, and decentralized finance (DeFi) through an advanced data availability and decentralized storage system. By providing a scalable, secure, and efficient platform, it seeks to address some of the most pressing challenges in the blockchain space, including scalability, data processing speeds, and interoperability.

The goal of 0G is to democratize access to blockchain technology and enable the development of applications that were previously not feasible due to technological constraints, thus bridging the gap between Web3 capabilities and Web2 performance standards.

Origin Story

0G Labs was founded to address the limitations of existing blockchain data storage and availability solutions. The founders recognized that as blockchain adoption grew, so did the need for efficient and scalable data solutions. Traditional blockchains struggled with data storage and throughput, leading to high costs and inefficiencies. 0G Labs’ mission is to create an infinitely scalable data layer that can handle the increasing data demands of modern blockchain applications.

Who are the 0G founders?

The founders of 0G are a group of individuals with deep expertise in blockchain technology, AI, and decentralized systems, each bringing a unique set of skills and experiences to the project:

Michael Heinrich- Acting as the CEO, Michael has a diverse background spanning software development, technical product management, and business strategy. Before 0G, he scaled a Web 2.0 company to significant heights, showcasing his ability to lead and grow technology-driven enterprises.

Ming WuAs the CTO, Ming brings an extensive research and development background from his time at Microsoft Research Asia, where he focused on distributed systems, storage, computation, and AI platforms. His expertise is crucial in building 0G’s technical foundation.

Fan LongAs Chief Strategy and Security Officer (CSSO), Fan combines his academic research in system security and blockchain with entrepreneurial experience. His academic tenure and co-founding experience in blockchain projects lend 0G a robust strategic and security direction.

Thomas YaoAs the Chief Business Officer (CBO), Thomas’s background in physics, self-driving technology, and venture capital provides 0G with strategic business insights and investment expertise. His experience in early blockchain investments and technology startups is invaluable for navigating the project’s business aspects.

Together, these founders form a well-rounded team equipped to tackle the complexities of building a next-generation blockchain infrastructure. They aim to make AI and other high-performance applications more accessible and efficient on the blockchain.

0Gs Architecture

0Gs high scalability hinges on separating the data availability workflow into:

a) The Data Storage Lane: This lane achieves horizontal scalability through well-designed data partitioning for large data transfers. Significant data can be stored or accessed nearly instantaneously.

b) The Data Publishing Lane: Guarantees data availability using a quorum-based system that assumes an honest majority, with the quorum randomly selected via VRF. This only takes up a tiny flow of data and avoids any data broadcasting bottlenecks, allowing space for the larger Data Storage Lane transfers.

0G Storage is 0Gs on-chain database comprising a network of Storage Nodes actively participating in a PoW-like mining process known as Proof of Random Access (PoRA). PoRA requires miners to correctly answer random queries relating to archived data, with the corresponding Storage Node rewarded accordingly. 0G focuses on rewarding nodes for their contributions rather than punishing them for misbehavior to encourage network participation in network maintenance and improve scalability.

0G DA is 0Gs infinitely scalable DA Layer directly built on top of 0G Storage. A quorum-based architecture provides DA confirmation using an honest majority assumption whereby nodes agree on available data. A VRF is used to randomize the quorum, while GPUs accelerate the erasure coding process needed to store data properly.

What Does 0G Solve?

The need for greater Layer 2 (L2) scalability has directly coincided with the recent rise of DA Layers, with L2s widely agreed upon as the solution to Ethereums scaling woes. L2s conduct transactions off-chain and settle on Ethereum for security purposes, meaning that they must post the actual transaction data somewhere so that it may be confirmed as valid. Publishing data onto Ethereum directly spreads its high fees amongst L2 users, increasing scalability.

DALs provide a more efficient means of publishing off-chain data and keeping it available for anyone to inspect.

That being said, existing DALs are inadequate for supporting the exponentially increasing amount of data arriving on-chain. They cannot store vast sums of data and have limited throughput, which is especially concerning for data-intense use cases like on-chain AI.

0G provides a 1,000x performance improvement over Ethereums danksharding and a 4x improvement over Solanas Firedancer, providing the necessary infrastructure to massively scale Web3s data needs. AI is a significant focus, as 0G Storage can store vast datasets while using 0G DA to quickly create AI models on-chain.

Beyond this, other use cases include:

0G Labs Use Cases

a) L1s / L2s: These parties may use 0Gs AI models or 0G for data availability and storage. Partners include Polygon, Arbitrum, Fuel, Manta Network, and more.

b) Bridges: Given that networks can easily store their state using 0G, state migration is possible between networks, facilitating secure cross-chain transfers. For example, relevant user balances can be stored as data and communicated cross-chain for fast, accurate transfers.

c) Rollups-as-a-Service (RaaS): a DA option and data storage infrastructure for RaaS providers like Caldera and AltLayer.

d) DeFi: 0Gs quick and scalable DA may support highly efficient DeFi on specific L2s & L3s due to fast settlement and storage, such as high-frequency trading.

e) On-chain Gaming: Gaming requires vast amounts of cryptographic proof-related data that need to be reliably stored on top of all regular metadata, such as a given players assets, points, actions, and more.

f) Data Markets: It makes the most sense that Web3 data markets truly store their data on-chain, which is currently only feasible on a large scale using 0G.

0G is the scalable, low-cost, and fully programmable DA solution necessary to truly bring vast amounts of data on-chain. This would not be possible without 0Gs complementary role as an on-chain data storage solution, which unlocks even more use cases (such as providing database infrastructure for any on-chain application).

0G can store any type of Web2 or Web3 data while efficiently proving its data availability. The benefit of this extends far beyond confirming Layer 2 transactions, as any data (large-scale datasets, a blockchains state, crypto.

How does 0G differentiate itself from projects like Avail, Eigen DA, Espresso, and Celestia?

0G sets itself apart by focusing on unmatched performance, scalability, and flexibility, tailored explicitly for high-performance applications such as AI, gaming, and DeFi. Heres how:

Performance: With speeds up to 50 gigabytes per second, 0G offers Web2-like efficiency with Web3’s decentralization. Its competitors dont match this level of scalability and cost-effectiveness.

Scalability: 0G’s storage network can scale horizontally without bottlenecks, outperforming systems like Celestia, which, while scalable, do not offer this level of flexibility.

Decentralized Storage: Unlike Eigen DA or Espresso, 0G integrates decentralized storage directly into its platform, supporting a wide variety of data types, including AI models and gaming assets.

Programmability: Developers can fully customize data persistence, replication, and location through smart contracts, offering flexibility unmatched by other DA solutions.

AI Focus: While others focus on general blockchain capabilities, 0G is designed to support AI and high-performance applications, ensuring long-term relevance in a rapidly evolving landscape.

What future developments can we expect from 0G?

Key advancements to look forward to include:

Enhanced Data Availability Services: Expanding its DA layer to support more complex applications and bridging the Web2-Web3 gap.

Deeper AI Integration: Introducing decentralized AI model marketplaces and computational frameworks to democratize AI development.

Community-Led Governance: Transitioning to decentralized governance, giving the community more control over 0Gs direction.

These developments will further 0G’s role as a pioneer in blockchain infrastructure, enabling the creation of previously thought impossible applications.

0G Labs Roadmap

0g Lbas is still in the planning phase for the official roadmap launch, and the current version you see was shared directly within their Discord community. Once everything is properly finalized, they will release the full, updated roadmap. Stay tuned!

0G Labs Investors

0G recently closed a $35 million funding round with backing from major Web3 investors like Hack VC, Bankless, Delphi Digital, and Polygon. This strong investor base highlights the potential and credibility of the project.

Compute Meets Infinite Scalability: Spheron and 0G Labs Unite

Spheron Network and 0G Labs have formed a strategic partnership to transform decentralized infrastructure. By combining Spherons expertise in scalable compute solutions with 0Gs advanced data availability layer, this collaboration aims to boost the performance of decentralized networks. Spheron will supply GPUs and CPUs to power 0G Labs, enhancing the efficiency of their on-chain database and data services.

Additionally, Spheron’s Supernoderz platform will make it easy for users to deploy 0G Labs nodes with a simple one-click setup. This partnership will create a stronger and more accessible decentralized ecosystem, enabling advanced applications in DeFi, AI, and more. Together, Spheron and 0G Labs will drive the growth of decentralized technologies and innovation in the Web3 space.

How can you get involved?

There are numerous ways to engage with 0Gs growing ecosystem:

Join the Community: Become part of 0Gs discussions on Discord, visit the official website, and subscribe to their newsletter.

Amplify Awareness: By following and sharing updates on Twitter, Telegram, LinkedIn, and relevant forums to help spread the word.

Contribute: Participate in content creation initiatives, submit feature suggestions, or attend webinars to deepen your knowledge of 0G.

Community contributors can earn rewards like early access to testnets, 0G tokens, and NFTs. Stay tuned to official channels for upcoming engagement opportunities.

Testnet Newton v2 is live

The journey to revolutionize Web3 begins now! 0GLabs Testnet Newton v2 is live, packed with powerful features to empower users and developers on the road to unlock groundbreaking on-chain use cases

Conclusion

0G Labs is at the forefront of solving some of the blockchain industry’s most pressing data scalability challenges. With its innovative architecture and growing ecosystem, 0G Labs is poised to play a crucial role in the future of blockchain and Web3 applications.

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Spheron and Heurist Take on AI Innovation

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Spheron and Heurist Take on AI Innovation


In this exciting crossover episode of Tech Fusion, we dive deep into the world of AI, GPU, and decentralized computing. This discussion brings together minds from Spheron and Heurist to explore cutting-edge innovations, challenges, and the future of technology in this space. Let’s jump straight into the conversation where our host Prashant (Spheron’s CEO) and JW and Manish from Heurist take center stage.

If you want to watch this episode, click below or head over to our YouTube channel.

Introduction to the Tech Fusion Episode

Host: “Welcome, everyone! This is Episode 9 of Tech Fusion, and we’re in for a treat today. This is our first-ever episode featuring four guests, so it’s a big one! I’m Prashant, and Prakarsh from Spheron is with me today. We have special guests from Heurist Manish and JW. It’s going to be a deep dive into the world of AI, GPUs, decentralized computing, and everything in between. So let’s get started!”

The Evolution of AI Models and Decentralized Computing

Prashant: “JW and Manish, let’s start by talking about AI models. We’ve recently seen advancements in AI reasoning capabilities, and it’s clear that decentralized computing is catching up. How long do you think it will take for end-users to fully harness the power of decentralized AI models?”

JW (Heurist): “Great question. First off, thank you for having us here! It’s always exciting to share thoughts with other innovative teams like Spheron. Now, on AI reasoning—yes, OpenAI has been making waves with its models, and we’ve seen open-source communities attempt to catch up. Generally, I’d say the gap between open-source and closed-source AI models is about six to twelve months. The big companies move faster because they have more resources, but the open-source community has consistently managed to close the gap, especially with models like LLaMA catching up to GPT-4.”

Challenges in Training and Inference with Decentralized GPUs

Prashant: “Decentralized computing is a hot topic, especially in how it relates to the scalability of training and inference models. JW, you mentioned some experiments in this space. Could you elaborate?”

JW: “Absolutely! One exciting development comes from Google’s research into decentralized training. For the first time, we’ve seen large language models (LLMs) trained across distributed GPUs with minimal network bandwidth between nodes. What’s groundbreaking is that they’ve reduced network transmission by over a thousand times. It’s a big leap in showing that decentralized compute isn’t just theoretical—it’s real and can have practical applications.”

The Role of VRAM and GPU Pricing in AI Models

Prakarsh (Spheron): “That’s fascinating. Something that I find equally intriguing is the premium we’re paying for VRAM. For instance, an H100 GPU has 80 GB of VRAM, while a A6000 has 48 GB. We’re essentially paying a high premium for that extra VRAM. Do you think we’ll see optimizations that reduce VRAM usage in AI training and inference?”

Manish (Heurist): “You’re absolutely right about the VRAM costs. Reducing those costs is a huge challenge, and while decentralized computing might help alleviate it in some ways, there’s still a long road ahead. We’re optimistic, though. With technologies evolving, particularly in how models are optimized for different hardware, we may soon see more cost-efficient solutions.”

Decentralized Compute’s Impact on AI Training and Inference

Prashant: “So, let’s dig deeper into the training versus inference debate. What’s the biggest difference you’ve seen between these two in terms of cost and resources?”

JW: “Great question. Based on our data, about 80-90% of compute resources are spent on inference, while only 10% goes to training. That’s why we focus heavily on inference at Heurist. Inference, although less resource-intensive than training, still requires a robust infrastructure. What’s exciting is how decentralized compute could make it more affordable, especially for end-users. A cluster of 8 GPUs, for instance, can handle most open-source models. That’s where we believe the future lies.”

The Vision for Fizz Node: Decentralized Inferencing

Prashant: “At Spheron, we’re working on something called Fizz Node, which allows regular computers to participate in decentralized inferencing. Imagine users being able to contribute their GPUs at home to this decentralized network. What do you think of this approach?”

JW: “Fizz Node sounds incredible! It’s exciting to think of regular users contributing their GPU power to a decentralized network, especially for inference. The idea of offloading lower-compute tasks to smaller machines is particularly interesting. At Heurist, we’ve been considering similar ideas for some time.”

Technological Challenges of Distributed Compute

Prakarsh: “One challenge we’ve seen is the efficiency of decentralized nodes. Bandwidth is one thing, but VRAM usage is a critical bottleneck. Do you think models can be trained and deployed on smaller devices effectively?”

Manish: “It’s possible, but it comes with its own set of complexities. For smaller models or highly optimized tasks, yes, smaller devices can handle them. But for larger models, like 7B or 45B models, it’s tough without at least 24 GB of VRAM. However, we’re optimistic that with the right frameworks, it can become feasible.”

Prashant: “I noticed Heurist has built several interesting tools like Imagine, Search, and Babel. How did those come about, and what’s the community response been like?”

JW: “The main goal of our tools is to make AI accessible and easy to use. When we launched Imagine, an AI image generator, the response was overwhelmingly positive. It stood out because we fine-tuned models specifically for our community—things like anime style or 2D art. It really showcased how diverse open-source AI could be. We’ve seen huge adoption in the Web3 space because users don’t need a wallet or even an account to try them. It’s all about creating a seamless user experience.”

AI-Driven Translation: Bringing Global Communities Together

Prashant: “Speaking of seamless experiences, I’m intrigued by your Discord translation bot. It sounds like a game-changer for communities with users from all over the world.”

JW: “It really is! The bot helps our community communicate across languages with ease. We wanted to make sure that AI could bridge language barriers, so now, anyone can send messages in their native language, and they’ll automatically be translated for the rest of the group. It’s been a huge hit, especially with our international users.”

Exploring Cursor: A Developer’s Dream Tool

Prakarsh: “Recently, I’ve heard developers rave about Cursor as a coding assistant. Have you integrated Cursor with Heurist?”

Manish: “Yes, we’ve tested Cursor with our LLM API, and the results have been fantastic. It feels like having multiple interns working for you. With AI-driven development tools like Cursor, it’s becoming much easier to code, even for those who’ve been out of the loop for years.”

The Future of AI: What’s Next for Spheron and Heurist?

Prashant: “Looking ahead, what are Heurist’s plans for the next couple of months?”

JW: “We’re working on some exciting things! First, we’ll be sponsoring DEFCON, and we’re collaborating with an AI partner to promote our Heurist API services. We’re also finalizing our tokenomics for the Heurist network, which we’re really excited about. We’ve been putting a lot of effort into designing a sustainable economic model, one that avoids the pitfalls we’ve seen in other projects.”

Final Thoughts: AI, GPUs, and Beyond

Prashant: “Before we wrap up, let’s talk about the episode’s title, AI, GPUs, and Beyond. What do you think the ‘beyond’ part will look like in the next few years?”

JW: “I believe AI will become so integrated into our daily lives that we won’t even notice it. From how we browse the web to how we work, AI will power much of it without us even being aware of it.”

Manish: “I agree. AI will blend seamlessly into the background, making everything more efficient. The future is in making these technologies invisible but essential.”

Conclusion

This episode of Tech Fusion was a fascinating exploration of how AI, GPUs, and decentralized compute will shape our future. From the challenges of VRAM usage to the exciting potential of Fizz Node and Heurist’s ecosystem, it’s clear that the landscape of technology is rapidly evolving. If you haven’t already, now is the time to dive into the world of decentralized AI and GPU computing!

FAQs

1. What is Fizz Node, and how does it work?
Fizz Node allows regular users to contribute their GPU power to a decentralized network, particularly for AI inferencing tasks. It optimizes small-scale devices to handle lower-compute tasks efficiently.

2. What is the difference between AI training and inference?
Training involves teaching the AI model by feeding it data, whereas inference is the process of applying the trained model to new inputs. Inference typically requires fewer resources than training.

3. How does Heurist’s Imagine tool work?
Imagine is an AI-driven image generation tool that allows users to create art in different styles, from anime to 3D realistic models, using fine-tuned models developed by the Heurist team.

4. What makes Heurist’s translation bot unique?
Heurist’s translation bot enables seamless communication across languages in Discord communities, automatically translating messages into the preferred language of the group.

5. What’s the future of decentralized GPU computing?
The future lies in making decentralized computing more accessible, cost-effective, and scalable, potentially competing with centralized giants like AWS. The goal is to decentralize much of the current AI compute load.



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How the $1.4 billion crypto prediction market industry took off in 2024 – report

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How the .4 billion crypto prediction market industry took off in 2024 – report


Receive, Manage & Grow Your Crypto Investments With Brighty

Prediction markets are experiencing growth, with platforms like Polymarket advancing the sector. Castle Capital reported in its latest deep dive that these markets enable users to bet on future events using crypto, moving traditional gambling into a decentralized domain. This shift allows participants to trade against each other rather than a centralized house, increasing transparency and resistance to manipulation.

Castle Capital outlined how prediction markets were historically centralized, limiting user participation and flexibility. The introduction of blockchain technology has allowed these markets to become decentralized, allowing users to create their own markets and conditions. Since the launch of another prediction market, Augur, in 2015, prediction markets have been recognized as a prominent application of blockchain technology, although mainstream attention has only recently intensified.

The sector’s total value locked has reached $162 million, significantly increasing user engagement and transaction volumes. Platforms like Azuro and Polymarket have facilitated this growth by offering different approaches. Polymarket, based on Polygon, operates using an order book model, focusing on major political and news-related events. It has processed over $1.4 billion in volume, becoming a key platform for betting on events like the US presidential elections.

Prediction Markets Volume (Castle Capital)
Prediction Markets Volume (Castle Capital)

Castle Capital explained that Azuro utilizes a peer-to-pool design, allowing users to provide liquidity to pools that serve multiple markets. This model diversifies risk and improves capital efficiency, catering primarily to sports betting. Azuro has handled over $200 million in prediction volume, attracting users who engage in recurring bets across various sports events.

Both platforms aim to expand their market offerings. Polymarket seeks to reduce its reliance on political events by adding more diverse markets, while Azuro reportedly plans to include political and news markets alongside sports. The growth of these platforms highlights the increasing interest in decentralized prediction markets as tools for gauging public sentiment.

Castle Capital outlined the challenges that remain for mainstream adoption, including liquidity issues, regulatory uncertainties, and the need for improved user experiences. Ensuring reliable oracles and data accuracy is crucial, as is addressing scalability concerns on blockchain networks. Overcoming these obstacles requires innovation and engagement with regulatory bodies.

As Castle Capital noted, prediction markets have the potential to provide accurate public sentiment on various topics, moving beyond seasonal hype to become integral tools for decision-making. Integrating artificial intelligence and expanded market offerings may enhance their utility and appeal. Prediction markets could offer news outlets decentralized sentiment data and influence political discourse.

The future of prediction markets appears promising, with platforms like Azuro and Polymarket at the forefront. Their continued growth and adaptation may solidify their position in the crypto landscape, offering valuable insights and opportunities for users forecasting future events.

According to Castle Capital’s report, the evolution of prediction markets reflects a broader trend of increasing adoption of decentralized applications. However, whether these platforms can sustain their momentum and navigate the challenges ahead to achieve mainstream acceptance remains to be seen.

Castle Capital’s complete deep dive report is available as part of its Castle Chronicles series.

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How Much GPU Memory is Required to Run a Large Language Model?

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How Much GPU Memory is Required to Run a Large Language Model?


With the growing importance of LLMs in AI-driven applications, developers and companies are deploying models like GPT-4, LLaMA, and OPT-175B in real-world scenarios. However, one of the most overlooked aspects of deploying these models is understanding how much GPU memory is needed to serve them effectively. Miscalculating memory requirements can cost you significantly more in hardware or cause downtime due to insufficient resources.

In this article, we’ll explore the key components contributing to GPU memory usage during LLM inference and how you can accurately estimate your GPU memory requirements. We’ll also discuss advanced techniques to reduce memory wastage and optimize performance. Let’s dive in!

Understanding GPU Memory Requirements for LLMs

LLMs rely heavily on GPU resources for inference. GPU memory consumption for serving LLMs can be broken down into four key components:

Model Parameters (Weights)

Key-Value (KV) Cache Memory

Activations and Temporary Buffers

Memory Overheads

Let’s examine each of these in more detail and see how they contribute to the total memory footprint.

Model Parameters (Weights)

Model parameters are the neural network’s learned weights. These weights are stored in GPU memory during inference, and their size is directly proportional to the number of parameters in the model.

How Model Size Impacts Memory

A typical inference setup uses each parameter’s FP16 (half-precision) format to save memory while maintaining acceptable precision. Each parameter requires 2 bytes in FP16 format.

For example:

A small LLM with 345 million parameters would require:

345 million × 2 bytes = 690 MB of GPU memory.

A larger model like LLaMA 13B (13 billion parameters) would require:

13 billion × 2 bytes = 26 GB of GPU memory.

For massive models like GPT-3, which has 175 billion parameters, the memory requirement becomes:

175 billion × 2 bytes = 350 GB.

Clearly, larger models demand significantly more memory, and distributing the model across multiple GPUs becomes necessary for serving these larger models.

Key-Value (KV) Cache Memory

The KV cache stores the intermediate key and value vectors generated during the model’s inference process. This is essential for maintaining the context of the sequence being generated. As the model generates new tokens, the KV cache stores previous tokens, allowing the model to reference them without re-calculating their representations.

How Sequence Length and Concurrent Requests Impact KV Cache

Sequence Length: Longer sequences require more tokens, leading to a larger KV cache.

Concurrent Users: Multiple users increase the number of generated sequences, which multiplies the required KV cache memory.

Calculating KV Cache Memory

Here’s a simplified way to calculate the KV cache memory:

For each token, a key and value vector are stored.

The number of vectors per token is equal to the number of layers in the model (L), and the size of each vector is the hidden size (H).

For example, consider a LLaMA 13B model with:

L = 40 layers

H = 5120 dimensions

The KV cache per token is calculated as:

Key Vector: 40 × 5120 = 204,800 elements

FP16 requires 204,800 × 2 bytes = 400 KB per key vector.

The value vector needs the same memory, so the total KV cache memory per token is 800 KB.

For a sequence of 2000 tokens:

2000 tokens × 800 KB = 1.6 GB per sequence.

If the system serves 10 concurrent users, the total KV cache memory becomes:

1.6 GB × 10 = 16 GB of GPU memory for KV cache alone.

Activations and Temporary Buffers

Activations are the outputs of the neural network layers during inference. Temporary buffers store intermediate results during matrix multiplications and other computations.

While activations and buffers usually consume less memory than model weights and KV cache, they still account for approximately 5-10% of the total memory.

Memory Overheads and Fragmentation

Memory overheads come from how memory is allocated. Fragmentation can occur when memory blocks are not fully utilized, leaving gaps that cannot be used efficiently.

Internal Fragmentation: This occurs when memory blocks are not filled.

External Fragmentation: This happens when free memory is split into non-contiguous blocks, making it difficult to allocate large chunks of memory when needed.

Inefficient memory allocation can waste 20-30% of total memory, reducing performance and limiting scalability.

Calculating Total GPU Memory

Now that we understand the components, we can calculate the total GPU memory required for serving an LLM.

For example, let’s calculate the total memory needed for a LLaMA 13B model with the following assumptions:

The total memory required would be:

26 GB + 16 GB + 9.2 GB (for activations and overheads) = 101.2 GB.

Thus, under this scenario, you would need at least 3 A100 GPUs (each with 40 GB of memory) to serve an LLaMA 13B model.

Challenges in GPU Memory Optimization

Over-allocating memory for the key-value (KV) cache, or experiencing fragmentation within the memory, can significantly reduce the capacity of a system to handle a large number of requests. These issues often arise in systems dealing with complex tasks, especially in natural language processing (NLP) models or other AI-based frameworks that rely on efficient memory management. Furthermore, when advanced decoding algorithms, such as beam search or parallel sampling, are used, the memory demands grow exponentially. This is because each sequence being processed requires a dedicated KV cache, resulting in even greater pressure on the system’s memory resources. Consequently, both over-allocation and fragmentation can lead to performance bottlenecks, restricting scalability and reducing efficiency.

Memory Optimization Techniques

PagedAttention: Reducing Memory Fragmentation with Paging

PagedAttention is a sophisticated memory management technique inspired by how operating systems handle virtual memory. When we think of computer memory, it’s easy to imagine it as one big block where data is stored in a neat, continuous fashion. However, when dealing with large-scale tasks, especially in machine learning or AI models, allocating such large chunks of memory can be inefficient and lead to memory fragmentation.

What is Memory Fragmentation?

Fragmentation happens when memory is allocated in a way that leaves small, unusable gaps between different data blocks. Over time, these gaps can build up, making it harder for the system to find large, continuous memory spaces for new data. This leads to inefficient memory use and can slow down the system, limiting its ability to process large numbers of requests or handle complex tasks.

How Does PagedAttention Work?

PagedAttention solves this by breaking down the key-value (KV) cache—used for storing intermediate information in attention mechanisms—into smaller, non-contiguous blocks of memory. Rather than requiring one large, continuous block of memory, it pages the cache, similar to how an operating system uses virtual memory to manage data in pages.

Dynamically Allocated: The KV cache is broken into smaller pieces that can be spread across different parts of memory, making better use of available space.

Reduced Fragmentation: By using smaller blocks, it reduces the number of memory gaps, leading to better memory utilization. This helps prevent fragmentation, as there’s no need to find large, continuous blocks of memory for new tasks.

Improved Performance: Since memory is allocated more efficiently, the system can handle more requests simultaneously without running into memory bottlenecks.

vLLM: A Near-Zero Memory Waste Solution

Building on the concept of PagedAttention, vLLM is a more advanced technique designed to optimize GPU memory usage even further. Modern machine learning models, especially those that run on GPUs (Graphics Processing Units), are incredibly memory-intensive. Inefficient memory allocation can quickly become a bottleneck, limiting the number of requests a system can process or the size of batches it can handle.

What Does vLLM Do?

vLLM is designed to minimize memory waste to nearly zero, allowing systems to handle more data, larger batches, and more requests with fewer resources. It achieves this by making memory allocation more flexible and reducing the amount of memory that goes unused during processing.

Key Features of vLLM:

Dynamic Memory Allocation:Unlike traditional systems that allocate a fixed amount of memory regardless of the actual need, vLLM uses a dynamic memory allocation strategy. It allocates memory only when it’s needed and adjusts the allocation based on the system’s current workload. This prevents memory from sitting idle and ensures that no memory is wasted on tasks that don’t require it.

Cache Sharing Across Tasks:vLLM introduces the ability to share the KV cache across multiple tasks or requests. Instead of creating separate caches for each task, which can be memory-intensive, vLLM allows the same cache to be reused by different tasks. This reduces the overall memory footprint while still ensuring that tasks can run in parallel without performance degradation.

Handling Larger Batches:With efficient memory allocation and cache sharing, vLLM allows systems to process much larger batches of data at once. This is particularly useful in scenarios where processing speed and the ability to handle many requests at the same time are crucial, such as in large-scale AI systems or services that handle millions of user queries simultaneously.

Minimal Memory Waste:The combination of dynamic allocation and cache sharing means that vLLM can handle more tasks with less memory. It optimizes every bit of available memory, ensuring that almost none of it goes to waste. This results in near-zero memory wastage, which significantly improves system efficiency and performance.

Managing Limited Memory

When working with deep learning models, especially those that require significant memory for operations, you may encounter situations where GPU memory becomes insufficient. Two common techniques can be employed to address this issue: swapping and recomputation. Both methods allow for memory optimization, though they come with latency and computation time trade-offs.

1. Swapping

Swapping refers to the process of offloading less frequently used data from GPU memory to CPU memory when GPU resources are fully occupied. A common use case for swapping in neural networks is the KV cache (key-value cache), which stores intermediate results during computations.

When the GPU memory is exhausted, the system can transfer KV cache data from the GPU to the CPU, freeing up space for more immediate GPU tasks. However, this process comes at the cost of increased latency. Since the CPU memory is slower compared to GPU memory, accessing the swapped-out data requires additional time, leading to a performance bottleneck, especially when the data needs to be frequently swapped back and forth.

Advantages:

Saves GPU memory by offloading less essential data.

Prevents out-of-memory errors, allowing larger models or batch sizes.

Drawbacks:

2. Recomputation

Recomputation is another technique that helps conserve memory by reusing previously discarded data. Instead of storing intermediate activations (results from earlier layers of the model) during forward propagation, recomputation discards these activations and recomputes them on-demand during backpropagation. This reduces memory consumption but increases the overall computation time.

For instance, during the training process, the model might discard activations from earlier layers after they are used in forward propagation. When backpropagation starts, the model recalculates the discarded activations as needed to update the weights, which saves memory but requires additional computation.

Advantages:

Drawbacks:

Increases computation time since activations are recalculated.

May slow down the training process, especially for large and deep networks.

Conclusion

Determining the GPU memory requirements for serving LLMs can be challenging due to various factors such as model size, sequence length, and concurrent users. However, by understanding the different components of memory consumption—model parameters, KV cache, activations, and overheads—you can accurately estimate your needs.

Techniques like PagedAttention and vLLM are game-changers in optimizing GPU memory, while strategies like swapping and recomputation can help when facing limited memory.

FAQs

What is KV Cache in LLM inference?

The KV cache stores intermediate key-value pairs needed for generating tokens during sequence generation, helping models maintain context.

How does PagedAttention optimize GPU memory?

PagedAttention dynamically allocates memory in smaller, non-contiguous blocks, reducing fragmentation and improving memory utilization.

How much GPU memory do I need for a GPT-3 model?

GPT-3, with 175 billion parameters, requires around 350 GB of memory for weights alone, making it necessary to distribute the model across multiple GPUs.

What are the benefits of using vLLM?

vLLM reduces memory waste by dynamically managing GPU memory and enabling cache sharing between requests, increasing throughput and scalability.

How can I manage memory if I don’t have enough GPU capacity?

You can use swapping to offload data to CPU memory or recomputation to reduce stored activations, though both techniques increase latency.



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Nextrope on Economic Forum 2024: Insights from the Event – Nextrope – Your Trusted Partner for Blockchain Development and Advisory Services

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Nextrope on Economic Forum 2024: Insights from the Event – Nextrope – Your Trusted Partner for Blockchain Development and Advisory Services


Behavioral economics is a field that explores the effects of psychological factors on economic decision-making. This branch of study is especially pertinent while designing a token since user perception can significantly impact a token’s adoption.

We will delve into how token design choices, such as staking yields, token inflation, and lock-up periods, influence consumer behavior. Research studies reveal that the most significant factor for a token’s attractiveness isn’t its functionality, but its past price performance. This underscores the impact of speculative factors. Tokens that have shown previous price increases are preferred over those with more beneficial economic features.

Understanding Behavioral Tokenomics

Understanding User Motivations

The design of a cryptocurrency token can significantly influence user behavior by leveraging common cognitive biases and decision-making processes. For instance, the concept of “scarcity” can create a perceived value increase, prompting users to buy or hold a token in anticipation of future gains. Similarly, “loss aversion,” a foundational principle of behavioral economics, suggests that the pain of losing is psychologically more impactful than the pleasure of an equivalent gain. In token design, mechanisms that minimize perceived losses (e.g. anti-dumping measures) can encourage long-term holding.

Incentives and Rewards

Behavioral economics also provides insight into how incentives can be structured to maximize user participation. Cryptocurrencies often use tokens as a form of reward for various behaviors, including mining, staking, or participating in governance through voting. The way these rewards are framed and distributed can greatly affect their effectiveness. For example, offering tokens as rewards for achieving certain milestones can tap into the ‘endowment effect,’ where people ascribe more value to things simply because they own them.

Social Proof and Network Effects

Social proof, where individuals copy the behavior of others, plays a crucial role in the adoption of tokens. Tokens that are seen being used and promoted by influential figures within the community can quickly gain traction, as new users emulate successful investors. The network effect further amplifies this, where the value of a token increases as more people start using it. This can be seen in the rapid growth of tokens like Ethereum, where the broad adoption of its smart contract functionality created a snowball effect, attracting even more developers and users.

Token Utility and Behavioral Levers

The utility of a token—what it can be used for—is also crucial. Tokens designed to offer real-world applications beyond mere financial speculation can provide more stable value retention. Integrating behavioral economics into utility design involves creating tokens that not only serve practical purposes but also resonate on an emotional level with users, encouraging engagement and investment. For example, tokens that offer governance rights might appeal to users’ desire for control and influence within a platform, encouraging them to hold rather than sell.

Understanding Behavioral Tokenomics

Intersection of Behavioral Economics and Tokenomics

Behavioral economics examines how psychological influences, various biases, and the way in which information is framed affect individual decisions. In tokenomics, these factors can significantly impact the success or failure of a cryptocurrency by influencing user behavior towards investment

Influence of Psychological Factors on Token Attraction

A recent study observed that the attractiveness of a token often hinges more on its historical price performance than on intrinsic benefits like yield returns or innovative economic models. This emphasizes the fact that the cryptocurrency sector is still young, and therefore subject to speculative behaviors. 

The Effect of Presentation and Context

Another interesting finding from the study is the impact of how tokens are presented. In scenarios where tokens are evaluated separately, the influence of their economic attributes on consumer decisions is minimal. However, when tokens are assessed side by side, these attributes become significantly more persuasive. This highlights the importance of context in economic decision-making—a core principle of behavioral economics. It’s easy to translate this into real-life example – just think about the concept of staking yields. When told that the yield on e.g. Cardano is 5% you might not think much of it. But, if you were simultaneously told that Anchor’s yield is 19%, then that 5% seems like a tragic deal.

Implications for Token Designers

The application of behavioral economics to the design of cryptocurrency tokens involves leveraging human psychology to encourage desired behaviors. Here are several core principles of behavioral economics and how they can be effectively utilized in token design:

Leveraging Price Performance

Studies show clearly: “price going up” tends to attract users more than most other token attributes. This finding implies that token designers need to focus on strategies that can showcase their economic effects in the form of price increases. This means that e.g. it would be more beneficial to conduct a buy-back program than to conduct an airdrop.

Scarcity and Perceived Value

Scarcity triggers a sense of urgency and increases perceived value. Cryptocurrency tokens can be designed to have a limited supply, mimicking the scarcity of resources like gold. This not only boosts the perceived rarity and value of the tokens but also drives demand due to the “fear of missing out” (FOMO). By setting a cap on the total number of tokens, developers can create a natural scarcity that may encourage early adoption and long-term holding.

Initial Supply Considerations

The initial supply represents the number of tokens that are available in circulation immediately following the token’s launch. The chosen number can influence early market perceptions. For instance, a large initial supply might suggest a lower value per token, which could attract speculators. Data shows that tokens with low nominal value are highly volatile and generally underperform. Understanding how the initial supply can influence investor behavior is important for ensuring the token’s stability.

Managing Maximum Supply and Inflation

A finite maximum supply can safeguard the token against inflation, potentially enhancing its value by ensuring scarcity. On the other hand, the inflation rate, which defines the pace at which new tokens are introduced, influences the token’s value and user trust.

Investors in cryptocurrency markets show a notable aversion to deflationary tokenomics. Participants are less likely to invest in tokens with a deflationary framework, viewing them as riskier and potentially less profitable. Research suggests that while moderate inflation can be perceived neutrally or even positively, high inflation does not enhance attractiveness, and deflation is distinctly unfavorable.

Source: Behavioral Tokenomics: Consumer Perceptions of Cryptocurrency Token Design

These findings suggest that token designers should avoid high deflation rates, which could deter investment and user engagement. Instead, a balanced approach to inflation, avoiding extremes, appears to be preferred among cryptocurrency investors.

Loss Aversion

People tend to prefer avoiding losses to acquiring equivalent gains; this is known as loss aversion. In token design, this can be leveraged by introducing mechanisms that protect against losses, such as staking rewards that offer consistent returns or features that minimize price volatility. Additionally, creating tokens that users can “earn” through participation or contribution to the network can tap into this principle by making users feel they are safeguarding an investment or adding protective layers to their holdings.

Social Proof

Social proof is a powerful motivator in user adoption and engagement. When potential users see others adopting a token, especially influential figures or peers, they are more likely to perceive it as valuable and trustworthy. Integrating social proof into token marketing strategies, such as showcasing high-profile endorsements or community support, can significantly enhance user acquisition and retention.

Mental Accounting

Mental accounting involves how people categorize and treat money differently depending on its source or intended use. Tokens can be designed to encourage specific spending behaviors by being categorized for certain types of transactions—like tokens that are specifically for governance, others for staking, and others still for transaction fees. By distinguishing tokens in this way, users can more easily rationalize holding or spending them based on their designated purposes.

Endowment Effect

The endowment effect occurs when people value something more highly simply because they own it. For tokenomics, creating opportunities for users to feel ownership can increase attachment and perceived value. This can be done through mechanisms that reward users with tokens for participation or contribution, thus making them more reluctant to part with their holdings because they value them more highly.

Conclusion

By considering how behavioral factors influence market perception, token engineers can create much more effective ecosystems. Ensuring high demand for the token, means ensuring proper funding for the project in general.

If you’re looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

How does the initial supply of a token influence its market perception?

The initial supply sets the perceived value of a token; a larger supply might suggest a lower per-token value.

Why is the maximum supply important in token design?

A finite maximum supply signals scarcity, helping protect against inflation and enhance long-term value.

How do investors perceive inflation and deflation in cryptocurrencies?

Investors generally dislike deflationary tokens and view them as risky. Moderate inflation is seen neutrally or positively, while high inflation is not favored.



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