kevin

@k3vin-wang

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71 Followers

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Here’s what I’ve learned from analyzing hypersub.xyz: • Prioritize retention and user experience (UX) over protocol in the early stages. • Retention fosters loyalty, reduces customer acquisition costs, and ensures sustained revenue. • Slower growth post-peak indicates a need for stronger retention strategies. • Enhance UX, personalize engagement, and continually provide value for both creators and users.

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Starting a conversation with OpenAI's advanced voice mode has been quite helpful in clarifying some thoughts.

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The overall error of multiple models is equal to the average error of a single model minus the diversity of the model’s predictions. In other words, if you have many different predictions, the overall performance may be better than relying on a single prediction. The theorem can be expressed as a mathematical equation: {Average Prediction} - {True Value})^2 = {Average Single Model Error} - {Prediction Variability} The left-hand side represents the error of the overall prediction. The first term on the right-hand side is the average error of a single model. The second term on the right-hand side represents the diversity (or variability) of the model’s predictions. The theorem does not imply that any collection of diverse models will be accurate. If all of the models share a common bias, their average will also contain that bias. The theorem does imply that any collection of diverse models (or people) will be more accurate than its average member, a phenomenon referred to as the wisdom of crowds.

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The Condorcet Jury Theorem is named after the 18th-century French philosopher and mathematician, Marquis de Condorcet. It is related to the broader ideas of collective intelligence and social information aggregation. This theorem has been influential in discussions about the epistemic reasons for democracy and the design of decision-making bodies. When a group of individuals independently makes a decision between two options, and each person is more likely to be right than wrong, majority voting is generally correct. As the size of the group increases, the likelihood of the majority being correct also increases. It applies in the following conditions: • There is an odd number of voters • Each person has a greater than 50% chance of being correct • People make decisions independently • There are only two possible answers (right/wrong) As the number of people in the group increases, the accuracy of the majority vote approaches 100%.

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I just collected Community.eth: An Ethereum Story (Official Poster) by Ethereum Stories https://ethereumfilm.xyz/ethereum-stories

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Here’s my latest AI stack for writing articles: > o1 preview - for reasoning and organizing logic > notebooklm + illuminate - for generating English dialogue > claude - for refining parts of the text > gemini 1.5 pro - for multimodal capture of videos and fragmented audio ideas > suno ai - for creating intro and outro music for podcasts

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This is one of the methods for identifying valuable consumers. Through my research, I’ve found that segmentation for on-chain customer loyalty is often underrated and worth more discussion. https://blog.tulsk.io/p/onchain-customer-loyalty-a-new-paradigm

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A feature, different perspectives (preparing for the next round of funding): > Founder/Director: Focus on narrative and emphasize the product’s value proposition for the VC pitch. > Development Team: Clearly distinguish between the goal scope and non-goal scope. > Product Team: Focus on effectiveness, meet the needs of all stakeholders, and optimize the resource-to-value ratio.

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The biggest UX pain point when using DeFi for transactions right now is that the browser in the crypto wallet does not provide a seamless browsing experience. I hope to see more PWAs or other user-friendly UX applications in the future.

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Collect many such cases ep 12: building for high-quality content with @dwr from many such cases :) on Pods!

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I’m posting early alfa on AlfaFrens before anywhere else. Get in early or expect to fomo hard.

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10 Moxie Passes available — mint yours to get access to Moxie, use Farcaster Fan Tokens, and more! cc @betashop.eth @airstack.eth

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The operating model of $FLY: Blackbird views it as a Utility Token, which in the Web2 world would be equivalent to Loyalty Points. When Diners make cash purchases, they are rewarded with $FLY. $FLY can be used for payments at any restaurant under the Blackbird network. Under the Tokenomics mechanism, the more $FLY consumed by Diners and Restaurants, the more $F2 tokens they will receive. However, the whitepaper doesn't elaborate much on owning $F2 tokens. Following the design principles of other protocols, $F2 tokens are likely to be closely related to future Governance applications

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I recently wrote an article "Deep Dive into Blackbird Whitepaper." While it doesn't discuss a specific dapp, it explores concepts from the "Ownership Economy" as outlined in Variants Fund's 2022 Thesis. The article examines how to create an exceptional dining experience by integrating both off-chain and on-chain infrastructure. A key focus of the piece is analyzing the project's approach from a Tokenomics perspective. It delves into how the system is designed to address the diverse values and interests of multiple stakeholders. I find this product development framework particularly compelling due to its maturity and comprehensiveness. This approach, I believe, represents a more evolved understanding of how blockchain technology can be applied to create practical, user-centric solutions in real-world scenarios like the restaurant industry. https://blog.tulsk.io/p/deep-dive-into-blackbird-whitepaper

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Crypto consumer apps should abstract the underlying blockchain technology, making it user-friendly. However, these apps should still leverage blockchain's benefits, particularly interoperability with other platforms, to provide an immersive experience.

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when i saw /nook from @slokh, it sparked a lot of my thoughts about why headless marketplaces and composability are crucial for Web3 consumer apps. After doing some research and finding interesting use cases, I wrote an article about Headless Marketplaces. Here’s my article ↓ https://blog.tulsk.io/p/headless-marketplace-explained-concept

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Good list! Love it!

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How we see events is shaped by the information around us. Social media algorithms label users and suggest popular topics. This seems logical since the system recommends stuff you "might" like. But what if the whole user group is biased? Interestingly, communities with barriers (like token-gated ones) or screening processes can keep content quality high because they have a clearer target audience. Here's what different platforms are known for: > Warpcast: web3 and new tech talks > Zora: tech art and digital media > Paragraph: web3 business & tech insights > Frame: meme and idea testing > Substack & Notes: deep knowledge and expert content > Twitter (X): tech trends and hot topics > Threads: middle-ground info sharing > Facebook: general info for all ages

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X is getting messy with too many posts. It's hard to find good stuff to read. Warpcast is better because it has more interesting posts and less junk.

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A headless marketplace is a type of headless app that facilitates trading without its own dedicated user interface. It operates by integrating with existing platforms and crypto wallets, enabling transactions through familiar digital spaces. Key aspects include: - No central interface: Functions through other platforms - Integration: Works with existing wallets and user accounts - Accessibility: Users interact where they're already active - Flexibility: Can be accessed through various front-ends Examples: Bountycaster on Farcaster, decentralized exchanges with multiple interfaces, NFT markets via social media bots.

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