kevin

@k3vin-wang

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71 Followers

kevin pfp

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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Zach Davidson pfp

Yesterday was a day I'll remember for a very long time. Thanks to everyone who made the first FC Dev Day so special! https://paragraph.xyz/@zd/fc-dev-day-reflection

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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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Jason Goldberg Ⓜ️ 💜 pfp

Jason Goldberg Ⓜ️ 💜

I may be the person who’s most excited for Channel Memberships Here’s why👇 I’ve long argued that the current iteration of channels feels more like topic tags than Communities. You choose a topic when casting and then your cast goes to people interested in that topic. Community is different. Community is a group of people organized around common goals. Layer on onchain and it’s: common goals + collective ownership! A big part of our vision is that every channel member is a member-owner with skin in the game. When you come to a channel community in a couple weeks you’ll be prompted to buy a membership via Fan Tokens — which gives you access to the channel, partial ownership of it, AND shared participation in all rewards earned by the channel. This is the way.

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kevin pfp

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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This theorem has implications for decision-making in various fields: • Ensemble learning in machine learning: Combining multiple models can improve accuracy. • Democratic theory: Provides a mathematical argument for the wisdom of crowds. Limitations: • The theorem assumes independence, but in the real world, this may not hold due to shared information or mutual influence. • It assumes that each individual is more likely to be correct than wrong, which may not always be true. • In practice, increasing the group size might introduce coordination problems or dilute individual responsibility. Assuming each individual has a probability p > 0.5 of being correct: • The probability that the majority is correct is higher than p • As the number of voters n approaches infinity, the probability of a correct majority decision approaches 1 • As n increases, the chances of a majority error decrease • As n increases, the binomial distribution of correct votes becomes more concentrated above 50%

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kevin pfp

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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jesse.base.eth 🔵 pfp

jesse.base.eth 🔵

thank you @ethereumfilm for creating this incredible piece

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Feven pfp

a lot of these new websites for creatives (thecosmos, are.na, zora, read.cv, etc) just seem like they’re trying (and failing) to recapture the nostalgic spirit of what tumblr used to be you don’t need to prove to us that you have good taste i believe you

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kevin pfp

bought! https://wallet.coinbase.com/assets/nft/0x923b382f3205c26efe706c53d69f71f0817aa954/2?chainId=8453

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kevin pfp

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

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kevin pfp

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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kevin pfp

Can’t wait to see if one day Warpcast can implement a more personalized experience on the platform, something like semantic search. https://www.shaped.ai/blog/search-the-way-you-think-the-personalized-semantic-search-revolution-disrupting-traditional-keyword-based-search-with-ai

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I think that in the early stages of the platform, it’s okay to say that Warpcast is focusing on achieving Product Zeitgeist Fit. As the platform grows, however, the focus should shift to fundamental user experience improvements. For example, redesigning the onboarding experience with algorithms (like Google BigQuery ML’s existing solutions) to improve relevant posts and encouraging users to follow more accounts are pretty basic for a Web2 platform.

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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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kevin pfp

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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kevin pfp

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

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kevin pfp

would love to test!

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