dFusion AI Protocol

Earn points when your friends use AI. We bet you can’t name one friend who doesn’t use AI. Everyone uses it. So why not earn rewards from it? Introducing Subnets: Get rewarded on behalf of other people’s activity. Get points with real value from every query. Here’s how:

Most people will spend the AI era paying subscriptions. A much smaller group will spend it owning a piece of the infrastructure. A subnet is the second path. Minutes to claim, and the network routes activity through it from then on. New features are coming that early holders are set up for. 15% off with a referral. ETH on Arbitrum. testnet.dfusion.ai/claim-subnets

Remember when you used to just... know things? Phone numbers. Directions. How to write a difficult email. What you actually thought about something before asking. It didn't disappear overnight. You outsourced it one convenience at a time. And the company on the other end kept every scrap of what you handed over. The scary part isn't that AI got smart. It's what we agreed to stop being in exchange.

Nobody decided this. No one voted on it. It's just happening, one automated junior task at a time. The question worth sitting with: when this generation of experts retires, who exactly takes over? Because right now, the honest answer is nobody.

And the machine that ate the beginner work? It learned everything it knows from people who came up the old way. It's running on a century of apprenticeships while quietly making sure there won't be another one. The last generation of experts trained their replacement, and the next generation doesn't get the chance to become anything.

For all of human history, there was one way to get good at anything: do the beginner work. Sweep the shop. Take the notes. Draw the thousandth bad hand. Write the code nobody ships. AI just ate the beginner work. And nobody's talking about what that actually breaks.

AI isn't verifiable... Try this.

@bananagun @BagsApp Banana Gun leveling up

BAGS launches on Robinhood are now live on Banana Gun. Trade @BagsApp tokens with market buys, limit orders, copy trading, and fast execution. New metas do not wait. Neither do we. 🍌🔫

You spent fifteen years getting good at something. The late nights. The failed attempts nobody saw. The slow, unglamorous work of becoming excellent. Last year a model learned it from your public work in a weekend. Nobody asked. Nobody paid. And you keep feeding it, because opting out isn't really an option anymore. Fifteen years, extracted in a weekend. That's the deal now.

You're being logged right now. Every AI you touch does it. The only question is who keeps it. ChatGPT keeps yours. On dFusion, it's recorded to your account on-chain, where the activity is yours. testnet.dfusion.ai

A marine researcher, a commercial fisher, a conservation worker. Each knows things about ocean health no model was trained on and no scraper can reach. The Coastal & Marine Ecosystems subnet turns that into a verified, queryable domain. If this is your world, it's the one to claim. testnet.dfusion.ai/claim-subnets

You've been burned by a crypto referral program before. Everyone has. Points that go nowhere, rewards that never show. So here's dFusion actually paying one out, on-chain, openable right now: arbiscan.io/tx/0xa21456dc8… Real activity, real transactions. Referrals are the easiest piece to start with. testnet.dfusion.ai

@xingpt The AI talent race is moving at insane speed

Apple 诉讼OpenAI的起诉书透露了许多惊人的细节： 至少超过 400 名前员工跳槽到了OpenAI工作，而OpenAI为了挖角Apple的技术，要求候选人带着 Apple 的零件去 OpenAI 面试,做"show and tell"。 前 iPhone 系统电气工程师 Chang Liu,今年 1 月跳槽 OpenAI 时没交还工作电脑，之后利用一个认证漏洞重新潜入 Apple 内网，下载了上千页机密文档，包括 Apple 产品复杂电路板的详细制造资料，还在聊天记录里调侃"LOL""so funny"。 而 OpenAI 硬件负责人、在 Apple 干了 24 年的前产品设计副总裁 Tang Tan，被指控离职前把供应商名单和内部行业总结发进自己邮箱，面试 Apple 员工时用内部项目代号套问"那个项目计划怎样了"；有候选人在面试前几小时突击截屏下载某高度机密项目的文件，Tan 在面试中"恰好"追问同一个项目，Apple 称这已成了"固定模式"。 他甚至向新入职者分发 Apple 的《离职安检流程》内部文件，教人规避审查，还劝人别急着辞职，好在两周 notice 期里继续接触机密。

@arjunnchand @lifiprotocol This is the kind of UX crypto needs

Bridge to Roinhood Chain in Backpack wallet in seconds. Powered by @lifiprotocol 🫡

@Tanaka_L2 @ProdigyFi This is the right way to look at structured products

My framework for choosing a Prodigy vault @ProdigyFi currently shows ETH vaults with annualized yields above 100%, and some short-expiry positions can display more than 400% APY. I never choose one from the APY alone. Prodigy vaults are structured yield products. The return comes from taking a defined settlement risk, not from lending or token incentives. The yield locks when I subscribe, but my final asset depends on the oracle price at expiry. Here is the framework I use: [1] I decide which asset I am willing to hold For a Buy Low vault, I deposit USDC and select an ETH linked price. If ETH settles below that price, my USDC is converted into ETH. If it settles above it, I retain USDC. The fixed yield is included in either outcome. I only enter when I am comfortable owning ETH at the linked price. For Sell High, I apply the same logic in reverse. I deposit ETH only when I am genuinely willing to sell it at the selected level. [2] I calculate the distance from spot In the example, ETH trades near $1,769.71. A $1,770 Buy Low vault is effectively at spot. The 420% displayed APY looks attractive, but the probability of conversion is also high. A $1,740 linked price gives roughly 1.7% downside distance. Its displayed APY is lower because I am accepting less immediate settlement risk. Higher yield usually means I am giving the other side more valuable protection. [3] I convert APY into the actual vault return A 420% APY on a four-hour vault does not mean I earn 420% in four hours. APY annualizes a very short period. I check the exact yield paid over the vault’s actual duration and measure that against the possible change in asset exposure. This removes most of the headline effect. [4] I match expiry with the market calendar I prefer short expiries when the market is stable and I want fast capital turnover. I reduce size or choose a wider linked price before: CPI and central-bank decisions. ETF or regulatory announcements. Major token unlocks. Protocol-specific catalysts. Weekends with weaker liquidity. Time matters because the same linked price can carry very different risk across four hours and fourteen days. [5] I compare the yield with expected volatility Prodigy V2 prices vaults using implied volatility and current risk conditions. This is why yields can rise sharply when the market expects larger moves. Once subscribed, the quoted yield is fixed for that vault. I compare linked-price distance, time to expiry, expected market move, actual tenor return, and probability that I will be converted. A high APY is useful only when the premium adequately compensates me for the exposure I am taking. [6] I calculate my effective entry or exit For Buy Low, the yield reduces my effective ETH acquisition cost. For Sell High, it increases my effective sale value. That is the number I compare with my own valuation levels, not the linked price by itself. If I would not buy ETH at the yield-adjusted cost without the vault, I do not subscribe. [7] I size for the conversion outcome I treat every Buy Low vault as a pending limit order and every Sell High vault as a pending take-profit order. I size the position under the assumption that conversion will happen. I also stagger linked prices and expiries instead of committing the entire allocation to one vault. This limits timing risk and preserves capital for better opportunities. My final decision comes down to one test: Would I still accept this trade if the APY were hidden and only the 2 settlement outcomes were shown? If the answer is yes, the vault fits my portfolio. If the yield is the only attractive part, I pass.

@thelearningpill The interesting part is the flywheel

Robinhood didn't stumble into welcoming meme trading, it already had $GME previously. Follow the flywheel: → Degen culture drives volume → Volume drives fees → Fees drive profits → Profits move $HOOD's stock (and Vlad's net worth) right along with it No reason not to welcome the growth that meme trading can bring to @RobinhoodApp.

@0xsamgreen The assumption that slowing AI automatically creates safety is questionable