I won two separate Kraken prizes in two different hackathons and Kraken is refusing to pay the second one. 1) AI Trading Agents hackathon Project (March 30 – April 12, 2026) : KarmaForge Swarm Prize: $1,200 (paid) lablab.ai/ai-hackathons/ai-t… 2) AI Agent Olympics / Milan AI Week Project (May 13 – 20, 2026) : xRegime Oracle Place: 3rd, Kraken Social Engagement Prize: $300 (unpaid) lablab.ai/ai-hackathons/mila… lablab.ai announced both wins. Kraken now says only one prize per person is allowed, so they paid the larger amount and called the rest final. This rule was never mentioned in either event. These were different hackathons with different prize pools. Applying a hidden “one prize only” limit after the fact is unfair. @krakenfx @krakensupport @lablabai I checked lablab ai terms of use and public prize pages. No general “one prize per person across different hackathons” rule appears. I also checked Kraken’s public terms and hackathon-related rules. No general “one prize per person across different hackathons” rule exists. Kraken’s current position does not match anything published in their general terms or the rules for the two events you entered. I don't know what is going on, but I won't let this slide, I have been following this for weeks only to get this reply today, which is so unprofessional and unfair, doesn't even make sense in common sense and reasoning Pls guys I need your help for me to get the prize I worked for

I built @OwnPay_ for Base builders quest I built OwnPay because a payment shouldn’t be the end of the story. On Base, people can pay, gift, or tip in USDC, then turn part of a qualifying payment into tokenized-stock ownership, with vesting and portfolio tracking built in. OwnPay is a @base native payments app that turns everyday money movement into programmable ownership. With OwnPay, creators, freelancers, businesses, and communities can share one link to receive Tokenized Stocks, send gifts, collect tips, and create ownership rules for qualifying payments. A selected portion of a payment can become eligible tokenized-stock ownership with vesting and claiming built into the experience. Recipients claim directly to their own embedded wallet and manage everything from a dedicated portfolio, including: - USDC balance - Owned tokenized stocks - Vested grants - Amount spent - Amount sent - Tips - Transaction history This video shows how OwnPay works on Base mainnet, from signing in and receiving a wallet to creating payment rules, claiming ownership, and viewing portfolio activity. @buildonbase

Spent the last few weeks building this ProphetPool is live on @BOTChain_ai Most RWA products just tokenize and hope. They have no real-time sense of whether the underlying asset is actually healthy. ProphetPool is different. ▪️AI agents continuously underwrite and monitor the assets. ▪️A live prediction market prices the risk. ▪️ When that risk crosses a threshold, the protocol can automatically protect capital, before a default hits. It’s not a dashboard, it’s a closed risk loop. Also wired in a Maple syrupUSDT yield path. Still in shadow mode for now, but the architecture is ready. This is the direction I believe RWA has to go: AI that doesn’t just observe risk, but actually responds to it on-chain. Do well to follow for more updates we are just getting started

Have you ever read a story or watched a video and thought, "I wish I could see what happens if it went this way instead?" Usually, taking a creator's work and remixing it gets you a copyright strike. With Gaffer, we made that exact urge a feature. If a fan wants a different ending, a localized angle, or a completely new video scene, they don't have to steal the content. They just stream a little USDC to steer it themselves. The AI generates their custom branch on the fly. The audience gets the exact version they want to experience. The creator captures 100% of the payment for that branch. Instead of publishing a piece of media and only earning from passive views, a creator can drop one great idea and get paid every time their audience wants to take it in a new direction. @arc @thecanteenapp

This one actually hits a bit too close for comfort. I have been in that cycle building agent workflows, you hardcode a model because it’s decent at reasoning or tool use for one step, then a few days later something cheaper or better at a specific capability drops and you are back in the code swapping names, testing again, and hoping the rest of the flow doesn’t break, or you try to force one model to handle everything and it just underperforms on certain parts of the job. It gets old fast once you’re running real multi-step stuff. Being able to write small policies instead, like “this step needs JSON mode and should be cheap” or “this one needs stronger reasoning and no logging” and have it route properly while still showing you exactly which models were considered, rejected, and why… that feels like a proper solution instead of another abstraction that hides the mess. Especially when you want to actually understand and trust what’s happening across a workflow. The fact that it’s open source and runs on your own keys makes it even more interesting. Nice ship.

▪️ @Qevorpay made it to the Top 30 finalists for the Turing Test Hackathon Demo Day Out of 120 shortlisted projects, we will be presenting on July 3 in the Consumer DApp track. Grateful for the journey so far. More updates coming. 🔥🔥

Qevor is building agent-first payment infrastructure on Mantle. Made for both regular users and AI agents, with safety and usability in mind. Any support on the community vote would mean a lot. If this pops up on your feed quote with anything, this is our entry for the mantle turning social track #MantleAIHackathon

Final submission for the @Bitget_AI Hackathon S1 🚀 This is VesperClaw, an accountable and self-improving AI trading agent built for Bitget. VesperClaw scans BTC, ETH, and SOL, generates trade ideas, and puts every idea through multiple checks before allowing any paper trade. It refuses weak setups, protects capital during losing streaks, and actually learns from its closed trades. The core idea is simple: a trading agent should not only show what it did, but also explain why it did it and why it refused certain trades. Here’s what’s included: ➖ Full trading loop with perception, decision, execution, monitoring, and learning ➖ Alpha Gate and AgentVault as dual risk layers that check both individual trades and overall portfolio exposure ➖ Profit Guard that makes the agent more defensive after losses Evolution memory that updates strategy behavior based on real closed trade results ➖ Meme Radar that analyzes trending meme coins and gives clear buy/watch/avoid signals ➖ Prediction market section for football and World Cup markets ➖ Full public paper trading logs and a live dashboard that shows every decision and refusal Built using Bitget Agent Hub, Playbook, and Qwen. Demo: zgzcjrl6.mule.page/#firewall Live dashboard: http://38.49.209.149:8501/ #BitgetHackathon

I'm building VesperClaw, an autonomous Bitget paper-trading agent that doesn’t just execute trades, it explains every decision and learns from its outcomes. Built for the @Bitget_AI Hackathon S1. It turns multi-source market data into explainable Signal Mandates, runs them through a strict risk firewall, and evolves its strategy behavior after every closed trade based on what actually worked in each market regime. More details and demo coming soon. #BitgetHackathon