Rewards on @merkl_xyz don't have to be always claim-based. More and more teams are using us us to push rewards straight to their users' wallets, with no claim needed. Imagine having to claim the dividends on your stocks, or the interest on your savings account. As finance moves onchain, payouts should just land. People already use our airdrop solution to reach millions of addresses at once. Next step: we're scaling direct transfers even further.

【Rewards Distribution Notice - Cinderella Special】 During the Doki Doki NIKKE Global Popularity Poll IV, Cinderella, Liberalio, Little Mermaid, Arcana: Fortune Mate and Snow White: Heavy Arms ranked as the Top 5 based on total votes. Plan B also received the most votes in the Cinderella Costume Voting poll. ✨ Commanders, thank you for your continued support. The Avatar Frame, Cinderella costume, and Theme Song rewards will be distributed in-game via the [Cinderella Special] event! Claim period 📅10/8 (After Maintenance) ~ 12/3, 04:59 (UTC+9) *After obtaining the Cinderella theme song, you will be able to listen to it in-game via [Command Center] - [Jukebox] - [Events]. *The Theme Song for the Top 1 most-voted Cinderella, and the celebratory illustrations for the Top 3 most-voted Nikkes will also be revealed on our official channels soon! #NIKKE#

[Rewarded Event] Pearl Spotlight - Reddit Event! Its time to shine a spotlight on Pearl, ✧ 1st Place in "I'll Be Right Here to Catch You" ✧! Create original art, memes or discussions focused on Pearl and post them on our subreddit for a chance to win gift cards worth up to $50! Join Now: #HonkaiStarRail# #StarRail#

[Rewarded Event] Discord Trivia Event! How well do you know Honkai: Star Rail? Pearl is ready to put everyone to the test! Join the event and answer the trivia questions that randomly appear in the server for a chance to win a $25 gift card! Join Now: #HonkaiStarRail# #StarRail#

【Rewarded Event】Pearl Strategy Discussion Event Pearl, ✧ 1st Place in "I'll Be Right Here to Catch You" ✧, has just released and we are inviting all trailblazers to join in a strategy discussion regarding tips, build suggestions and team setups to achieve their maximum potential! Join the conversation for a chance to win a $25 gift card! Join Now: #HonkaiStarRail# #StarRail#

Rewards update. The StonkFun rewards pipeline has been upgraded. Rewards no longer wait for a large pot to accumulate before harvesting transfer tax. Fees are converted and distributed continuously. The upgrade allows for separate horizontal scaling at each stage. There are also distinct thresholds for each stage: • Harvesting • Claiming & swapping • Planning and funding distributions • Sending distributions to holders If a conversion ever lags behind volume, rewards can be distributed in the coin itself so payouts keep flowing without delay or excessive harvesting. Live for new and existing coins launched on LaunchLab, with the upgrade rolling out to legacy coins (CLMM) soon. The pipeline is still being tuned for optimal performance, further concurrency and prioritization algorithms are being worked on.

Reward AI is based in the SF Bay Area. The founders are two of Stanford's standout robot-learning researchers: Zipeng Fu (Cofounder, CEO), advised by Chelsea Finn, ex-Google DeepMind. Lead author on Mobile ALOHA (the viral $32k robot that cooks and cleans) and HumanPlus (humanoids learning by shadowing humans). Chen Wang (Cofounder, CTO), advised by Fei-Fei Li and Karen Liu. Lead author on DexCap, the portable dexterous-manipulation capture system OM-1's wearable hand is built on.

Reward AI just came out of stealth and unveiled OM-1, a robot policy learned entirely from humans. No teleoperation, no on-robot data. One Model, Any Body. 1. Capture. A wearable 7-DoF hand (Omnibody Hand) lets people demonstrate manipulation naturally while working, cooking, or sorting. Sensors record tactile, proximity, force and hand pose at full human speed. 2. Learn. OM-1 trains on this human data alone. No teleop, no on-robot experience. Then it runs across bodies from industrial arms to humanoids, and scaling is just adding more human data. 3. Control. A high-frequency control layer, trained with RL in sim, executes those actions on any robot, holding steady through unknown loads like a stuck fridge door. The result: OM-1 picks up a brand-new task from under 30 minutes of data.