How to do crypto airdrops with AIIM in 2026?

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AIIM (AI Internet-Meritocracy) is a perfect platform for crypto airdrops:
  • We will soon support multiple blockchains with ERC-20 tokens on them.
  • The funds will be distributed as a salary to scientists – one of the most active users. And they join our app only with one purpose — use delivered to them funds.
  • We are going to create a non-custodial wallet for your money to be saved.
  • You do not pay for gas, we do.
crypto airdrops

However, currently (27 Sep 2025) the app is not yet production quality, the wallet is custodial, and ERC-20 are not supported. Be sure to check us in the future, if you want to make an airdrop.

👉 Donate for science.

Support Independent Science

Our flagship product is AI Internet-Meritocracy - an app, that unlike universities distributes money directly to researchers and open source developers, without traditional bureaucracy.

AIIM’s dependency-aware allocation model is currently being tested. Support the next testing milestone.

Supporting independent science is not only a matter of fairness to researchers whose expertise and work are often underfunded. It is also essential for addressing systemic failures in scientific publishing that delay discoveries and leave important results unnoticed. In science and software, even one missing component can prevent an entire system from working.

Help valuable research and open-source infrastructure move forward. Please make a donation to support independent scientists and free software developers.

Disclaimer

Experimental-system notice: AI Internet-Meritocracy is an experimental funding system. Its AI-generated evaluations are heuristic judgments based on available public or connected-account evidence; they are not validated measurements of a person’s causal economic or scientific impact. The current beta uses custodial and administrative components. Decentralized governance, non-custodial wallets, and complete on-chain auditability remain under development. Evaluations may contain factual errors or biases and should be interpreted together with audit logs, appeals, human oversight, and published test results.

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