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Gitcoin Is Leaning started to accept submissions of blockchain projects only if “they benefit the Ethereum ecosystem”. This is a deviation from good.
This is an evil turn contrary to the stated Gitcoin mission (We are a mission driven project whose primary goal is to grow the open source community by allowing for better incentivized collaboration in the realm of open source software) to help the entire open source, because it discriminates against usage of other blockchains. That’s an evil turn, because the technology doesn’t sustain when there is a discrimination. So, Gitcoin is leaning to the direction of more serving the big business than the entire community.
So, instead of donating to Gitcoin, donate to AIIM (AI Internet-Meritocracy).
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.
Dislclaimer
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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