{"signature": "0x4bb31ab8c01fa2635de3795d170c6cfb306d6299fd5975bd8adb14d6bb14ff617632e0bd9787bf66c83cc439987c384c164ed67ebbec10515960e6af424dc7a81b", "application": {"round": "0x0C0412DDB846096Ea1e37de717921EBF4fEF9A39", "answers": [{"type": "email", "answer": "anshuman@push.org", "hidden": true, "question": "Email Address", "questionId": 0}, {"type": "paragraph", "answer": "*Amount*: 50.9896 USD\n*Donors*: Hippocrat, Desci Asia\n*Platform*: FunDeSci.com (Asian-based Grant Initiative) ", "hidden": false, "question": "Funding Sources and Total prior funding for project in USD", "questionId": 1}, {"type": "number", "answer": "1", "hidden": false, "question": "Team Size", "questionId": 2}, {"type": "paragraph", "answer": "https://x.com/anshuman_ecc", "hidden": false, "question": "Profiles or socials of other main team members publicly associated with project", "questionId": 3}, {"type": "short-answer", "answer": "anshuman_ecc_eth", "hidden": true, "question": "Telegram username", "questionId": 4}, {"type": "short-answer", "answer": "11 months", "hidden": false, "question": "How old is the project? (Months)", "questionId": 5}, {"type": "paragraph", "answer": "*Problem Statement*: Legal research is plagued by multiple inefficiencies resulting from siloed annotations. The absence of transparent and reliable incentives for sharing notes results in gridlocked collaboration and unnecessary duplication. On the other hand, the absence of high-quality, ethically sourced, human-annotated datasets impedes the development of reliable, domain-specific language models for the legal field. Current AI models often rely on unethically sourced data (or noisy and synthetic data parsed from documents), leading to inaccuracies and hallucinations, making them unsuitable for legal applications. There is an urgent need for a system that recognizes and rewards the semantic labor of legal researchers while providing high-quality datasets for AI training, evaluation and benchmarking. \n\n*Solution*: Hyvmind offers an elegant solution to this problem by creating a reward channel which turns siloed legal annotations into community-owned vector databases.  \n\n*Roadmap*: Presently, the project is in the community development phase. The immediate goal is to solidify institutional partnerships (with civil society organisations, law colleges, other legal-tech projects) by positioning Hyvmind as a tamper-proof and community-owned semantic data layer for legal research.   \n\nThe concrete goals over the next year are as follows: \n- July 2025: launch a 'digital internship pilot' with partner organisations \n- Aug 2025: build an initial cohort of law students (20-50) around a particular working group (say 'Indian banking law) through blockchain-based rewards (e.g. Push Points) and/or poaps\n- Oct 2025: conduct at least 3 workshops and seminars to onboard fresh cohorts and deepen institutional partnerships\n- Nov 2025: make datasets available through public repos for wider consultation and use; another round of internships and onboarding \n- Dec 2025: launch beta version with a minimal frontend and user profiles (with rewards, reputation points and annotation marketplace)\n- Feb 2026:  feedback, review, finalisation of tokenomics\n - May 2026: launch of v1 app for broader public use", "hidden": false, "question": "Describe your project in a few paragraphs (ideally including a problem statement, solution, roadmap, and timeline for implementation)", "questionId": 6}, {"type": "paragraph", "answer": "- *Digital Public Infrastructure*: there will be two layers to Hyvmind, an openly accessible layer for black letter law (no weights), and a royalty-embedded layer for interpretations/notes (weighted) [see 'annotation value chains' in the whitepaper for details]\n- *Ethically Sourced Datasets*: each 'location' (say art. 14 of the Indian Constitution) will be owned and controlled by its own sub-DAO [see 'location contracts' in the whitepaper for details]\n- *Professional Recognition*: rewards, reputations and annotation weights will combine to form a reliable metric for professional contribution (outside traditional metrics such as cases won, publications etc.) \n- *Scientific Peer-Production*: the open-source layer of black letter law will be thoroughly vetted by peers, embedding peer review and co-production into the architecture. [see the use of 'inter annotator agreements' in the whitepaper for details]\n- *Progressive Decentralisation through subDAOs*: each location will begin with small working groups which will be progressively decentralised through subDAOs. ", "hidden": false, "question": "How does your project support the mission or goals of DeSci?", "questionId": 7}, {"type": "paragraph", "answer": "- Technical Development (app interface, open-source repos, annotation marketplace, reputation system)\n- Community Growth (workshops, internship pilots, content creation)\n- Research and Development (DAO structuring, tokenomics, royalty parameters, stakeholder discussions)", "hidden": false, "question": "Describe how funds from the grants program will be used for your project.", "questionId": 8}, {"type": "paragraph", "hidden": false, "question": "If you participated in past grant rounds (i.e. DeSci GR15/ Beta/GG 20DeSci round/GG21 DeSci round) using a different project payout wallet address, please share it here.", "questionId": 9}, {"type": "paragraph", "answer": "not applicable", "hidden": false, "question": "If this project participated in past DeSci grant rounds, please share any new updates or milestones since the last round.", "questionId": 10}, {"type": "paragraph", "answer": "For full disclosure: I am currently part of the DeSci Asia, Desci India and Gitcoin Desci Round Ops teams (coordinator), and work as a technical content writer with Push Chain.  ", "hidden": true, "question": "Is there any other information you would like to share about your project, previous work, other project affiliations, or potential conflicts of interest? (or anything not covered elsewhere in this application that you feel is relevant to share)", "questionId": 11}, {"type": "dropdown", "answer": "No", "hidden": false, "question": "Does 50% of your team reflect the diversity of the community your project serves with a particular emphasis on including women, Black, Indigenous, and People of Color (BIPoC), individuals with disabilities, and members of the LGBTQ+ community?", "questionId": 12}], "project": {"id": "0x878e85001221af5cab52d50c488befcba883ee27282fcf8db1811e2366c9dc0b", "title": "Hyvmind", "logoImg": "bafkreiakn6x6q72llsqp4hdil4h6yfepix7nestwhdeiopxqrdtkuq7h6y", "metaPtr": {"pointer": "bafkreieycbms5gdgwzelkqe2tyqznid7iwqqqlscpc3ih347vofgwdrh3i", "protocol": "undefined"}, "website": "https://hyvmind.xyz", "bannerImg": "bafkreib6753r3ddvxazw2rwa6x6aqhkf3ofesqr3fqaff7psudsmx4eckm", "createdAt": 1743243892079, "userGithub": "anshuman-ecc-eth/", "credentials": {"twitter": {"type": ["VerifiableCredential"], "proof": {"type": "EthereumEip712Signature2021", "created": "2025-03-29T10:24:17.287Z", "@context": "https://w3id.org/security/suites/eip712sig-2021/v1", "proofValue": "0x6e81723892521f5b339f6e8eb672cdfe18685ebd6e4684f08eda39cdffcb1f8534d6d5318b0114df14edf90cb420521bbb4b5ec95a35a137666f2e84edd279021c", "eip712Domain": {"types": {"Proof": [{"name": "@context", "type": "string"}, {"name": "created", "type": "string"}, {"name": "proofPurpose", "type": "string"}, {"name": "type", "type": "string"}, {"name": "verificationMethod", "type": "string"}], "@context": [{"name": "nullifiers", "type": "NullifiersContext"}, {"name": "provider", "type": "string"}], "Document": [{"name": "@context", "type": "string[]"}, {"name": "credentialSubject", "type": "CredentialSubject"}, {"name": "expirationDate", "type": "string"}, {"name": "issuanceDate", "type": "string"}, {"name": "issuer", "type": "string"}, {"name": "proof", "type": "Proof"}, {"name": "type", "type": "string[]"}], "EIP712Domain": [{"name": "name", "type": "string"}], "CredentialSubject": [{"name": "@context", "type": "@context"}, {"name": "nullifiers", "type": "string[]"}, {"name": "id", "type": "string"}, {"name": "provider", "type": "string"}], "NullifiersContext": [{"name": "@container", "type": "string"}, {"name": "@type", "type": "string"}]}, "domain": {"name": "VerifiableCredential"}, "primaryType": "Document"}, "proofPurpose": "assertionMethod", "verificationMethod": "did:ethr:0xd6f8d6ca86aa01e551a311d670a0d1bd8577e5fb#controller"}, "issuer": "did:ethr:0xd6f8d6ca86aa01e551a311d670a0d1bd8577e5fb", "@context": ["https://www.w3.org/2018/credentials/v1", "https://w3id.org/vc/status-list/2021/v1"], "issuanceDate": "2025-03-29T10:24:17.285Z", "expirationDate": "2025-06-27T10:24:17.285Z", "credentialSubject": {"id": "did:pkh:eip155:1:0x19439fCdB55A5A16f44d9AdEEa68670414B3a5C6", "@context": {"provider": "https://schema.org/Text", "nullifiers": {"@type": "https://schema.org/Text", "@container": "@list"}}, "provider": "ClearTextTwitter#anshuman_ecc", "nullifiers": ["v0.0.0:/mcmPokKnb9BJ7C42mUd/ejBFaS5GdAIMmHZI73DsGk="]}}}, "description": "*What*\nHyvmind is a 'research to earn' app for lawyers. It rewards them for contributing to an open source semantic-data layer.\n\n*Why*\nHuman annotated datasets play a critical role in training language models. But sourcing them is considered expensive and time-consuming. The irony is that researchers invariably create such annotations for themselves, without being aware of their value. \n\n*How*\nImagine an app where legal researchers could (a) own each annotation that they made on a public document, (b) encrypt and preserve it as a private asset or deposit it in a common pool for rewards, (c) collect assets shared by others, (e) combine owned and collected assets to create private curations and knowledge subgraphs.", "lastUpdated": 0, "projectTwitter": "anshuman_ecc"}, "recipient": "0x5D21f6803902A93018eCcA706cb3f853aDa1CCd2"}}