Bridging Community Signals and Strategic Priorities for Crowdfunding Open-Source Software

What is this talk about?

Grant programs publish where the money went. But can we quantify if it reached the problems that mattered most? This talk shows how we visualised $1M+ in community-driven funding for open-source software: not as a list of funded projects, but as capital flows from ecosystem challenges to the work that addresses them. This work engages questions about visualising complex allocation systems for designing feedback loops between community signals and strategic priorities.

Core ideas and context

Most grant programs show you transactions: who received how much. That's transparency of allocation, not strategic clarity. In crowdfunding, the harder question is whether the aggregate of thousands of funding decisions actually maps to the priorities an ecosystem cares about.

We worked with a crowdfunding mechanism (Quadratic Funding) where donors collectively decide which open-source projects receive matching funds. It's radically transparent, where every donation and matching calculation is public. But transparency alone doesn't answer:

Are the most critical problems getting funded, or just the most visible ones?

Community funding has a structural bias. Work that's narratable, immediately useful, and easy to explain attracts donations. Ecosystem critical work that's long-horizon, coordination-heavy, or infrastructural struggles to compete for attention, even when donors intellectually agree it matters.

This data analysis bridged that gap. Starting from curated problem catalogues developed through domain sensemaking, including stakeholder interviews, ecosystem analysis, and expert judgment, we used AI-assisted methods to align each project to primary and secondary problem areas. The resulting visualisations traced funding flows from problem categories to individual projects, making it possible to ask:

Did the problems we identified as most critical actually attract the funding needed to solve them?

Speaker Bio

Rohit Malekar designs analytics systems that help blockchain-native communities translate messy grant data into clear, actionable insights to make better capital allocation decisions. He designs tools that reduce information asymmetry and make funding decisions more fair, transparent, and evidence-based for funders, donors, grant allocators, and domain experts. His work spans the grants ecosystem in Gitcoin, analytics with Open Source Observer, and climate impact initiatives with Atlantis. He believes the best visualisations don’t just inform. They coordinate action. More at rohitmalekar.in

Structure and flow

1. Beyond Transparency (3 min): What blockchain-native funding already solved and the strategic question it leaves open
2. The Approach (8 min): From problem catalogues to funding flows, a review of design choices, AI-assisted alignment, and visualisation trade-offs
3. The Patterns (4 min): What the visualisations revealed and a reusable framework for problem-level funding analysis

Intended audience

Practitioners designing visualisations for civic, nonprofit, or grant contexts—anyone building decision-support tools where users navigate unfamiliar domains under uncertainty.

Key takeaways

1. A diagnostic question: "Does your dashboard show where money went, or whether it reached what mattered?" offers a new lens for evaluating grant effectiveness.

2. A mental model: Position analysis as an invitation for inquiry, not a final judgment. Stakeholders are more likely to engage when they're co-investigators, not defendants.

3. Design principle: AI can handle the first-pass categorization at scale; humans refine and validate. This makes problem-mapping feasible for large portfolios.