Hi @deltajuliet ,
It looks like Hydrapad’s report was generated by LLM based on Mathilda’s template, with the primary goal of selling Hydrapad’s solution.
Tools Used:
- Root cause analysis of startup failure metrics
- Comparative study of fundraising mechanisms (SAFTs, ICOs, bonded curves)
- On-chain liquidity analysis (Dune, DeFiLlama)
Source Key Finding Severity Dune Analytics 68% presale tokens crash >90% Critical GG19 - 23 92% founders lack mentor access High Hydrapad [value proposition] Solution Data clustered around 3 themes:
- Capital Access: Fragmented tools increase failure rates.
- Operational Burden: Compliance/KYC slows launches.
- Liquidity Mismatch: Static presales cause volatility.
This is as far from complexity-informed sensemaking that @owocki described as possible.
Unfortunately the current framework is vulnerable to this kind of “sensemaking through AI-generated reports”. If Gitcoin wants to address Ethereum ecosystem as a complex environment (which it is), it’s important to implement the “probe-sense-respond” approach and make sure that reports are not about “solving problems”, incl. pitches or self-advertising. Sensemaking isn’t about creating the proposals for what should be done, but rather understanding the environment.
A few members of Sensemaking Scenius (including myself) have collaborated on this proposal: [Gitcoin 3.0] Complexity-Informed Sensemaking Pilot
We would really appreciate your feedback. Our intention is not to challenge the existing framework, but rather to invite the Gitcoin community to a serious conversation about sensemaking.