Sarah Drinkwater
@sarahdrinkwater
Knowledge beachcomber interested in the ownership economy, flourishing communities, ecosystem building, magic, and knowledge as an end to itself.
Sarah Drinkwater
DAO bounties: For workers, it’s a way to get a taste of working in crypto, to help their favorite project move forward, or to earn tokens that will give them ownership in the new Web3 economy. For crypto start-ups and DAOS, it’s an affordable way to source cheap labor for development or marketing without having to hire full-time employees with... See more
Blockchain Socialist • 'Cowboy Bebop' Was A Warning
Gitcoin Kernel and She256 are doing a lot of work to build educational onramps.... But what if @dhof and the whole Loot Discord dedicated time to mentoring a group of people who don’t have any way into the space? (not white friends who work in bigcos)
Spirit of Toby Shorintwitter.comWeb 3.0 and
Publishing and
Somewhat to my surprise, I found an answer waiting.I want to follow people who are interesting to me, in a way that’s simple, expressive, and predictable.
Robin Sloan • Specifying Spring '83
Amsterdam traded worldwide. The 17th-century city had four times the income per capita of Paris. Foreigners flocked here, and were left largely unbothered. Shorto notes that Amsterdam’s tolerance was – and is – above all pragmatic. Tolerance was good for business, for social peace and innovation. 17th-century Amsterdam published perhaps 30 per cent... See more
Amsterdam: A History of the World’s Most Liberal City, by Russell Shorto
Future of software and AI tooling
One question kept coming up:
Why haven’t incumbent software providers been able to leverage their valuable data assets yet?
Both giants like Salesforce and growth-stage companies with 10+ years of collected data seem to be struggling to deliver truly differentiated AI experiences. Most of us would assume these companies are sitting on data gold mines that should give them a massive advantage. But is that actually true?
One perspective I found compelling: The historical data these companies have collected simply isn't the right kind to make AI models truly effective. The workflow data, outcomes data, or database info they've aggregated over the years might not be as useful as we'd think. What's most valuable in building agentic AI isn't just workflow data or outcomes data—it's a granular understanding of how humans actually perform tasks.
One of the reasons that there is so much confusion around token design, and value accrual specifically, is that tokens, and the DAOs and protocols that issue them, are so multifaceted. Sometimes, the issuers want them to behave like shares in a corporation. Others issue “governance” rights to skirt regulations while insiders pump the tokens in the... See more