Sarah Drinkwater
@sarahdrinkwater
Knowledge beachcomber interested in the ownership economy, flourishing communities, ecosystem building, magic, and knowledge as an end to itself.
Sarah Drinkwater
Stop trying to raise a debut venture fund -- go for the SPV instead
techcrunch.com
We’re Selling Entrepreneurship Short
bryce.medium.com
The way managers are taught to run companies seems to be like modular design in the sense that you treat subtrees of the org chart as black boxes. You tell your direct reports what to do, and it's up to them to figure out how. But you don't get involved in the details of what they do. That would be micromanaging them, which is bad.
Hire good people and give them room to do their jobs. Sounds great when it's described that way, doesn't it? Except in practice, judging from the report of founder after founder, what this often turns out to mean is: hire professional fakers and let them drive the company into the ground.
Crafting a compelling product vision | Ebi Atawodi (YouTube, Netflix, Uber)
youtube.comThe core problem is not that open source projects are not sharing the money received. The problem is that, in total numbers, open source is not getting enough money. $2.5 million is not enough. To put this number into perspective, startups get typically much more than that.
André Staltz • Software below the poverty line
The first and largest “user” of a Web3 platform — defined as a software service running on an open blockchain (and often called a “protocol” in Web3 parlance) — is usually the product team that built the underlying platform. Web3 teams building these platforms are wise to build the first product on top and give it away for “free,” as a commodity, i... See more
Jesse Walden • Product vs. Protocol: Finding a Balance in Web3 – Variant
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.