Sarah Drinkwater
@sarahdrinkwater
Knowledge beachcomber interested in the ownership economy, flourishing communities, ecosystem building, magic, and knowledge as an end to itself.
Sarah Drinkwater
NoNoise Ventures
nonoise.vcMurmurations in software!
What Here Comes Everybody has me thinking about in 2022 is whether or not the dynamic era of organizational experimentation that flourished in the 2000s was way shorter than we predicted it to be, and if it is now effectively over. The apparently open-ended organizational possibilities of the 2000s web resolved in practice to a limited handful of... See more
Tim Hwang • Here Went Everybody
Because we don’t ship our shit back to where the food comes from, we keep perpetuating the redistribution of nutrients on the planet. Soils grow barren, so we use synthetic fertiliser, which isn’t anywhere near as good as the real shit, and also is very polluting to produce. Scientists call this phenomenon the metabolic rift.
Lina Zeldovich • Our excrement is a natural, renewable and sustainable resource – if only we can overcome our visceral disgust of it
Inside the race to train AI robots how to act human in the real world
https://www.latimes.com/people/nilesh-christopherlatimes.com
As with other forms of Cheems, there is obviously an important distinction here between ‘friends giving you helpful advice about obstacles you’ll need to overcome, or trade-offs to be aware of’, and ‘reflexive cynicism’.
#1: In-group Cheems
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.
