> To that end, making our compute available for rent means that we can only take it back if we can make more money on it ourselves; the only way we can do that is by leaning into what we are good at, not what I have spent too long wanting us to be. To put it another way, our best product decisions have been intuition validated by data and revealed preference; that’s how we’re going to approach AI.
What a great way to frame the strategy + opportunity cost. "We will make a bare minimum via reselling, and it's now up to us to prove we can do better."
Your concept sounds interesting. I highly recommend you add a link to your startup on the job board or in your HN profile. Searching for Pascal with NYC, CPG, etc. key words doesn't yield your home page and I can't tell if teampascal.com has anything to do with you given it doesn't discuss CPG.
Basically OpenClaw but with investing dashboards for my portfolio, additional tools specifically for investing, and exploring an AI-Human collaboration on researching economics (check the 'community' tab).
The data models are all in markdown and Excel so that there's no lockin and you can manually edit positions, personalities, etc.
This comes from frustration around most investing tools basically scraping your personal data + forcing you to lock into subscriptions. I think it's now possible to just vibe code most of what one needs, aside form raw data subscriptions.
I'll add one more point. If you scroll through his Substack, a lot of his posts are incredibly negative and unproductive. I was (and continue to be) someone who cares deeply about responsible AI... But there's a difference between working on AI responsibly or pushing the debate, versus simply criticizing everything that is done as folly, useless, crap, etc.
"Everything can be taken from a man but one thing: the last of the human freedoms—to choose one’s attitude in any given set of circumstances, to choose one’s own way."
I'm a huge Malick fan. If you are curious about his very unique style, this 20-minute video outlines why his cinematography is so unique and so powerful: https://www.youtube.com/watch?v=waA3RXy13aA
The article talks about how prediction markets' sports books are significantly more profitable. This has less to do with financial structures and more to do with who wants to make bets and where.
According to the article, prediction markets make magnitudes more money on potentially illegal (by today's standards in the US, anyway) sports betting than true event contracts.
Amazing. If this means no more management of Celery workers, then I am so happy! So nice to have this directly built _into_ Django, especially for very simple task scheduling.
Do you ever pair trade or hedge your shorts by buying indices? For example, short the quantum stocks but buy NASDAQ index (or call options) in case everything keeps going up?
> There is another thing called world models that involves predicting the state of something after some action. But this is a very very limited area of research. My understanding of this is that there just isn't much data of action->reaction.
Folks interested in this can look up Yann LeCun's work on world models and JEPA, which his team at Meta created. This lecture is a nice summary of his thinking on this space and also why he isn't a fan of autoregressive LLMs: https://www.youtube.com/watch?v=yUmDRxV0krg
Fair point, and I should be more clear. The AI researchers I speak with don't expect AGI and are more reasonable in trying to build good tech rather than promising the world. My point was that these AI researchers aren't the ones inflating the bubble.