cl42

Goodbye Zachary's Karate Club, hello Sumida Penguin Romance Chart!

I'm building AthenaOS: https://athena-os.ai/

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.

It's all open source, too: https://github.com/wgryc/athena-os

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."

Fantastic book.

I hate all the portfolio tracking tools out there + don't understand why tools like FactSet or CapitalIQ cost so much.

... so I'm building an open source version.

Track all your trades in Excel, and get Sharpe ratios, Sortino ratios, or even pass it on to an LLM to have it recommend trades based on news feeds.

Planning to open source it in the next week or two, once I add the proper tests and docs! :)

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

That's a fascinating point, thanks for sharing. I wonder if prediction markets will 'asymptotically converge' into similar sports betting strategies.

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.

Have you considered launching your own weather prediction market instead?

Parametric insurance, energy traders, etc could be good markets.

Seems like this is the _President_ of the division, so sounds like there's a nontrivially-sized team to manage.

> Background Tasks.

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.

That’s awesome, thanks!

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?

This Yann LeCun lecture is a nice summary of the conceptual model behind JEPA (+ why he isn't a fan of autoregressive LLMs): https://www.youtube.com/watch?v=yUmDRxV0krg

> 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

Thanks for doing this. The song is beautiful and I highly recommend folks listen to it on Spotify/Youtube/whatever.

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.

Great points. I am bullish on AI but also wary of accounting practices. Tom says Nvidia's financials are different from Lucent's but that doesn't mean we shouldn't be wary.

The Economist has a great discussion on depreciation assumptions having a huge impact on how the finances of the cloud vendors are perceived[1].

Revenue recognition and expectations around Oracle could also be what bursts the bubble. Coreweave or Oracle could be the weak point, even if Nvidia is not.

[1] https://www.economist.com/business/2025/09/18/the-4trn-accou...

Not sure why you're getting downvoted.

If you speak with AI researchers, they all seem reasonable in their expectations.

... but I work with non-technical business people across industries and their expectations are NOT reasonable. They expect ChatGPT to do their entire job for $20/month and hire, plan, budget accordingly.

12 months later, when things don't work out, their response to AI goes to the other end of the spectrum -- anger, avoidance, suspicion of new products, etc.

Enough failures and you have slowing revenue growth. I think if companies see lower revenue growth (not even drops!), investors will get very very nervous and we can see a drop in valuations, share prices, etc.

Former management consultant here from a long, long time ago. It blows my mind when I see the consultancies cheer how they are adopting Gen AI to automate deck building, Excel analysis, etc.

... like, don't you see that your clients are READING your announcements and wondering "What the hell am I paying for?"

If I pay you $10K/month (let alone $100K+) and you're sending me anything AI generated, you will never work for me again.

Thank you! That definitely sounds interesting.

A few years ago I went to a military museum in Vietnam, where they have a lot of equipment used by the US military on display -- helicopters, planes, guns, etc.

What surprised me is how the equipment had labels around where to open things to rescue people, where to pour fuel, etc... It was labelled such that someone with limited (or no) exposure to the vehicle model would know what to do without referencing any sort of manual.

Very different today.

In the spirit of July 4, John Lewis Gaddis explores a similar theme in "On Grand Strategy". This is one of my favourite explorations, where he compares Abraham Lincoln and John Quincy Adams:

> Compare Lincoln’s life with that of John Quincy Adams. Great expectations inspired, pursued, and haunted Adams, depriving him, at critical moments, of common sense. Overestimations by others—which he then magnified—placed objectives beyond his reach: only self-demotion brought late-life satisfaction. No expectations lured Lincoln apart from those he set for himself: he started small, rose slowly, and only when ready reached for the top. His ambitions grew as his opportunities expanded, but he kept both within his circumstances. He sought to be underestimated.

The point -- being too ambitious can slow you down if you're not strategic.

I was going to recommend a thorough study of "Seeing Like a State" by James C. Scott until I saw your </satire> tag. You got me. :)

I search through them via text-based/exact matches. I also try to review the notes every once in a while + do use tagging.

I'd love to add an LLM layer onto this at some point but haven't done that yet.

Does Zettelkasten count? I started using a more formal approach to note-taking and idea review and it's been fantastic in helping me generate ideas, hypotheses, topics to write essays on, and so on.

It requires a bit of rigour but it's helped my intellectual productivity immensely.

"The Power Broker: Robert Moses and the Fall of New York" by Robert Caro

This is an incredibly long biography of a man who figured out how to build an urban empire. While he wasn't an "entrepreneur" per se, he figured out how to generate huge amounts of revenue via tolls/bridges, how to manage and manipulate public policy, and how to attract the best urban planning talent.

... and you can then read about how it all fell apart.

Regardless of your opinion on Robert Moses / NYC, it's an incredibly fascinating read or (~90-hour) audio book.

Gotcha, thanks! Updated.

Thank for the feedback! :) I included “No” because that’s the paper’s conclusion. Hoping to save people the effort of reading.

I've been skeptical about the "time series" LLMs papers from earlier, so this is interesting to see. Curious if others disagree with this paper!