Love this idea, and +1 on Jon Gjengset and Neel Nanda.
For finance though, I’m skeptical of your recommendations (DeepFuckingValue and Martin Shkreli). The former made his money pumping-and-dumping meme stocks, and I get the impression the latter has been selected for fame (like recommending Neil deGrasse Tyson to learn physics).
In general, I think finding good resources in finance requires a much stronger epistemic immune system than nearly any other field! There’s so much adverse selection, and charlatans can hide behind noisy returns and flashy slide decks for a very long time. I’ve worked at a top quant trader long enough to spot BS, and the KL-divergence between what competent looking YouTubers say and what actually works is extreme.
My recommendations would be:
Blogs with great tacit knowledge: bitsaboutmoney.com, thediff.co, Money Stuff
Trustworthy news sources: Financial Times, Bloomberg News, Reuters
Other recommendations:
Git version control: Scott Chacon’s So You Think You Know Git videos
I’ve played Figgie a fair bit and don’t think it’s a good tool for teaching epistemics outside of a Jane Street trading internship.
To actually learn with it, you first need a number of motivated playing partners. They need to be quite skilled for their actions to be informative, and the game sucks before they reach this point. In my experience people don’t reach this skill level within their first couple hours of play. It also requires N custom-made decks for N rounds of play, or for everyone to install a specific app. So it’s really only practical for a trading firm internship program.
I have what I would call unusually good epistemics, and I mostly got there by obsessively reading the financial news (Bloomberg, Reuters) and making concrete predictions about how various stories would develop, and seeing where I went wrong. I’d ask myself questions like “how, concretely, did this article come into being?” I did this for about 9 months, and by the end I was rarely surprised by news items, could spot the signs of failure modes like circular reporting (super common), and had working mental models of many important institutions.
The other valuable thing I did was putting probabilities on everyday occurrences (65% confident I get home within 15 mins, etc.). I recorded these, and reflected on what went wrong when I deviated too far from perfect calibration.