I often write with Claude, and at work we have Gemini code reviews on GitHub; definitely these two catch different things. I'd be excited to have them working together in parallel in a nice interface.

If our ops team gives this a thumbs-up security wise I'll be excited to try it out when back at work.

This I don't understand, maybe someone help me out? Say the real growth rate of capital (or interest rate available to me, whatever) is 2%/year and I have a 10 year time horizon. So $1.00 today is ~$1.22 in 10 years. More generally, if I have wealth X today I will have 1.22X in 10 years. And if X is not a constant but a random variable and I want to maximize future expected wealth (not log wealth), that's just max(E[1.22X]) and by linearity of expectation I should just maximize wealth today to maximize in 10 years time.

So Kelly being appropriate must have some other conditions, right? Wanting to maximize log wealth is surely sufficient (and individually probably ~rational). What else?

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Tiny example (which blends Julia-the-language and Julia-the-ecosystem, for better and worse): I just timed reading the most recent CSV I generated in real life, a relatively small 14k rows x 19 columns. 10ms in Julia+CSV+DataFrames, 37ms in Python+Pandas...ie much faster in Julia but also not a pain point either way.

Remote: strongly prefer in person or hybrid

Willing to relocate: I'm very happy in the Boston area, so only under extraordinary circumstances

Technologies: data science, machine learning, technical communication, simulation, optimization, physically motivated models, heuristics; Julia, Python (Django, numpy, scipy, sklearn, cupy, pandas), AWS, Postgres/Postgis, Snowflake, Gurobi, NLopt, etc.

Resume: https://www.dropbox.com/scl/fi/wevhopcf81vx6vdoarn5a/efields...

Email: in the resume

Blurb: PhD in operations research -> startup employee #2 -> scale startup, gradually moving from data science IC to VP of data science + software engineering -> now looking for mission-meaningful hands-on technical work. I'm particularly good at technical communication as well as translating from the physical world (science/business) to math models. Open to companies of any size, especially motivated by the biosecurity, biotech, robotics, and clean energy sectors.

Something cool I built that's not work related: https://nomai-writing.com/ in the spirit of Outer Wilds

Remote: strongly prefer in person or hybrid

Willing to relocate: I'm very happy in the Boston area, so only under extraordinary circumstances

Technologies: data science, machine learning, technical communication, simulation, optimization, physically motivated models, heuristics; Julia, Python (Django, numpy, scipy, sklearn, cupy, pandas), AWS, Postgres/Postgis, Snowflake, Gurobi, NLopt, etc.

Resume: https://www.dropbox.com/scl/fi/wevhopcf81vx6vdoarn5a/efields...

Email: in the resume

Blurb: PhD in operations research -> startup employee #2 -> scale startup, gradually moving from data science IC to VP of data science + software engineering -> now looking for meaningful hands-on technical work at a company inventing something in the world of atoms

Something cool I built that's not work related: https://nomai-writing.com/ in the spirit of Outer Wilds

* Car users are quite dependent on the government for transportation, e.g. the many billions (in the US) of public dollars spent on roads each year. Hard to get around without them!

* Even if we hand-wave that away and assume car users aren't dependent on the government for transportation, surely _everyone_ is dependent for other reasons like enjoying public goods (national defense, clean air and water, rule of law), access to the social safety net, etc.?

I know spaced repetition is super helpful and I should be making and study cards to help with language learning and other topics I'm studying, but it always feels like a slog to try to find a deck (which won't end up being what you want) or manually make a bunch of cards, the UI is a little meh, etc.