I can't understand how professional software developers are so attached to days of manually coding. Was objectively a top-tier software engineer (e.g. hired as a quant dev to build high performance systems) and honestly not having to type another line of code feels like such a good riddance. People being so forlorn about agents being good at coding is insane to me. You can continue to ride horses as a hobby, no one can take that away from you, but you should be happy for everyone else to have engines. Coding has and should always (just) be a means to an end. The product was and always *should* be the point. It's undeniable that agents are just objectively better at getting to the point now. Go ahead and manually write code as a hobby.

Even for quants, as a pm there are many ways you can hurt your career from piss poor decision making in high stress environments: mostly around trying to make the pain stop // chasing comfort // protecting an identity. Making the pain stop: cutting the book rapidly after some drawdown and not being able to recover in the subsequent reversion. Chasing comfort: sizing up at the peak of your strategies, sizing down at the trough of your strategies (see making the pain stop). The above are papercut sequences where you see teams just drawdown slowly until management loses patience and eventually cut them. Protecting identity: trying to hold on to the image as a perpetual rainmaker, and doubling down on a dying strategy/book without understanding if the drawdown is a result of alpha decay and/or some systemic change. This is the blow up sequence, where teams will hit enormous drawdowns from doubling down, sometimes outside pre-agreed risk limits.

Pressure is a privilege.

One thing I’ve realized about life is that when people lose there are people who want to only talk about the loss and then there are people who want to only talk about how not to lose the next time and they might as well be speaking in different languages.

Me: When it rains, i want you to cover me with an umbrella. Anthropic: There are two halves to this. And the half where the user does not want to get wet is the one that matters. OpenAI: The user enjoys using umbrellas in the rain.

At this point, if you don't believe that Fable/Astra is generalized intelligence it's most certainly a skill issue. Increasingly, it seems the primary value of a human is to pick a series of options in an infinite sequence of options.

Life is chosen suffering - that's all it is, we have to pick the suffering that gives us the most meaning in exchange. IF we do not pick, we are assigned a form of suffering; e.g. mediocrity in your career is a form of suffering. Being obese and unfit is a form of suffering. To live is to suffer, but being able to pick your choice of suffering is a form of bliss.

In a knife fight, one guy dies on the street and the other guy dies on the way to the hospital.

A simple mental framework for how long an agent takes to complete a task: A task can essentially be broken down into the number of tokens by the implementation model x its harness needed to achieve it. Consider a very difficult task, a very smart model might need to iterate 10x before it gets it right, and might require 10mn toks. A less smart model might need to iterate 1000x before it gets it right, and might require 1bn toks. A dumb model will never get it right even if you let it iterate 1mn times, and will require an infinite toks. -- Between the very smart model and the less smart model above, if the very smart model outputs tokens at 10 tok/s and the less smart model outputs tokens at 1000 tok/s, they actually complete the task at the same time. So it's not JUST about intelligence. The same thing is true about costs, it's temping to look at $/mn toks and state that a model is cheaper than another, but what you're actually interested in is the $/difficult task for the work you are doing. The "more expensive" model might end up using $100 for $10/mn toks for 10mn toks to complete the task, while the "less expensive" model might end up using $1000 for $1/mn toks for 1bn toks to complete the task. -- Lastly, there are some tasks where anything LESS than frontier (e.g. Fable) literally cannot iterate towards the correct solution. This is why frontier models continue to be very attractive even if cheaper models are available. Until a supermajority of "very hard" tasks can be solved by OSS models, OpenAI and Anthropic will likely continue winning over more and more prosumers. -- Bonus: There are some domains that are "so difficult", which is another way of saying that the solution space is so far away from the training data available to the models (e.g. quant finance), that you will literally need to provide iterative feedback for the model to arrive at a reasonable solution.

One thing you realize is that most ultimatums in life are binary, if you fall long enough, it's up or out. Every man faces this demon on things he really cares about at least a few times in his life. You have to stare into the abyss and really contemplate if you're willing to be out, if not, the only other option is up. Then, you just have to make it work.

seconded

This is a fair take and a counterpoint to my meme.

1. Factor ETFs are often not orthogonalized to other factors and are themselves a myriad of factors. Often times due to long-only construction. Whilst one could argue they are more "actionable", they are most definitely not "better" at representing the underlying factors for analysis. 2. "High IQ"/professional/institutional factor models are constructed using the mfm methodology where beta is observed and returns are estimated. This is not intuitive for non-quants since returns are normally the observed variables. See en.wikipedia.org/wiki/Multip…. That's where the "numbers" come from. 3. Multi factor models (the real stuff) has been used extensively to great success at all real quant firms. One could probably get away with NOT using MFMs, or using a watered down version of them, but it's certainly not a gimmick!

Every fiber of my body is telling me to build consumer apps that demand attention in 10min intervals, targeting people who are restless from having interspersed pockets of time.