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Joined 2 years ago
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Cake day: June 16th, 2023

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  • Based on the amount of vitriol I’ve personally received on this site for renting one property while I am temporarily relocated to attend school, the answer is yes.

    For some reason everyone views being a landlord as easy money. But in reality returns on investment are worse than the stock market for being the landlord of a single family home.

    Edit: Isn’t it funny how the critics below didn’t even ask questions about a specific situation where it does make sense to rent out an owned home? Instead of trying to understand why someone might make the choice they make, they sling insults and make wide sweeping assumptions to reinforce their skewed world view. Honestly it’s this shit that’s why Trump won. Leftists can’t see the forest for the trees and are willing to engage in ever escalating purity tests that only alienate other sympathetic voters to leftist causes.

    I worked hard to be able to own my own house. Saved money and took out a loan. I never received a penny from my parents or some inheritance from a family member that died. A greater return on investment can absolutely be made by investing in the SP500, returns on investment for single family homes will be worse. The SP500 can be expected to rise an average of 10% per year. A single family home on the other hand will increase by 4.3% per year. With interest rates being higher than that level appreciation, there is effectively no profit from the leverage that can be typically seen by borrowing money. Renting is typically 37% cheaper than buying on a month-to-month basis. Owners don’t expect to Break-even on a home until after 5-10 years of ownership (depending on the city). Over 2/3 the cost of a mortgage go towards loan interest and taxes. Now what does a house get you then if there are all these downsides? Freedom. Freedom to decorate how you choose. To remodel, to build a deck, install Ethernet throughout the house, add an extension. But most of all, it gives long-term stability. After that 5 year period where a homeowner is taking a loss because of buying, they are finally ahead financially of a renter. This is why it doesn’t make sense to sell a home due to short-term circumstances, because owning a home is inherently a long-term benefit. Especially when one loses 10% of the the value of a home selling it when it would take 3 years for the home to even grow to the point where that cost is covered by increases in home value, which is not even remotely guaranteed, as evidenced by home values only increasing 0.12% after falling by 5% the previous year.


  • Valve is a unique company with no traditional hierarchy. In business school, I read a very interesting Harvard Business Review article on the subject. Unfortunately it’s locked behind a paywall, but this is Google AI’s summary of the article which I confirm to be true from what I remember:

    According to a Harvard Business Review article from 2013, Valve, the gaming company that created Half Life and Portal, has a unique organizational structure that includes a flat management system called “Flatland”. This structure eliminates traditional hierarchies and bosses, allowing employees to choose their own projects and have autonomy. Other features of Valve’s structure include:

    • Self-allocated time: Employees have complete control over how they allocate their time
    • No managers: There is no managerial oversight
    • Fluid structure: Desks have wheels so employees can easily move between teams, or “cabals”
    • Peer-based performance reviews: Employees evaluate each other’s performance and stack rank them
    • Hiring: Valve has a unique hiring process that supports recruiting people with a variety of skills


  • This is done by combining a Diffusion model with ControlNet interface. As long as you have a decently modern Nvidia GPU and familiarity with Python and Pytorch it’s relatively simple to create your own model.

    The ControlNet paper is here: https://arxiv.org/pdf/2302.05543.pdf

    I implemented this paper back in March. It’s as simple as it is brilliant. By using methods originally intended to adapt large pre-trained language models to a specific application, the author’s created a new model architecture that can better control the output of a diffusion model.