• 5 Posts
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Joined 2 years ago
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Cake day: June 18th, 2023

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  • This is definitely a simplification, which is why I pointed out the possibility of distributing costs among the consumers based on how much of the total consumption each consumer is responsible for.

    I think the major point still stands though: In order to take advantage of production at scale, you need to build some minimal size production facility. For stuff like hydropower, that minimum can be quite high, depending on available geography.

    If marginal cost is zero, it makes most sense to charge some form of flat rate to have access to power, rather than a consumption-based price, because it’s not necessarily feasible to downscale the facility, even if there’s low demand (in that sense, hydro or nuclear would be better examples than solar).

    The details of how this more or less flat rate should be distributed among consumers is a discussion in itself (should those living further away pay more since they require more power lines? etc.)


  • You’re making the argument yourself here:

    A 1000 A transformer costs more than a 10 A transformer

    Yes. And that is true regardless of how heavily it is used, which means you should pay a flat rate for maintenance of the infrastructure you use, and another rate for the power you draw.

    Residential buildings use standardised infrastructure, which then leads to the same standard fee for everyone. Industry that needs heavier equipment pays a different fee, because they require different infrastructure.


  • No, they’re arguing that the price of power should be split:

    • A fee for grid maintenance (equal for all)
    • A fee per unit of consumed power (scales linearly with consumption)

    This makes sense, because regardless of you much power someone uses, the costs associated with maintaining the infrastructure that allows them to draw any power at all remain the same. This also happens to be the model used in Norway, so it’s not an untested concept.

    Another option, relevant when the cost of building the power plant is large and the cost of energy production is negligible, is that everyone connected to the grid pays a near-flat fee in total, which is distributed among consumers depending on how much power they use. I’ve never heard of that option being used before.


  • I’m all for eating the rich, but I’m still going to point out why exactly this can make sense.

    Let’s say you have an energy company that owns a solar farm, you’re not looking to turn a profit, just provide clean energy to the world: You produce electricity at effectively zero cost.

    However, your solar farm needs to be paid down within its lifetime of ≈30 years, which is independent of energy consumption. So you decide to charge a rate that ensures 1/30th of your production costs are paid back each year, so that you can replace the solar farm after 30 years.

    This effectively means you are charging a constant rate for access to energy supply, independent of consumption. This again means that the rate per kWh goes up if average consumption goes down.

    Individual customers can still save money by reducing consumption relative to the other customers, but nobody saves money if everyone reduces consumption. This makes complete sense when your “marginal cost” (i.e. the cost of producing energy) is negligible compared to the initial investment of building the power plant, and also applies more or less to nuclear, hydropower, and wind power as well.

    Given that this is not an ideal organisation though, I wouldn’t put it past them to increase the rate such that it more than offsets the decrease in consumption, thereby increasing their profit. In that case: Fuck them.

    I just think we should be aware that our current understanding of energy prices as linked to day-to-day consumption (because the primary expense for a thermal power plant is the cost of fuel), will become outdated as we move to clean energy sources. At some point, we should be paying a near-flat rate for “access to power”, rather than a rate for each unit of power consumed.



  • Not running any LLMs, but I do a lot of mathematical modelling, and my 32 GB RAM, M1 Pro MacBook is compiling code and crunching numbers like an absolute champ! After about a year, most of my colleagues ditched their old laptops for a MacBook themselves after just noticing that my machine out-performed theirs every day, and that it saved me a bunch of time day-to-day.

    Of course, be a bit careful when buying one: Apple cranks up the price like hell if you start specing out the machine a lot. Especially for RAM.






  • I wholeheartedly agree: In my job, I develop mathematical models which are implemented in Fortran/C/C++, but all the models have a Python interface. In practice, we use Python as a “front end”. That is: when running the models to generate plots or tables, or whatever, that is done through Python, because plotting and file handling is quick and easy in Python.

    I also do quite a bit of prototyping in Python, where I quickly want to throw something together to check if the general concept works.

    We had one model that was actually implemented in Python, and it took less than a year before it was re-implemented in C++, because nobody other than the original dev could really use it or maintain it. It became painfully clear how much of a burden python can be once you have a code base over a certain size.


  • I have next to no experience with TypeScript, but want to make a case in defence of Python: Python does not pretend to have any kind of type safety, and more or less actively encourages duck typing.

    Now, you can like or dislike duck typing, but for the kind of quick and dirty scripting or proof of concept prototyping that I think Python excels at, duck typing can help you get the job done much more efficiently.

    In my opinion, it’s much more frustrating to work with a language that pretends to be type safe while not being so.

    Because of this, I regularly turn off the type checking on my python linter, because it’s throwing warnings about “invalid types”, due to incomplete or outdated docs, when I know for a fact that the function in question works with whatever type I’m giving it. There is really no such thing as an “invalid type” in Python, because it’s a language that does not intend to be type-safe.


  • That depends on what level you’re working at: If you hire a company to do electrical work as a part of your construction project, you’ll typically rely on that company to provide paperwork confirming that everything is in order. As your company does not have the qualifications to do the certification (hence why you are hiring a subcontractor), you cannot be expected to cross-check the work.

    If the building catches fire due to an electrical failure, it’s the subcontractor that signed off on the paper whose held liable, not the company that delivered the end-product.

    Similarly, if I buy a product and receive a certificate that it holds some standard, I’m permitted to assume the certificate is valid and re-sell the product, unless there’s some express reason I should have understood that something is wrong.


  • To be fair, if the lead is added by a middle man selling to the company, then the company isn’t making any more money.

    I can definitely see a situation where that’s the case. It would be comparable to buying something off someone, you look at it and it looks like everything is in order, after you sell it on it turns out the stuff was stolen.

    I’m not 100% sure, but I don’t think you can be held accountable in such a situation unless it’s proven that you either knew or should have known that you were selling stolen goods.