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Joined 2 months ago
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Cake day: August 4th, 2026

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    • Working with git instead of 10 folders called “version x”
    • to read faster I first had to learn to read at all. I was always only scimming and skipping words and since I focused on retention and understanding I started to read better.
    • or more general: I need to invest time to improve/ get good at things. There is no shortcut and talent alone only gets you so far. A nice body comes from putting work in. Learning languages comes with practice. Etc…
    • listening to my body (still not good at it tho) and rest when I feel tired. I’d feel urged to do something and can’t rest and end up burned out and need to rest anyways – but with brainfog and wasting my rest time on my phone or youtube…

  • Oh and the connection between LLMs, Tokens and ANNs.

    ANN = LLMs, LLMs are a subset of ANNs that have the purpose of doing anything language. Most times you’d want maybe to predict a single thing (e.g. a temperature, a color, a letter). The specific output depends on the usecase. LLMs simply output characters and predicts one character at a time whats the most likely output.

    To make sense of this nonesens you’d need to output and input things that are larger than a single character. These are tokens. I don’t know much about that, but basically it is the whole reason why LLMs took off and they got invented by some research done by google in 2017 (idk the title of the paper anymore, maybe something along the lines of “a new way of thinking”). Funnily enough google ignored the paper and some guys at google got mad and founded their own AI company cough Anthropic cough

    Anyways tokens let the AI-Model predict 4 characters or so at a time, which enables the LLM to predict actual words.

    I am not sure exactally how it works from there, maybe it gives the whole in- and output so far back into the model as a new input to generate the next token? Sounds kinda inefficiant, but the whole thing is just fucking inefficient and useless. AI has usecases but LLMs are billionare madness in terms of calculation power needed.


  • To answer a lesser answered sub question of a question: what is a weight?

    Imagine you have an output, its either a or b. You have an input, whatever it is. And then there is a weight in the middle. The weight determines wether the output will be a or b.

    In math it would be a function input*weight=output.

    ANN (Artificial Neural Networks, just afancy name) is a chain of a whole bunch of weights. There are layers and each layer generates an output from a bunch of weights and the next layer uses it as inputs. The name comes from the visualization, which looks like neurons firing to other neurons (from biology, things that make your brain work).






  • (you don’t have to be witty when you want to be savage.)

    They don’t seem to be nice people, so I wouldn’t recomment to try being friends with them. If they bother you and you want to change it then speak up that they are out of line, but do so at your own accord, not because someone told you to, because you need to assess the situation. If they are reasonable then they might just not realise they are out of line, if they actually want to insult you then then it might just fuel the flames. I’d write something like “stop bothering me”






  • I stuck with superproductivity. It supperts boards (you can configure your own board, but kanban and eisenhower are build in i think (i dont use that feature) i like how they handle projects and task storage. They have a philosophy of not adding task from a project to the daily clutter, you chose yourself. If helps to not feel overwhelmed and it can store tasks.

    Its not that good for lists of things imo, its better for for timetracking. But you can create a task and easily do subtasks and use it as a list. Also I think it is installable via docker but I don’t know, I havent used docker, but a search gave some results.