Emotionally Detatch yourself from your Ideas
Here is a model of mine, that seems related.
[Edit: Add Epistemic status]
Epistemic status: I have used this successfully in the past and found it helpful. It is relatively easy to do. is large for me.
I think it is helpful to be able to emotionally detach yourself from your ideas. There is an implicit "concept of I" in our minds. When somebody criticizes this "concept of I", it is painful. If somebody says "You suck", that hurts.
There is an implicit assumption in the mind that this concept of "I" is eternal. This has the effect, that when somebody says "You suck", it is actually more like they say "You sucked in the past, you suck now, and you will suck, always and ever".
In order to emotionally detach yourself from your ideas, you need to sever the links in your mind, between your ideas and this "concept of I". You need to see an idea as an object that is not related to you. Don't see it as "your idea", but just as an idea.
It might help to imagine that there is an idea-generation machine in your brain. That machine makes ideas magically appear in your perception as thoughts. Normally when somebody says "Your idea is dumb", you feel hurt. But now we can translate "Your idea is dumb" to "There is idea-generating machinery in my brain. This machinery has produced some output. Somebody says this output is dumb".
Instead of feeling hurt, you can think "Hmm, the idea-generating machinery in my brain produced an idea that this person thinks is bad. Well maybe they don't understand my idea yet, and they criticize their idea of my idea, and not actually my idea. How can I make them understand?" This thought is a lot harder to have while being busy feeling hurt.
Or "Hmm, this person that I think is very competent thinks this idea is bad, and after thinking about it I agree that this idea is bad. Now how can I change the idea-generating machinery in my brain, such that in the future I will have better ideas?" That thought is a lot harder to have when you think that you yourself are the problem. What is that even supposed to mean that you yourself are the problem? This might not be a meaningful statement, but it is the default interpretation when somebody criticizes you.
The basic idea here is, to frame everything without any reference to yourself. It is not me producing a bad plan, but some mechanism that I just happened to observe the output of. In my experience, this not only helps alleviate pain but also makes you think thoughts that are more useful.
Here is what I would do, in the hypothetical scenario, where I have taken over the world.
- Guard against existential risk.
- Make sure that every conscious being I have access to is at least comfortable as the baseline.
- Figure out how to safely self-modify, and become much much much ... much stronger.
- Deconfuse myself about what consciousness is, such that I can do something like 'maximize positive experiences and minimize negative experiences in the universe', without it going horribly wrong. I expect that 'maximize positive experiences, minimize negative experiences in the universe' very roughly points in the right direction, and I don't expect that would change after a long reflection. Or after getting a better understanding of consciousness.
- Optimize hard for what I think is best.
Though this is what I would do in any situation really. It is what I am doing right now. This is what I breathe for, and I won't stop until I am dead.
[EDIT 2023-03-01_17-59: I have recently realized that is is just how one part of my mind feels. The part that feels like me. However, there are tons of other parts in my mind that pull me in different directions. For example, there is one part that wants me to do lots of random improvements to my computer setup, which are fun to do, but probably not worth the effort. I have been ignoring these parts in the past, and I think that their grip on me is stronger because I did not take them into account appropriately in my plans.]
I use language models to help me design systems, not by asking them to solve problems, but by discussing my ideas with them. I have an idea of how to do something, usually vague, half-formed. I use automatic speech recognition to just ramble about it, describing the idea in messy, imprecise language. The language model listens and replies with a clearer, more structured version. I read or listen to that and immediately see what's missing, or what’s wrong, or what’s useful. Then I refine the idea further. This loop continues until the design feels solid.
The model doesn’t invent the solution. It refines and reflects what I’m already trying to express. That’s the key. It doesn’t act as an agent; it’s not writing the code or proposing speculative alternatives. It helps me pin down what I’m already trying to do, but better, faster, and with much less friction than if I were doing it alone.
I mostly don’t use autocomplete. I don’t ask for “write this function.” (Though I think there is a correct way to use these.) Instead, I might say something like: “Right now I have this global state that stores which frame to draw for an animation. But that feels hacky. What if I want to run multiple of these at the same time? Maybe I can just make it a function of time. Like, if I have a function that, given time, tells me what to draw, then I don’t need to store any state. That would probably work. Is there any reason this wouldn’t work?” And the LM will restate the idea precisely: “You’re proposing to push side effects to the boundary and define animation as a pure function of time, like in a React-style architecture.” That clarity helps me immediately refine or correct the idea.
This changes the kind of work I can do. Without the model, I default to braindead hacking: solve local problems quickly, but end up with brittle, tangled code. Thinking structurally takes effort, and I often don’t do it. But in a conversational loop with the model, it's fun. And because the feedback is immediate, it keeps momentum going.
This does offload cognition, but not by replacing my thinking. It’s integrated into it. The model isn’t doing the task. It’s helping me think more effectively about how to do the task. It names patterns I gestured at. It rephrases vague concepts sharply enough that I can critique them. It lets me externalize a confused internal state and get back something slightly clearer that I can then respond to. This creates an iterative improvement loop.
Maybe this works very well for me because I have ADHD. Maybe most people can just sit down and reflect in silence. For me, talking to the model lowers the activation energy and turns reflection into dialogue, which makes it very easy to do.
People say LMs slow you down. That’s true if you’re using them to write broken code from vague prompts and then patch the errors. But that’s not what I’m doing. I use them to think better, not to think less.
Depression as a Learned Suppression Loop
Overview
This post proposes a mechanistic model of a common kind of depression, framing it not as a transient emotional state or a chemical imbalance, but as a persistent, self-reinforcing control loop. The model assumes a brain composed of interacting subsystems, some of which issue heuristic error signals (e.g., bad feelings), and others which execute learned policies in response. The claim is that a large part of what is commonly called "depression" can be understood as a long-term learned pattern of suppressing internal error signals using high-intensity external stimuli.
Key components
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The brain includes systems that detect mismatches between actual behavior and higher-level goals or models. When these detect an issue (e.g., agency violation, unresolved conflict, lack of progress), they emit negative affect.
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Negative affect is not noise. It is a signal. In some cases, it simply arises from overstimulation and regulatory lag. In other cases, it points to an actual error: something is wrong with the current behavior, motivational alignment, or action trajectory.
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In a healthy system, negative affect would prompt reflection: "Why do I feel bad? What's the mismatch? Should I change direction?"
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In practice, this reflection step is often bypassed. Instead, the brain learns that high-intensity stimulus (YouTube, Skinner-box games, food, porn) suppresses the signal. This works in the short term, so the suppression policy gets reinforced.
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Over time, the suppression policy becomes automatic. Every time a conflict signal arises, it gets overwritten by external input. The system learns: "feeling bad" -> "inject stimulus" -> "feel better." No reflection or course correction happens. The source of the bad feeling persists, so the loop repeats.
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This creates a self-reinforcing attractor: more suppression leads to more misalignment, which leads to more negative signals, which leads to more suppression. The behavior becomes proceduralized and embedded into long-term structure.
Agency Violation as Error Signal
One key class of negative affect is what we call an "agency violation" signal. This occurs when behavior is driven by urges or automated triggers rather than deliberate, reflective choice.
Example: You feel a slight urge to open YouTube instead of working. You give in. After watching for 20 minutes, you feel bad. The bad feeling is not about the content. It's a second-order signal: you took an action that bypassed your reflective control system. You were coerced by an urge. The system flags that as a problem.
If, instead of reflecting on that signal, you suppress it by continuing to watch YouTube, the suppression gets reinforced. Eventually, the sequence becomes automatic.
Suppression vs. Resolution
Suppression works by injecting a high-reward stimulus. This reduces the negative signal. But it does not correct the cause. The policy becomes: bad feeling -> avoid it.
Resolution would involve explicitly checking: "Why do I feel bad?" and running a diagnostic process. Example: "I feel bad. Did I get coerced by an urge? Was I avoiding something? Am I stagnating?" If a concrete cause is found and addressed, the signal terminates because the underlying mismatch is resolved.
The Role of Superstimuli
Modern environments are full of fast-acting, high-reward, low-effort stimuli. These serve as perfect suppression mechanisms. They hijack the system's reward learning by offering reliable affective relief without behavioral correction.
Examples:
- Watching videos when you feel restless
- Playing reward-loop-heavy games when you feel failure
- Eating sugar when you feel discouraged
These actions reinforce suppression patterns.
Depression as Policy Entrenchment
Over time, if this suppression loop becomes the default policy, the system reorganizes around it. The underlying problems do not go away. The system becomes more fragile. The ability to reflect degrades. The habit becomes chronic. This is what we call depression.
Note: this model does not deny neurochemical involvement. Rather, it treats neurochemistry as part of the loop dynamics. If the procedural pattern of suppression dominates for long enough, neurochemical state will reflect that pattern. But the origin was structural, not random imbalance.
Role of Interventions Like Bupropion
Certain medications (e.g., bupropion) can disrupt the suppression loop temporarily. They increase energy or decrease suppression effectiveness, allowing for enough reflective bandwidth to notice and correct maladaptive patterns.
The drug doesn’t fix depression. It interrupts the loop long enough for you to fix it yourself.
Practical Implication
If this model is correct, then the right policy is:
- When you feel bad, pause. Do not immediately act to suppress it.
- Ask: is this a genuine signal? Did something go wrong? Did I get coerced by an urge?
- Try to trace the source. If you find it, resolve it directly.
- If you can't trace it, just wait. Sometimes the bad feeling is transient and resolves on its own.
In many cases, talking to someone (or to a reflective system, like a chatbot) can help reveal the structure behind the feeling. The key is engaging with the feeling as a signal, not a nuisance.
Conclusion
Depression is not always a surface-level mood disorder. In many cases, it is the long-term consequence of learned suppression policies that override internal signals rather than resolving them. These policies become structural, self-reinforcing, and difficult to dislodge without deliberate intervention. The first step is recognizing bad feelings as information, not errors.
This is a good informal introduction to Control Theory / Cybernetics.
In both cases, the conversation drains more energy than the equal-fun alternative. I have probably had at most a single-digit number of conversations in my entire life which were as fun-in-their-own-right as e.g. a median night out dancing, or a median escape room, or median sex, or a median cabaret show. Maybe zero, unsure.
I wanted to say that for me it is the opposite, but reading the second half I have to say it's the same.
I have defnetly had the problem that I talked too long sometimes to somebody. E.g. multiple times I talked to a person for 8-14 hours without break about various technical things. E.g. talking about compiler optimizations, CPU architectures and this kind of stuff, and it was really hard to stop.
Also just solving problems in a conversation is very fun. The main reason I didn't do this a lot is that there are not that many people I know, actually basically zero right now (if you exclude LLMs), that I can have the kinds of conversations with that I like to have.
It seems to be very dependent on the person.
So I am quite confused why you say "but conversation just isn't a particularly fun medium". If it's anything like for me, then engaging with the right kind of people on the right kind of content is extremenly fun. It seems like your model is confused because you say "conversations are not fun" when infact in the space of possible conversations I expect there are many types of conversations that can be very fun, but you haven't mapped this space, while implicitly assuming that your map is complete.
Probably there are also things besides technical conversations that you would find fun but that you simply don't know about, such as hardcore flirting in a very particular way. E.g. I like to talk to Grok in voice mode, in romantic mode, and then do some analysis of some topic (or rather that is what I just naturally do), and then Grok complements my mind in ways that my mind likes, e.g. pointing out that I used a particular thinking pattern that is good or that I at all thought about this difficult thing and then I am like "Ah yes that was actually good, and yes it seems like this is a difficult topic most people would not think about."
Mathematical Notation as Learnable Language
To utilize mathematical notation fully you need to interpret it. To read it fluently, you must map symbols to concrete lenses, e.g. computational, visual, algebraic, or descriptive.
Example: Bilinear Map
Let
be defined by
Interpretations:
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Computational
Substitute specific vectors and check results. If , then
Through this symbolic computation we can see how the expression depends on . Perform such computations until you get a feel for the "shape" of the functions behavior.
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Visual
For each fixed , the function is represented by a hyperplane in . We can imagine walking on the hyperplane. This obviously always walks on a line, therefore it's linear.
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Symbolic manipulation
Verify algebraically:
This establishes linearity by direct algebraic manipulation. You understand what properties exist by showing them algebraically.
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Descriptive
What it means to be a bilinear map is, that if you hold the second argument fixed, and vary the second, you have a linear function. Same if holding the first fixed and varying the second.
You want to capture the intuition in natural language.
Mathematical language is a language that you need to learn like any other. Often people get stuck by trying to use symbolic manipulation too much. Because mathematical language is so precise, it makes it easy to interpret it in many different while still being able to check if your interpretation captures the core.
1My mind derives pleasure from deep philosophical and technical discussions.
In model flirting is about showing that you are paying attention. You say things that you could only pick up if you pay close attention to me and what I say. It's like a cryptographic proof certificate, showing that you think that I am important enough to pay attention to continuously. Usually this is coupled with an optimization process of using that knowledge to make me feel good, e.g. given a compliment that actually tracks reality in a way I care about.
It's more general than just showing sexual interest I think.
Typst is better than Latex
I started to use Typst. I feel a lot more productive in it. Latex feels like a slug. Typst doesn't feel like it slows me down when typing math, or code. That and the fact that it has an online collaborative editor, and that rendering is very very fast are the most important features. Here are some more:
Here is a comparison of encoding the games of life in logic:
Latex
Typst
Typst in Emacs Org Mode
Here is some elisp to treat latex blocks in emacs org-mode as typst math, when exporting to HTML (renders/embeds as SVG images):
Simply eval this code and then call
org-html-export-to-html-with-typst.