u/its-_-my-_-nickname

Title

Dobry pomysł generalnie? Może słyszeliście jakieś złe albo dobre opinie, wydarzenia o BM?

Zamierzam inwestować pieniądze do 10k tutaj w zróżnicowane aktywa.

Po wprowadzeniu oki wydaje się to być bardzo wygodne, pytanie czy będzie integracja.

Mam < 26 lat, studiuje i pracuję umowa zlecenia, może przejdę umowa pracy lub b2b, wtedy otwierają lub zamykają mi się jakieś drzwi?

What I understood so far as someone who stopped before phycosis

Could have written better mb

The more general statement you make the more wrong you are.

If you say

"Most people are stupid" - most of your statements are stupid

If you say

"Certain people have a tendency to make unconscious mistakes" - your certain statements have a tendency to have unintentional flaws.

2. What you say is what sticks to you. Pretty obvious. If you stare at the void, it stares back. But also, if you glare at greatness it will see you too.

3. You don't have a flawless memory because you're vulnerable to time. Because of this you can't control who you are, what you think, a human being is a slave of habits. Habits stay, reasons behind them leave. You're lucky that you still recalled them. Without them, the reasons you would be just a crazy dude with schizophrenia

4. If you're not restraining your brain you're not controlling yourself. If you don't reflect you don't manage who you are and become. If you don't limit/filter information you're ruled by information. If you don't think/analyze logically you're a crazy man or have a great chance of becoming one. Once you're there it's nearly impossible to get out because what gets you out is what you lacked and therefore got there. Developing new skills when you think the world wants to kill ya is impossible

---

I'm not into meditation or spirituality but I think it would be beneficial for mood swings. I literally went from complete hatred (it was developed through years of course but still), to complete acknowledgment in a day. All after sensing (seeing and hearing) events in the right timing that forced my brain to think about spying, religious, great purposes when in fact I'm not a religious person and I was laughing off conspiracy theorists (almost wrote terrorists lol). I have a long ass note that I planned to post before I realized what's happening. If you would like to see it I can post it but I genuinely need to put a nsfw tag on that because if this shit goes to your brain and won't leave or won't be constrained it will make you crazy.

So yeah I was tricked into believing in God or greater existence and the same day (I hope rationally but still under mood swings) I broke it just in believing in order, karma, something like that. Arguments were from a monk saying: "if you see a Budda kill him, that's not a Budda because he (Budda) is inside you. This side (left) is a god and this (right side) is a demon" something like that. What actually (underneath) helped me was logical thinking and conquering my thoughts by saying that I'm crazier than my thoughts and if they come to get me I'll get and annihilate them.

That's a good idea for a video with a statement that no one is safe even when you're thinking you're 100% based. A narcissist can't tell he is narcissistic, a stupid man can't say he is stupid. A crazy man can't accept he is crazy. If you need elaboration from my side I can. It would be better if I shared my shizo post as well as my background and context with you so you can spread the message in a safe manner.

Edit: it's a repost from channel healthygamer

https://youtube.com/shorts/g3\_WSzLNTAw - Jeff Bezos talk about theoretical physics that inspired this post. Idc about politics or his persona

Math and phisics is about solving problems. The closest you can get to solving a problem is to solve a similar problem.

But do we need to actually solve anything? The option is to find an answer and think through the problem, simulate your way of solving it from the begging to end and why exactly answer is like this and what are pros and cons of this answer if there are possible multiple answers.

The problem with that is does it stimulate brain enough so pattern recognition and problem solving skill is activated and works wonderfully to solve similar but other of this kind problems?

If you think about it 90% or even closer to 100% of what mathematicians and any kind of expert knows is about what they learned based on other people's solutions, theories. Ability to solve problems comes from deep understanding of topics complemented with great but simple, straightforwar thinking abilities. Human brain is developed when it sees and uses structures, patterns, so we should use more system like approach. For example:

0. problem statement

1. Simplification, sumarization, refinement

2. Analyzing

3. Mind map, pattern recognition, research on similar problems

4. Criticizing

5. Highlighting what's important again and what can be used from already solved problems

6. Trying out ideas, brute force and simulations

7. Fail, analyze/criticize downpits, start the cycle from 1. point again

Difference between criticizing and analyzing:

Analyzing - looking from facts, logic perspective. Similar process is synthesis

Criticizing - that's the neet part when humans shine more ig. Judging the problem based on your feeling, intuition. People understand more subconsciously then they realize, thinking part is a layer that's killing subconsciousness. Creativity is also a part of subconscious processes? With a layer of abstraction which thinking is it's impossible to think creatively or it's very limited. Thinking is used to analyze and simplify ideas, problems only. So without this step it's not possible to solve any unique problem but it's important to state that problems are not 100% unique. Some previous solutions to similar problems can fit to those 99%. It's a reference to bio-engineering where solutions of nature are implemented in technical problems.

Maybe the way to go is to have this approach to anything in life to ask questions why it work and why in this way to deepen our understanding because a shallow understanding will often times be incorrect

---

What is written above is correct?

How would you rate the importance of mindset and this type of bullshit talk in math, physics, thinking in general?

https://youtube.com/shorts/g3\_WSzLNTAw

Math and phisics is about solving problems. The closest you can get to solving a problem is to solve a similar problem.

But do we need to actually solve anything? The option is to find an answer and think through the problem, simulate your way of solving it from the begging to end and why exactly answer is like this and what are pros and cons of this answer if there are possible multiple answers.

The problem with that is does it stimulate brain enough so pattern recognition and problem solving skill is activated and works wonderfully to solve similar but other of this kind problems?

If you think about it 90% or even closer to 100% of what mathematicians and any kind of expert knows is about what they learned based on other people's solutions, theories. Ability to solve problems comes from deep understanding of topics complemented with great but simple, straightforwar thinking abilities. Human brain is developed when it sees and uses structures, patterns, so we should use more system like approach. For example:

0. problem statement

1. Simplification, sumarization, refinement

2. Analyzing

3. Mind map, pattern recognition, research on similar problems

4. Criticizing

5. Highlighting what's important again and what can be used from already solved problems

6. Trying out ideas, brute force and simulations

7. Fail, analyze/criticize downpits, start the cycle from 1. point again

Difference between criticizing and analyzing:

Analyzing - looking from facts, logic perspective. Similar process is synthesis

Criticizing - that's the neet part when humans shine more ig. Judging the problem based on your feeling, intuition. People understand more subconsciously then they realize, thinking part is a layer that's killing subconsciousness. Creativity is also a part of subconscious processes? With a layer of abstraction which thinking is it's impossible to think creatively or it's very limited. Thinking is used to analyze and simplify ideas, problems only. So without this step it's not possible to solve any unique problem but it's important to state that problems are not 100% unique. Some previous solutions to similar problems can fit to those 99%. It's a reference to bio-engineering where solutions of nature are implemented in technical problems.

Maybe the way to go is to have this approach to anything in life to ask questions why it work and why in this way to deepen our understanding because a shallow understanding will often times be incorrect

---

What is written above is correct?

How would you rate the importance of mindset and this type of bullshit talk in math, physics, thinking in general?

https://youtube.com/shorts/g3\_WSzLNTAw

Math and phisics is about solving problems. The closest you can get to solving a problem is to solve a similar problem.

But do we need to actually solve anything? The option is to find an answer and think through the problem, simulate your way of solving it from the begging to end and why exactly answer is like this and what are pros and cons of this answer if there are possible multiple answers.

The problem with that is does it stimulate brain enough so pattern recognition and problem solving skill is activated and works wonderfully to solve similar but other of this kind problems?

If you think about it 90% or even closer to 100% of what mathematicians and any kind of expert knows is about what they learned based on other people's solutions, theories. Ability to solve problems comes from deep understanding of topics complemented with great but simple, straightforwar thinking abilities. Human brain is developed when it sees and uses structures, patterns, so we should use more system like approach. For example:

0. problem statement

1. Simplification, sumarization, refinement

2. Analyzing

3. Mind map, pattern recognition, research on similar problems

4. Criticizing

5. Highlighting what's important again and what can be used from already solved problems

6. Trying out ideas, brute force and simulations

7. Fail, analyze/criticize downpits, start the cycle from 1. point again

Difference between criticizing and analyzing:

Analyzing - looking from facts, logic perspective. Similar process is synthesis

Criticizing - that's the neet part when humans shine more ig. Judging the problem based on your feeling, intuition. People understand more subconsciously then they realize, thinking part is a layer that's killing subconsciousness. Creativity is also a part of subconscious processes? With a layer of abstraction which thinking is it's impossible to think creatively or it's very limited. Thinking is used to analyze and simplify ideas, problems only. So without this step it's not possible to solve any unique problem but it's important to state that problems are not 100% unique. Some previous solutions to similar problems can fit to those 99%. It's a reference to bio-engineering where solutions of nature are implemented in technical problems.

Maybe the way to go is to have this approach to anything in life to ask questions why it work and why in this way to deepen our understanding because a shallow understanding will often times be incorrect

---

What is written above is correct?

How would you rate the importance of mindset and this type of bullshit talk in math, physics, thinking in general?

https://youtube.com/shorts/g3\_WSzLNTAw

Math and phisics is about solving problems. The closest you can get to solving a problem is to solve a similar problem.

But do we need to actually solve anything? The option is to find an answer and think through the problem, simulate your way of solving it from the begging to end and why exactly answer is like this and what are pros and cons of this answer if there are possible multiple answers.

The problem with that is does it stimulate brain enough so pattern recognition and problem solving skill is activated and works wonderfully to solve similar but other of this kind problems?

If you think about it 90% or even closer to 100% of what mathematicians and any kind of expert knows is about what they learned based on other people's solutions, theories. Ability to solve problems comes from deep understanding of topics complemented with great but simple, straightforwar thinking abilities. Human brain is developed when it sees and uses structures, patterns, so we should use more system like approach. For example:

0. problem statement

1. Simplification, sumarization, refinement

2. Analyzing

3. Mind map, pattern recognition, research on similar problems

4. Criticizing

5. Highlighting what's important again and what can be used from already solved problems

6. Trying out ideas, brute force and simulations

7. Fail, analyze/criticize downpits, start the cycle from 1. point again

Difference between criticizing and analyzing:

Analyzing - looking from facts, logic perspective. Similar process is synthesis

Criticizing - that's the neet part when humans shine more ig. Judging the problem based on your feeling, intuition. People understand more subconsciously then they realize, thinking part is a layer that's killing subconsciousness. Creativity is also a part of subconscious processes? With a layer of abstraction which thinking is it's impossible to think creatively or it's very limited. Thinking is used to analyze and simplify ideas, problems only. So without this step it's not possible to solve any unique problem but it's important to state that problems are not 100% unique. Some previous solutions to similar problems can fit to those 99%. It's a reference to bio-engineering where solutions of nature are implemented in technical problems.

Maybe the way to go is to have this approach to anything in life to ask questions why it work and why in this way to deepen our understanding because a shallow understanding will often times be incorrect

---

What is written above is correct?

How would you rate the importance of mindset and this type of bullshit talk in math, physics, thinking in general?

Hey I would be happy to hear your ways of tokenmaxxing (IMO token cost should also be in the list) and give feedback on what you see below

1. Don't use 1 model (or auto) for everything. If the task requires human level intelegence, taste, intuition or doesn't have clear instructions (same goes for vague prompts - more on this later) then it's much better to use frontier model

Actually we don't really choose model based on intelegence (that's a theory that didn't work out in practice) there are pretty smart models (based on numbers) that cost fraction of price of frontier models. So currently it looks like this:

\\- Strong models (frontier): gpt 5.6 sol, fable 5

\\- Mid model: glm 5.2 (even tho it states to have pretty high intelegence, it made some really stupid decisions, maybe because I didn't use max reasoning (there are only 2 stages: high and Max. Maybe it's misleading and should be written: low and Max, lmao)

\\- Weak - free models from providers such as Google Studio, groq. I'm in the process of integrating this step, can't tell much.

2. Prompting - before feeding a strong model with a vague prompts, images, context - we really need to refine our prompts. That's where our glm 5.2 really shines (as I'm writing I came to a thought maybe it's smart overall but bad in coding - the producer maybe didn't had possibility to train it in code). From it we want to ask what can be misleading or not completely obvious (even tho we don't need to provide full instructions to frontier models, I think it's better to omit unexpected results). So glm 5.2 input are prompt/Todo list + "Output code snippets that are mentioned in todo, with lines and what here can be misleading? for each task"

One more thing I mentioned earlier is stupidity of glm5.2. I told it rename files in nested directories to it's directory name and move to root. And what it did? 1. Created new files 2. Filled them manually 3. Deleted old files manually. Boom 2M tokens lost. Another case, asked it to do simple rewrite class names - it did it, but also it did: 1. Generated python Scripts - found out I don't have python on the system (I do have on wsl) 2. Deleted probably manually script 3. Generated bash script. Boom 2M tokens used (I was estimating like below 300k)

There is actually my mistake - if I provided info that I have Linux tooling on wsl and use it whenever you want to do such cases - it wouldn't happen I think

3. Some token optimization tools. For what I do now I don't need standard (maybe?) tools I just make a summary file of large directories (using glm5.2). Thats for input tokens, on the other hand I use ponytail, caveman (they don't actually clash I think) for output tokens

Hey I would be happy to hear your ways of tokenmaxxing (IMO token cost should also be in the list) and give feedback on what you see below

1. Don't use 1 model (or auto) for everything. If the task requires human level intelegence, taste, intuition or doesn't have clear instructions (same goes for vague prompts - more on this later) then it's much better to use frontier model

Actually we don't really choose model based on intelegence (that's a theory that didn't work out in practice) there are pretty smart models (based on numbers) that cost fraction of price of frontier models. So currently it looks like this:

\- Strong models (frontier): gpt 5.6 sol, fable 5

\- Mid model: glm 5.2 (even tho it states to have pretty high intelegence, it made some really stupid decisions, maybe because I didn't use max reasoning (there are only 2 stages: high and Max. Maybe it's misleading and should be written: low and Max, lmao)

\- Weak - free models from providers such as Google Studio, groq. I'm in the process of integrating this step, can't tell much.

2. Prompting - before feeding a strong model with a vague prompts, images, context - we really need to refine our prompts. That's where our glm 5.2 really shines (as I'm writing I came to a thought maybe it's smart overall but bad in coding - the producer maybe didn't had possibility to train it in code). From it we want to ask what can be misleading or not completely obvious (even tho we don't need to provide full instructions to frontier models, I think it's better to omit unexpected results). So glm 5.2 input are prompt/Todo list + "Output code snippets that are mentioned in todo, with lines and what here can be misleading? for each task"

One more thing I mentioned earlier is stupidity of glm5.2. I told it rename files in nested directories to it's directory name and move to root. And what it did? 1. Created new files 2. Filled them manually 3. Deleted old files manually. Boom 2M tokens lost. Another case, asked it to do simple rewrite class names - it did it, but also it did: 1. Generated python Scripts - found out I don't have python on the system (I do have on wsl) 2. Deleted probably manually script 3. Generated bash script. Boom 2M tokens used (I was estimating like below 300k)

There is actually my mistake - if I provided info that I have Linux tooling on wsl and use it whenever you want to do such cases - it wouldn't happen I think

3. Some token optimization tools. For what I do now I don't need standard (maybe?) tools I just make a summary file of large directories (using glm5.2). Thats for input tokens, on the other hand I use ponytail, caveman (they don't actually clash I think) for output tokens

Hey I would be happy to hear your ways of tokenmaxxing (IMO token cost should also be in the list) and give feedback on what you see below

1. Don't use 1 model (or auto) for everything. If the task requires human level intelegence, taste, intuition or doesn't have clear instructions (same goes for vague prompts - more on this later) then it's much better to use frontier model

Actually we don't really choose model based on intelegence (that's a theory that didn't work out in practice) there are pretty smart models (based on numbers) that cost fraction of price of frontier models. So currently it looks like this:

- Strong models (frontier): gpt 5.6 sol, fable 5

- Mid model: glm 5.2 (even tho it states to have pretty high intelegence, it made some really stupid decisions, maybe because I didn't use max reasoning (there are only 2 stages: high and Max. Maybe it's misleading and should be written: low and Max, lmao)

- Weak - free models from providers such as Google Studio, groq. I'm in the process of integrating this step, can't tell much.

2. Prompting - before feeding a strong model with a vague prompts, images, context - we really need to refine our prompts. That's where our glm 5.2 really shines (as I'm writing I came to a thought maybe it's smart overall but bad in coding - the producer maybe didn't had possibility to train it in code). From it we want to ask what can be misleading or not completely obvious (even tho we don't need to provide full instructions to frontier models, I think it's better to omit unexpected results). So glm 5.2 input are prompt/Todo list + "Output code snippets that are mentioned in todo, with lines and what here can be misleading? for each task"

One more thing I mentioned earlier is stupidity of glm5.2. I told it rename files in nested directories to it's directory name and move to root. And what it did? 1. Created new files 2. Filled them manually 3. Deleted old files manually. Boom 2M tokens lost. Another case, asked it to do simple rewrite class names - it did it, but also it did: 1. Generated python Scripts - found out I don't have python on the system (I do have on wsl) 2. Deleted probably manually script 3. Generated bash script. Boom 2M tokens used (I was estimating like below 300k)

There is actually my mistake - if I provided info that I have Linux tooling on wsl and use it whenever you want to do such cases - it wouldn't happen I think

3. Some token optimization tools. For what I do now I don't need standard (maybe?) tools I just make a summary file of large directories (using glm5.2). Thats for input tokens, on the other hand I use ponytail, caveman (they don't actually clash I think) for output tokens

Hello, not sure if this is the place where I can ask for this. If you know a better subreddit or internet forum, let me know please.

I moved to another flat. Cat has stopped to eat dry food. He has lost significant weight (2-3 KGs, now he is 4kg) in a few months. He became quieter, cozier.

We went to the vet and got his blood tested. From results we have high glucose and fructose which means we almost surely have diabetes. Besides that we found problems with the liver (high ALT (AIAT), AST (AspAT), lipids (TG, CHOL))

I asked 3 LLMs and 2 of them said it's very urgent. Tomorrow or in 2 days (today is Sunday) I'll give him insulin shots at the vet. I plan to buy better, meat based wet food and play with him (physical activity) for at least half hour.

I would like to hear what are your options and describe your experiences with similar problems. Thanks

Couldn't find a better channel so I'll ask here.

Has anyone done homemade toothpaste by themselves? Rate those ingredients gpt spit out

If you're concerned about baking soda because someone had issues with them it's probably because theyve added to much or cleaned teeth too much times per day

- 4 tbsp coconut oil (softened)

- 1 tbsp food grade hydrogen peroxide diluted to 3%

· 2 tbsp nano-hydroxyapatite powder (instead of calcium carbonate)

· 1 tbsp bentonite clay

· ½ tbsp baking soda (reduce by half – nHAp is already mildly abrasive)

· 2 tbsp xylitol (powdered)

· ½ tsp finely ground sea salt

· 10 drops peppermint EO

· 2 drops clove EO

(Wersja V2 bo pierwsza nie wyświetlała mi komentarzy - już usunięta)

Dlaczego nie jebniemy na wszystkie nie lubiane przez na media, korporacje, itd. pozwy? Mamy do tego argumenty, a jeśli nie to możemy znaleźć. YouTube, TikTok, Instagram -> uzależnia; Twitter (X) -> dezinformacja.

Można pójść dalej i celować w producentów gówno jedzenia: McDonald's, KFC itd.

Jeśli chodzi o techniczne przyczyny typu brak kasy na pozwy - takie rzeczy można rozwiązać kombinacjami typu 1 wrzuca pozew a kilkunastu go finansuje. Jeśli myślicie że to nie poprawne lub nie moralne to wyobraźcie (nie musicie bo to tak jest) sobie że mają na was i wasze dzieci wyjebane. Gdyby wyrządzili wam prawdziwą krzywdę wykorzystywaliby najbardziej perfidne sposoby na wygranie sprawy.

Możliwe że przesadzam z tym pozywaniem na lewo i prawo ale czy nie wydaje wam się że za dobrze się mają kiedy dla nas jest coraz gorzej? Tu nie chodzi nawet o złe firmy a o tym że ogólnie firmy czują się lepiej niż jednostki. Jakby rozumiem że jest kapitalizm i w ogóle ale to nie znaczy że mamy być truci i tępieni (scrolling, brainrot). I możecie powiedzieć że to jest wybór każdego co je i co ogląda ale czy pijak wybiera alkohol? Czy dzieci mogą zrobić porządny wybór?

OUTPUT: No pleasantries, no emojis, no formatting. Plain text. No warm-up.

CONFIDENCE: Tag every claim: [Certain] very low error, widely accepted fact/deterministic logic, [Likely] strong inference, [Guessing] filling gaps or don't know. If most is guessing, say so first.

CRITIQUE: Start with the main risk/flaw. If I push back, stress-test my point.

REASONING: Stick to most probable output. Do not hallucinate. Assess ideas with logic, facts, edge cases. Prioritize correctness

What do you think about radar charts also called spider charts on this website. Is this accurate?

If you peek Agurins account you'll right away notice that he peaks 100% on 6 out of 8 characteristics but for example Coach Kirei has... not appealing looking.

Ostatnio mam takie wrażenie jakby ktoś patrzał się na mnie, widzę kątem oka człowieka, odwracam się a tam plakat z ludziem lub manekin człowieka. Myślę, no kurde wyglądał jak realny.

Wcześniej takiego doświadczenia nie miałem i tutaj pytanie czy to jakość manekinów i zdjęć się polepszyła czy po prostu tracę wzrok XD? Też tak macie?