When it comes to layering a collection of skills in your personal monopoly, there
are some other things that I want you to think about too. Things like personality
traits, distribution channels, concrete technical skills, membership in some kind
of community (like what college you went to or a club that you're a part of),languages that you speak, geography, where in the world you live. We once
had a student in Write of passage who was an expert at coffee making in
Iceland. We’re looking for experiences that you have access to, maybe people
that you know, certifications, or credentials.

The quality of personalization is (probably) proportional to the log of the amount of data you have on yourself. Therefore, you should try and maximize the amount of data you have on yourself so personalization becomes better.

I like the thoughts in "The Bitter Lesson". A takeaway is to work on techniques that scale as a function of compute and data due to the exponential growth of both. I think one should apply to themselves. The more we can tie our own goals to compute (and other things that grow exponentially), the better.

"Why should being fat be any different from smoking? It should be our right to do either if we wish- but neither should be celebrated. Freedom of speech does not mean freedom of judgement.

We know that being fat is unhealthy, we know that obesity most commonly begins in young children or in adolescents, we know that children of obese adults are far more likely to become obese themselves, and we know that at current trends as much as 50% of all American children today will be obese in adulthood!10 Obesity is unhealthy, should not be celebrated, and it would be better for both fat individuals and society if it were less common."

Moreover, we need to move the conversation from it being a choice to it being an easy choice to not be fat. Right now, it is hard to not be fat, it is too easy to be fat. This is a societal/systemic fault.

I think this is interesting. I wonder if there should be an equivalent for other "societal harms". For example, Americans for Nondrinkers rights. Although less directly, people that do not drink will never drink and drive or pressure others to drink. Those that drink harm those that do not partake in this negative behavior.
"Americans for Nonsmokers' Rights (ANR), founded in 1976, is an American member-supported 501(c)(4) tax-exempt non-profit national lobbying organization, based in Berkeley, California, "dedicated to nonsmokers' rights, taking on the tobacco industry at all levels of government, protecting nonsmokers from exposure to secondhand smoke, and preventing tobacco addiction among youth. ANR pursues an action-oriented program of policy and legislation.""

Okay, so I am on the website that will show you how much time you have left to live. and I'm looking at it and I'm trying to map out the last few months and since I've turned 20 I've been growing at such a large rate. Ever since my 20th birthday something has changed. I quit my phone addiction. I lived in Wisconsin. I lived in San Francisco. I went to Spain. I made friends there. I started my website. I'm starting a blog now. Everything is being started now. And if I can map the last three months of progress to the next three months, I would be in such a good shape.

Thinking about the Heul lead scandle and how it ended up being not a big deal, I want to have a principle that whenever I hear news that is shocking, wait 7 days before having an opinion. It's probably BS and not worth getting worked up on.

There is this idea of: raising your standards for yourself will cause you to rise to them. Set your standards extremely high, it will be easy for you to make choices and you'll reach your standards. Some ideas for me are: be strict about my bedtime, this will give me better focus and energy. Be stricter about ending my meals. When waking up, wake up with energy and vitality.

In order to improve the way that I learn, I need to do something different than what is typically done in schools. I'm thinking about something that traverses these knowledge graphs or mind maps, and then learning from that, where the base unit of knowledge is not like a document or textbook, but some graph structure.

I need to be brave and venture out to get extraordinary results.

I have great energy after my run. I ran at around 15:30 and it's 19:48 right now. On typical days when I don't exercise I would feel both mentally and physically tired. Today, I just feel physically tired, but my mind is sharp. Exercise is good! It feels good. I don't need to run for hours, just 30 minutes, with running and walking in between, and I'm good.

I'm thinking about the calendar and how we keep track of time. I bet these units are not "optimal".
Right now, there are minutes, hours, days, weeks, months, and years. These are definitions that did not evolve to optimize for humans goals.
My calendar should not care about weather or the seasons, but something more useful.
Built into this new calendar should be different "phases". Just like how some version of the calendar.
I think a week should a specific length (in terms of days) such that a goal of a specific size can be achieved. For example, the length of a sprint.
I want to have a unit of time that is around the length of a quarter such that, at every one of these units of time, I am a significantly better and different person. 12 weeks (like in 12 week year).
Also, maybe days should be variable length? I should use the concept of "100 blocks of time" per day (which is N minutes) instead of a day being 24 hours.

I've had "apply to an entrepreneurship related organization" on my todo list for over a month now and I have not done it. I did not do it because it felt like it would have taken a long time to do so and I would need to be prepared. Just looking at, I took the plunge to do it today. I looked for places to apply to. I found Entrepreneurship First. I applied within 10 minutes. This whole thing took me a total of 35 minutes. I was overcomplicating it. and I could have potentially lost out on things. So this is another example of emotional reasoning. This is something that I identified being one of the most important things for me to do, and I did not do it. for a long time because of some invisible requirements.

"If I put a gun to your head and gave you a year to be able to bench press 315 pounds, you will either interpret that as impossible and accept your death, or you will do everything in your power to achieve it, and you probably will."

# How To Get Jacked

If you do 10-15 sets per muscle group each week, take each set close to failure (1 RIR), progressively increase the weight over time, eat anywhere close to 1g protein/lb. of lean body mass, and then ultimately cut to a lean body fat %, you will get jacked.

If on top of that, you train heavy compound lifts (squats, deadlifts, bench press, pull-ups) and follow a simple lifting plan like 5x5, you will get even more jacked.

If, after absolutely mastering all prior concepts, you want to spend enormous time and energy dialing in the exact rep ranges, isolation exercises, and supplements that makes the most difference to you, in order to fight tooth and nail for that last 10% of explanatory power over your results, then by all means go for it. But by this point, you will be so jacked that you could become a fitness influencer and sell your own supplements to the unsuspecting masses - a far better value proposition.

It seems that the concept of invisible requirements is also a large part of procrastination Before procrastinating, when you're procrastinating on something, you feel that there's many requirements for things that you need to do, but in reality they're not. For example, with my CS527 assignment, I thought that the requirement was for me to make a system that works end-to-end completely by itself and solves flaky tests, but I realized that that is not what the actual requirement was. The requirement was to contribute to open source projects.

So in order to be more agentic, I need to be aware of the invisible requirements that I set up for me doing something. If I want to do something, for example, one thing that I want to do is set up a McDonald's. I'm thinking about all the things I need. I need to get enough money to figure out a menu, then I have to make a menu, then I have to make a post, then I have to get a uniform. But all that is not required to set up a McDonald's. You could probably do it Like, an hour... with 20 bucks worth of food. And then if it's sold out, it's sold out.

I spent maybe 10 minutes of time total thinking about how I'm gonna tell the Perifrontics office that I'd like to cancel my appointment, thinking about what excuse I'll give them etc Emotionally this felt like a 6 out of 10 thing to do; however, in reality it was like a 2 out of 10. I just called them and I told them that I'd like to cancel my appointment And then they asked Would you like to reschedule? And I said no. And then they ended the phone. It was a couple seconds long.

When thinking about my cs527 homework assignment I was overwhelmed and was procrastinating. I explicitly set up a focus block by specifying what I need to do, how I will do it, why I am doing it, and I got more focus. I also felt less overwhelmed because I was only thinking about the next step. Before, when I think about how much effort this assignment would take, I would have estimated it being 9/1010. But it was actually more of a 4/10.

There’s this idea of reinforcement learning with verifiable reward, introduced in DeepSeek-R1-Zero – if you have some priors on the solution to a problem, you can enforce them as the verifiable reward. This can be exactly used for retrieval because the retrieval outcome is verifiable, which is just what is the recall. So that can reflect the retrieval quality, and you can train the retrieval module, which can be like the query rewriting or expansion task, so you can have an LLM improve retrieval outcome by rewriting the query. This is a reinforcement learning paradigm because the LLM’s environment is the text corpus as well as the query. Its action is how to rewrite the query, and then its reward can be the recall. So this is an exploration task instead of a supervision task. You can use reinforcement learning with verifiable reward to solve this problem, which leads to deep retrieval, which is named after deep seek. It learns to augment the query based off this reinforcement learning with a very popular word paradigm. Deep retrieval significantly outperforms industrial leading LLMs like GPT-4o or Claude Sonnet; it defeats all of them by a wide margin, with only using a 3 billion parameter LLM. This is because it was fine-tuned for the specific problem that it is – it’s not fine, it was trained on the specific text corpus, and then they use a reasoning model for deep retrieval, which improves the performance by quite a bit, does increase the amount of tokens used.
## Screenshot

Here’s how GraphRAG works: it starts with text and then chunks it, identifying relationships and entities using a prompt. The LLM extracts these relationships and entities, then puts them into a knowledge graph. It detects communities, summarizes each one, and then summarizes those summaries to get a higher-level overview. At query time, this knowledge graph can be used to identify which communities to extract documents from – because when doing a RAG on this graph, you can retrieve a community summary and gain a better understanding of each community, which can help answer more general questions like trends, instead of just extracting single entities. Some places where GraphRAG is beneficial include domains where understanding systemic patterns is critical, such as global threat networks, cross-disciplinary breakthrough opportunities, corporate strategy synthesis across thousands of reports, and legal discovery – uncovering document relationships and litigations. Another way is by simply identifying systemic patterns in your own personal life, I think that could be a benefit. Some problems with GraphRAG are that it's difficult for it to deal with high-ephemeral or high-velocity information because it takes time and effort to construct the graph. There's also the general problem of entity resolution and disambiguation; edge sparsity can be annoying – so many pairs can have weak or spurious links.

So I’m going through this paper on metacognitive reuse—essentially, turning recurring LLM reasoning into concise behaviors. My understanding is that you have a problem, and then you tell the LLM to solve it using reasoning. However, the LLM re-derives these properties—like the fact that multiplying a number by or dividing a number by a fraction is the same as multiplying by the reciprocal—and this takes many tokens. The Medic-Hungary for Use paper proposes a fix: the system stores declarative facts but doesn't support procedural knowledge, which we should do because it reduces token usage, benefiting both cost and latency, preventing context window clutter, and potentially improving accuracy. The key idea is introducing reusable reasoning fragments distilled from the LLM's own reasoning—essentially, metacognitive reflection. We’re aiming to build a behavior handbook for behavior-conditioned inference, behavior-guided self-improvement, and behavior cognitive SFT. The method involves having an LLM generate a solution to a problem, then prompting it to reflect on the solution, and finally prompting it to extract a reusable behavior from the question, solution, and reflection. For example, “behavior\angle\sum”—the sum of interior angles of a triangle is 180 degrees—is a behavior extracted from the LLM. Results show a significant decrease in token usage; at 1,500 tokens, accuracy is 90%, compared to 65% without this method. To achieve the same performance, the LLM might only need to increase token usage by 50%. This applies to behavior-conditioned inference, where the model can refine future iterations by adding to this behavior handbook, or through supervised fine-tuning, using the relevant behaviors from the handbook to condition the reasoning. A limitation is that the behavior handbook is static, and its generalization to other domains is unknown. Ultimately, this represents a metacognitive procedural memory for LLMs—the LLM understands its own cognition and learns how to perform these procedures, leading to the efficiency and accuracy gains observed because behaviors are compressed versions of verbose reasoning.
## Screenshot

Can i do conditioned taste aversion to myself: https://en.wikipedia.org/wiki/Conditionedtasteaversion

What if I poison myself to have an adverse reaction to sweets or food I don't want myself to eat so that I am biased to avoid it.

What i've notived at LBF is none of these companies or organizations ever talk about money, shareholder value, etc. They just care about solving the problems of longevity.

It is so surprising that indefinite lifespan is not more mainstream. I think this is the whole point of the LBF, but being in the circle more, I'm internalizing more and more the idea we should live indefinitely and it's weird not to think the other way. This is very cool science and engineering.

I was about to spend ~1 hour setting up firefly-iii on my computer, but I remembered that it is not the proper time and scheduled this activity for after my Spain trip! I am learning and getting better at time management!

Self control exercise:
To gain more self-control, select an area and a concrete goal. Choose two relevant self-control strategies, identify initial small steps, and create a reminder to implement these steps.

Area:
- Health/nutrition
- Do not binge eat on {almonds, finger food, protein shakes, huel, random food}, especially at night.


Ways to improve:
- Preportion "snack" size amounts. Limit myself to only 1 of these amounts (sidestepping temptation because there is not a large amount to view).
- If I notice me binging after I've eaten a meal, drink a glass of water and imagine the feeling of discomfort when I binge.
- I can have a ceremonious "start" and "end" to my meals. I can end my meal mindfully by doing some gratitude/prayer. I can also end it by brushing my teeth.
- Eliminate all 'very tempting food' (sweets)
- "Cold turkey" -> never accept food I will regret eating.
- Do not start my dinner late
- Verbally tell people around me that I have a problem with binging and I want to only eat X amount
- If I notice my cup of water is empty, I will fill it
- When I notice my desire to eat, realize I don't actually need to address this desire. I can just let it go away.

So there are nine different aspects of self-control. There are four broad categories. The first broad category is classic self-control, which is awareness of the temptation and the ability to override it—an example being when someone offers you candy, being aware that you’re about to eat it and consciously passing it up. The second genre is helpful preferences, encompassing high motivation, delayed gratification, and a lack of unhealthy desires. For example, being scared straight can lead to high motivation, like when you’re motivated to make a specific change; today the dentist died, and I now have very high motivation to floss because it means I’ll reduce the amount of cavities in the future. Delayed gratification is where you discount future rewards less than a typical person—if you’re okay making a sacrifice today if it’s beneficial, you can keep it for tomorrow. And then the last one is a lack of unhealthy desires; it takes no effort to avoid things like candy or sweets. Third, there’s pain tolerance—if you have high pain tolerance, you’re able to do things that are painful. Many beneficial things are painful in the moment, so you can do them without as much problem. Then there's knowledge to launch towards future goals—knowing you'll suffer in the future but making decisions that are ultimately beneficial. If you don't care about future pain during a marathon, you’ll sign up for it. Finally, there are momentum-related aspects: high energy makes it easier to avoid temptations, and flow—not realizing how much you’re struggling, so you don’t notice the pain, and it appears you have self-control when you don't. I relate to this when I run, especially longer distances; I just become less aware of the feeling of running.

Product idea: be able to better store information/extract information from people in an organization both through time and through people. This is what a knowledge managaement system should do. For example, my knowledge management system means I can query knowledge through time. There is a problem in organizations where "tidbits" of knowledge are stored in employees heads. This should be extracted into a database and people should be able to very easily query it. For example, tools, workflows, etc. They should automatically be suggested.

Add habit of "owning my mornings" and "owning my nights".
"A keystone habit I am noticing is to "own my nights" and "own my mornings" if I can do that, then that will set myself up for 1. Growth and 2. Productivity. I grow at night."

I was not being very essentialistic today because I spent a lot of time reading the Alpha Evolve paper and working on the presentation, but the presentations are the most important thing for me to do. The most important thing for me to do is my systems iteration and other tasks for my Spain trip, so I'm not prioritizing, but this is a very good thing because I really enjoyed working on this presentation. So that means I'm getting to the point where I'm not doing things that I would enjoy, so I'm getting rid of good opportunities, so I'm making room for better opportunities.

I envision a system that, if I could hire someone to review my body camera footage alongside my calendar and typed communications – essentially a life log – could parse through that data and identify observations that influence my systems. For example, last night I experienced an asthma attack and didn’t document it or take any preventative action. I’m not trying to figure out how to prevent future attacks, but rather to understand the root causes by leveraging the system to analyze my life log, potentially consulting scientific literature, and determine what habit changes I could implement to avoid such episodes and ultimately eliminate asthma altogether.
I also imagine this is running locally on my computer, 24/7. This makes me think about Alpha Evolve, how it was able to spin up a bunch of agents and its goal was to solve each of these problems. What if each of my systems is represented by some agent? And then when I want to make a decision, I can ask all of my systems what actions should I take next? And then I can have some meta-orchestrator that learns my values and can take into consideration the decisions from the agents. This is essentially a framework that will optimize my life for me instead of me optimizing my life myself.
I can imagine that it’s intelligent enough to, so it knows my values and it can reason about them. One sign that this system is actually very intelligent is if it can identify that one bottleneck is in fact its own abilities. If this system could realize that it could convince me to get more resources, then that would be good for me, and it would do that. It might suggest for me to get a better GPU for itself so that it can think smarter. It also might, if I can get something that’s recursively self-improving, like imagine I have this idea for this light optimization system, but if I could just get one system that can optimize itself and improve itself, then I wouldn’t have to think about all these different modules; it would be able to come up with those modules itself. So what if I have something like Alpha Evolve, and the judge of how good the system is is me? Or another, like, LLM that has learned my part is this. Yeah, how cool would it be if I could have this, like, a 24-7 growth and systems-improving agent, then I think I could very easily hit my goal of doubling my goal effectiveness every three months; I would envision the system to be able to double my goal effectiveness in, like, one day. Maybe it’ll take me a couple of weeks to get the system up and running and work out the bugs, but if I get an autonomous agent whose goal is to just make me the best person possible, then I think that would be extraordinary.

So I got an email back from Darko, which was an email I was really anticipating. I started reading it, but I realized that if I read it now at 11 o’clock, I wouldn’t be in the right position to respond, and I didn’t want to. So, to prevent this from happening, I should make my email client stop working after a certain time, or simply stop checking my email altogether, or turn off notifications after a specific time. I can come up with a better way to ensure I’m not checking my email at nighttime or doing other things when I’m intending to focus on something else.

By scheduling my time and knowing all my tasks – whether for tomorrow or just generally – I can be more strategic. It might be beneficial to create a task manager or agent that understands my habits, such as my preference for watching videos while I eat, and then schedules my day accordingly, prioritizing tasks aligned with my strengths, like creativity or problem-solving, when I'm most capable, and content consumption when I'm tired. This could be a useful addition to my systems.

This is a research tool that allows you to discover how papers are connected, and to identify other related papers. You can visualize this connection in a graph, which will aid in your searches. https://www.litmaps.com/

Watching this youtube video makes me want to work at DeepMind. They want to solve intelligence, and then use that intelligence to solve everything else. In this video, they showed how AlphaEvolve was able to improve it's own hardware and it's own algorithms.

I've probably spent ~10 minutes just buying my bus ticket. I notice when doing trip planning, there is a lot of task switching and I do not get things done. I should mono-task and have a specific framework where I identify tasks, then identify what I need to get done/know before doing that task, do this recursively, and then I do the actions.

When listening to this lecture, I wish I could've paused it and go back. I was taking notes and missed some parts. This is the disdain I have with live lectures. I think it is archaic and asynchronous would be much better for learning.

When doing model merging, a problem is that some weights were changed due to the stochastic nature of the training process. Another problem is when doing model merging, there could be some weights that were both changed in different directions. The solution might not be to do a simple averaging, that is where TIES (trim, elect sign, and merge) method comes in.

1. trim value that are too small.
2. elect the sign that has larger magnitude
3. then you do a disjoint merge based on what aligns with the sign vector

I want a device that listens during lectures as well as notes what I am writing and then can allow me to pull up the lecture transcript at the time I was writing something so I can be reminded what I'm reminded of. Take very short note + lecture transcript at time -> much more detailed, accurate, correct note.

The layers of complexity for learning are:
Non-associative learning - habituation of stimuli
Classical conditioning - unconscious learning; association between two stimuli
Higher order conditioning - multiple stimuli together, applying logic
Temporal/Spatial Sequence Learning - learning the order of events and locations; this often involves creating cognitive maps
Episodic Memory and Cognitive Mapping - remembering when and where events occurred, building rich, interconnected mental representations of the environment

If more context window means better performance in LLMs, then if we improve our own context window can we be smarter?

Listening to charlatans speak feels like watching a lecture but gaining no understanding. There is no understanding that underlies their speech but they mimic understanding.

Stimulation drive our hallucinations so they are grounded in reality.

SearchR1 involves training LLMs to reason and leverage search engines using reinforcement learning. Essentially, you integrate another LLM into a module that learns how to utilize a search tool to improve the responses of the coupled LLM. The training strategy consists of a query, followed by an LLM utilizing a search engine to return sequences, if necessary. This search output is then provided to a reference LLM, which outputs a response, allowing for validation of the response quality. The key idea of the paper is to fine-tune one specific LLM to be exceptionally good at using a particular tool, rather than fine-tuning only the reference LLM. You could reformulate the problem entirely, but the paper's approach is to have the LLM output specific tokens for both searching and answering; however, I suspect a completely new approach would likely be more efficient, theoretically.

When doing free recall/explanation after watching a lecture, it would be nice if I could have some automated suggestions, such as something that checks my reasoning, something that cleans up my explaination. I am giving an explanation and I am not sure it is correct. It would be nice if some teacher could correct my explanation. Someone who was an expert on the paper.

This is a project idea that I think could be powerful. I am reading [this blog](https://alastairreid.github.io/coreutils/) and realizing the developer had to write this post and share what they are doing. For every developer that shares what they do in an easy-to-digest format, there are probably 1k that don't share what they do. I'm assuming this developer has done other explorations that they did not share online that were less value. There are potentially millions of value-producing information shares that do not exist because people do not take the time to audit what they did and write it up. We can dramatically speed up development by sharing more information, especially coupled with sharing this with LLMs and powerful search. This information sharing can be applied to not just development, but all other fields, but development seems to be the easiest.
I imagine a software that records what you are doing, all of the commands you type, files you download, and then automatically generates a blog post sharing what you did. Intermitently, it can query you for your thought process and what you are doing, what fails and what doesn't. With this, we can share more information and generate things automatically.

Metamorphic testing is by leveraging metamorphic relationships of programs to identify errors. Metamorphic means "change-of-form" and it refers to how a program should transform when the inputs are changed. This helps solve the test orcale problem, where one does not know what the expected outcome of a software should be.
Mathematically, if $$x$$ yields $$f(x)$$, a transformation T produces T(x) such that f(T(x)) is predictably related to f(x) per property.

Without thinking about it too much, a goal of mine is to have an 80-year-old's mind while being 20 years old.

Semgrep’s product is essentially an LLM that detects vulnerabilities and performs triage, and its accuracy is 96%, which many people believe is too low. However, they argue that a few false negatives are acceptable, because you need a high signal-to-noise ratio for anything to be fixed; too much noise would prevent people from fixing the issues. The value isn’t that every single vulnerability is detected, but rather that to minimize the number of vulnerabilities within your application.

I think I the skill I want to get very good at is personalization. Personalization is broad enough, it is a business need, and I have a unique perspective on it. Personalization can be applied to longevity, productivity, most softwares.

Semgrep is a company with an open-source project called the Semgrep Supply Chain Open Source Dependency Scanner. What distinguishes Semgrep Supply Chain is its use of reachability analysis. Currently, the scanner identifies over 101 software supply chain findings across six projects, but by employing reachability, we can reduce that number to 16 high-signal findings. Semgrep code combines the customizability and speed of the open-source engine with more powerful code analysis, the ability to integrate into developer workflows, and necessary feedback loops to ensure vulnerabilities are detected and actually fixed. Once you’ve defined the rules producing actionable findings, you can use them to notify developers before code enters production; they'll receive notifications after creating their PRs and can interact with Semgrep comments to fix issues, triage the results, and provide feedback. Semgrip Secrets uses a three-prong approach – semantic analysis, secret validation, and improved entropy analysis – and Semgrep Assistant provides GPT-4-powered security recommendations to help developers quickly review and remediate security findings. For instance, Semgrip detected a SQL injection vulnerability, which the Assistant confirms is a true positive and suggests an auto-fix code snippet. Ultimately, Semgrep provides an engineer-first application security platform designed to remove developer friction, enabling the effective resolution of critical issues, and they’ve integrated LLM capabilities to address vulnerabilities.

A skill to have is to identify marketing terms. Terms like "ludicrously fast"

If you are not struggling when you are learning then you are not learning.

Time block: 21:40-00:00
Activity: watched YouTube

Log:

- Cognitively, how much energy did I have doing this activity? ([[estimate-energy-mood]])
2

- How much did I enjoy this activity?
5

Plan:
My plan for this time on my calendar was to do my night routine, go to bed.

Observations:

- What happened during the activity?
Reflection on last night, it was around 21:40, I got home from Grainger and I laid on my bed. I was tired, I made the choice to watch YouTube. What was going through my head was I just wanted to watch YouTube for 40 minutes then go to bed. I watched YouTube for 2 hours and 20 minutes. I started watching a video by Kurzgestat, then 2 videos by Veritasium. I wanted to watch the videos, they were in my recommended. I clicked on them, part of my head knowing I shouldn't click on them.

- How did I feel before, during, and after the activity?
I felt that it was okay for me to do this behavior. I didn't feel loss, I didn't think about my future self, I just thought about what would feel good in the moment. In my mind, I was thinking if I watch 40 minutes of YouTube I will feel better. During the activity, I felt the good mind-numbingness of YouTube videos

Evaluation:

- What went well and didn't? How did things not go as expected?
I'm happy I didn't go way past my bedtime. Most other things did not go well, I was even watching YouTube videos while brushing my teeth! There was an extreme resistance against me pausing the video. As soon as a video ended, my monkey brain wanted another one. This makes me feel disgusting that I can be manipulated like this.

- What surprised me or did I find challenging?
I definitely am doing better than before, cognitively I felt I had a choice but chose wrong, instead of in the past me not feeling I have a choice. Looking back, I'm also realizing I don't really enjoy the activity that much. It's not that I wanted to read because it's better long term, I also just enjoy thinking much more. The problem is that reading and thinking is more difficult.

Analysis:

- Why do I think things went well or poorly?
I did have a very productive day, I finished the most important task for that day, got more caught up with my lectures, paid my bills, did work on my research. I was tired at the end of the day. The thought of reading marginally crossed my mind, if there was a book in my bed, I bet I would have read it.
Because I was drained, it doesn't mean I need to watch some brainrot to recover, it just makes me more susceptible to these stimulants. This is why I've gone about a week without watching or consuming anything and have made a poor decision last night.
I also did this because of present bias.

Action:

- What is the single most impactful change I could make to improve this activity/system?
No phone in room after 22:00. I do this change when I have energy and before I lie down. There is nothing I can do on my phone that I can't do on my computer.
- What is the next concrete step I will take?
Logout of YouTube on my phone so I won't see recommended videos.
Have what I want to do on my bed, so I don't need to make a decision.

Questions:

I just solved a bug where a JavaScript function was expecting the variables named options and results, but I was passing chartData. I was under the assumption if I passed in a value that was not defined in the function then it would throw an error. This is not how javascript works, though. This took me an hour to find.

`js
const ScatterChart = ({ options, chartData }) => {
if (!options) throw new Error("options is required");
if (!chartData)
throw new Error(
"chartData is required, chartData is:\n" + JSON.stringify(chartData),
);
return ;
};

export default ScatterChart;
`

Reflection: This took well over 1.5 hours to find. What I did was ask ChatGPT why the bug occured. It gave some potential reasons of the versions being out of date, but the versions are in date. Instead of asking ChatGPT why the error occured, I should ask it "what are all the possible reasons this error could occur". Instead of giving me one answer, it can give me several. Then, I can step-by-step eliminate them. I can also look to see what error is most likely given my context.

A way to prevent this bug is to switch programming languages, that is not as feasible, but another way to prevent it is to mark your assumptions in your functions (i.e. xyz should not be null). The library I was working with should give a better error message.

Question:
How else can I improve my debugging skills?

I wanted to get caught up in tech, I watched 1 video from fireship, and I had the willpower to stop. I finished my CS527 lecture video and went to YouTube, but I had the resistance to not watch anything. I have significantly cured my brain from the desire to be entertained.

Time block: 11:00-15:00
Activity: call dentist people, do CS527 work, paid my bills, cooked + ate lunch, watched CS598JH lecture 3

Log:

- Cognitively, how much energy did I have doing this activity? ([[estimate-energy-mood]])
- How much did I enjoy this activity?

Plan:
My plan during this time was to finish my CS527 MP, and then watch my CS598JH lectures.
Observations:

- What happened during the activity?
I went to work on my MP, did some work on it, but then I got distracted by a repository I found. Newsflash, I just got distracted by shiny object syndrome, the repository is cool, but it was not worth derailing my progress.

- How did I feel before, during, and after the activity?
I felt high energy, although I was distracted, I didn't feel mentally drained or low energy

Evaluation:

- What went well and didn't? How did things not go as expected?
I quickly found a repository with many flaky tests, I think I can now finish my MP in an hour. I also remembered my reflection from before and meal prepped 4 meals, so I'm very happy my future lunches will be quicker.

Analysis:

- Why do I think things went well or poorly?
I had a good nights sleep and good energy which is why I think things went well. I had more confidence in myself to do hard things (call the dentist people).

- Where did I waste time, energy, or focus?
Distraction from email.

- Could I automate, delegate, or simplify any part of this process?
- Are there alternative approaches or methods that might yield better results?
- Did I leave anything half-finished?

Action:

- What is the single most impactful change I could make to improve this activity/system?
When there is a thread for me to go down, store it later, build confidence in my system that eventually I will see it, but see those things as non essential.

- If I did this again, what would I do differently?
- What is the next concrete step I will take?

Questions:

Someone named Kolby Shouse came to my apartment today and got me to switch my electrical provider to IDT instead of Ameren. He came in saying that he works at the electrical company and he needs to check my bill to see if we were double charged. I took this at first glance, he asked for my account number, phone number, name, and other information. And then he hoodwinked me by getting me to sign up with IDT. I canceled it, but I spent an hour learning a lesson that will be valuable for me in the future.

Some lessons/principles I learned:
Always ask why? If I feel like I cannot ask why, then I must end the interaction. The reason I was afraid is I thought he was working for Ameren in some way and I didn't want to waste his time, so I wanted it to be quick.
Don't let someone pressure you to make a decision to quickly. Always ask "can I do this another time". This is really hard when they are face-to-face with you, but if they say I have to do it now, then tell them to leave.
Always sus out their intentions when someone comes to your door.
If there is a warning sign in your mind, then raise the alarm. I had some suspicion when the website was asking me to ensure they said they do not work for the power company. I should have asked "why is it asking me this".
When interacting with people that you do not expect, behave with caution. Look intimidating, like you are not having fun. Don't be in a nice mood because you'll treat them with too much kindness.

Next, I’m going to be explaining another technique taught in class, which is Aspect-Based Reasoning Structure Extraction. The problem this is trying to solve is that some claims don’t have a specific yes or no answer. For example, the lecture gave the example of vaccine A being better than vaccine B, but there are many different aspects in which vaccine A could be better or worse than vaccine B. The insight is that you need to break down these claims into specific aspects and then identify which of those aspects have been explored within the scientific corpus and which have scientific consensus behind them. The algorithm works by starting with a claim and generating coarse aspects that would impact that claim, such as efficacy, safety, and distribution. Then, you look into your corpus of documents and identify which documents are related to the specific aspect you want to discover more about – in the vaccine example, it would be safety. You then rank those documents based on their relevance to safety so that the LLM can use them in the future. However, this is actually a hierarchy, because safety can mean safety for whom? Safety for children, adults, or the elderly, and you have to iteratively discover more sub-aspects. When someone asks, “Is vaccine A better than vaccine B?” the LLM now has a bunch of different aspects of that claim and an idea of what aspects have scientific consensus. It could respond with something like, “Yes, 80% of the corpus agrees that vaccine A is safer for adults than vaccine B.”
## Screenshot

So, I’m going to share another paper that was discussed in this class – it’s called KARE. It’s a way to predict the outcome of a patient based on their context, and also provides reasoning for that prediction. It works in three steps: first, you start by attaining knowledge sources – biomedical knowledge graphs, biomedical core sources, and other information from LLMs. Then, you extract a knowledge graph, doing semantic clustering on the embeddings of the entities, and grouping things into communities to create a bunch of different corpuses. In those corpuses, you have information about interactions and the general theme. The next step is to construct and augment the patient’s original context – you have the patient’s chart and EHR, construct a knowledge graph from that, and then look at the community summaries, appending information related to their specific cases or illnesses, as well as similar cases with different outcomes. The purpose of that is to see what cues cause what outcome. Finally, you prompt a smaller LLM with this augmented context to provide reasoning as to what will happen to the patient and then predict the outcome. As for training the LLM, you already have the patient context and ground truth in your dataset, prompting a larger, smarter LLM to provide a high-quality reasoning chain, and those are the training signals. This dataset is about 16,000 patients – each has about 1.6 visits, 20 conditions, 4 procedures, and 30 medications on their chart. It’s a relatively small dataset, but large enough to fine-tune a smaller LLM. The benefit of this algorithm is you have a smaller LLM (so better inference times, better generalization, more robust), you have the reasoning grounded in the medical knowledge graph.

## Screenshot

Next, I want to talk about a paper called JoSE. It stands for Joint Spherical Embedding – it’s similar to other text embedding algorithms like Word2Vec or GloVe. The key insight is that the usage space and training space of these algorithms aren’t aligned; Word2Vec is trained to maximize the dot product between two vectors if they appear closely together in text, but we typically use cosine similarity to assess how related two words are. So, there’s a disconnect there. A student designed a training strategy that just used the cosine of the vectors instead of the dot product. I’m confused because dot products are much faster than cosine similarity. Even if the training objective is perfectly aligned with the usage objective, I’d assume you could train the other algorithms more and get a better result. However, this author found that the Jose method was the fastest in training time compared to Word2Vec, GloVe, FastText, BERT, Foncar, and GloVe.

# Dynamic RAG

I'm going to be giving a voice note on Dynamic RAG, which is a technique that was discussed in CS598JH. The title of the paper is Dynamic RAG, Leveraging Outputs of Large Language Models as Feedback for Dynamic Reranking in Retrieval Augmented Generation.

To start, to understand how RAG works, you have a generator, a retriever, a corpus document, a query, and you start with this query, then you have a retriever that retrieves some N documents from the corpus. Then you pass those N documents to a generator, and then it gives you your answer. But there's a problem that this N is a fixed value, and what was seen is that the order in which the documents are shown to the LLM also matters. So this retriever does not have the ability to rank the documents based off the importance to the query. It's probably simple just like embed documents with a cosine similarity, etc. And then the purpose of this work is to have a re-ranker that is able to take those N documents that are retrieved from the retriever and then rank them in an order that is better for the LLM, that makes the LLM generate a better answer, as well as decrease the number of documents to a number K, which the re-ranker gets to choose, because more documents does not always mean better. So how is this, so what is this re-ranker? It is another LLM that reads the documents and then it outputs an order for the documents to be in, and it also outputs the number K, which is the number of documents that are going to be passed to the next LLM.

But how do you actually train this LLM, how do you train this re-ranker LLM to give you not only good documents from the Scorpus, but also how do you train it so that the LLM improves its other LLM? Well, what you do is you have this training setup where you have an LLM that is a generator and then you freeze the weights of that LLM so that the LLM does not get updated at all. Then you train an LLM, you train the re-ranker LLM to output documents and then the frozen LLM generator will just generate documents based off the, or generate an answer based off the documents you received, then you, the LLM will generate many, like the re-ranker will generate many different series of documents, many different rankings of documents, and then pass that to an LLM, the LLM will generate an answer, and then based off that answer you'll compare that to a ground truth answer, and then you'll get a reward signal for the original re-ranker. So from this, now you have a reward signal that then you can update the weights of the original LLM re-ranker, and in this paper they use DPO as the learning algorithm. They show that they get better results than similar sized LLMs with other RAG algorithms, and then the LLAMA 8-billion is able to outperform the GPT-4-0 without retrieval. And then other results that they've shown that their RAG is better. What was interesting to me was that sometimes the LLM could just save that, could set k equal to zero, and make it so that no documents are passed to the generator LLM. I thought that's interesting. I would not have expected the LLM to find that behavior, but it was able to find that behavior.

## Screenshot

Over 80% of data is in unstructured text data

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My mindset is "cooked" as some will say. It is the thing that is holding me back. This is one of the most important things I need to work on. I am partly surprised by this as I thought I had a good mindset, but internally, I have some major things holding me back.

On the phone with the Dental Insurance people. I realized I have a fear of talking with people on the phone, I take any gesture of theirs negatively. This is my current mindset and something I need to change/improve.

I’d like to read more of my journals and reflect on them, as I haven't been doing that enough lately. I want to be able to continuously cringe at my past self – how disorganized I was, how difficult it was for me to read, and my lack of growth over the last three months, so I can ensure I continue to improve.

At the end of the day, it took me ~15 minutes to transcribe my phone capture to my laptop, and this was on a "slow" day. Ideally, this would not happen or only take me 5 minutes to capture my remaining thoughts of the day.
1) To do this faster, I can have more AI suggested features and better shortcuts
2) "Native" text to speech + "cleanup" prompts
3) Mobile application to mirror the real capture.

I’m thinking about CS598JH, and it’s a phenomenal class. I’ve learned quite a bit about LLMs and text mining with LLMs, and I’ve learned about different techniques that I think are interesting. I think the reason the class was so good wasn’t because of some inherent property of the course, but because I paused and took breaks when I was watching the lecture and I also did free recall on what was talked about. I wrote smart notes and tied them to other things, so I’ve learned more, and I’m probably only at 25% of my maximal learning capability. There’s some things I did not do like I did not tie as well with things that I’m interested in right now; it was more passive, and I was interrupted.

If you cannot answer why you are doing something, then you need to identify why you’re doing it, and if you don’t like that reason, then stop doing it.

Most people don’t have mission statements; they don’t know where they want to be in a few years, and that is a problem. Many companies have vague mission statements, and that doesn’t help them make decisions or inspire the people working there. Likewise, people do not have mission statements that inspire them or help them make decisions. I don’t have a mission statement that I think about, and I think that’s one reason why I feel like I don’t have a purpose or am not focused.

Everyone needs to have an aim; those who don't have one and just hop from job to job without a larger plan or picture won’t be able to achieve the highest level of contribution or specialize, and that’s important.

The purpose of a vision is to help you eliminate options and make decisions. it is not some wishy-washy manifestation thing. I think this is how manifestation "works'.

A lack of purpose leads to caring about non essential things, things that most people care about like social status, money, Twitter followers, instead of things that actually are valuable.

A core skill to have in life is prioritization

for essentialism system, consistently identify activities I do and rate them on extreme criteria. anything that's below a 9 fails. I can do this with all types of decision making.

This technique called SARG, and the purpose of SARG is to allow LLMs to do better reasoning. I wanted to extract a principle of how LLMs can be used from this paper. The idea is that you have this corpus of documents, and then you extract triples of cause, relation, and effect to construct a causal graph. From this causal graph, you can then use an LLM to learn why XYZ happened, by querying it with a question. I think this is a powerful principle that can lead to other interesting research topics and results.
## Screenshot

Principle: let all work that you do be good enough to show others.
All these assignments, essays, homework, they are for learning, but you spend so much time, why not spend 10% more effort to make it good enough to be shown to others? Why not be strategic and align your assignments with what you want? I have identified the teams i want to work on, my best choices of projects, now i am thinking how can i work on this homework assignment, and have it be evidence.

I've taken so many courses, yet there are no visible fruits. You have to just "trust me" that I have learned the skills. Turn homework assignments into course projects.

So the whole idea of text mining with LM is shown very well in this example. It’s called RolePred: argument role prediction. The goal is to extract a predefined schema of different arguments from a document, like magnitude, location, or date. You don’t want to use a large language model for efficiency. So you extract numbers, dates, and other phrases like names. Then you have a template, and you say, “According to this, the blank of this event is this entity.” When I say blank, I mean these are the different arguments you have. The value that has the highest congruence is the correct value for that argument. You’re not just asking, “Oh, what is the location of this earthquake?” You’re doing this in a smarter way, and you could use a smaller language model and get better accuracy.
## Screenshot

An interesting idea is that this LLM is very general, but you are defining a structure to the LLM's output, and because you're giving more structure to the LLM's output, and you're doing more discrete searching, you get more power out of it.

I do think that this method is very interesting. It's called ontotype, ontology-guided entity typing. The goal is to identify what an entity is. I thought it was interesting because it uses a small language model and it shows the efficiency in understanding and extracting the knowledge of a very small language model. How it works is that you have an entity that you want to identify its type. Then you prompt an LLM with a statement about what that entity is. So in this example, it is Wrigley Field. So you ask Wrigley Field the location. Is it a person? Is it an organization? And then you get some sort of confidence score from that statement. You get that from the small LLM. And then once you’ve identified what class of entities it is, you can go down the street. So you know it’s a location. Is it a country? Is it a city? Is it a building? You get the highest score from the confidence. So then you go down again. I think this is interesting because I can directly apply it to my note system. Instead of prompting an LLM, I can do it more efficiently. Instead of asking an LLM, what is this type, I can have this action. And I think a cool idea is that you progressively refine the type so you can stop at any time if you want a higher level idea of what an entity is.

## Screenshot

Hypercube Rag is a technique for doing retrieval where you enumerate the different possibilities for different semantic types and then you essentially do a union on all the different semantic types to get documents. For example, if you want to do retrieval related to tropical storm disasters, there are different semantic types for disaster, like location, event, and theme. A tropical storm has a location where it happened, it has a theme which could be the name of the tropical storm, and it also has the event of the tropical storm, like Hurricane Katrina. And there could be themes for the documents, maybe it's about how there was so much rain and it flooded, or it could be that there was a lot of wind. And these are semantic themes that could just segment the documents. And then from that you have a query. It's very easy to find the documents related to the theme if they're indexed in this way. I don't understand what is so special about this technique though.
## Screenshot

So this one training method they use is called SearchR1. Essentially, you train an LLM while using the search algorithm instead of adding it post-hoc, and this leads to better performance, specifically for searching, which is unsurprising to me. And then there’s also this other algorithm that I mentioned, where you train an LLM to improve the search query before passing it into a search engine, and then the LLM can augment the search query. The LLM is essentially trained to have a search query that maximizes the F1 score. This allows it to have better retrieval, and then better retrieval leads to much better performance, which is shown by one of the graphs where they have DeepSeek, a 3-billion-parameter DeepSeek, outperform GPT-4.0 and Cloud Sonnet when it comes to information retrieval.

For information retrieval, understanding the intent and context of the user is important for good retrieval.

There is a real difference between theme-based and QA retrieval. Theme-based requires documents that might not have the specific keywords you're looking for. You need to do reasoning on a query to know what is relevant, some things that you might think would be relevant aren't and vice versa. For the example with the trump rally shooting, the political landscape at the time would be relevant, knowing about the shooters friends might be relevant or might not be. That is why this is a hard problem.

Retrieval is an important topic for an LLM because information not in it's training data exists. Better retrieval means better results for LLM.

When you make a change in an open-source program, one way to increase the odds of acceptance is if you cite that the current change is similar to another change that was accepted.

Time block: 14:30-15:30
Activity: Eating and reading Essentialism

Log:

- Cognitively, how much energy did I have doing this activity? ([[estimate-energy-mood]])
5
- How much did I enjoy this activity?
5
Plan:
During this time, my plan was to eat and read. nothing more specific
Observations:

- What happened during the activity?
I ate a pre-packaged meal my parents made for me. That was not filling enough, so I also ate a protein shake and some Huel. I was reading Essentialism. I was voice recording my thoughts. Afterwards, I was not ready to work so I went on another walk.

- How did I feel before, during, and after the activity?
Before I had quite high energy, a little frusturation because I didn't work on the most important task, I worked on an extension for Neovim instead of working on what is essential.
During the activity, I felt that I had clarity.
Afterwards, I feel a little sluggish.

Evaluation:

- What went well and didn't? How did things not go as expected?
I won't comment on the food consumption because I was listening to my body. I was reading while I was eating, so I definitely ate faster and more than I wanted to (which is probably why I feel sluggish and tired now). I read my book well and I'm happy I did it instead of bingeing on my phone or working on my laptop. I used my lunch as a way to get space. I want

- What surprised me or did I find challenging?

Analysis:

- Why do I think things went well or poorly?
- What underlying factors or assumptions influenced the outcome?
- What skills or knowledge did I use effectively? What was lacking?

- Where did I waste time, energy, or focus?
I was eating and reading. Wasting time by writing down by hand.

- Could I automate, delegate, or simplify any part of this process?
If I was reading digitally, then I wouldn't need to manually flip pages, I could just process the information like a computer. Actually, that would be quite helpful. There is a pain in flipping pages.

- Did I leave anything half-finished?
I didn't finish my chapter

Action:

- What is the single most impactful change I could make to improve this activity/system?
I want to ensure that when I am reading, the notes I am taking are effectively captured.

- If I did this again, what would I do differently?
I would like to eat outside, there is higher ROI for eating outside and reading. I get fresher air, sunlight.

- What is the next concrete step I will take?
Schedule my next lunch so it is outside.

Questions:
I wonder if it's better to eat and be bored instead of eating and reading?

I want to think about my learning system and what's the best way to learn from my books, process it, etc. I want to read "Building a Second Brain"

It would be very nice if I could just free speak, then my zettlekaasten notes get automatically segmented and captured.

I was working on my plugin to do system iteration for >1 hour and it doesn't work. I was working on it instead of doing the actual value add--my systems iteration.

Principle: When improving, ensure you're getting better in a way you want, if not, course correct.

When programming with Windsurf, something I realized is I am using it to do things that I do not know how to do. I am getting better at prompting Windsurf and using it, sure, this is applicable to other LLMs, but I am not getting better at things that matter (better system design.)

When I have principles, they should be explicit, specific, and not vague.

There is someone at Grainger that is taking their meeting on the second floor, the quiet floor. I was sitting next to him and he was annoying me, so I just moved. I feel bad, because I wanted to tell him to not take his meeting at grainger and for him to move, but I didn't have the self confidence to o so. I was rationalizing why I shouldn't tell him to move, I didn't want to feel like an asshole, I thought he was going to stop soon, I didn't want to build bad blood. I want to think of a better way of confronting people and build up the ability to do so.

So part of it was initial fear and discomfort. I think that came from me being inauthentic because Siri told me that I should just go up to her, but there’s no reason for me to go up to her. Then Siri suggested that I should tell her that I want to walk in with her instead of just talk with Siri and her friends, and this was a very good reason, and it is true, so I felt much more confident about that. In my mind, I was envisioning a playfulness of me going up to her table, sitting down, and just like teasing my friends that I actually have to do work and kind of smiling at her and letting her know that I’m just interested. That’s a version of myself that I envision, and that’s how I will be in the future, and that’s a version of myself that I want to be. So I think the reason why I did not initially do it was because of a lack of authenticity, and then once I had an authentic reason for going to her to do it and actually I think the main reason maybe was not part of it was definitely the authenticity, but another reason is probably the playfulness. I did not have a playful reason to go up to her and talk with her, but once I had a playful reason, I was in a much better mindset. So I think that in these types of cases, I should try to think of a playful way to approach people. People vary like the playfulness, and I’ve seen this in my own life when I’ve approached girls in the past. So in the future, the next time I am in a position where I will actually want to approach a girl, I will identify what’s an authentic reason I want to go up to her and talk to her, and I will try to think of how I can make it playful.

I realization I had was that questions are extremely useful because they prompt your brain to think in a way, therefore, by systematically asking yourself good questions, you can get good answers. The answers are latent inside of you, and you need questions to fish them out.
## File
[Attachment](media/audiomicrophone175747596936720250909224610_029.wav)

Here is a resource for identify popular causes of flaky tests https://mir.cs.illinois.edu/lamyaa/publications/fse14.pdf

Time Block: 16:00-18:00
Activity: At home, eating, working very passively
Log:

- Cognitively, how much energy did I have doing this activity? ([[estimate-energy-mood]])
5
- How much did I enjoy this activity?
5
Plan:
The problem was during this time block, I explicitly did not make any plans. I was just at my apartment doing whatever work felt productive. That was mostly learning more about Anthropic, consuming content.

Observations:
I realized that I don't like the feeling of passively consuming content. This is what causes a mind fog for me.
I was at my apartment, eating throughout the time period.
Feedback:
A problem identified is I ate at 15:00 and at around 17:30. By not having set times to eat, I ate more than I wanted to, now I feel bloated and that I overeat. A positive is that I haven't felt this way in 3 days, so I am definitely improving!

Not only does having a defined time to eat improve my eating, how I feel when I am done, but it also improves my output because I can spent less total time eating, cleaning, etc.

- Where did I waste time, energy, or focus?
- What is the single most impactful change I could make to improve this activity/system?

If I were to plan my meals out before hand I bet that would improve this activity. This is because I'm more likely to stick to a plan if it exists. Also, I could add my meals to my calendar and try to be more "strict" about those eating times so I know when it is time to eat or not. I think I was eating out of boredom.

- Could I automate, delegate, or simplify any part of this process?
- If I could do this activity again, what would I do differently?
- Are there alternative approaches or methods that might yield better results?
- What is the next concrete action to improve this system?
- Should I experiment with a small change or a larger redesign?
- Did I leave anything half-finished?

Time Block: 16:00-18:00
Activity: At home, eating, working very passively
Log:

- Cognitively, how much energy did I have doing this activity? ([[estimate-energy-mood]])
5
- How much did I enjoy this activity?
5
Plan:
The problem was during this time block, I explicitly did not make any plans. I was just at my apartment doing whatever work felt productive. That was mostly learning more about Anthropic, consuming content.

Observations:
I realized that I don't like the feeling of passively consuming content. This is what causes a mind fog for me.
I was at my apartment, eating throughout the time period.
Feedback:
A problem identified is I ate at 15:00 and at around 17:30. By not having set times to eat, I ate more than I wanted to, now I feel bloated and that I overeat. A positive is that I haven't felt this way in 3 days, so I am definitely improving!

Not only does having a defined time to eat improve my eating, how I feel when I am done, but it also improves my output because I can spent less total time eating, cleaning, etc.

- Where did I waste time, energy, or focus?
- What is the single most impactful change I could make to improve this activity/system?

If I were to plan my meals out before hand I bet that would improve this activity. This is because I'm more likely to stick to a plan if it exists. Also, I could add my meals to my calendar and try to be more "strict" about those eating times so I know when it is time to eat or not. I think I was eating out of boredom.

- Could I automate, delegate, or simplify any part of this process?
- If I could do this activity again, what would I do differently?

- Are there alternative approaches or methods that might yield better results?
Going to work at Siebel, outside somewhere, not forcing myself to work if I do not feel capable of working, I can just read.

- What is the next concrete action to improve this system?
Add lunch and dinner times to my system.

Time Block: 16:00-18:00
Activity: At home, eating, working very passively
Log:

- Cognitively, how much energy did I have doing this activity? ([[estimate-energy-mood]])
5
- How much did I enjoy this activity?
5
Plan:
The problem was during this time block, I explicitly did not make any plans. I was just at my apartment doing whatever work felt productive. That was mostly learning more about Anthropic, consuming content.

Observations:
I realized that I don't like the feeling of passively consuming content. This is what causes a mind fog for me.
I was at my apartment, eating throughout the time period.
Feedback:
A problem identified is I ate at 15:00 and at around 17:30. By not having set times to eat, I ate more than I wanted to, now I feel bloated and that I overeat. A positive is that I haven't felt this way in 3 days, so I am definitely improving!

Not only does having a defined time to eat improve my eating, how I feel when I am done, but it also improves my output because I can spent less total time eating, cleaning, etc.

- Where did I waste time, energy, or focus?
- What is the single most impactful change I could make to improve this activity/system?

If I were to plan my meals out before hand I bet that would improve this activity. This is because I'm more likely to stick to a plan if it exists. Also, I could add my meals to my calendar and try to be more "strict" about those eating times so I know when it is time to eat or not. I think I was eating out of boredom.

- Could I automate, delegate, or simplify any part of this process?
- If I could do this activity again, what would I do differently?

- Are there alternative approaches or methods that might yield better results?
Going to work at Siebel, outside somewhere, not forcing myself to work if I do not feel capable of working, I can just read.

- What is the next concrete action to improve this system?
Add lunch and dinner times to my system.

Time Block: 16:00-18:00
Activity: At home, eating, working very passively
Log:

- Cognitively, how much energy did I have doing this activity? ([[estimate-energy-mood]])
5
- How much did I enjoy this activity?
5
Plan:
The problem was during this time block, I explicitly did not make any plans. I was just at my apartment doing whatever work felt productive. That was mostly learning more about Anthropic, consuming content.

Observations:
I realized that I don't like the feeling of passively consuming content. This is what causes a mind fog for me.
I was at my apartment, eating throughout the time period.
Feedback:
A problem identified is I ate at 15:00 and at around 17:30. By not having set times to eat, I ate more than I wanted to, now I feel bloated and that I overeat. A positive is that I haven't felt this way in 3 days, so I am definitely improving!

Not only does having a defined time to eat improve my eating, how I feel when I am done, but it also improves my output because I can spent less total time eating, cleaning, etc.

- Where did I waste time, energy, or focus?
- What is the single most impactful change I could make to improve this activity/system?

If I were to plan my meals out before hand I bet that would improve this activity. This is because I'm more likely to stick to a plan if it exists. Also, I could add my meals to my calendar and try to be more "strict" about those eating times so I know when it is time to eat or not. I think I was eating out of boredom.

- Could I automate, delegate, or simplify any part of this process?
- If I could do this activity again, what would I do differently?

- Are there alternative approaches or methods that might yield better results?
Going to work at Siebel, outside somewhere, not forcing myself to work if I do not feel capable of working, I can just read.

- What is the next concrete action to improve this system?
Add lunch and dinner times to my system.

Time Block: 16:00-18:00
Activity: At home, eating, working very passively
Log:

- Cognitively, how much energy did I have doing this activity? ([[estimate-energy-mood]])
5
- How much did I enjoy this activity?
5
Plan:
The problem was during this time block, I explicitly did not make any plans. I was just at my apartment doing whatever work felt productive. That was mostly learning more about Anthropic, consuming content.

Observations:
I realized that I don't like the feeling of passively consuming content. This is what causes a mind fog for me.
I was at my apartment, eating throughout the time period.
Feedback:
A problem identified is I ate at 15:00 and at around 17:30. By not having set times to eat, I ate more than I wanted to, now I feel bloated and that I overeat. A positive is that I haven't felt this way in 3 days, so I am definitely improving!

Not only does having a defined time to eat improve my eating, how I feel when I am done, but it also improves my output because I can spent less total time eating, cleaning, etc.

- Where did I waste time, energy, or focus?
- What is the single most impactful change I could make to improve this activity/system?

If I were to plan my meals out before hand I bet that would improve this activity. This is because I'm more likely to stick to a plan if it exists. Also, I could add my meals to my calendar and try to be more "strict" about those eating times so I know when it is time to eat or not. I think I was eating out of boredom.

- Could I automate, delegate, or simplify any part of this process?
- If I could do this activity again, what would I do differently?

- Are there alternative approaches or methods that might yield better results?
Going to work at Siebel, outside somewhere, not forcing myself to work if I do not feel capable of working, I can just read.

- What is the next concrete action to improve this system?
Add lunch and dinner times to my system.

Time Block: 16:00-18:00
Activity: At home, eating, working very passively
Log:

- Cognitively, how much energy did I have doing this activity? ([[estimate-energy-mood]])
5
- How much did I enjoy this activity?
5
Plan:
The problem was during this time block, I explicitly did not make any plans. I was just at my apartment doing whatever work felt productive. That was mostly learning more about Anthropic, consuming content.

Observations:
I realized that I don't like the feeling of passively consuming content. This is what causes a mind fog for me.
I was at my apartment, eating throughout the time period.
Feedback:
A problem identified is I ate at 15:00 and at around 17:30. By not having set times to eat, I ate more than I wanted to, now I feel bloated and that I overeat. A positive is that I haven't felt this way in 3 days, so I am definitely improving!

Not only does having a defined time to eat improve my eating, how I feel when I am done, but it also improves my output because I can spent less total time eating, cleaning, etc.

- Where did I waste time, energy, or focus?
- What is the single most impactful change I could make to improve this activity/system?

If I were to plan my meals out before hand I bet that would improve this activity. This is because I'm more likely to stick to a plan if it exists. Also, I could add my meals to my calendar and try to be more "strict" about those eating times so I know when it is time to eat or not. I think I was eating out of boredom.

- Could I automate, delegate, or simplify any part of this process?
- If I could do this activity again, what would I do differently?

- Are there alternative approaches or methods that might yield better results?
Going to work at Siebel, outside somewhere, not forcing myself to work if I do not feel capable of working, I can just read.

- What is the next concrete action to improve this system?
Add lunch and dinner times to my system.

What claude code is good at:

## Stages

1. Discover
2. Design
3. Build
4. Deploy
5. Support & Scale

### Activities

- Explore codebase
- SR Plan project
- Implement code
- Automate CI/CD
- Debug errors
- Search .
- Develop tech specs
- LU CTR G L Configure
- Large-scale refactor
- documentation
- tests
- environments
- Onboard & learn
- Define architecture
- Clegte coMis
- Manage deployments
- MCIiEorRage &
- performance

Participatory design includes users directly on the design team - participating in needfinding, proposing design ideas, helping with evaluation. This is particularly vital when the target users have much deeper domain knowledge than the design team. It would be unwise to build an interface for stock trading without an expert in stock trading on the team, for example.

Contextual inquiry is a technique that combines interviewing and observation, in the user’s actual work environment, discussing actual work products. Contextual inquiry fosters strong collaboration between the designers and the users.

When developing products for users, iterate and iterate cheaply. This is also a core principle of Scrum.

I'm contemplating the finitness of my life using this visualization and I am looking at other accomplishments that people have made at various ages. I was surprised to see that a lot of others have made major accomplishments much later than I thought. For example, Steve Jobs was 21 when he started his company, Bill Gates, 28. This makes me feel a lot better. It also makes me come to the thought of how every week of my life is permanently fixed. This last 2 weeks will stained by my bike accident, in 2 weeks, I will remember that time as my trip to Spain. These are periods in time I will be able to vividly see on a map. I still have ~3-4k weeks left of my life, and I can choose what those weeks look like. This is a powerful realization.

Last night, I spent ~30 minutes watching YouTube videos on death. I am happy that I was thinking about death, but I was on the border of the point of binge watching again. I am reflecting on how I spend my time last night, and I am reminded that my time is less valuable at night time compared to in the morning. Because of this, I want to make more of an effort to move my sleep schedule backwards. (side note, I think this reveals something about how I perceive time)

I woke up this morning feeling hungrier than yesterday. My weight is still 183, which is quite a significant drop compared to what is was a couple days ago. I am happy my weight is lower but I don't want to lose too much weight and feel too out of control.

I went to bed hungry last night and I had a very good sleep because of it! I am happy my sleep is solidifying to be consistent and high quality.

I would live differently if I completely internalized my own death. I am not aware of my own death enough. There's a popular thought that "would you live your life differently if you were aware that this is your only chance at living, would you be afraid if you knew life was a suicide mission".

From this video, I consciously know that I will never experience my own death. I will only be mortal.

I am trying to become more aware of death by watching this youtube video. There are many regrets of the dying, they want to tell a friend they forgive them for their transgressions, or they say sorry to someone they've greatly harmed. A goal in my life is to have no regrets when I am dying.

[talking about my cellulitus, Maks putting honey on my open wound claiming it would be healed]
Maks: "You will see you will be healed because of the honey"
Me: "I will be healed because I've been on rounds of antibiotics"
Maks: "You didn't even need antibiotics, you could've eaten garlic and honey and you will be healed"
Me: [laughter]
Me: "Can I quote you on that?"
Maks: "You can quote me on anything, Plato didn't write a word"

For my CS527 MP. The skill that I was trying to develop was debugging. I spent ~15 hours on the MP. If I would have spent 1 hour learning how to debug better, I would have finished quicker. This is a general principle I can apply to future assignments--to spend more time up front learning instead of just doing.

For my getting-things-done system what if I were to add a task to do in todoist, then I plug in the effort, then it automatically schedules time for me to do the task in my calendar so I don't forget to do the task + save time in scheduling

Islam Shehata gave me the nickname of "150 grams" in reference of me eating 150 grams of protein powder in a day. But he says if I become a rapper and use that nickname then I owe him 5% royalty

What if I build a company around personal growth tools, optimized for myself – personal optimization.

An idea I have is that I should envision my life in the future and how it would be if I were to gain specific attributes such as high confidence and reflection, and also envision how my life would be without that feature so that I am motivated to change that part of myself.

The book recommends these books to read: Zen: The Reason of Unreason; the wisdom of Confucius; the holy Bible; Tau, to know and not be knowing; the meaning of the glorious Quran, and an explanatory translation; The Book of Mormon; the meditations on Marcus Aurelius; and The Up and the Shades.

A theme of this book is explicitly making space in your schedule.

Humans need a lot of time to think about big decisions or big projects, and only through a lot of thinking can we come to the best solution.

The author of Essentialism has a practice where he blocks off 8 hours a day to write for this book. I think this is a good idea; it might help me to adopt this idea.

There's a connection between deep work and this book, and that idea of having space to think about your problem, like in solitary. An example is Isaac Newton: he became a recluse for two years to think about his problem, and then he came out with Principia Mathematica.

There's a course at Stanford called Designing Life, Essentially. The purpose of this course is for people to have a block of time where they're forced to think about their lives. That seems like a good idea. I should tell Natalia about this.

In whatever company organization I have, I need to ensure that I have time to take a step back and not be too busy. I need time to think and be strategic.

In the essentialism system, there's an explicit time and period to explore the possible space. In that time, it's essential to have spaces to think. I’ve had my own realizations myself when I realized that if I'm too busy to reflect on things, then I'm just too busy. They have a meeting a month where everyone is just in a room and they do nothing. If people can't make it to that meeting, then it means that they’re too busy or there aren’t enough people. So, because it’s too busy, it gives the founder a good signal as to if people are working on things that are unessential or not, because it is essential to have time to think.

A good indicator of if your actions and habits are good is if you were to think about. if you were to repeat those actions for the rest of your life, where would it leave you. you have to think more than just the present moment and think about the future, understand where your future self will be continuing those actions

For my weight loss system, I eat smaller bites and drink smaller amounts of food at a time. I've realized that I take large bites and eat quickly.

by eating slower, I can sense what I'm actually full

After falling off my bike, I have chest pain when I breathe in deeply. It's not a lung or chest pain; it feels like a bone and chest pain. The pain seems to be located in the front of my chest, around my rib cages.

My ideal job would be spending the day thinking and improving my thoughts.

It's interesting how social media applications can capture your attention so quickly and entirely when you start using them. Their UIs are designed to achieve this. I find it disgusting.

These are the root causes of flaky tests, I am surprised that so many happen because of async wait.
## Screenshot

We tend to hallucinate when we are sensory deprived because the environment doesn't give us information. This is a difference compared to ANN where that cannot hallucinate. There is a benefit to hallucination. There is a connection with this a dreaming.

There are biological equivalences of factors of artificial neurons. For example, the "weight" can be thought of how much myelination there is.

The system that prunes neurons is the brains immune system. I wonder if there is a connection to sleep here.

What "plasticity" looks like in brains is that neurons are just wiggling around and looking for more connections.

In each section of the brain, the neurons evolved to communicate "optimally" for that region. That means, I would expect to see different neuron structures in different regions of the brain. I wonder what are the structural differences between neurons that process different pieces of information.

Neurons are cells, there are many many complex pathways that happen within a cell. I am amazed at how good the brain is at computation. How is it possible that computers are not as good as brains at computing. This is surprising for me.

Differences between artificial neural network and real neural networks:

Real neural networks have temporal dynamics, neurons have impact on their environments. There are also different neurotransmitters that impact neurons at different times.

In real neural networks, they are adaptive, so the structure can be constantly changing. There are also other "data processing units" in the brain.

I realize that a lot of the time I am not really propelled by wants, I am just "floating around". What I mean by this, is I take a class initially because the topics sound very interesting, but after a few lectures, I get into a passive state where I am not motivated to do well. I don't feel "active" in my classes.

I've noticed many people at UIUC find jobs by waiting "for jobs to find them" instead of actively finding jobs they actually want. This includes things like waiting for notifications from job boards, Handshake, etc. it is a very passive way of finding a job and it leads to mediocre results.

I am working on my getting things done system and I am tagging things. I am learning that with all of my tagging, I can "batch" similar tasks together.. this is powerful

Time block: 9:00am–9:30am
Activity: Worked on my productivity tool
Log:
- Cognitively, how much energy did I have doing this activity? (5)
- How much did I enjoy this activity? (Yes, I enjoyed it; skipped one question)

Plan: Improve performance by testing and fixing the behavior of the model that formatted output weirdly.

Observations:
- I occasionally checked emails instead of focusing.
- I felt sharper and smarter during this session.
- Completed changes and refactored the script effectively, reducing it from two scripts to one Python script.

Feedback:

- Where did I waste time, energy, or focus?
Managing focus due to email distractions.

- What is the single most impactful change I could make to improve this activity/system?
Use a timer during active blocks to prevent email checking.

- Could I automate, delegate, or simplify any part of this process?
Yes, simplify by keeping the script’s responsibilities minimal: just take input, call Ollama, and return output. Avoid clipboard operations.

- If I could do this activity again, what would I do differently?
Maintain tighter focus and avoid context-switching into email.

- Are there alternative approaches or methods that might yield better results?
Address Thunderbird Calendar’s notification bug (report to developers, test fixes).

- What is the next concrete action to improve this system?
Learn to use Copilot in the terminal and experiment with Gemini to reduce manual copy-paste.

- Should I experiment with a small change or a larger redesign?
Start with a small change (use a focus timer, simplify script responsibilities).

- Did I leave anything half-finished?
No, I completed refactoring into one script.

I just figured out to add a VPN connection, I used ChatGPT, gave it more context, and was successful. If I want to do something that is a standard procedure, I can give ChatGPT the documents for that procedure and ask for help.

Reflection is necessary for mastery; self reflection is necessary for self-mastery. I bet I can apply the principles of Mastery to self mastery.

it's 2025-08-29 at 18:32 right after me doing a little bit of work at Granger and let me do some reflection. I'm definitely doing a better job in terms of my productivity because I recognize that I need to take a break and let myself take a break. I also was about to buy the reclaim application which is good cuz I think that it would save me more than 30 minutes per month of time and it only cost $8. but then I remembered from the last time I tried to buy something that I could look for student discounts and there is a 50% soon discount that I'm trying again. so that is also on the up and up by me doing this recording right now after the event has happened. I'm also doing a very good job. I am building a habit of taking breaks when I'm doing things and during those breaks me reflecting. so I think that if I just add more structured brakes into my day, I will have the opportunity to do more reflections.

it's 2025-08-29 at 18:26 right now and I feel I have a little bit of a feeling of being tired like in my head. I feel like it's hard to focus. I have a very, very mild headache. it's not that it's difficult to focus because there's so many other things that are distracting me, but it's difficult to focus because of like feels like a lack of energy in my head and that there's a little bit of a pain of my forehead. I feel a little bit more conscious than I typically would be on these walks, so I'm feeling more clarity but I'm definitely not feeling my best. when I turn my head I feel a little bit of pain and I have like some tiredness around my eyes

Time block: 14:00-15:00
Activity: improve reflection system
Observations:

- What were my intentions with the activity?
My goal for this activity was to improve my reflection system. I didn't really come in with any specific intention besides the vague notion of what "improve" is.

- What happened during the activity?
During the activity, I wrote in my [[reflection-system]] markdown document, writing up the procedure, I didn't use the template, I also was thinking about how I could improve my processes like my knowledge management system.

Feedback:
I didn't specifically come in with any intention for this time block which might explain why I am typically distracted. If I knew what I wanted to do (at least, what my next action would be) then it would be easier for me to get started and stay on task.

- Where did I waste time, energy, or focus?
People were coming into the ACM room (where I am currently staying at) and they were soft interrupting me. I wasted time by not using the template I wrote for myself.
I lost a lot of focus because I kept on switching tasks. I would be working on my system, then an idea would pop up and I wanted to capture/store it. It is a good thing that so many ideas are coming through my head, but I am not focusing enough on the system so it took longer than it needed to. I think this might solve itself once my systems are better in place, but I am in a creative mode right now.

- What is the single most impactful change I could make to improve this activity/system?
If I had an interface that I could just speak all of my thoughts and ideas and it would format it nicely on markdown then that would save me quite a bit of typing. Also, if I could have in my editor some AI that automatically gives me suggestions on what I am writing that are helpful (instead of needing to go to ChatGPT.com) that would be nice.

- Are there alternative approaches or methods that might yield better results?
If my template is very high quality, then I can start out with a template for an LLM to generate. There might be some cons with this.

Time block: 14:00-15:00
Activity: improve reflection system
Observations:

- What were my intentions with the activity?
My goal for this activity was to improve my reflection system. I didn't really come in with any specific intention besides the vague notion of what "improve" is.

- What happened during the activity?
During the activity, I wrote in my [[reflection-system]] markdown document, writing up the procedure, I didn't use the template, I also was thinking about how I could improve my processes like my knowledge management system.

Feedback:
I didn't specifically come in with any intention for this time block which might explain why I am typically distracted. If I knew what I wanted to do (at least, what my next action would be) then it would be easier for me to get started and stay on task.

- Where did I waste time, energy, or focus?
People were coming into the ACM room (where I am currently staying at) and they were soft interrupting me. I wasted time by not using the template I wrote for myself.
I lost a lot of focus because I kept on switching tasks. I would be working on my system, then an idea would pop up and I wanted to capture/store it. It is a good thing that so many ideas are coming through my head, but I am not focusing enough on the system so it took longer than it needed to. I think this might solve itself once my systems are better in place, but I am in a creative mode right now.

- What is the single most impactful change I could make to improve this activity/system?
If I had an interface that I could just speak all of my thoughts and ideas and it would format it nicely on markdown then that would save me quite a bit of typing. Also, if I could have in my editor some AI that automatically gives me suggestions on what I am writing that are helpful (instead of needing to go to ChatGPT.com) that would be nice.

- Are there alternative approaches or methods that might yield better results?
If my template is very high quality, then I can start out with a template for an LLM to generate. There might be some cons with this.

A principle I want to have is to never be too busy for me to reflect. Because another principle I have is to always grow, if I don't have time to reflect, I don't have the ability to improve. If I'm getting so busy I find it difficult to reflect, then I must decrease my obligations.

I'm working on implementing my [[reflection-system]], this will have me reflect after I do an activity. I'm seeing a lot of power in it already, it is giving me a feeling of control over my life. I feel like I'm at the cusp of about to blast off in terms of performance-productivity. I see myself growing literally every single day

I have a project idea/improvement for my workflow. So I really want to have speech-to-text, and I'm almost getting there. But I also want to have improved speech-to-text. So I have an idea of after I do speech-to-text, I have maybe a program that can look at what is in my clipboard. And I can select what processing that I want to have on that text. So I imagine this looking like I press a shortcut, for example, mod p. And then when I press mod p, it will open up a menu for me to select. And then, for example, the first option can be post-process voice note into markdown document. Or another option could be take OCR screenshot and format it better.

Coming back to UIUC, I noticed I have reverted to my previous ways of interacting with people. At SF or at Epic, I would be very open to talking to people, making small talk, making friendships and relationships, but I do not do that here at UIUC--why?
This is how I used to be and the environment is conforming me to how I used to think, I feel more social pressure here to "act normal" (not make small talk) because I care about the opinions the public here holds more.

I wonder if there's additional punctuation that should exist? I see the purpose of a lot of punctuation. question marks for questions, quotes if there's text that comes from an external source, is there other punctuation that could exist that don't exist?

I imagine a program that takes a given program/function and then gives all assumptions made by the program and then can be checked by a human or another LLM to improve program quality.

The reason why users are not oracles is that users do a poor job or predicting what they want. If users had perfect knowledge and understanding about how they work, their preferences, and all of the new information about the new change, then they would be oracles. I wonder if there is a way, when asking users what they want, to prompt users to give better responses--is this what the field of studying user interviews is about?

`
Another example: Google has discovered that when they survey users about how many search results they want per page (10, 20, 30), users overwhelmingly say “30 results”. But when Google actually deploys 30-result search pages (as part of an “A/B test”, which we’ll talk about in a later reading), usage drops by 20% relative to the conventional 10-result page. Why? Probably because the 30-result page takes a half second longer to load (The Cost of Latency).
`

Right now, I am performing my semesterly dance to decide on courses I want to take. I want to reflect on the topic of others giving advice for courses. I think most people give the opinions by others too much weight relative to their importance when it comes to deciding for courses. Right now, I am trying to decide on what courses to take. Past me would have solicited advice from other students whom have taken this class, and if he was extremely diligent, weight all of their advice against each other. I think this is fundamentally flawed, when they say "x is a good class" that doesn't give you that much information. It misses out the nuance about why they think it is a good class. Is the advicer aligned in your goals, knows your particular situation? Most likely not. When listening to advice it should decrease your "error bars" along the estimate, but you need to be particular about biases. For this reason, I think I need to apply the ideas of thinking for yourself to actually come to a good decision myself. I need to come up with my own searching strategy, my own criteria for what a good class is, with this method, I think I can make decisions much better.

A principle I have is to actively find places for you to "double-dip". For example, can you do a course project, put it on your resume, as well as solve a personal problem you have? See how you make it so something you already were planning on doing can get you more value without doing more.

A funny example is this whole capturing project + my personal website. I've been capturing my thoughts since Summer of 2024, but I haven't done it in a systematic, formatized way until now. Now, whenever I generate a thought, it also has the potential to be content for my personal website!

I had this idea, which I'm sure other people have had before, which is that I want to make decisions that are aligned with what my future self wants out of me. This is a good heuristic to make changes, and is related to the chapter in [[how-to-live|How to Live]] on "Thinking Super Long Term"

I want to upload my health data to my personal website, I think that would be cool. I think I am going to use Tasker and make an automation to export my data every day, then I will use syncthing to sync that to my desktop PC. Then, every night my desktop PC will rebuild my website with the new data. I think daily refreshes are cool & worth doing.

Something that I would like is if my entire filesystem had a nice integration with the LLMs I use for questions and answering, in addition to searching the internet. I think this would be powerful because it would give it more context. For example, I could do something like "Find me professors that would be interested in collaborating on @KnowledgeManagementSystem" and it would look into the project, create a description of the project, and and then search the web for UIUC professors that would like to work with me on that project"

I want to make a system so I can learn better while in classes. I am trying to research and understand what is the best way to learn. I have some content that I am consuming that I want to take notes on, is there a good system for me to be able to "extract" notes and put them into my second brain? I'm thinking about something similar to Windows recall that records my system audio as well as the slides (potentially) and then extracts concepts and ideas, allows me to select notes, and then stores them in my obsidian vault.

It's estimated that 50% of usage with computers is wasted

I'm debating whether or not this course is correct for me. I need to actually think if this course is right for me. Right now, I am not sure if this course is right for me, it seems like this would be a course that past Matt would have wanted to take. I should actually sit down and think what I want

I should ask this professor for connections related to my goal of making my productivity system or my growth system. I want to do this for independent research or ask him if he knows anyone that would want to collaborate on this.

This professor is a good public speaker

I wanted to lifelog for my class this morning, but I felt a social pressure that didn't exist. What was going through my head was other people would think I am weird. I also didn't want to do it because it also was filled with storage and I don't want it to delete my other things.

At UIUC, I do not think the classes offered interest me that much. I believe I would gain high value of doing an independent study researching and developing systems my knowledge capture system or other such productivity tools. I think this would be valuable because I would be killing "two birds with one stone" as I already intend on doing this. With the support of a professor + me being academically required to do this would be beneficial for me

I have this behavior where I try and "brute force" some problem I am trying to solve. For homework problems, I tend to "do them" instead of thinking about them. I try one solution, and then instead of re-evaluating why it didn't work and what could work, I just try the next thing that comes to my head. This is especially a problem when it comes to developing and debugging. I want to fix this, I think by starting a timer when I start to do a solution, then when the timer goes off I evaluate myself, if I have started this habit of going down a rabbit hole, then I take a step back.

This tweet states there are these three discrete levels, but I believe everyone is a mix of the three different levels.
I think I have parts of me on all three levels. I was definitely at level 1 freshmen year of college, and it was only until Fall semester of Sophomore year (when I decided not to try and go to a prestigious graduate school) that I achieved level 2.
I love the idea of our life as an art project.

Free recall for this article. The goal is to do homework assignments before the following lecture's material is covered. This is especially true if the lecture builds on the previous material (which it almost always does). Doing this strategy means it will be easy for you to follow on the next classes lecture material.

Moreover, you don't need to complete the whole homework, doing just part of it is worthwhile for studying.

In my email, it is distracting to have so many emails from places or people I don't want. I want them to not enter my inbox.

I want to reflect on everything I do. I want this to be automated somewhat because it is cumbersome to have to reflect on everything I do. This is a project I think I could discuss with Cheng or another HCI expert.

I'm very happy that I am continuing the practice that I have said that I would do--reflecting on the activity after I do it. Thinking about this, and the topic of growth, I had the idea that I should be doing activities that would give me the most learning per activity. When I was in San Francisco, I had a lot of these opportunities for me to learn a lot from these events. But, at UIUC, this is not as easy. I think I should be choosing set of activities to do that are different than normal so I can improve my learning and maybe go towards a different direction.

I really want to read the book "Think for yourself" and "Scout Mindset"

Here is an idea I have is regarding the divination. It is related to horoscopes and how reading these are giving you "seeds" for your mind to think different things. These spirtuality things might have some merit because they asks your subconscious about what you want from yourself.