The Internal Protest: Parenting Your Own Procrastination

January 25, 2026 Personal, Productivity No comments

My 9 year old son has been giving me a bit of a challenge lately, like protesting all the time, not wanting to go out, not listening to anything, saying “no” to everything, well you get it, all kids do this at some point and then again at some other point. So tiresome, but, well, this post is not about parenting.

While he has an adult to protest against, I realized that his external protesting is identical to the internal protesting I feel fairly often, and each one of us feels at times. We just don’t feel like doing something. It is likely a very similar mechanism to what kids experience except we are adults to ourselves. One part of our brain knows we “have to” do something, while the other part resists, looking for a shortcut or a break, like telling yourself that you will do it tomorrow. You know, tomorrow that mysterious place where 99% of human productivity is preserved.

Gemini LLM generated hacking of the Temporal Motivation Formula based on contents of this blog post.

In adults, this resistance usually looks like procrastination. Sometimes it is just watching Netflix or doing literally nothing. This form of procrastination is bad if it is supplemented with feelings of guilt. But often procrastination masks itself as productivity. One of my past coworkers once told me he “procrastinates by doing work.” He would avoid the one terrifying, high-impact task by doing ten smaller, easier tasks.

I do this too. If I have to work on a complex design document that requires untangling a messy legacy system, I might find myself refactoring some code instead or tidying the issue tracker. It feels like work, but it’s actually a form of avoidance. There is a term for this. It’s called structured procrastination.

I’ve often wondered if people who push through the pain actually get further (“no pain – no gain”) or if this is a recipe for burnout and failure. Do the structured procrastinators find their own path to success?

In the past I found some personalized ways of dealing with my own procrastination, such as: self-imposed deadlines, external visibility and accountability. Structuring things and setting very clear goals works best for me. While these mechanisms work fairly well, they have a ceiling pushing beyond which leads to frustration and burnout. So I was thinking what is that key solution that would truly keep you going in a sustained manner and bring internal satisfaction.

By looking at some of the psychological research on this, it appears a few things are key to this:

  • Self-determination theory suggests that human motivation relies heavily on a sense of autonomy. My son does not like it when I tell him to go out for a run or a walk because he has no choice, but he sometimes joins me in the garage to kick the punching bag or “lift weights”. You might not enjoy that your project is basically given to you by your boss without your own input.
    • Lowering activation energy. When dealing with large tasks oftentimes the hardest is just overcoming the initial 2 minutes of starting the work and that’s it. The protest is usually about the start, not going. Once my son starts doing something he usually gets through it. When I start some unthankful task at work I usually bring it to the end.
  • Not feeling guilty. Procrastination is a stress-relief mechanism in a way. If you feel guilty about procrastinating you feel stressed, which makes you want to procrastinate even more. Beating yourself is counterproductive, instead just resetting next time without guilt is so much better. In one of my posts I wrote “starting something and then failing is OK as long as you start the cycle again and again, pushing the time of a failure further and further away in any given cycle” as such connecting periods of higher motivation together creating an upward trend.
  • Temporal motivation theory explains why “tomorrow” is a graveyard for human productivity. Here is the formula:
  • Where:
    • Expectancy: How likely am I to succeed?
    • Value: How big is the reward?
    • Impulsiveness: How easily distracted am I?
    • Delay: How far away is the deadline?

If we don’t see value in something or if we think our chances of succeeding are not high our motivation drops. Likely, my son doesn’t see much value in going out for a run with me and his expectancy of enjoying or succeeding is very low. Similarly, if the deadline is approaching sooner our motivation increases. Having the formula above in mind is helpful, so instead of pushing with “willpower” you can do few tricks:

  • Decrease the denominator by shortening the delay via micro-deadlines (like completing sub-task by end of day).
  • Increase the numerator either by clearly connecting the value of your work to a big goal (like this weightlifting today will make me more attractive) or increasing chances of success (I cannot run 10k, let me run 5k so I can do it).

Conclusion

When my son protests he isn’t trying to be difficult. He is just experiencing a low in his motivation equation, maybe the task feels imposed, too difficult, or low value. As an adult, I have to parent myself with the same empathy. Instead of beating myself up for “being lazy”, which only increases stress and procrastination, I need to move the levers that I know work and have worked for me in the past:

  • Restore autonomy. Remind myself I choose to do this.
  • Lower activation energy. Just start for 2 minutes.
  • Not feeling guilty. Instead, restart with new energy and do better next time.
  • Hack the formula. Create micro-deadlines and easier wins.

Either way, we need to find proper ways to self-parent ourselves. Good luck.

.


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Frameworks for Generating Ideas

January 18, 2026 Uncategorized No comments

Is there a framework for generating ideas? Do ideas just happen? Today I struggled to come up with ideas for my next blog post. None of my initial ideas truly resonated with me. Next I just started thinking about what my process for coming up with ideas looks like. Usually it is just writing down a bunch of random thoughts until one of those sparks interest in me. But then a realization came to me:

Sometimes ideas come as visualization at a later time – the other day I could not finish a bouldering (climbing) problem, always falling at the last move. I was walking and visualizing myself climbing and different moves and could not solve it. I was walking later in the day and a ‘toe hook’ idea came to me. The next day I went to the gym and simply flawlessly executed.

Similarly today I was looking at my keyboard and struggling to come up with ideas and then it came to me: ideas are not some kind of “magic” that appear from nowhere, but rather they are processes and you can make them happen and come to you. I don’t pretend to have a patent on generating ideas, otherwise I would sit on a pile of billions of dollars, but, anyway,  some thoughts:

Step 1: Input Loading. Any idea out there is some variation of previously existing idea, some synthesis of multiple existing ideas, or some application to the new context. We can say that Idea: Idea = Input A + Input B (in a new context). For example my blog post about Global Maximum was a combination of computer science “hill climbing” and my career story.

Step 2: The Constraint. Staring at a blank page is very intimidating because there are way too many degrees of freedom, but having constraints is helpful. One constraint I’m operating under right now is time (it is 6:00AM, I’m at a coffee place, and I have 1h20m to write my blog post). Another type of constraint is topic based (like how I connect athletic performance to software careers, blog post). There are more constraints to come up with “artificially”.

Step 3: Diffuse mode thinking. Everyone knows the cliche about the best ideas coming in the shower. This is actually a known phenomenon called Diffuse Mode thinking. It is a key step in James Webb Young’s technique for producing ideas:

  • Gather new material.
  • Work over the materials in your mind.
  • Put the problem completely out of your mind.
  • Your idea will come back to you with a flash of insight.
  • Shape and develop your idea into practical usefulness.

Conclusion
This “framework” might not be complete but I think next time around I will open this blog post to remind me that this is simply a process and I can be in control of it and use techniques.


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Building Personal AI Agent

January 11, 2026 AI, AI Agent No comments

Rich people always had access to assistants (+chief of staff) that would help them with all kinds of chores, would advise them on things, or just do things behind the scenes. We live in an interesting time of AI, where anyone has access to these fabulous LLMs that can do some of those things for us. Like, I’m sure most of us are doing our travel plans with some help from LLMs, or we make buying decision, etc. It is just mind boggling how good these things are becoming!

In parallel, as software engineers, we keep hearing about AI agents all the time. We use AI agents at work. The most useful and prominent example of LLM agents are coding agents. You are likely using Claude Code at work. I already heavily rely on LLMs to track many of my personal goals, to critique me, to give suggestions, etc.

But what if I build a personal AI Agent to avoid repeating things and to make it watch me more proactively?

Vibe Coding AI Agent

There we go! Let’s build something simple first. My use cases for LLMs are fairly simple, nothing too crazy and very closely tied to my Life Goals and areas of life. For example, I have chats with Gemini labeled like “Nutrition”, “Finance”, “Career”, etc. When I eat my breakfast I snap a picture of it and estimate my nutrition intake. When I’m considering stock buying I do research with Gemini. When I plan a trip I build an itinerary with LLM, etc. I track my weekly progress in google docs. I track my finances in spreadsheets. The more I think about this the more I realize there is a room for a personal AI agent that is highly tuned to my personal needs and would orchestrate all of this. Additionally there won’t be any ready solution online, because this is so personal, so I’ve got to build one agent for myself!

Email Digest Agent after ~3 hours of Vibe Coding

So what I’ve built in 3 hours is a “personal AI agent that automatically generates daily digest emails by fetching data from multiple sources in parallel: it retrieves stock market insights for ~X tickers, extracts the current week’s goals (with checkbox tracking) from a tabbed Google Doc, pulls my monthly focus items, pulls net worth data from a Google Sheets dashboard, then uses Gemini to generate a professionally formatted HTML email summary and sends it via SendGrid. The system is built with LangGraph for workflow orchestration, uses OAuth2 for secure Google API access, and preserves formatting details like checkboxes (✅/⬜) by converting them to email-compatible emoji before direct insertion into the final email.”

Tech Stack

  • LangGraph 0.2+ – Workflow orchestration with parallel execution
  • LangChain 0.3+ & Gemini API (gemini-2.0-flash-exp) – LLM integration
  • Google Docs/Sheets API – Data sources with OAuth2
  • Alpha Vantage API
  • SendGrid – Email delivery
  • Python 3.9+ – Runtime

Conclusion

This project is just scratching the surface of what is possible to be built very quickly for personal needs. The most exciting realization for me wasn’t the technical implementation but how accessible it is to connect things together. Setting up all of the API keys and then vibe coding all together is so straight forward that it is just unbelievable.

I will continue this project next week to make it actually properly work for my needs and then will host it on some server to send me those digests. Next steps would be to supplement it with prompts to LLMs to give me quick ideas for what I should focus on, better tracking, etc so I can achieve my goals quicker. So exciting!

Go ahead and build something for yourself!


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2025 Recap. 2026 Plan.

January 3, 2026 YearPlanReport No comments

I skipped similar posts for the last three years because I didn’t feel like exposing too much of my personal seemingly worryless life all the while so many of my relatives and people in my home country of origin are in distress. But, maybe, I’m wrong to close myself for reasons I cannot control. I did set goals for 2023, 2024, 2025 and did track them the same as for the past 16 years (!). Sometimes I think that I should have had more success in life by now if I was setting the right goals and if they were ambitious enough. At the same time, everyone has their own path in life, contributing variables, including luck and misfortune, and demons to fight in their heads. I am of the opinion that life is a struggle. A struggle to fight off suffering and grow, otherwise there is no purpose. Time will show how mistaken I have been, but after all, at the scale of this universe, I’m nothing. My life is for me to live.

2025 Recap

Moved to the US. Joined Meta. Made almost 2x more money than previous year. Wrote 24 blog posts. Read 4 books (a lot less than I wanted). Built a habit of single coffee a day and waking up at 6:30AM. Learned some Muay Thai (2x/week). Took swimming lessons and took kids through swimming classes. Went to 7 metal concerts and went into the death wall mosh pit a few times. Cut my social media consumption almost to 0. Failed at building more muscle. Failed to run 10k <48m. Averaged 348 active min / week. Made new climbing friends. Climbed outside. Explored WA, OR, HI with family. Solo-hiked a volcano. Drove on sand dunes. Drove fun sports cars. Built unforgettable experiences. Paddle boarded and biked. Vibe-coded bunch. 3D printed bunch. Realized that time has no mercy. Didn’t give up.

2026 Goals

I have very rigorous and detailed plans for 2026 with weekly tracking, milestone check-ins, and mechanisms to make it work. Over the years, I tried, simple lists, % based approaches, OKR-based mechanisms, latest I have is some hybrid approach with multi-layers, weekly routines and check-ins, and AI assisted course-correction:

Vision (Level 0) → Areas (Level 1) → Annual Goals (Level 2) → Weekly Routines (Level 3)

Vision: Live a healthy, worry-free, experience-rich, financially independent, and fulfilling life. Close the gap between reality and the dream lifestyle, while building a legacy and having no regrets.

  • Health & Sport: Stay healthy for long, have a healthy strong attractive body and sharp mind. Have a comprehensive health protocol that covers strength, cardio, mobility, and nutrition. This can include weights, kickboxing, climbing, running, and rest days to have proper recovery periods. Balanced food. Good sleep. Enough water.
  • 2026
    • Gain extra 8lbs (4kg) muscle weight (150lbs->158lbs).
    • Run 10k race (good time ~52min or less despite extra weight).
    • Maintain climbing performance despite extra muscle weight (V4-V7 range).
    • Can swim 4 full laps freestyle non-stop. Take 2+ swimming lessons.
    • Video of perfect shadow boxing I can be proud to share online.
    • 3 macrocycle rest weeks with minimum activities (50-70% less)
    • Weekly routines:
      • 400 active minutes / week (vigorous 2x, regular 1x).
      • 3+ cardio / week (e.g. 2 kickboxing + 1 run).
      • 2+ strength / week (e.g. 2 climbing + strength session in the end).
      • 1 dedicated strength training w/ barbell / week
      • In bed at 10:00PM, alarm 6:25AM
      • Daily intake: creatine [counter], D3 and/or multi [counter], protein [counter]
      • Healthy food: 3 times a day, diverse, calorie surplus daily (3,000kcal) tracked with Gemini.
      • Min 2.5L (85oz) water. Tracked with Gemini.
  • Financial / Career / Professional: Goal: Increase net worth and streams of income to enjoy freedom of time and life experiences. Target: $XM net worth by age 47. Explore alternative streams of income and potential new terrains. Semi-retirement post 47.
    • 2026 FAANG high-comp (optimize RSUs, bonuses, 401k etc). Climb the corporate ladder as long as I enjoy it and as long as it contributes to the other areas of life. Spendings <30% of post tax income to supercharge savings/investments.
    • Goals..
      • Max out 401k & Backdoor Roth IRA
      • …
      • Weekly:
        • Spend 1h time-boxed on investing (learn & act).
        • … 
  • Family: Set up kids for success in life…
  • Worry-free: …
  • Experience-rich: …
  • Legacy / Growth / Learning / Fulfillment: …
  • No Regrets: …

Each and every section is as rigorous as my “Health” section. The mechanisms that make these things work for me:

  • Routines & Habits
    • For example, I had a desire for a single coffee a day routine. I built it in 2025, for 2026 I don’t even have to write it down as it is going to be a default.
  • Weekly
    • On a weekly basis I do 2 things:
      • reviewing annual goals and deadlines to see if there is anything I can do next week or if I’ve done the past week
      • being accountable by copy-pasting my weekly report template and filling-it in with completion
  • Course-correction and AI support
    • We live in the age of AI. I found that this year AI will be indispensable for planning, tracking, and adjusting my trajectory to achieve these goals.
    • Specifically for each Area I have a running thread with LLM asking it to identify blind spots, critique me, explore, brainstorm, etc.
    • Tracking with AI. For instance, I found it working perfectly to track my nutrition intake: “Add 22oz water”, “add glass of water with creatine”, “add 2 turkey bacon white sandwiches from Starbucks”, etc. It tracks my day perfectly.

In life “you can have virtually anything you want, but you can’t have everything you want”. It might look like I’m trying to do too much with my plan. The reality is that there is the cost in not doing many things. Like, not watching TV, killing my social media consumption, not socializing enough, not having many friends. The cost is in pushing through bad moods and grinding at work when I’d rather be doing something else. It is the constant fear that I might not be spending enough time with my kids or enjoying the money I have right now.

To decide is literally to cut off, so I’m trading off things. I am cutting off comfort to make space for these goals. There will be collateral damage, but I would rather pay the price of trying than the price of drifting. Time will tell if the trade was worth it, “but in the end, it doesn’t even matter”.


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Escaping the Local Maximum: Hill Climbing and Paying Debts

December 27, 2025 Career, Personal, Success 2 comments

I would like to talk about something that might be hard to accept and might trigger the feeling of regret but that’s an important topic we must entertain in our brains. Ask yourself a question: If you are climbing a hill right now, is that the highest hill you are capable of climbing?

In computer science, a Hill Climbing algorithm can get stuck at a Local Maximum, a peak that is higher than everything immediately around it, but significantly lower than the highest possible peak, called the Global Maximum. To reach the Global Maximum, you first had to walk down the hill leaving comfort, taking risks, and crossing the valley of uncertainty to reach the right hill and then climb again.

I have climbed too many local maximum hills in my life. The most prominent was my time at the United Nations (IAEA) in Vienna, Austria. UN is a highly prestigious place to work, offering a tax-free salary, numerous perks, that might include education subsidy, extra long paid vacations, great pension payments, etc. If you get high enough you even get a diplomatic passport and be treated as a VIP anywhere in the world. Life in Austria is very stable, you get an incomparable quality of healthcare, and a great free education for kids. It is just the pinnacle of stability and quality of life you can get in central/western Europe.

One of the interesting aspects of working there as a software engineer was that I had to read some very old code. There were not many people I could consult about that code, as the people who wrote it have either retired, or…  died. I recall my interactions with much older colleagues at work and this made me realize that that place, while very prestigious and comfortable is exactly that – way too comfortable. A place to work towards your retirement, not the place to thrive and grow.

The problem is that you cannot realistically climb any higher. Even if I were to spend a good 10 years to reach a director level (unlikely) I would still be limited by “Noblemaire Principle” and my income and net worth, despite being very high in comparison to other salaries in Austria, will grow very linearly. Just to pull some numbers, a director at UN would probably make just <200K$ net, when a senior engineer with just a handful of years of experience at FAANG in the US would be taking home (after tax) a lot more than that. In a summary: D1 at UN is the peak of that specific hill. Hard to get, hard to maintain, capped upside. When merely L5/L6 at FAANG is still close to the base of a gigantic tech hill that is almost uncapped upside.

Moving to Canada, going through years of uncertainty (another immigration process), was my climbing down of the Local Maximum hill only to climb a bigger hill. In a way that was paying “Immigration Debt” in the valley. I worked for Amazon for a good 2.5 years and after switched to Google, which was a great boost to my income and career trajectory. Unfortunately I was still climbing the wrong hill out there. Yes, a lot bigger than the previous hill, but still not the biggest hill on the horizon. At the same time, gaining more certainly by becoming Canadian was my walk along the valley and staying at the “base camp” for a period of time just to get to the next big hill more comfortably.

The debt is not always just temporary paycheck cuts or discomfort of moving, sometimes the debt comes in the form of relationships. I had my university friends back in Ukraine, and my connection with them slowly and gradually decreased as I moved to Austria. These days we don’t even wish each other happy birthdays. The same happened when I moved to Canada. I still have a base of good friends in Austria, but the timezone difference made it challenging to keep the connection. When I visited Austria two years ago it was great to meet all of them, but unfortunately that’s the high price I am paying for moving around. The same has happened again by moving to the US, some friends are just north across the border. I have friends everywhere but the depth of connection is dissolving.

I am now at Meta in the Seattle Area, looking up at this very big mountain. It is a challenging, rewarding ascent, and I am focused on the path ahead. The “risk” of down-climbing from Vienna paid off with a trajectory I couldn’t have imagined back in Europe. Because I have down-climbed before, I no longer fear the descents. Life is a struggle, I accept it, if in some years spot a higher peak, maybe one with a different terrain or climate, whether it’s an updated career growth or something else, I won’t hesitate to pack my gear, walk down into the valley, and start climbing again, ready to pay the price again.


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Planning and Direction

December 21, 2025 YearPlanReport No comments

“Plans are useless, but planning is indispensable” is probably one of my favorite quotes about planning. This is to mean that things are rarely going exactly as planned, but the value of going through the exercise of planning is so great because it prepares you for many possible scenarios, it makes you think about the problem more holistically with the end in mind and spread over time.

The new year is coming soon and I am working on my next new year resolution. I’ve been doing those since 2011 (yup, that’s about 14 years). I’m often obsessed with creating annual plans and meticulously following them. Oftentimes this brings me the results. I think the reason why I ended up at Meta in the US was because it was on my “Career Strategy” document and I did plan for it, I did put some work behind that. Other years I’ve built some habits like doing exercises often or finally made myself wake up early in the morning after many years of struggle. So, yes, I do see very and very tangible results of my annual plannings.

Now, where can this go wrong? You can have a perfect plan and execution but if your direction is wrong you end up in the wrong place. The way to think about this is in mathematical vectors, vectors have both a direction and a velocity. Too much velocity off course and you are further away than steady slow pace but in the right direction. This is both applicable to projects at work, like writing perfect code for a feature that no user cares about, or personal life, like pushing to buy a big house to only realize later in life you missed out on experiences in life.

As I write my next annual plan I am trying to be more deliberate about the direction. Yes, I will still have my spreadsheet and my habit trackers, I can’t help it. But I think this time around I will  be placing more emphasis on long term goals and if my plans bring me there and also challenging the plans some some proper checks, like:

  • The Why Check: If I achieve this goal perfectly, will I actually be happier/better off, or am I just doing it because it seems like the “next logical step” or “just the right thing to do”?
  • The Vector Check: Am I optimizing for speed in a direction I might want to abandon next year?

Being honest with answering these questions might be challenging, but that’s the right thing to do. I encourage people to do planning for all of the aspects of their life. The mental exercise of understanding your constraints and resources is indispensable. Just going through that exercise might reveal something you didn’t realize earlier and even if things won’t go as you planned, you would know to do a better job next time and succeed next time around. Give it a try.


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Passion vs. Fear: What Tony Fadell’s BUILD Taught Me About Overthinking

December 14, 2025 Book Reviews, Opinion, Personal No comments

I needed a reminder today that things sometimes get tough. It is important to understand that everybody ‘fights their own demons’ and you are not alone. Everyone around is struggling, sometimes you see this, but most of the time you don’t. 

To be brutally honest, I’m a big overthinker. I go through all possible and impossible scenarios in my head. This often leads to me spending too much time on some problem, almost getting to the state of paralysis, but sometimes this does pay off. I remember years ago, I worked on a migration of a service, I simply could not get to sleep before checking each and every edge case. The thing worked perfectly, but how much mental capacity it had consumed was probably overboard.

Even the most successful founders and people around have similar struggles. I just finished reading “Build: An Unorthodox Guide to Making Things Worth Making” by Tony Fadell, creator of iPod and Nest. I’m bringing it in this context because there are chapters on personal growth, struggles of building something, making mistakes, learning from those mistakes, and rising again after making those mistakes. Fadell doesn’t pretend the anxiety and stress aren’t there. Instead, he offers a framework for navigating the chaos. Here are some takeaways:

Killing Yourself for Work

One of the chapters in the book talks about the difference between ‘working hard’ and ‘killing yourself’, one coming from passion and being driven to build great things, and another coming from fear (being terrified of what happens if you don’t work hard). I found for myself that I do like to work hard, but also need to be honest about the source of that drive. If passion that’s sustainable, if fear then it is not.

Failure is the Data; Fear of being wrong

Fadell talks about his time at General Magic as a spectacular failure that taught him everything. It reminded me that my ‘overthinking’ is really just fear of being wrong. I am often fixated on avoiding mistakes, but the only people who do nothing make no mistakes. Failure is just data for the next iteration.

Data vs. Opinion; Avoiding “analysis-paralysis”

My paralysis usually comes when I’m looking for data that doesn’t exist. The book simply says that sometimes data isn’t there (yet) and it is ok to bet on your intuition and just move (“just do it” as Nike’s slogan says). Overthinking without data or with made up data is just spinning wheels and wasting energy.

The Crisis Point

The author argues that any meaningful project will have a ‘Crisis’ point. I realized that in my career I had so many projects where it really felt that things were about to fall apart only to find a way to still ship things in the end. Stress during those times is normal, it is just part of the process. If you had zero stress, then it is likely you were not pushing yourself or your team outside of the comfort zone.\

Conclusion

There are so many other great chapters in the book, like “Why Storytelling”, “Assholes”, “Heartbeats and Handcuffs”, “How to spot a great idea”, and so many more, but one lesson I’m taking from the book today is that the path of success and growth is always hard, often filled with failures, stress, and hard work. If you are driven by passion and wanting to make a difference, then push for this, but if your hard work is coming purely from fear, it might be a good time to take a break and see what you can learn.


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A/B Test: Optimizing Career Through Mentorship

December 7, 2025 Career No comments

In sports, no matter how elite the athlete is, they always have a coach. Even absolute world champions have someone who will critique their form, strategize with them about the next event, encourage and push them when they are tired. Yet, in software engineering, I often see people don’t get any help and just try to power through their own career. Imagine, that you can ‘test run’ your career decision, run A/B test on it, and then make the best decision to be deployed to production. This is essentially what mentorship is about.

Q: Why would you want to have a mentor?

There are always people who have been there before. There is someone who went through the same promotion, joined the company you are considering joining, works in the domain you are interested in, built a startup, or is simply in a similar situation. You want to talk to them and learn from them. You will benefit from leveraging their experience. Instead of doing your own O(n) you can learn about optimized O(1) approaches right away.

Q: Shouldn’t you make your own mistakes?

Yes, absolutely. Make them and quickly recover, at the same time there is only so much of this life. You can live a lifetime and not make enough mistakes, and some of them are painful, especially if they are ‘one way door decisions’. So I say: make mistakes naturally while learning how to avoid them and knowing about the mistakes and lessons of others. Other people’s experience is so much cheaper to learn from than making costly mistakes on your own. I think the room for making mistakes is infinite, so, maybe, make them when it is more of ‘two way door decisions’ but get as much advice as possible for irreversible decisions.

Q: How do you get access to mentors that seem inaccessible?

Let’s say you are interested in some particular tool or technology and you know of a person who is voice in that field, say for AI that would be Andrew Ng or Andrej Karpathy. You probably won’t get 1:1 with Andrew Ng, but what you can do is find people who report to them or work with them, who appear on the same white papers as them and get in touch with those people. You can also be very specific when you reach out asking about a very specific part of their work and offering something in return. And I am not talking nonsense – I personally had small exchanges with major bloggers (back then) and with authors of some tech tools, books. What I’m saying is that people are more accessible and open than you might think. This requires high effort, but the ROI on a single response can be career-defining.

Q: Why would anyone be interested in helping you?

Although it might seem you are asking for someone’s time like if it was their charity to you, but that is not the case. There are multiple reasons why they will be interested in helping.

  1. Teaching you forces them to sharpen their own thinking. This is the top of the learning pyramid. 
  2. Networking. More senior engineers are looking for high-potential engineers and mentorship is often a way to build the bench and network across. It’s a win-win.
  3. Sometimes it just gives people satisfaction to know that someone acted on your advice and was able to make progress.
  4. A way of staying in touch with what is happening on the ground, technology stacks, career challenges, etc.

Q: How to ‘match’ your mentor?

One of the mentors I had once said that finding a good mentor is a bit like dating. Not everybody ‘clicks’ even if they seem to be the right fit on paper. In my opinion you need to have a session or two with them to understand. If you are in a big company there are official channels to establish mentorship and I encourage you to try it out. If you are looking for a mentor outside, it might be a bit weird to ask someone ‘Will you be my mentor?’ (sounds almost like a marriage proposal on a first date), but instead you probably want to ask for specific advice on some topic, share your own thoughts on the topic of their interest and build the relationship naturally. Oftentimes for mentorship to be effective you don’t have an official ‘mentorship’ label, and it can just be occasional lunch.

Q: How do you spot a good mentor?

In my opinion, a good mentor will be asking you questions that make you think a lot. They would challenge your thought process. Give new perspectives. Good mentors give you better questions than the ones you come up with.

Q: How do you prepare for the first session?

Treat the first session like a system design interview. Have requirements and constraints in mind. Explain the goals of ‘design’, give some timeframes. For example: “I want to learn how to deal with ambiguity, and this is the situation I’m in …, my goal is to close this ‘gap’ for my next promo by x”.

Q: How do you get the most out of mentorship?

Follow up! Yes – that’s the best advice I have. After each meeting with your mentor, make sure to follow up on the action items. It does multiple things: you are not wasting their or your own time; you are showing respect to your mentor; you are building discipline in yourself. Going to your mentor with the generic “How do I grow?” The question is a bit discouraging. Your mentor will have to put a smart face on and give you a generic answer. Who needs that? Go with very specific questions, create action items, report back on them, put work in. Close the loop!

Q: What’s my model for working with mentors?

I like being accountable and keeping people accountable. As such for my mentorships I keep a list of Action Items and generally strictly follow up on them. This isn’t the only valid approach. Some people find value in more casual and relaxed ‘coffee chat’ conversations.

Q: Do you need a ‘board of directors’ or just one single mentor?

I think getting more perspectives is always healthy. You need one person who is technically better than you, one who is politically savvier, and one who understands your personal values. There is, obviously, a limit to how many mentorships you can handle. Personally I have 1 or 2 mentors at any given time and besides them I have some network connections that work a bit like mentorships where I learn from them, even though there is no label ‘mentor’.

Q: When and how to end the mentorship?

Sometimes you outgrow a mentorship relationship. Let’s say you reached the goal you wanted to achieve. If you have mentorship within the bounds of a company, this is super easy to do, just tie it to the performance review cycle, thank them, leave them good feedback they can use in their perf review and move on. It is fair, a good mentor wants you to outgrow them, it’s awin-win.

If the mentorship is outside of a company, then it is more likely to have natural evolution. Never ‘ghost’ your mentor. Instead give it a logical conclusion, thank them, mention switching to more of ‘ad-hoc’ sessions instead.

Conclusion

You cannot really A/B test your entire life and career. If there are ways to move faster and learn from others then I would say mentorship is definitely one of such ways and you should leverage it. Whether you are looking for a mentor or are ready to be one, remember: we grow faster when we grow together.

P.S. This post is a ‘Thank You!’ to my current and past mentors as well as my self-reminder to leverage this even more in my life for other areas, such as my sports, personal growth, building ideas, etc.


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AI Toolkit Research for Software Engineer 

November 29, 2025 AI, Personal No comments

Disclaimer: I do not promote any of the tooling mentioned in this blog post nor have I used the vast majority of them. All of the text written by me is my personal opinion. All of the text generated by LLM is its creation – I will be explicit where it is LLM and where it is me.

Unlike most posts where I make emphasis on my own experience and writing by myself, this blog post will be assisted with LLM, but in a good way. I want to create a comprehensive AI toolset list to explore for myself. Although it will have some tools very specific to my personal needs (like climbing) I believe you can apply the same approach to get your ideal list. This could have been LinkedIn post “Definite list of AI tools you must use” but I cannot stand those posts. This is just idea on how you can come up with your own list:

Step 1: Refine research plan

Prompt: “Refine this prompt: Perform research on the landscape of currently available LLM products and AI tooling for both professional and personal development for the user profile and wider audience, using referenced examples of already used tooling. User Profile: A Software Engineer at a big tech company in the Seattle area. Family with two kids, sports (climbing, Muay Thai, running), writing blog posts, coding for fun, and wondering about life in general. Researching topics such as: health, finance, 3d printing, writing about tech, coding, taxes, parenting, relationships, career decisions, trip planning, real estate, etc. Reference examples:  ChatGPT, Gemini (deep research, chat, image gen), Claude Sonet for coding, locally running small LLMs, trying out different AI startups for intermittent tasks.”

The above text was me. The below text is the proposed research plan by LLM:

(1) Research the current landscape of major LLM products (such as ChatGPT, Gemini, Claude) and emerging AI startups in late 2025, specifically analyzing their capabilities for professional software engineers (coding agents, debugging, architecture design).
(2) Investigate the ecosystem of open-source and locally executable LLMs (compatible with Ollama) and privacy-focused tools suitable for a tech-savvy user running consumer hardware.
(3) Search for specialized AI tooling and applications tailored to the user's specific hobbies and creative interests:
(a) Sports performance analysis for climbing
(b) Sports performance analysis for Muay Thai
(c) Sports performance analysis for running
(d) 3D printing modeling and slicing optimization
(e) Technical blogging and creative writing assistance
(4) Find AI resources and tools assisting life management, covering:
(a) Parenting, education, and kids' activities in the Seattle/Bellevue area
(b) Real estate market trends and housing analysis in Vancouver, BC
(c) Personal finance, tax preparation, and investment tracking in the Seattle/Bellevue area
(5) Explore AI interfaces designed for broad life topics, such as philosophical conversation, relationship coaching, career development planning, and travel logistics.
(6) Synthesize the findings to categorize tools by professional utility versus personal development, comparing them against the user's current stack (ChatGPT, Gemini, Claude, Ollama) to identify gaps and superior alternatives.

Step 2: Run multiple researches and synthesize takeaways

I’ve run 4 reports with the above prompt or some variations of it. In the end I got four 20 page documents. After skimming through them it is obvious that either: a) some info is outdated, like recommending Claude Sonet 3.5 when I’m already using 4.5; b) recommendations are too generic; c) some sections were too verbose and some too short for my needs; d) some recommendations are contradictory, for instance using Ollama for privacy and then suggesting some startups that suggest uploading private data to small startups.

One idea I had was to feed all documents into another research specifically asking for summary with bullet points. Unfortunately the result was a 5th document of not better quality. Another approach I took was: “Create a maximum 5 pages summary from the four similar documents. Do bullet points. Keep links to websites. Do NOT do research.” This gave much better results.

Step 3: Edit the result

So I’ve got approximately what I need. The next step was to iterate a few times on the list. E.g. I asked LLM to add a list of general health and longevity tooling following the same format. Copy-paste, read through, and add more sections. The appendix text in the end is the list by AI, with minor edits from me.

Step 4: Action Plan

It is obvious that I won’t be trying all of the tooling (that would be crazy) but to do exploration of what’s available and within my area of interest. As an action plan I highlighted some tooling to use and play with below or use more actively:

  • llms.txt – a file to be added to this blog post so AI knows how to read it.
  • Ollama: The CLI standard for running local models. Already using it, but probably use even more. (got M4 apple processor with 32Gb memory so some LLMs run just fine).
  • NotebookLM: Google’s AI-Powered research partner. Have seen demos of this one and should try.
  • Belay AI: analyzing center of mass and hip trajectory when climbing. Didn’t know such a thing existed. Would be curious to try out.
  • Garmy: Python library and MCP server linking Garmin data to Claude Desktop or Cursor for agentic analysis. Sounds like something I would like to play with next.
  • Meshy: Converting prompts to 3D models. Already tried but wan’t too happy with the results. Will give it another try.
  • Layla & Wanderlog: AI travel agents. Used general LLMs for my travel planning before but will be curious to try tailored tool.
  • Orai: Pocket AI coach analyzing recordings for filler words, energy, and clarity.

Conclusion

I believe the AI and LLM tooling landscape is very saturated. There is a tool or a startup for almost anything you can think of. The point is not the specific list but how I arrived at it and how it is tailored to my needs. In this blog post I provided a method at arriving at your own list of AI tooling that is applicable specifically to you.

Alert: wall of text below.

APPENDIX: THE RESULT

Disclaimer: I am not advertising or promoting any of the tooling below, have no affiliation to any of the companies or products mentioned. The text below is generated by LLM. I only reviewed it.

Executive Summary: The Shift to Agentic Workflows

The technological paradigm has shifted from “Chatbots” (passive Q&A) to “Agents” (active execution). The competitive advantage in 2025 belongs to the “Augmented Architect” who orchestrates specialized AI entities to manage full-stack development, complex financial engineering, and physical performance.

1. The Professional Engineering Workbench

The modern workflow bifurcates into Integrated Agents (living in the IDE) and Headless/Terminal Agents (operating autonomously).

The Battle for the IDE: Cursor vs. Windsurf vs. Copilot

  • Cursor: The “Architect’s Instrument”.
    • Best For: Heavy refactoring and legacy codebases.
    • Key Feature: “Composer” and “Shadow Workspace” index the entire codebase to predict multi-line edits and handle global refactors.
    • Model: Uses Claude 3.5 Sonnet for superior code structure nuance.
    • Pricing: $20/month; generally preferred by power users over Copilot.
  • Windsurf: The “Flow State” Facilitator.
    • Best For: Greenfield projects and rapid prototyping.
    • Key Feature: “Cascade” flow tracks user actions (terminal commands, clipboard) to infer intent, actively running tests and fixing errors.
    • Differentiation: Focuses on keeping the developer in “flow” rather than granular control.
  • GitHub Copilot: The Enterprise Incumbent.
    • Best For: Corporate environments with strict compliance needs.
    • Status: Now includes “Agent Mode,” but critics note the chat often feels disconnected from the editor compared to AI-native rivals.

Command Line & Autonomous Agents

  • Claude Code (CLI): A terminal-resident agent that replaces the chat interface. It can navigate directories, read files, execute Unix commands, and handle large-scale refactoring.
  • Cline / Roo Code: Open-source VS Code extensions that act as “Headless Developers.” They can execute terminal commands and create files autonomously, allowing for a “Bring Your Own Key” (BYOK) model.
  • Deep Think Models: Google’s Gemini 2.5 Pro utilizes “parallel hypothesis testing,” allocating a “thinking budget” to simulate System 2 thinking for architectural reviews and debugging race conditions.

The Sovereign Stack: Local Inference on Apple Silicon

For IP protection and privacy, running models locally on M3/M4 Max chips (Unified Memory) is the standard.

  • Ollama: The CLI standard for running local models (like Docker for LLMs).
  • LM Studio: A GUI alternative for discovering and testing models.
  • Top Open-Source Models:
    • DeepSeek-Coder-V2: Uses Mixture-of-Experts (MoE) for high reasoning with efficient inference; ideal for logic-heavy tasks.
    • Qwen 2.5 Coder: The premier open-source choice for daily coding, rivaling GPT-4 in benchmarks (88.4% HumanEval) and running on 32GB+ RAM.

Career Engineering & Strategic Presence

Interview Intelligence (The “Copilot” Era)

  • Final Round AI: Real-time “Interview Copilot” offering transcription and live hints.
  • InterviewBee AI: Adaptive mock interviews that dynamically adjust difficulty.

Strategic Networking & Personal Branding

  • Supergrow: LinkedIn growth tool generating tone-matched content from past posts.
  • Taplio: Identifies viral technical topics and drafts high-visibility posts.

Performance Engineering (Automated Brag Docs)

  • Lattice AI: Auto-drafts reviews, translating engineering metrics into business impact narratives.
  • Fellow: Auto-generates “Brag Docs” by tracking wins from 1:1s year-round.

Public Speaking & Leadership Intelligence

Voice Cloning & Auditory Feedback

  • ElevenLabs Voice Cloning: Clones your voice to objectively audit delivery and identify awkward phrasing before speaking.
  • Orai: Pocket AI coach analyzing recordings for filler words, energy, and clarity.

Simulation & Real-Time Coaching

  • Yoodli: Simulation platform providing private analytics on eye contact and pacing during practice speeches.
  • Poised: Real-time meeting assistant offering live, private feedback on speaking speed and confidence.
  • VirtualSpeech: VR-based training for practicing presentations in realistic 3D environments (e.g., boardrooms).

2. Wealth Management

For a Seattle engineer with RSUs and complex taxes, standard budgeting apps are insufficient.

The “Finance as Code” Approach (Privacy-First)

  • Beancount: A Python-based double-entry bookkeeping system that stores financial records in plain text. It treats finance like code (version control, CI/CD).
  • Fava: The web UI for Beancount.
    • Fava Investor Plugin: Calculates IRR and tracks asset allocation across disparate accounts.
    • Automation: Python scripts (e.g., wash-sale-tracker) can parse trade history to track wash sales and automate RSU vesting tracking.

The SaaS Optimization Route

  • Prospect: Specialized for tech employees with ISOs/RSUs. It models tax implications of exercising options and calculates the “AMT crossover point” to prevent surprise tax bills.
  • Compound Planning: Tracks net worth across illiquid assets and models scenarios for RSU vesting and tax cliffs.
  • StockOpter: Specifically addresses equity compensation guidance and AMT modeling.
  • HouseSigma: Essential for the Vancouver, BC market. Uses AI to provide “Sold” history and valuation estimates, unlocking data previously gated by realtors.
  • VanPlex: Analyzes zoning data to identify “under-utilized” lots suitable for multiplex development (Bill 44), aiding in investment arbitrage.

Financial Education & Investment Intelligence

Tools for deep research, interactive learning, and risk-free simulation (Paper Trading).

Deep Research & Earnings Intelligence

  • AlphaSense: Institutional “semantic search” for broker research and earnings calls; highlights off-script management answers.
  • Quartr: Mobile access to live earnings calls/transcripts; “search across audio” finds specific keyword mentions instantly.
  • FinChat.io: “ChatGPT for Finance” providing sourced answers from verified 10-Ks/10-Qs to minimize hallucinations.

Interactive Education & Family Literacy

  • Magnifi: AI investing tutor; explains portfolio balance and stock fit via conversational interface rather than just charts.
  • Zogo: Gamified literacy app for families; breaks complex topics into bite-sized modules with rewards.

Simulation & Practice (Paper Trading)

  • Thinkorswim: Institutional-grade paper trading with “PaperMoney” to test strategies risk-free.
  • Webull: UX-friendly platform for beginners to practice trading mechanics before deploying capital.

3. The Quantified Athlete: Physical Intelligence

AI is transitioning from logging data to providing active biomechanical coaching.

Rock Climbing

  • Belay AI: Uses computer vision (pose estimation) on a smartphone to analyze center of mass and hip trajectory, identifying micro-inefficiencies in movement.
  • KAYA Pro: Digitizes climbing sessions and calculates “Workload” to prevent overtraining. It filters “beta” videos by body morphology (e.g., finding beta for a specific height).
  • Lattice Training: Uses datasets to benchmark finger strength and build periodized training plans.
  • Crimpd: Utilizes analytics to prescribe hangboard workouts and manage training loads.

Muay Thai & Running

  • Sensei AI: A virtual coach for Muay Thai that analyzes shadow boxing via camera. It corrects hip rotation on kicks and guard retraction.
  • RunDot: The data scientist’s choice. Uses “Environment Normalization” to adjust pace targets based on heat/humidity, ensuring constant physiological stimulus.
  • Runna: Focuses on UX and community, gamifying the training process for better adherence.

General Health & Longevity Intelligence

The intersection of “Quantified Self” and AI for preventative health and programmable biology.

Developer-Friendly Health Data

  • Garmy: Python library and MCP server linking Garmin data to Claude Desktop or Cursor for agentic analysis.
  • HealthGPT: Open-source iOS app using on-device LLMs to query Apple Health data privately.

Longevity & Preventative Analytics

  • InsideTracker: “Programmable” biology platform; integrates wearable APIs and uses Terra AI to map blood biomarkers to peer-reviewed optimization protocols.
  • Function Health: Deep clinical baseline with a 100+ biomarker panel for early detection and chronic disease prevention (closed system).
  • Superpower: Accessible longevity diagnostics and biological age tracking at a subscription-friendly price point.

4. Creativity & Fun

Bridging the gap between software and physical artifacts.

3D Printing Stack

  • OrcaSlicer: The “Open Source Victory” for 2025. Offers granular control (jerk/acceleration settings) and “Scarf Joint Seams” for aesthetics.
  • Obico: AI failure detection (spaghetti detective). It monitors the print bed via camera and pauses prints to prevent fire/waste. Can be self-hosted on a Raspberry Pi.
  • Zoo (formerly KittyCAD): “Text-to-CAD” API. Generates editable, parametric CAD models (code-based) rather than simple meshes.
  • Meshy: Generates 3D assets from text prompts, best for rapid prototyping or game assets.

Knowledge & Blogging Pipeline

  • Repo-to-Blog: A workflow using Gitingest or GitHub Actions to convert codebases into token-optimized summaries. These are fed into LLMs to automatically generate technical “DevLogs” from commit history.
  • Obsidian + Smart Connections: A “Second Brain” setup where the plugin uses local embeddings to allow you to “chat” with your notes vault.
  • llms.txt: A new standard for 2025. Adding this file to a personal site makes it indexable by AI agents.

Music Exploration & Vibe Matching

  • Spotify AI Playlist: Generates playlists from creative text prompts (e.g., “songs for a rainy cafe”).
  • PlaylistAI: Creates playlists from text prompts, images, videos, or festival posters.
  • Maroofy: Search engine that matches songs by “audio vibe” rather than just artist similarity.
  • Cyanite: Advanced “Free Text Search” to find songs matching specific moods or descriptors.
  • Music-Map: Visual tool that creates a floating “cloud” of related artists based on fan affinity.
  • NotebookLM: Google’s research tool; upload your concert history/venue calendars to create a custom event finder.
  • HyperWrite: AI agent capable of browsing the web to find specific tickets or venue schedules for you.

5. Life Logistics

Tools acting as “Chief of Staff” for the household.

  • Ohai.ai: Ingests unstructured data (screenshots of flyers, voice memos) to manage family calendars and conflicts.
  • Milo: An SMS-first family assistant (powered by GPT-4) that manages logistics via natural conversation.
  • HomeZada: A digital home management platform that uses AI to predict maintenance costs, schedule seasonal repairs.
  • Familymind: Synthesizes school PDFs and sports schedules into a master calendar.
  • Layla & Wanderlog: AI travel agents. Layla handles discovery/booking; Wanderlog optimizes daily routes and logistics while traveling.
  • Magic School: Generates personalized tutoring content and educational activities.


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Don’t Outsource Your Thinking: Why I Write Instead of Prompt

November 23, 2025 AI, Blog, Opinion, Personal No comments

Original content by Andriy Buday

I’ve been asked multiple times about my writing process, how I keep consistency, and why I write blog posts at all. Who in their right mind spends multiple hours weekly to write when there are LLMs that generate the same quality text within a minute? Let me share my secrets and answer these 4 questions:

  1. Why do I write at all?
  2. Isn’t it all just a waste of time because of LLMs?
  3. What’s my writing process?
  4. How do I stay consistent?

Image credit: Gemini Nano Banana Pro. I admit the image is cheesy, lol, but it’s also fun.

Why do I write at all?

Writing is structured thinking

Over time I confirmed to myself that ‘writing is thinking’ but also unlike speaking or thinking in my head writing is a structured way of thinking. You get the privilege of ‘parking’ some thoughts for further elaboration, you get the privilege to validate your thoughts with external research, you get all the privilege to mold things and shuffle them around, decompose and synthesize again.

Writing is a transferable skill

Undeniably writing is a skill, but, in my opinion, it is a transferable one. By working on improving my process of writing it becomes easier for me to write documents at work and the easier for me it becomes to write my personal documents (financial planning, goal setting, emails, etc). The more I write the easier it is to overcome that initial ‘hurdle’ of starting a new document. I am a doc producing machine at work: meeting notes – I’ve got it; short design doc – I’ve got it; just documenting my work trip – I’ve got it; producing ‘announcement’ document – I’ve got it. None of it seems daunting. I also wrote a post on “Why documenting everything you do at work matters” believing it is beneficial for your career, especially performance reviews and promos.

Isn’t it all just a waste of time because of LLMs?

Producing vs. Consuming

Here is a big secret, my dear readers: I’m writing mostly for myself, and I have a strong argument why it is worth my time instead of just prompting LLMs. For the sake of argument, I just kicked-off Gemini’s ‘Deep Research’ on the topic of tech writing, answering 4 questions from above. I’m confident that in ~3 minutes I will have a PhD level research paper on this topic. What do I gain from that research? What do you gain from that LLM research? Well, we become consumers – I can read that research paper and, for sure, that will have many punchy arguments and external pointers to like 100+ websites to learn from, but this trains our “info => brain” path, this does not train our “brain => info synthesis” path. Very specifically, next time when you need to produce new information, your retrieval/producing ‘paths’ in your brain are not trained for that.

Numbers

Let’s also do some numbers to see the worthiness of this activity:

  • Range of 2-5 hours per week writing blog posts. It is closer to 2h for writing itself like this post and closer to 5h for larger technical/coding posts.
  • 370 blog posts so far.
  • 800 comments with praise/admiration and additional insights I was missing.
  • Only 3k pageviews/month and only 50 mail subscribers.
  • I have no ad income (I made some <200$ in the past as an experiment).
  • In a way, the blog is an ‘Ad’ of myself.
  • Up-scalling my tech writing skills.
  • Hardly measurable influence on my career growth, but it’s definitely there.

What’s my writing process?

Sourcing Topics and Info

To be honest, at times it is very challenging to come up with new blog post ideas and even when I have an idea expanding on it is also quite a tedious process. I have a “blog post ideas” document which just sits there in my Google docs. Whenever something crosses my mind I would add it there. Another source of ideas is just some question I would get from someone either at work or in my personal conversations. For instance, this blog post was inspired by a person asking about my writing process as he was struggling a bit with writing some roadmap/design document at work. I hear you. This blog post is for you.

Writing Process Itself

At very early stages I usually start with just pouring thoughts and ideas in raw, unfiltered, and very unstructured ways. This is just the expansion step of my framework of dealing with ambiguity. At this stage focusing on quality, perfection, structure is counter-productive. If this is a technical design document, then some template for structure is usually already given, so that ‘pouring’ thoughts happens in compartments. Then, once I have lots of unstructured thoughts, I do more of research, I try to find key points and rephrase where needed, this is where trimming also happens. At later stages I would use LLMs to help me out, but I am generally against using LLMs for everything, and definitely not using for my blog writing. At work, generating summary or bullet points or initial structure is definitely easier with LLMs, and it would be a mistake not to use it.

LLMs

Yeah, I do use LLMs – but not for writing or structuring my thoughts but for other purposes. The main one: finding blind spots in my thinking. I have made many profound realizations of missing some key arguments thanks to LLMs, not only that, even in my personal life I came to realize that there are things I perceive simply differently to other people – eye opening. Another use of LLM is to suggest refinements to text, but not so much proof-reading, unless this is obvious typo catches. Honestly, sometimes, I just cannot stand all this ‘sophisticated flowery’ text generated by LLMs. When I see people write ‘significant impact drastically improving leverage of comprehensive coverage of’ – I know it is LLM and it sucks. You can know these are my own words, because LLMs avoids confrontation. Another way I’m using LLM for my writing is coming up with a common theme in my thinking and generating ideas for the best title.

Focusing on Experience and own Opinions

In the light of LLMs I found it to be ever more important to focus on my own experiences, strong opinions, and on building my own personal expertise. That’s the main distinguishing factor. No LLM has my brain or knows my thoughts.

How do I stay consistent?

Connect Inspiration Periods in One Continuous Chain

This is another question I get quite often. The answer: Consistency is Hard. The way it works for me is a multi-year habit building. I failed many times and I had to re-start it over and over again until it actually started working in a connected chained manner. The analogy I give is from rock climbing: to climb a mountain you connect multiple single pitch climbs. We get inspired at times (new year resolution, someone inspires you) and then you do a few iterations and then you fail. I say: get inspired more often, on purpose, and then connect into a continuous chain! Get inspired by books, people you admire, your mentors, your super-smart colleagues, someone you love, and last but most importantly, your future self!

Conclusion

Writing is not always just the output, but also the process. I see writing as “gym for the brain”. For sure, LLMs can lift weights for you, but that won’t make your thinking and writing stronger. It is like watching fit and strong people on Instagram and wishing you will be one like them without doing anything. The world is dominated by consumption and in my opinion being able to structure your thoughts and clearly articulate is something that will make you stand out.

APPENDIX: Additions by Gemini 3 PRO LLM

Below are some interesting extracts from the 17 page ‘Research Paper’ produced by Gemini on my ‘deep research’ request about technical blow writing and from analysis of the above text:

Blind spot: The “Quality” Trap

You mention LLMs generate “flowery” text that sucks. While often true, advanced prompting can mimic style fairly well. Suggestion: Strengthen the argument that the value isn’t the final text, but the struggle to produce it. Even if an LLM wrote a perfect post in your style, it would still be a “waste” because you didn’t do the cognitive lifting to get there. It’s like sending a robot to the gym for you.

Luck Surface Area

Blogging increases an engineer’s “Luck Surface Area.” This concept suggests that the amount of serendipity (job offers, speaking invitations, consulting opportunities, co-founder inquiries) one encounters is directly proportional to the number of people who know what one is doing.

Beyond the internal mechanics of a current employer, blogging functions as a potent “sales aid” for the engineer’s broader career trajectory.2 Recruiters and hiring managers at elite firms increasingly value communication skills as a primary differentiator. According to industry data, a significant majority of recruiters prioritize communication skills, sometimes even above raw technical proficiency, because technical knowledge can be taught, whereas the ability to articulate complex logic is a rarer trait.

Linearization of Thought and Feynman Technique

The process of writing requires the linearization of thought. Code can be non-linear; it jumps between functions, modules, and asynchronous callbacks. Prose, however, must flow logically from premise to conclusion. This forcing function exposes gaps in understanding. As noted in the analysis of engineering blogging benefits, writing a blog post often reveals that the author does not understand the code as well as they thought they did. This aligns with the “Feynman Technique,” which posits that one does not truly understand a concept until one can explain it in simple terms to a layperson.

Transfer of Experience

However, LLMs struggle with context, nuance, and novelty. They cannot hallucinate genuine experience. They can explain what a circular dependency is, but they cannot explain how it felt to debug one at 3 AM during a Black Friday traffic spike, nor can they navigate the specific political and technical constraints that led to that dependency in the first place.

The value of human writing has shifted from Transfer of Information to Transfer of Experience. The “Small Web” movement is a reaction to this; it is a flight to authenticity. Readers are looking for the “red hot branding iron” of human personality—the idiosyncrasies, the opinions, and even the biases that signal a real person is behind the text.15 As AI content proliferates, the premium on “human-verified” knowledge increases.

Case Study: Gergely Orosz (The Pragmatic Engineer)

Gergely Orosz serves as the gold standard for the modern technical writer. His transition from engineering manager at Uber to full-time writer was built on a specific process 39:

  • Crowdsourcing via Surveys: Orosz often gathers data before writing. For an article on “Developer Productivity,” he surveyed 75+ engineers and managers across the industry.39 This provides proprietary data that no LLM can access.
  • Structured Workflow: He treats writing with the discipline of coding, using outlines and working with editors/publishers to force progress.41
  • Mimicry: He openly advises starting by mimicking role models.42 If you admire a specific engineering blog, analyze its structure and replicate it until you find your own voice.


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