AI

Conway’s Game of Life, Coded From a Mall

March 29, 2026 AI, AI Agent No comments

When I was in university I coded “Conway’s Game of Life”, which is a primitive simulation of cellular life, if there are a certain number of live cells around they produce more cells and if conditions are not favorable cells die off. I don’t know what the meaning of life is. Would at some point the argument come to say that AI is alive? This is very philosophical, this post is instead a very practical showcase of some of the most recent tools and advances in AI as I play with them.

Just have a look at this photo:

As you can see I’m somewhere in a mall, asking my Claude Cowork running on my mac at home to create a folder ~/Projects/gameoflifecowork and generate a single HTML page with Conway’s game of life implementation. I came home and there it was an html page right in that folder with the implementation. It is perfectly working. I’m adding it here. If you are reading from e-mail you would need to open the blog to see it in action.

[if you are reading from e-mail you might need to open in browser to see The Game Of Life]

Cowork is an extremely powerful (and dangerous tool). It is not yet a very smooth experience. Sometimes I need it to prompt multiple times, most of the time there is no proper feedback in the Mobile app so I don’t know what is happening. For instance, I also asked it to go to my “Downloads” folder and locate any concert tickets and tell me how much I paid for them. Unfortunately there was no way to see on the mobile app the answer to the tickets (not in any chat), but when I came home I saw a dedicated chat open on my computer that had the answer.

Under the hood Cowork runs Claude Code to implement the game of life, so I was wondering if I can compare different models and how good of a job they do, so I installed Open Code and connected Gemini API, Anthropic API, and also local LLM!

With Gemini API I generated a really great fast full screen Game Of Life, which cost me about 0.20$. Local llama3.1 unfortunately is not suitable for this task, I had to give it many additional instructions and it messed up every time until in the end I got an empty html file with some broken functionality, which I fixed with Copilot just to get it render:

Screenshot Lllama3.1 implementation of GameOfLife (with lots of manual effort)

Gemini’s Game of Life was full screen and rendered perfectly:

Gemini PRO Game Of Life

I then switched to Claude Haiku 4.5 to generate the Game of Life you can see above.

This implementation cost me about 0.33$. 

What was a small mini-assignment at university to code the Game Of Life, which I did with C++ and probably took me few days, now turns out to be just 0.20$-0.30$ throwaway code just to test different API integrations. Right now I understand why those cells live and die as I wrote this algorithm myself in C++ but I’m wondering if at one point we will not know what is happening inside implementations generated by AI.

Life remains for the most part a mystery to science, though we are getting closer and closer to understanding how it works. At the same time a reverse is happening with AI we are slowly getting further and further from understanding what goes into the beautiful implementation of Game of Life.


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Completely Sold On AI

March 22, 2026 AI, AI Agent, Personal No comments

Today I’ve done multiple personal things that fully sold me on AI. No way back. Sorry.

Use Case 1: Complex Tax Filing

No question using AI for Tax purposes is useful, but today I got it to the next level. To understand my tax situation: I moved from Canada to the US in 2025, I have rental property, stocks bought and sold in both Canada and US, retirement accounts in both countries, stocks/ETFs transferred from different institutions back and forth, etc. With AI handling all of this is basically avoiding nightmare and frustration taking many days on.

For specific example, I had VFV.TO which is ETF traded on Toronto stock exchange, not only I got some purchases and sales starting 2021 they tracked through multiple brokerages and income from dividends was reinvested back into shares, not only it is over many years, banks, currencies, countries, but also dozens of statements and documents, it is also treated in special way in USA where they treat it as Passive Foreign Investment Company (PFIC). Other than that the day I moved countries I “deemed disposition” of my assets. If I had to do all these things manually I would just get petrified, but instead I can just throw about 30 documents at AI and ask it to generate a final spreadsheet with all of the transactions, and also have source documents referenced so I can double-check. It all worked really-really well.

Use Case 2: Organizing My Files since 2000

Yes, I crossed the line and allowed Claude Cowork access to my local files.

I keep files dating from the early 2000s, keeping most of the things archived in Dropbox. I would keep such things as any of my university course works, archives of code snippets I have written in 2005, archives of my blog, some old presentations, tax documents, all kinds of agreements, all kinds of things that basically track my life since it became digital. It grew over many years. I tried to organize things in some iterations in the past, but then mostly gave up on just naming things more nicely and using search. But with AI I was just able to say “Create a plan to organize files in this folder X”, then iterate over the plan and let it execute things. The best part is that this is just natural language.

Use Case 3: Organizing Life

I previously posted that I do track things in life and that I use AI running chats for each area of life, like “Health”, “Finance”, “Career”, etc. This is all good, but then I wanted to build my personal AI agent to help me with tracking, but tools like OpenClaw and Claude Cowork made that unnecessary. Now, I’m playing with migrating my life planning into simple .md files. My life is essentially one repository that I can query and manage with natural language. Nuts.

Use Case 4: Posting on Instagram

Haven’t posted my climbing videos on instagram for a while, but I thought maybe I would post one latest video. I thought that analyzing video is not great with LLMs, but no. I uploaded my climb, it recognized that I was wearing martial arts t-shirt, it recognized at what points major moves where happening and I asked it to suggest music that would work well for the video matching my style but also the pace of events in the video. This is nuts. As next step I was thinking of fully automating video edit and posting on my behalf. Nuts.

Use Case 5: Work

While I cannot talk too much about work, I would just say that there was a step-function improvement in AI use and productivity. Producing code is faster, iterating over ideas is faster, getting things out just gets faster. I need to constantly adapt so that I don’t become a dinosaur that dies with a fallen AI asteroid.

Conclusion

It appears I grew from an AI skeptic, to AI learner, to fully embracing it by now. No way back.

edited with Claude Cowork Computer Use from mobile phone


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Surviving the AI Asteroid: Hype Cycles, Overreactions, and What Comes Next

March 15, 2026 AI, AI Agent, Opinion No comments

Previously I compared AI to an asteroid, with the simple premise that if your job is boilerplate CRUD, your job is a dinosaur, and if your job involves high leverage of AI, including building AI-integrated systems, your job is the mammal that survives.

AI Hype is real, it is everywhere, and honestly is somewhat annoying. My LinkedIn feed is oversaturated with all of the AI noise, I keep overhearing “AI” when walking past random people on the street. Everyone has their say on AI, including me. AI washing is a real marketing tactic used by many companies. Many people would get into AI just purely because of FOMO. And, honestly, that fear is justified, because what if you are really missing something and will be left behind. No one wants to be left behind. As a simple example, it is even hard to get a Mac mini because everyone is buying them to run their personal OpenClaw. I’m playing this game as well. I did set up OpenClaw on Docker just to see what I could do, but until I have a sustained workflow, I won’t be buying a dedicated machine. It’s easy to confuse playing with new tools for actual productivity. But, maybe, I’m missing out.

There is no smoke if there is no fire. Last week Anthropic released a labor market impact study claiming that hiring has slowed in highly AI-exposed roles since ChatGPT launch. For us, software engineers, the study claims that AI can theoretically automate about 90% of our jobs and it appears current automation is only at about 30%. If this is true and if this is happening soon, some kind of a combination of the two will happen: 1) our jobs will transform by a lot so that we are building ever new and more complex things that AI cannot and/or 2) there will be significant reductions in software engineering jobs. I don’t know if 1 or 2 would be a larger component of transformation but we should be preparing for both!

Image credit: Anthropic https://www.anthropic.com/research/labor-market-impacts 

As I use Claude Code, it becomes apparent how it becomes more and more capable over time. It is no longer a question of whether the threat is real. It is absolutely real. The asteroid is here! It has hit the ground already. The transformation is already happening and if you don’t see it, you might be in trouble (that is unless you are a plumber or someone with a low exposure job). I still see value in myself by figuring out what problems to solve and then directing the work to my AI agents, sometimes finding myself directing 4 of them at once, which is really cool to be able to make progress on 4 things at once, but it is also terrifying. Does it mean I’m now 4x productive? Does it mean we need ¼ of engineers to do the same job? Or we will simply see another instance of the Jevons Paradox. Historically, making software development cheaper/faster didn’t mean we hired fewer engineers. What we have seen is that demand for more software has increased thus increasing the number of software engineers. But still, there are so many open questions that come with this transformation: like

  • where do we get senior engineers if there is no need for juniors?
  • would we ever reach “lights out” codebases not requiring human code reviews?
  • would we hit the energy limits when it is more cost effective to pay a biological human rather than energy consuming AI? … and so many questions.
  • what’s your question?

What goes up will go down. Things happen in cycles. What is born will die. I made the mistake of buying real estate in Vancouver at the peak of the market in early 2022, my property price has not yet recovered. Do you remember the COVID tech hiring frenzy followed by layoffs? Our industry over-hired only to lay off people after that. Do you remember the 2008 financial crisis and what happened to real estate prices only to recover some years after that? I am not old enough to remember the dot com bubble but it is all the same all across. My argument here is that we will see some kind of cycle of overreaction with AI as well. Some companies will over-invest in AI and not get anything out of it. Some companies will lay off too many people only to hire back. What we are looking at are micro movements, but what is more interesting is what will this bring us long term, what is the macro movement here?

Image credit: Gemini on my prompt. The image is too colorful for my taste but it is kind of fun.

Dear reader, prepare for multiple outcomes. They say to have peace, you must prepare for war. Build a strong financial safety net, constantly stay on the lookout for what is changing, and adapt relentlessly. Every technological shift creates massive, unseen upsides. The rules of the game are changing and instead of panicking, the goal is to understand the new rules so you can keep playing.


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Surviving the Half-Baked AI Hype

March 1, 2026 AI No comments

I was looking at my social feeds recently, and it feels like everyone is suddenly an AI productivity guru or all knowledgeable about AI (ironically I write about AI a lot as well). People are just throwing together these half-baked agents and AI solutions, not even checking if they solve a real problem or if any quality is there, and bragging about inventing some “10x workflow”. Well, not really bragging themselves, but rather asking AI to brag about it, which makes it even worse.

I believe this year is just so much FOMO and oversaturation fatigue for all of us, software engineers. I no longer know where it is worth spending my time. Last weekend I spent some time setting up OpenClaw because that seems to be a hot thing right now. Before that I was vibe coding different things, playing with LLM integrations, Agents, tools or whatever the latest cool thing was. You can spend lots of time learning a tool or an approach, and a month or two later, it’s completely obsolete because the next “best thing” just dropped. It’s overwhelming. For the most part, it is all just noise. It is increasingly more and more difficult to figure out what the signal is. The signal-to-noise ratio just went really really bad. These days whenever I see a post by someone I try to quickly gauge if that is typical AI text and I mostly ignore it in those cases, if not, I try to see if there is some substance to whatever is written and if there are any opinions expressed, if so, that seems to be a genuine piece of work and it draws my attention. I am starting to develop an allergy to AI generated text.

I am not an AI denier. It is extremely useful and great but the hype is just over the board. What goes up will inevitably settle down, and we just need to figure out how to ride the waves. I’ve written about this in the past. The tools will inevitably change but the underlying shift in our industry is permanent. Our software engineering jobs are destined to change. There is no question about that. There is also a lot of uncertainty over which other jobs will be displaced by AI. With the current trends, it looks like anything that has to do with text and image processing can be replaced and anything that has anything to do with operating in the physical world (think plumbing) or requiring human judgment might take longer to get replaced. I spoke to some of my non-tech friends and they express fear of being affected by AI as well.

All I can say for now is that we need to keep adapting to remain relevant. So while I don’t like all of the hype, if I don’t sample around, try things out I might miss on one of the things that wasn’t a hype and be left behind as the industry moves forward.


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My 1-Hour Open Claw Setup: Docker, Llama 3.1, and Telegram

February 22, 2026 AI, AI Agent, HowTo No comments

I saw all the fuss about Open Claw online and then spoke to a colleague and she was saying she is buying a Mac mini to run Open Claw locally. I could not resist the temptation to give it a try and see how far I can get. This post is just a quick documenting what I was able to do in like one hour of setup.

If you’re like me and find it difficult to follow all the latest AI hype and missed it, Open Claw is an open-source AI agent framework that connects large language models directly to your local machine, allowing them to execute commands and automate workflows right from your terminal or your phone.

A quick preview below. This is just nuts. In one hour I was able to run OpenClaw on Docker talking to llama3.1 running locally and communicating with this via Telegram bot from my phone 🤯.

Back in the old days I would open some kind of documentation and follow steps one by one and unquestionably get stuck somewhere. This time I started with Gemini chat prompting it to guide me through the installation and configuration process. This proved to be the best and quickest way.

Decision 1: Running locally or in some isolation.

I think this one is an obvious choice. Giving hallucinating LLMs permission to modify files on my primary laptop sounds like a recipe for disaster. Decided to go with Docker container but if I find the right workflows I might buy Mac mini as well. 

Commands were fairly simple, something along these lines:

git clone https://github.com/openclaw/openclaw.git
cd openclaw
./docker-setup.sh
docker compose up -d openclaw-gateway

Decision 2: Local LLM or Connect to something (Gemini, OpenAI)

This is a more difficult decision to make. Even though I’m running an M4 with 32GB, I cannot run too large of a model. From reading online it is obvious that connecting to large LLMs has an advantage of not hallucinating and giving best results but at the same time you’ve got to share your info with it and run the risk of running into huge bills on token usage. Since this was purely for my self learning and I don’t yet have good workflows to run, I just decided to connect using a small model llama.3.1 running via Ollama. Since it was running on my local machine and not docker, I had to play a bit with configuration files but it worked just fine. And yeah, the answers I would get are really silly.

Later I found that ClawRouter is the best path forward. Basically you use a combination of locally run LLM and large LLMs you connect to. I might do this in the next iteration.

Decision 3: Skills, Tools, Workflows

This is just insane how many things are available. Because this can run any bash (yeah, in your telegram you can say “/bash rm x.files” – scary as hell) on the local the capabilities for automation with LLMs are almost limitless.

Conclusion

I can barely keep up with all of the innovations that are happening in the AI space but they are awesome and I’m inspired by the people who build them and feel like I want to vibe code so much more instead of spending my time filling-in my complex cross-border tax forms over the weekend.


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From Alien Translators to Human Translators

February 15, 2026 AI, Opinion No comments

Just wanted to share some quick thoughts on AI again. It does change our jobs (see my earlier thoughts “AI is asteroid and your tech job is a dino”, “Is AI redefining software craftsmanship?”). It completely rewrites how much can be done in a short period of time (see a bunch of vibe coding posts from me: blogger agent, typing game, AI powered snake, etc). And while I expressed some doubts and expected a ceiling to its advancements, I am now more deeply convinced that the time to fully embrace AI is now. Almost any knowledge work you do with your brain can get some help from AI. And while I advocate for limited use of AI in writing (“Don’t outsource your thinking to AI”) it has undeniably changed how I do my writing and what value I think I bring or don’t bring. Writing generic advice, anything that can be searched online, is an absolute waste of time, unless it is supplemented with opinions or experiences. Writing coding blog posts with technical details, as I used to do in the past, is also worthless. The entire stack overflow is now not receiving much traffic.

I liked to think about Software Engineers as these super smart almost alien-like translators. We used to translate requirements and business needs into cryptic code that most people couldn’t understand, just to make the software work. While fundamental knowledge is still relevant and our role as translators still remains, the destination language is changing to be more English-like. Instead of typing code we orchestrate AI work. What still matters is what AI cannot do and is very unlikely to be able to do soon, which is doing human things. The things that revolve around judgment, our lived experience, and our authentic connections.

An LLM can write technical documentation, generate a summary, and write lots of code. It works perfectly for transfer of knowledge, but it still is not good at transfer of experience and understanding what we really need and mean as humans. Translators are still needed, but instead of being more alien-like we might need to be more human-like and do more human things.

P.S. I resisted the urge to use AI for this blog post.


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Built a personal “AI Slop” Generator in 3 Hours

February 8, 2026 AI, Opinion No comments

Presenting you with AI Slop by Andriy Buday and Gemini: https://aislop.andriybuday.com 

I was recently challenged on why my weekly blog posts are not written by AI. I do have my strong opinions on this and arguments against it but before I delve into them I wanted to accept the challenge. So in about 3 hours of vibe coding I built an automated GitHub and Google Gemini powered workflow that picks either an idea from ideas.md file or one of my older blog posts on this website and (re-)writes a new blog post based on that and then uploads it to my dedicated aislop subdomain.

Solution based on GitHub Actions, FTP, Google Gemini

The entire project took about 3 hours from initial concept to deployment. This was pure vibe coding of ~40 git commits, a bit of setup in my bluehost, and some setup on github.

I learned about GitHub Actions fairly recently, but basically you can build a workflow based on yaml definition that would be triggered on a periodic basis. Additionally you can put your secrets into GitHub repo configuration. I placed my Gemini API key into secrets as well as I then placed my FTP access details (yes, I know it’s insecure and old school, but this is a 3-hour hack project). For FTP I created a dedicated account and only allowed a specific folder on my bluehost, where I also created a subdomain.

How it works: The workflow and Tech Specs

I asked Claude to summarize the technical details because this is what AI shines at:

Workflow:

  • GitHub Actions → triggers workflow daily → Python script picks unprocessed blog post or idea from ideas_tracking.json or blog_posts.json → load content and prompt Gemini via API with custom prompt → postprocess text to HTML → create HTML file and update blog_posts.json (database) → connect to FTP and upload files → git push changes for tracking and backup

Core Development Phases

  • Scaffolding & Engine (75m): Establishing the repository and building the Python script to handle web scraping via BeautifulSoup4 and AI integration.
  • Automation & Queueing (35m): Configuring GitHub Actions for CI/CD and implementing a JSON-based status tracker to manage 370+ URLs.
  • Refinement & UI (80m): Enhancing Gemini’s prompting for authoritative content, fixing Markdown-to-HTML rendering bugs, and building a responsive progress dashboard.

Technical Stack Highlights

  • AI & Logic: Python 3.9+, Google Generative AI (Gemini 2.0 Flash), and markdown2
  • Infrastructure: GitHub Actions for scheduling, Bluehost via FTP, and GitHub Secrets for API key management
  • Frontend & Data: Vanilla HTML/CSS/JS for the dashboard, with JSON and CSV files handling all state-tracking without a database

Thoughts (Why I am against AI generated content)

Is this the future of blogging? Maybe. Is it a future I’m excited about? Not entirely. I am definitely not going to share my AI Slop sub-blog unless that is purely to prove the point. I can barely stand all of these huge walls of text that are clearly written by AI but presented as if humans had written it. Why would you read it? You can just prompt your favorite LLM to give you answers you really need. I almost want to vomit from all this clearly AI generated text with no personal substance or real opinions. Sorry for being this vivid, but again: AI would not write that it wants to vomit because of the text it has written. 

And just to be clear, I do use LLM as a tool to help with my writing, but just not to write instead of me: Don’t Outsource Your Thinking: Why I Write Instead of Prompt

So where does the value of blog posts come from?

In my opinion the value comes from giving your own perspective, from sharing your opinions, driving your own arguments, and, yes, while bloggers can and do use LLM to find blind spots and to arrive at a stronger argument, the arguments should still come from the author, otherwise it is all just crappy AI Slop (unless that was the intention originally).

My ‘AI Slop’ bot can publish 100 posts a day, but it can’t build its own perspective. It can only synthesise perspective based on data it has received before.

My concluding argument is that efficiency in generating text does not equal value in reading text.


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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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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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