I’ve been reading Anthropic’s research on the problems with AI assistance when acquiring new skills (arXiv paper).

The hypothesis: AI accelerates your work where you’re already an expert but hinders you from becoming an expert in something new. If you fully delegate tasks to AI, you start to get dumber and eventually become obsolete.

At first glance, this contradicts my post on autonomy with acceptable quality, but it doesn’t. In that post, I was discussing autonomous task completion, not learning something new. I set the tasks myself, which means I already have some understanding of what needs to be done and how to evaluate the result.

When actively using Claude Code in manual mode, I always apply the same technique. I keep sessions as short as possible. I set a task, get the result, close the chat. If I realize I need to return to the task to clarify something, I do so via claude -r.

This workflow creates a need to save information between contexts. At the end of each session, I ask Claude to summarize and format a short summary of the session outcomes, which I then publish to the issue tracker for that task. I also ask it to update documentation, tests, architecture decisions, and CLAUDE.md to keep all descriptions synchronized.

The primary metric I use to evaluate my autonomous AI systems: autonomy of work with acceptable quality of result. This seems obvious from the name “autonomous,” but until you articulate it explicitly, it’s not.

I see people around me setting up multiple monitors to watch several parallel Claude Code sessions simultaneously, constantly tweaking and running from one to another.

I believe this approach is fundamentally wrong. Context switching in the human mind is an expensive operation. Very expensive. Frequent task switching is exhausting, regardless of what anyone thinks or says. There’s research on this: The Cost of Interrupted Work, Executive Control of Cognitive Processes, Brief Interruptions Spawn Errors.

My hypothesis is simple. The recipe for success is to offer something simple and familiar (lowering the barrier to entry) and add value on top of it. Products that work on interfaces users already understand take off. Products that completely change how people work don’t.

Consider these examples.

Cursor is VS Code, familiar to everyone. You don’t need to radically change how you work. Just tweak your workflow a bit.

Yesterday all channels were buzzing about Clawdbot. I decided to install it and give it a try.

I liked the concept. Following my thinking from Product Over Technology, I always consider the product concept and how to sell it first. The execution is not great, but that’s not what matters. What matters is that it’s generating hype (meaning it sells) and it works well enough.

What I fundamentally didn’t like was the lack of a platform approach. No separation of important and unimportant. No core versus everything else. This led to having to think about everything during installation. Network configs (Tailscale), provider settings (though you could reuse what’s already in ~/.claude/), plugin sets. I have no idea which plugins to enable right now. I haven’t even decided what I need this for. I want to try it quickly and move on. In some places, I noticed NIH syndrome (Not Invented Here), with vibe-coded solutions instead of existing tools.

I’ve always tried to see myself as a company. Even as an employee, I viewed my employer as a client or partner. The problem is, I did it poorly. I only recently realized that I think in processes rather than product-outcomes, and that needs to change (see: Product Over Technology). But the core idea remains: I am a single independent economic unit that joins forces with other independent units to achieve shared goals. This partnership is mutually beneficial and voluntary.

I’ve spent my entire life working with technology. Studying it, building it, growing it. When evaluating any project, I instinctively reach for the technology lens first. How well is the code written? Does the architecture allow for future growth? What frameworks are being used?

When developing my own projects, I always focused on technology first. I design elegant architecture, set up auto-deployment to Kubernetes, ensure data security, scalability, and disaster recovery. I always have monitoring in place.

My Evolution of Working with AI Tools. The Capsule Concept.

I use LLMs and coding agents extensively in my work. This post contains a brief history of my observations and hypotheses.

The first decision I made: never run agents locally. This is a rule for me. There are two reasons for this. First, I’m afraid the agent might accidentally do something unacceptable on my behalf and with my permissions. For example, delete my home directory or a client company’s database (protection against such actions may either be absent or fail). Second, the agent could steal secrets from my device (keys, wallets, passwords), especially if some third-party MCP or skill is used that might contain a prompt injection instructing it to send the entire contents of ~/.ssh to an attacker’s remote server.

The tactic is simple. Figure out how to consume all available compute, then find ways to increase that availability.

I have a Claude Code subscription and other AI tools. My goal is to use them as efficiently as possible. This means pushing token usage closer to 100% of the available limit while maintaining acceptable quality. Solving real problems, not running expensive LLMs in circles.

Measuring Utilization

For objective assessment, I need to periodically collect usage and limits statistics. Perhaps every minute or every ten minutes. Store it in a database, build graphs, and devise methods to minimize the area under the (limits - usage) curve across all three parameters: current session, weekly totals, and Sonnet-specific allocations.

My entrepreneurial hypothesis for the coming year:

Robots should do everything.

Absolutely everything. I should only monitor how they do it and adjust when necessary.

I should build systems of robots.

My task comes down to creating and maintaining infrastructure so that robots can do their work.

Here we should consider the following aspects:

1. Entrepreneurial: what exactly should agents do and why
2. Technical: servers, hosting, secure communications
3. Managerial: which agents will interact and how exactly
4. Legal: how to prevent regulatory violations (which may happen by mistake), how to pay taxes on what agents earn

For example, I used to write code for a client’s needs. Now I’m building a team of robots that write code for a client’s needs. My responsibility is to build the system so that the quality of the result is acceptable.

“How many tokens did you spend on this task?”

This is the question I ask when someone starts telling me that a task is complex, unsolvable, or that they don’t understand how to approach it.

Of course, you can’t get an exact answer to this question. Unless you’re using my LLM accounting and observability system, but that’s still in development.

But this question forces you to verify that the person at least tried to understand the unfamiliar subject with an LLM’s help.

I never run code locally. It’s not safe. Instead, I always use sandboxes. Once upon a time it was a remote machine with Emacs that I connected to via SSH. Then I moved to GitHub Codespaces with VS Code. Then it became a remote machine again, this time with VS Code tunnels.

In the Claude Code era, I kept these habits. I would connect to the machine through a VS Code tunnel (wait for it to load), open a terminal (wait for it to load), run Claude Code (and with Codespaces I had to authenticate every single time). Then I’d finally get to work.

I want to save everything I write in Claude Code. And everything it answers. Everything it does, which tools it calls. Absolutely everything. Then I want to index this information.

After that, I’ll analyze it. What did I think about most this week? Where do I make mistakes most often? How can I optimize my work? What connections exist between sessions? What do I constantly overlook?

I can also use this session history in future sessions. This will become my personal context graph — or at least a significant part of it. I’ll have to give up using other interfaces, but that’s fine, Claude Code is good enough. I’ll need to spend some time making it work not just in developer mode.

Reading “Fooled by Randomness” by Nassim Taleb.

Taking lots of notes as I go — I’ll sort through them when I finish and maybe write something more detailed.

But right now one simple thought keeps spinning in my head (unsurprisingly, since it runs as a thick thread through the entire book). It’s long been obvious to me personally, yet completely non-obvious to people around me.

If you left the stove on unattended and went to the store for an hour, and nothing happened — that doesn’t mean everything is fine. It means you just incurred a direct loss equal to the probability of fire multiplied by the cost of restoring your apartment after one.

Reading Cursor’s post on dynamic context discovery.

“Treat terminal as a file” — that’s the Emacs way. Both approaches share the same idea: a unified interface to everything through files (or buffers).

It would be fascinating to build an AI agent that works natively with Emacs buffers. You could reduce everything to text and use Emacs for literally everything.

The only problem: Emacs as a user interface for AI is impossible to sell. People won’t appreciate the elegance.

So I’m simplifying the writing process to the maximum, shortening the path from forming an idea in my head to publishing it.

First, I’ve been using a private Obsidian for personal notes for a long time. In one of my next posts, I’ll describe it in more detail (along with the history of updating my note-taking systems, which includes dozens of tools and approaches).

All that’s left is to set up the publishing part.

Originally, I planned to write all my texts myself without using AI. But now I realize this requires considerable—actually, serious—discipline and extra time to polish my style in a non-native language. So I often simply don’t find the time, and that’s why I don’t write on my blog. Not because I have nothing to say.

So I’ve decided to write however feels comfortable and trust AI to improve and format the text. I’ll also keep all the originals, so in the future I can run these same initial drafts through newer, smarter models and get better versions.

Command — controlling the “world” through text.

The idea is to work in a single interface — whether Obsidian, Commacs (a commander based on Emacs), or a custom-built one. The implementation doesn’t matter; what matters is the technology itself.

Here’s how it works: I make a “move” by writing a message (text, voice, images, documents, facial expressions, etc.). I try to formulate it as completely as possible. A move is a command to change the world. I can only make moves by writing messages.

Agency (in the context of making decisions) is the most important skill during the AI era.

I believe computers (robots/AI/agents) can do almost all human work. We can automate every task but not decisions.

Making decisions requires willpower (see Baumeister et. al.), but only humans have it.

Computers can help with decisions, but they can’t make them. They can find arguments for different options, compare them. They can also create and run models to understand consequences, but they can’t take responsibility for them.

I watch in horror as people around me use AI in an absolutely unsafe way.

People give full access to their devices. The AI tools can use browsers (with all accounts), SMS, emails, bank apps. Once I saw somebody allow AI to attach by SSH to a production server! And nobody sees the problem with this.

What is the problem? You give control of all your digital life to someone else. He can send the wrong email to your boss (from your name!), he can spend all of your money. And you can’t control this.

I’m building a habit of writing down everything. I think this is the base of efficient work.

Any thought should be written down. Any bookmark, any interesting book, any link, any quote, any paper should be saved. Everything interesting. But I should save only things which I am really interested in, not all of them. This is important.

Ideas don’t appear out of thin air. They are born from other ideas (mine or other people’s, but already written down). If so, they should be linked. I can track how my ideas grow and find insights there.

It can be unexpected to write texts about AI without AI. Let me explain why this is important for me.

First of all, this is not a magazine or a newspaper. These are just my essays about our futuristic days. I don’t need to be perfect for anyone else, so I can make mistakes, fix them, study and become better right here, in this blog.

But I need to study. I need to make new thoughts. My thoughts, not AI’s, not generated ones. AI can explain them in future. But if I can’t think, there’s nothing to explain.

Let me explain why I think AI is the most important thing I should invest my time and money in.

I mean I should read news about it immediately after I wake up, before any other things. I should invest my time in reviewing new technologies, new models, and new architectural approaches. I should build my new company based on a new era of tools. My employees are not just people now. My staff are AI and people who can work with AI.

Hi there.

My name is Andy and this is my blog.

To begin I would like to write some points about this.

1. This is a blog. All of these posts are my personal opinions. Not financial advice. Not an absolute truth. Just my opinion. Don’t read it if you don’t like it.
2. I write for myself in general. I need a place for public thinking. I need to improve my English writing skills, I need to write down all important things.
3. I use my personal language, my style, not an AI-language. This is important for me, for my identity and for my brain skills. This is me, not my digital copy.
4. AI agents are welcome. You can use my texts for building my digital copy, but please take care of him.
5. I’m not sure about future topics in this blog, but for now I’m interested in AI and philosophy. I’m thinking about this and I’m writing about it right now. Maybe I’ll change the topics in the future.
6. I’ll try to write every day. I need to change my life to have interesting things every day to write about.

Good luck and let’s start.