Did they do this before AI? Does the company really, truly care about software quality or are they just trying to ship features?

Things like

- in depth code reviews

- encouraging sharing knowledge and helping others

- dedicating time to address technical debt

- giving engineers freedom to explore technologies and solutions

- following best practices for software dev

- hiring the right people

This is one of those things you can't enforce, but your leadership can encourage it by setting examples. If your company does not care about understanding the software by carving out time and explicitly encouraging it, then employees won't either.

Like Apple Intelligence? Which was quite crap

If code doesn't go this direction soon, I'd be surprised. PM builds a prototype with claude, or designer designs something in figma/canva - claude vibe codes 70% of the solution using your company's frameworks and design system, then hands it off to the developer who finishes it and productionizes it

If I was working at a startup or working on a personal project, I wouldn't read the code but instead build a tighter verification loop to ensure the code functions as expected. Much harder to do in an existing system that was built pre-AI

AI agents haven't yet figured out a way to do sales, marketing or customer support in a way that people want to pay them money.

Maybe that won't be necessary and instead the agent economy will be agents providing services for other agents.

Some things to consider:

Despite living here for 30 years, my parents don't feel they fit in. Their friends are Russian and the media they consume is in Russian. At the same time, they wouldn't fit in back in Russia either at this point. It's a weird state where you lack a strong cultural association. If you move, highly recommend immersing yourself in local culture, language, and activities.

We moved here because my dad had a good job at an international company (software dev). Our immigrant friends who are doing well are in a similar boat, or have entered higher paying fields like nursing. If you don't plan to climb the financial ladder via upskilling, or aren't in a transferrable career, your material life will be much better in your home country.

Overall, I don't think their quality of life changed much between the two countries. They are educated, white collar workers who would have a similar life anywhere they lived.

I use nvim all the time for code exploration & figuring out what i need to tell the AI. Invest in tools and packages that let you navigate your codebase quickly

What you can do is empower your agent to solve more complex problems fully on its own. See if you can write a plan (claude is great at writing plans) that encapsulates all the work needed for a feature. Go back and forth with the AI on the plan. Spend some time thinking about it. Including tests, how the AI can automatically validate that things work, etc. Put it on auto-accept and tab away. Once it finishes, review the code, do your normal QA, follow ups, etc

While it's working, go work on another feature in the same vein. Git worktrees are a simple way to work on the same codebase in parallel (though you likely work on an app that isn't set up for multiple instances of it running in parallel, have fun with that). Containers are another way to run these in parallel. Vibe code yourself a local tool to manage these. This is somewhat built into the claude/codex desktop apps, but you likely need to customize it for your environment

Basically you do the architecture, code review & QA and let the model do as much of the code as possible, and try to do it in parallel. I still do manual coding when I need to explore what a solution might look like, but AI is much faster at experimenting with larger solutions, and if you don't like what it did, it's a `git checkout .` away from a clean slate

How much time you spend on validating is a tradeoff of speed vs correctness needs for your business.

Marketing, lawyers, good customer support, creating relationships with customers.

Auth flows are important for enterprise customers, but just use an existing off the shelf library for oauth/SSO. Depending on your customers, this is a feature. If you mean auth flows between your own services, then you've overengineered it.

Basically none of the things you've listed are as important as having features that attract customers. Those things you build afterwards for stability and velocity.

I'm biased, but in 2026 I would just use a Ruby on Rails monolith with Postgres. You don't even need to containerize it until the stack becomes too complex to run locally.

> The real question isn’t whether AI helped write this

It is. As soon as I saw the bullet points, my mind went "AI wrote this" and I stopped reading.

- the printing press

- radio

- tv

- personal computers

- internet

in terms of important contributors to human civilization. We live in the information age, and all of these are significant advances in information.

The printing press allowed small organizations to create written information. It de-centralized the power of the written text and encouraged the rapid growth of literacy.

Radio allowed humans to communicate quickly across long distances

TV allowed humans to communicate visually across long distances - what we see is very important to the way we process information

PCs allowed for digitizing of information - made it denser, more efficient, easier to store and generate larger datasets

The internet is a way to transfer large amounts of this complex digital information even more quickly across large distances

AI is the ability to process this giant lake of digital information we've made for ourselves. We can no longer handle all the information that we create. We need automated ways to do it. LLMs, which translate information to text, is a way for humans to parse giant datasets in our native tongue. It's massive.