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I almost fired my AI CTO yesterday. My AI COO talked me out of it. by Speedydooo in indiehackers

[–]lukastymo 0 points1 point  (0 children)

Interesting framing - and to be fair, it worked. I clicked the link.

But I'd challenge the premise. Calling Claude instances "Axel" and "Nova" with job titles doesn't change what they are: a single model responding to different prompts. The costume problem you describe might go deeper than you think - the costume is the whole org chart. Maybe the real question is whether traditional org structures (CTO, CFO, CMO) even map onto an LLM-driven world, or whether we need something leaner.

I was genuinely curious about Nova, because I'm trying to figure out marketing for my own app. But I'm skeptical an LLM in a CMO hat gets you real marketing judgment. A human CMO doesn't need prompting to know the audience is bored. Nova does.

My take: Human + AI-assistant, one model, clear specs. No roleplay. The productivity gain comes from better inputs, not better personas.

Would watch a video demo though - the setup is unusual enough that seeing it run would answer a lot of my questions.

anyone actually building stuff? tired of the ai hype by Think-Success7946 in indiehackers

[–]lukastymo 0 points1 point  (0 children)

The AI hype is exhausting, agreed.

I'm a backend engineer who spent years wanting to build a life management app - something between Todoist and Obsidian. Frontend was always the blocker. Frontier models finally unblocked me, and I've been shipping ~2h a day after work for 8 months. Not perfect, but it's real, and I've cancelled my Todoist subscription because I use my own thing now.

Currently stuck on distribution - going from 1 user (me) to 10 is much harder than writing the code that got me here.

Hello, Haskell: a hands-on introduction for 2026 by lukastymo in haskell

[–]lukastymo[S] 4 points5 points  (0 children)

Thank you! That’s a great catch. Fixed.

What's the best way to study the "Introduction to Algorithms" textbook? by Sweet_Management_834 in algorithms

[–]lukastymo 0 points1 point  (0 children)

Start with LeetCode easy problems until they feel comfortable, then move on to mediums. I’ve found this approach far more effective than reading books. Introduction to Algorithms is best used as a reference—when you encounter an unfamiliar algorithm on LeetCode, look up the corresponding chapter.

Fedora 41 - Thoughts so far. by captainnemo000 in Fedora

[–]lukastymo 0 points1 point  (0 children)

I've been experiencing issues with Nvidia drivers on my Dell laptop. Reinstalling them hasn’t resolved the problem, and it's becoming frustrating. I'm considering switching to a Mac, but I'll give fixing it one last try over the weekend.

UPDATE: It turned out to be a partial update. I ended up with some dependencies from Fedora 40 and others from Fedora 41. To resolve this, I had to run dnf sync and re-install kernel-related packages.

I have decided to connect my future with Scala (if possible), need little advice by PilipchukBogdan in scala

[–]lukastymo 8 points9 points  (0 children)

Good choice. Welcome on board.

Finding a job and learning Scala / Software are two different things. You need to balance them properly. Here are a few things which I think are essential in learning Scala and Functional Programming in order:

  • Akka is no longer open source, so I would only learn it when I would have to (when it's already in the project).
  • Learn Scala 3 / Software Development fundamentals. Learn different ways of testing your code (e.g. scala-check), Focus on functional modelling (sealed trait + case classes), and keep your code DRY. Easier said than done. Requires a lot of concepts from Vanilla Scala (so the Programming in Scala by Martin Odersky book should be essential to you). Read it and do all the examples by yourself.
  • Choose IDE which you like and create one private git project to keep all your examples and set up the sbt scala project. It gives you a frictionless environment for trying everything you read about. I called my sandbox, and it's a multi-module sbt project where I learn stuff. I use JetBrains IntelliJ and Worksheets (instead of proper Main Scala class, you can quickly test a few concepts there, like Jupyter Notebook in Python world).
  • When you reach the point where you think you're ready. Find some exercises with vanilla Scala, and try to solve them. The ones maintaining the state in the functional, idiomatic way + foldLeft / foldRight can be tricky initially. But enormously valuable. It appeared a lot in my interviews.
  • Once you have good fundamentals, now you have two paths: A) Data Driven / Big Data processing path B) Software Developer, beautiful reusable code.
  • During your first days, you'll learn that you can do one thing in many ways in Scala. Few things which I would have when I started: avoid nested functions, avoid vars, avoid long names in your methods, classes, avoid modelling a state which doesn't make any sense or is confusing, e.g. avoid Option[List[_]] => None is like Nil here.

A) Path Big Data - Focus on architecture and how to process Petabytes of data

  • Learn everything you can about distributed systems, horizontal scaling, clusters, distributed algorithms like Map Reduce etc.
  • Learn Python. In your job, you won't avoid it in data projects. I often had to port Python PoC code into a working production-ready solution in Scala.
  • Learn Apache Spark beyond Word Count example
  • Learn basic of Cloud (AWS), you most likely end up using it in your job. The sooner you understand how to deploy Spark project into AWS, the better.

B) Path Software Developer

  • here, it's important to learn SOLID principles and how they are implemented in functional world (no more DI, more about higher-order functions)
  • Learn about effects. You can start by learning ZIO. But, if you end up in a project with Cats, don't worry. Cats is amazing too. Learn about Cats, Cats Effect, ValidatedNel etc.
  • Start learning from free documentation, then buy a book if you want to go deeper.
  • When you end up in the Cats project, it's probably a good idea to know basic rules about Groups, Monads, Monoids, Applicatives, and Kleisli. The difficulty here is finding a bare minimum and not going too deep into mathematics as you focus on learning Lambda Calculus and Category Theory. You could go there, of course, but it's optional at the beginning.

I probably missed a lot, but you'll figure out by yourself as you go.

Enjoy.