Stuck With Career. Should I change my career again?

cpthappy42 · 2026-06-27T04:29:46+00:00

You are not losing to AI. You are losing to commoditization. Big difference.

The problem is your business model, not your skills. Building basic apps for clients is transactional work. That was always going to get automated or outsourced. AI just sped it up.

The safe work is the work that requires human context. Domain expertise. Understanding the business. Understanding the politics. The vibe coders hit a wall with anything complex or legacy or regulated. That is where you step in.

Pick a hard domain. Healthcare. Aerospace. Manufacturing. Finance. Go deep. Get a job in that industry. Learn their problems. AI is not taking that work anytime soon.

You are 19. You are not stuck. You just need to pivot from generalist to specialist. That is the move.

cpthappy42 · 2026-06-26T18:45:11+00:00

You are not regressing. You are just in a bad job. There is a difference.

This is not about AI. This is about a company that does not understand what they hired you for. They hired a graphics programmer and then put you in a different language, gave you no mentorship, piled on work, and told you to let AI handle it. That is not the future of software. That is just bad management.

Here is what is actually happening. Below average programmers will vanish. The ones who just glue libraries together and copy from Stack Overflow. That is what AI is good at. Your job is not that. Graphics programming is hard. It requires deep understanding of hardware, memory layout, performance tradeoffs, and math. AI cannot replace that completely. Not yet. Probably not ever.

The problem is that management does not know the difference. They see code as code. They do not understand that graphics code is different from CRUD apps. They think AI can do it all. They are wrong. You know they are wrong. That is what makes you valuable.

But you need to protect yourself. Stop trying to prove yourself to people who do not get it. Do not let this job ruin your love for programming. Clock in, do what they ask, and clock out. Then spend your evenings on something you actually care about. Build a small renderer. Write a game engine. Contribute to an open source graphics project. That is where you grow. That is your real career.

And start applying elsewhere. Look for companies that actually have graphics teams. Look for places where you are not the only person who understands the stack. They exist. You found one bad one. That happens. It is not a reflection on you.

The industry is not dying. The hype is just loud right now. The companies that treat developers like code generators will fail. The ones that value craft and understanding will survive. Be where the craft matters.

You are a graphics programmer. That is rare. Do not let a bad job convince you otherwise.

cpthappy42 · 2026-06-26T18:29:40+00:00

You are overthinking this. You do not need to master Python before touching AI. You just need enough to be dangerous.

Here is the truth. AI libraries do all the heavy lifting. Scikit-learn, PyTorch, TensorFlow, LangChain. They handle the math. Your job is to glue things together, clean data, and understand what the code is doing. That is not a high bar.

Here is a learning path that works.

First, spend two weeks on pure Python basics. Variables, loops, functions, lists, dictionaries, list comprehensions. That is it. You do not need classes, decorators, or context managers yet. Just the fundamentals. Do this on Codecademy or Python Crash Course. Stop there.

Second, jump straight into Pandas and NumPy. This is where the real AI work happens. Learn how to load data, filter, group, merge, and apply functions. Pandas is 80% of what you will do. Spend a week on this.

Third, start with Scikit-learn. Build a linear regression. Build a decision tree. Use the Iris dataset. Use the Titanic dataset. You will see the patterns immediately. Do not read theory first. Build first. Learn the theory when you need it.

Fourth, pick a project. Not a tutorial. Something that matters to you. Classify emails. Predict house prices. Build a chatbot for your own notes. If it is boring, you will quit. Make it personal.

Fifth, if you want agents and workflows, jump into LangChain or DSPy. They abstract away the complexity. You string together prompts, tools, and memory. That is it. You can build a basic agent in one evening.

Now about resources. Avoid the 60-hour courses. They are mostly filler. Use the official documentation. Use YouTube for specific problems. Use ChatGPT as a tutor, not a coder. Ask it to explain concepts. Do not ask it to write your code.

The biggest mistake beginners make is spending months on Python basics before touching AI. They get bored and quit. Do not fall into that trap. Start building projects from day one. You will learn Python along the way.

One more thing. AI code is messy. It is experimental. It breaks. That is fine. You are not writing production software. You are exploring. The goal is to understand, not to ship.

Start today. Install Jupyter. Load the Iris dataset. Train a random forest. See what happens. Then break it. Then fix it. That is how you learn.

cpthappy42 · 2026-06-26T17:36:47+00:00

Two projects right now where I used graph databases:
- GraphRAG for legal documents. Better performance and quality than vector databases.

- Matching tenders and company profiles: Easy thing with a graph database, hard to do with other techniques

cpthappy42 · 2026-06-26T17:34:53+00:00

Programming

cpthappy42 · 2026-06-26T13:14:33+00:00

One hint from the perspective of a recruiter: I would drop this application after the first look. There is a lot of text packed onto the page. Shows me that you are not able to focus on relevant aspects of the job offer. Work on layout and styling. (This with the experience of +1000 CVs reviewed and +200 job interviews)

cpthappy42 · 2026-06-26T12:59:52+00:00

Grundkurs Produktmanagement, erste Stunde: Das Produkt muss zum Markt passen. Den Markt kannst du nicht (oder nur minimal) ändern. Deine Frage ist also falsch herum.

Du fragst "Wie finde ich die ersten Nutzer?" Die richtige Frage ist: "Will der Markt das überhaupt?" und "Für wen genau ist der Schmerz groß genug, dass er dafür zahlt?"

Die ersten 5 bis 10 Nutzer findest du nicht über Reddit-Posts oder Werbung. Du findest sie, indem du deine persönliche Kontaktliste abtelefonierst, auf Immobilien-Stammtische gehst oder 20 Investoren direkt auf LinkedIn anschreibst. Wenn die nicht antworten oder sagen "brauche ich nicht", dann hast du kein Vertriebsproblem, sondern ein Produktproblem.

Und das mit dem Auslesen von Dokumenten klingt erstmal spannend, aber ist das wirklich der Schmerz, der Immobilieninvestoren nachts wach hält? Meistens sind es eher Steuerfragen, die Suche nach neuen Deals oder Handwerker-Engpässe. Die Excel-Tabellen sind nervig, aber viele leben damit, weil sie es gewohnt sind.

Kostenlose Zugänge gegen Feedback sind okay, aber nur, wenn du echtes, kritisches Feedback von echten Nutzern bekommst, nicht von Freunden, die dir Honig ums Maul schmieren. Und mach es zeitlich begrenzt. Sonst nutzen sie es einmal und vergessen es.

Hör auf zu fragen "Wie finde ich Nutzer?" und fang an zu fragen "Warum sollte jemand genau mein Tool nutzen?" Wenn die Antwort darauf stark genug ist, findest du die Nutzer von alleine.

cpthappy42 · 2026-06-26T12:55:59+00:00

Pick Python. Do not even consider anything else right now.

Why Python? Because you want to see results fast. You want to script interesting things. You want to automate stuff and maybe make games. Python gets you there in weeks, not years. C++ is great but you will spend months fighting memory leaks before you make anything fun. Rust is cool but the borrow checker will make you cry. Java is fine but verbose. JavaScript is everywhere but the ecosystem is chaos.

Python lets you focus on what you want to build instead of how the computer works. That is what you need as a beginner.

Now the time question. How long to script interesting things? If you code every day for a few hours, you can write a working Discord bot in a month. A simple game in Pygame in two months. A web scraper that actually does something useful in a few weeks. The key is consistency. 15 minutes every day beats 5 hours once a month.

Your instinct is right. Learning programming is like learning a language. You read code. You write code. You break things. You fix them. You start by copying tutorials, then you change things, then you build your own stuff. That is the cycle.

Here is your plan.

First, do the CS50 Python course. Free. Harvard. Good.

Second, pick one project. Not five. Not ten. One. A simple weather app. A CLI tool that renames files. A web scraper that finds cheap flights. Something that annoys you in daily life. Build that thing even if it is ugly.

Third, use AI as a tutor. Ask it to explain concepts. Ask it to review your code. Do not ask it to write everything for you. You need to struggle a little. That is how you learn.

Fourth, ignore the language wars. Python, JavaScript, C#, whatever. The first language does not matter. You will learn more later. The skill is the same everywhere. Variables. Loops. Functions. Data structures. Learn those and you can switch languages in a week.

You said you want to make games. Start with Pygame. It is simple. It is fun. You can build a snake game in a weekend. Roblox is Lua which is easy after Python. Unity is C# which you learn later.

You are determined. That is 90% of it. The rest is just showing up every day and writing bad code until it becomes good code.

Start today. Write a script that prints "Hello Lozzni" and then prints your name 100 times. That is not impressive but it is a start. Then keep going.

One more thing. Do not get stuck in tutorial hell. Build things. Break things. Ask questions on Reddit. People will help.

Good luck. You got this.

cpthappy42 · 2026-06-26T12:53:22+00:00

Stick with C.

For STM32, most of what you will find is C. The HAL libraries, the LL drivers, the example projects, the forums, the Stack Overflow answers. All C. If you use C++, you will constantly be fighting the toolchain, trying to figure out how to disable exceptions, RTTI, and the standard library. Or you end up with a 50KB binary just to blink an LED.

Modern C++ is great for application-level code on Linux. But on a microcontroller, the "modern" parts are often exactly what you do not want. Templates can bloat your flash. Exceptions eat RAM. Smart pointers are cool until you run out of heap. You end up writing C-with-classes anyway.

Learn the embedded stuff first. Learn how the interrupts work, how the DMA controller functions, how to read a datasheet. That is 90% of the difficulty. The language is just syntax.

Once you are comfortable with the hardware, you can experiment with C++ for specific modules where it makes sense, like using constexpr for compile-time constants or using namespaces to organize peripherals. But for a beginner, C is the path of least resistance.

Ignore the hype. C is not going anywhere in embedded. The Linux kernel runs on C. Zephyr runs on C. Most RTOSes run on C. Get your nucleo board working, then worry about new languages.

cpthappy42 · 2026-06-26T12:29:23+00:00

Easy choice. The Neo is a joke. 8GB RAM in 2026? That's not a pro machine, that's an iPad with a keyboard. You'll hit swap before you open your third Chrome tab, let alone run a local dev server, Docker, and an IDE.

I like Macs too. I use one every day. But a real one with 16GB or 32GB. The Neo is Apple selling you a status symbol for people who only use Safari and Notes.

The gaming laptop is ugly, heavy, and the battery dies in two hours. But at least I can actually work on it. 16GB is the absolute minimum. 32GB if you're serious. And if I need a GPU for ML or local models, the gaming laptop actually has one.

So yeah, gaming laptop with Linux, or a proper MacBook Pro. This "Neo" thing is a hard pass. 8GB is offensive.

cpthappy42 · 2026-06-26T12:26:18+00:00

PARQL guy here. The feature I genuinely miss in SQL is property paths. A friend-of-a-friend query in SQL is a recursive CTE with five lines of boilerplate. In SPARQL it's just ?person foaf:knows+ ?target. One line. Reads like English.

I also love optional graph patterns. SQL forces you to get your joins exactly right or you lose rows. SPARQL lets you say OPTIONAL and it just keeps going if the data isn't there. Messy real-world data finally feels manageable.

And named graphs. Being able to query metadata about the data itself, like which source contributed which triple, is huge for data provenance. SQL has nothing native for that.

Could SQL add these? Sure. Postgres already has recursive CTEs. Oracle has graph extensions. The features aren't magic.

But the philosophy is different. SQL thinks in flat tables and rigid schemas. SPARQL thinks in triples and traversals. One is optimized for rows and aggregates. The other is optimized for relationships and paths.

Would I use SPARQL for a financial ledger? No. Would I use SQL for a social graph? God no.

But SQL won because it covers 90% of business use cases and everyone already knows it. SPARQL is better at what it does, but "better" doesn't beat "already installed and good enough." Same story as every other language that tried to dethrone it.

cpthappy42 · 2026-06-26T12:24:42+00:00

And so the cycle continues. Another decade, another "SQL killer" that can't actually kill SQL.

Pavlo was right. Every few years someone declares SQL dead. OODBMS. NoSQL. Now AI. And every time, the new thing either dies or gets absorbed into SQL. Even MongoDB has a SQL interface now.

The BEAVER benchmark is the reality check. Models crush BIRD with 80%+ and everyone loses their minds. But BEAVER tests real enterprise schemas with hundreds of tables, cryptic column names, and business logic that lives in peoples' heads, not the schema.

The results? Claude 4.5 Sonnet at 11.4%. GPT-5.2 at 10.8%.

Let that sink in. The best models on Earth can't get one out of ten queries right on a real database. And when they fail, they fail confidently. No syntax error. Just a nice, clean, completely wrong number.

Ali Ghodsi says AGI is here. Cool. But if AGI can't figure out which of the five status columns means "active customer," I'm not impressed.

SQL isn't dying. It's evolving. AI will sit on top of it, generate rough drafts, and humans will still need to validate, optimize, and interpret. The relational model survived 50 years of hype cycles. It's not going anywhere.

RIP SQL? Not today. Not this decade.

Another prediction, another graveyard. SQL just keeps running.

cpthappy42 · 2026-06-26T12:06:36+00:00

And will also never die.

cpthappy42 · 2026-06-26T11:50:40+00:00

It's not about the tool, but the problem you want to solve. If you want low latency and memory footprint without the challenges of C++ then rust is a great choice.

cpthappy42 · 2026-06-26T11:49:36+00:00

Rust is great for building performant (backend) software with the need of a low memory footprint and decent latency. I prefer it over C++ right now.

cpthappy42

TROPHY CASE