Oğuz Dumanoğlu

You can’t always solve real world problems by throwing them technology. Technology is only a leverage in most cases. One simple example; if there is no food supplies available, then food delivery apps would just be useless. This is most tech people don’t get.

Jevons paradox is happening in real time. Companies, especially outside of tech, are realizing that they can now afford to take on software projects that they wouldn’t have been able to tackle before because now AI lets them do so. We’re going to start to use software for all new things in the economy because it’s incrementally cheaper to produce. Marketing teams at big companies will have engineers helping to automate workflows. Engineers in life sciences and healthcare will automate research. Small businesses will hire engineers for the first to build better digital experiences. And as long as AI agents still require a human who understands what to prompt, how to review when an agent goes off the rails, how it guide back, how to maintain the system that was built, how to fix the ongoing bugs, and more, we will still have humans managing these agents. This is why all the advice you get of not going into engineering is wrong. The world is going to increasingly be made up of software, and the people that understand it best will be in a strong economic position. This will happen in other roles as well where output goes up and demand increases.

The comfortable middle is over for software. Growth has stalled but true profitability still hasn't arrived — most of the sector is stuck: too slow for a premium growth multiple, too diluted for a fortress multiple. a16z's David George says CEOs have 12-18 months to pick exactly one of two paths: a16z.news/p/there-are-on…

The 24-29 year old engineer will soon become the most valuable asset in technology. Pre-AI principles + Post-AI speed is an undefeated combo

I rather take 1000 shots where I try something new and fall on my face in front of everyone vs be the guy that has 1000 ideas and never goes after any of them.

start a company assuming: 1) models will become 10x better 2) the only bottleneck for humans is making as many well informed decisions as fast as possible in a great interface lovable are so impressive, they understood this at gpt 3.5 and the same logic still holds

Building software is cheap Maintaining it remains expensive

If you wonder what is “Peaceful Tea” in NoWarNoCry country, let’s meetup and discover together in the @istanbulphp event! 🤓We will talk about building RAG with Symfony AI library and tools! @symfony @official_php Tomorrow 20:30 Turkish time, but this time it will be in Turkish!

a year later and this is still the most important distinction nobody talks about. agency is more powerful than intelligence and it’s not close.

Software engineering accounts for nearly 50% of all AI agent tool calls. Healthcare, legal, finance, and a dozen other verticals are barely touched, each under 5%. That's a hundred AI unicorns waiting to be built. garryslist.org/posts/half-the…

İstanbul PHP olarak yeni bir etkinlikle sizlerleyiz! 🎉 InterNations ekibinde Sr. Backend Software Engineer olarak çalışan @oguzdumanoglu, "Symfony AI ile RAG Sistemleri Geliştirmek" sunumu ile 13 Mart Cuma günü, online olarak bizimle olacak. Etkinlik detayları ve kayıt için👇

All that advancements in AI, official Nvidia event and the goddamn pointer still doesn’t work 😅😅 event guys seem to be safe.

AI writes 90% of my code and I handle the remaining 90%.

software is still about thinking software has always been about taking ambiguous human needs and crystallizing them into precise, interlocking systems. the craft is in the breakdown: which abstractions to create, where boundaries should live, how pieces communicate. coding with ai today creates a new trap: the illusion of speed without structure. you can generate code fast, but without clear system architecture – the real boundaries, the actual invariants, the core abstractions – you end up with a pile that works until it doesn't. it's slop because there's no coherent mental model underneath. ai doesn't replace systems thinking – it amplifies the cost of not doing it. if you don't know what you want structurally, ai fills gaps with whatever pattern it's seen most. you get generic solutions to specific problems. coupled code where you needed clean boundaries. three different ways of doing the same thing because you never specified the one way. as Cursor handles longer tasks, the gap between "vaguely right direction" and "precisely understood system" compounds exponentially. when agents execute 100 steps instead of 10, your role becomes more important, not less. the skill shifts from "writing every line" to "holding the system in your head and communicating its essence": - define boundaries – what are the core abstractions? what should this component know? where does state live? - specify invariants – what must always be true? what are the constants and defaults that make the system work? - guide decomposition – how should this break down? what's the natural structure? what's stable vs likely to change? - maintain coherence – as ai generates more code, you ensure it fits the mental model, follows patterns, respects boundaries. this is what great architects and designers do: they don't write every line, but they hold the system design and guide toward coherence. agents are just very fast, very literal team members. the danger is skipping the thinking because ai makes it feel optional. people prompt their way into codebases they don't understand. can't debug because they never designed it. can't extend because there's no structure, just accumulated features. people who think deeply about systems can now move 100x faster. you spend time on the hard problem – understanding what you're building and why – and ai handles mechanical translation. you're not bogged down in syntax, so you stay in the architectural layer longer. the future isn't "ai replaces programmers" or "everyone can code now." it's "people who think clearly about systems build incredibly fast, and people who don't generate slop at scale." the skill becomes: holding complexity, breaking it down cleanly, communicating structure precisely. less syntax, more systems. less implementation, more architecture. less writing code, more designing coherence. humans are great at seeing patterns, understanding tradeoffs, making judgment calls about how things should fit together. ai can't save you from unclear thinking – it just makes unclear thinking run faster.

A K shaped future makes sense since Team B loses to Team A 10 times out of 10, and now coding AGI is an accelerant

we are living in the industrial revolution of knowledge work

Love this @garrytan! youtube.com/watch?v=ZVpVvG… In Turkish, we say: “Don't say what I am, say what I will be.”