by gergely-orosz
The abstraction ladder never stops moving — compilers, assembly, even hand-written code are becoming irrelevant. AI-enabled teams of 2-20 people will rival big company output, but the vampiric burnout effect means you might only get 3 productive hours at max speed.
by gergely-orosz
Software engineering is undergoing a printing-press moment: coding as a gatekept craft is dissolving, generalists who ship prototypes and adapt weekly to new model capabilities will define the next era.
by gergely-orosz
Programming language choice matters more in the AI era, not less — AI code generation quality varies dramatically by target language, and the runtime tradeoffs a language imposes don't disappear just because a machine writes the code.
by gergely-orosz
Every major abstraction shift in software engineering has triggered the same existential crisis among developers, and each time the industry expanded rather than contracted — the current AI shift is no different.
by gergely-orosz
Effective agentic coding requires closing the loop—giving AI agents the ability to verify their own work through tests and tooling—while developers shift from writing code to orchestrating parallel agents and designing systems for verifiability.
by gergely-orosz, chip-huyen
Chip Huyen explains how AI engineering differs from ML engineering, walking through the practical developmental path from prompts to RAG to fine-tuning.
by gergely-orosz, martin-fowler
Martin Fowler argues AI represents the biggest shift in software engineering since assembly to high-level languages—not because of abstraction level, but because we now work with non-deterministic systems.
by gergely-orosz
A critical look at AI hype vs reality in the tech industry, examining where Silicon Valley's assumptions about AI may be misguided.
