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 margaret-anne-storey
AI accelerates code production faster than human comprehension can follow — the resulting understanding gap is cognitive debt, and it's more dangerous than technical debt because it's invisible until the team hits a wall.
by bryan-finster
AI amplifies existing capability rather than replacing it—developers and organizations with strong engineering foundations gain exponentially while those without see problems compound faster.
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 mia-heidenstedt
Developers who treat AI as an autonomous coder lose control of their codebase—effective AI collaboration requires explicit architectural decisions, layered verification systems, and human-written specification tests that resist shortcuts.
by geoffrey-huntley
Back pressure—automated feedback on quality and correctness—is the key mechanism that enables AI agents to handle complex, long-horizon tasks.
by geoffrey-huntley
Coding agents are just 300 lines of code running in a loop—demystifying AI tooling reveals that the model does the heavy lifting, and understanding these primitives transforms you from AI consumer to AI producer.
by simon-willison
Reasoning models have made LLM-generated code undeniably good, and 2026 will bring both a major security incident from coding agents and the resolution of the sandboxing problem.