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- Tags: fav programación
[!summary]AI now generates and explains code, acting like a helpful but imperfect pair programmer.
That lets nonexperts build things fast via “vibe-coding” but also encourages shotgun debugging and security slip-ups.
It may erode deep craft and learning, so engineers must stay alert and keep fundamental skills.
Highlights
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At the risk of appearing to legitimize AI hype merchants, the security concerns around vibe-coding, in my estimation, are something of a bogeyman — or at least the net effect may be non-negative, because AI can also help us write more secure code. Sure, we’ll see blooper reels of “app slop” and insecure code snippets gleefully shared online, but I suspect many of those flaws could be fixed by simply adding “run a security audit for this pull request” to a checklist. Already, automated tools are flagging potential vulnerabilities. Personally, using these tools has let me generate far more tests than I would normally care to write.
De acuerdo con esto. En relación a las preocupaciones sobre la seguridad del código generado por IA, creo que es algo que la evolución de los modelos pronto dejarán offside. Sólo basta considerar Mythos. Adicionalmente, el cuidado del código se puede desplegar con instrucciones específicas para que el modelo haga reviews, que es algo que hoy se puede promptear y que probablemente en el futuro venga incorporado.
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Another way to see this is as the natural progression of programming: the evolution of software engineering is a story of abstraction, taking us further from the bare metal to ever-higher conceptual layers. The path from assembly language to Python to AI, to illustrate, is like moving from giving instructions such as “rotate your body 60 degrees and go 10 feet,” to “turn right on 14th Street,” to simply telling a GPS, “take me home.”
El vibe coding como el último paso en la progresiva evolución del nivel de abstención en el que le damos instrucciones a las máquinas
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The jury is still out on whether AI-assisted coding speeds up the job at all; at least one well-publicized study suggests it may be slower. I believe it. But I also believe that for AI to be a true exponent in the equation of productivity, we need a skill I’ll call a kind of mental circuit breaker: the ability to notice when you’ve slipped into mindless autopilot and snap out of it. The key is to use AI just enough to get past an obstacle and then toggle back to exercising your gray matter again. Otherwise, you’ll lose the kernel of understanding behind the task’s purpose.
A propósito del deskilling consecuencia del uso de IA y formas de evitarlo.