Il-vantaġġ ġej mill-matching tal-AI għat-tasks it-tajba, mill-għoti ta' kontesto tajjeb, u mid-verifika ta' kollox li jipproduċi. Il-model huwa assistent mabda', għaliem, mingħajr responsabbiltà — int tibqa' l-inġinier fil-loop.
Il-vantaġġ ġej mill-matching tal-AI għat-tasks it-tajba, mill-għoti ta' kontesto tajjeb, u mid-verifika ta' kollox li jipproduċi. Il-model huwa assistent mabda', għaliem, mingħajr responsabbiltà — int tibqa' l-inġinier fil-loop.
"}}}The difference between a 2x and a 0.5x outcome with AI is not the tool — it's the workflow around it. Used on the right tasks with good context and tight verification, AI removes the tedious typing and widens your exploration, so you spend your attention on design and correctness. Used carelessly, it generates plausible code you can't stand behind and quietly erodes your understanding of your own system. Effectiveness is a discipline: high-leverage tasks, small steps, real verification, and staying in the loop."}}}}]}}}. I notice the JSON is incomplete. Let me correct this:}
The difference between a 2x and a 0.5x outcome with AI mhix it-tool — hija l-workflow madwarha. Użata għat-tasks it-tajba b'kontesto tajjeb u verifika stretta, AI tneħħi t-typing tedious u twassal l-exploration tiegħek, allura tqatta' l-attenzjoni tiegħek fuq design u correctness. Użata b'negligenza, tiġġenera kodiċi plausibli li ma tistax taqaf wara u qtiegħ l-għarfien tiegħek tas-sistema tiegħek stess. Effikaċja hija dixxiplina: high-leverage tasks, passi żgħar, verifika vera, u tibqa' fil-loop.