3 ms·

Where did they learn sensibility for higher-level of abstraction? AI is the opposite, it will do what you prompt and never stop to tell you its a terrible idea,

Where did they learn sensibility for higher-level of abstraction? AI is the opposite, it will do what you prompt and never stop to tell you its a terrible idea, you will have to learn yourself all the way down into the details that the big picture it chose for you was faulty from the start. Convert some convoluted bash script to run on Windows because thats what the office people run? Get strapped in for the AI PowerShell ride of your life.

ikerino 1y ago
How is that different than how any self-taught programmer learns? Dive into a too-big idea, try to make it work and learn from that experience. Repeat that a few hundred times and you'll have some strong intuitions and sensibilities.
- pessimizer 1y ago
  The self-taught programmer's idea was coded by someone who is no smarter than they are. It will never confuse them, because they understand how it was written. They will develop along with the projects they attempt. The junior dev who has agents write a program for them may not understand the code well enough to really touch it at all. They will make the wrong suggestions to fix problems caused by inexperienced assumptions, and will make the problems worse. i.e. it's because they're junior and not qualified to manage anybody yet. The LLMs are being thought of as something to replace juniors, not to assist them. It makes sense to me.
- skydhash 1y ago
  > Dive into a too-big idea, try to make it work and learn from that experience. Or... just pick up that book, watch a couple of videos on Youtube and avoid all that trying.
CamperBob2 1y ago
AI is the opposite, it will do what you prompt and never stop to tell you its a terrible idea That's not true at all, and hasn't been for a while. When using LLMs to tackle an unfamiliar problem, I always start by asking for a comparative review of possible strategies. In other words, I don't tell it, "Provide a C++ class that implements a 12-layer ABC model that does XYZ," I ask it, "What ML techniques are considered most effective for tasks similar to XYZ?" and drill down from there. I very frequently see answers like, "That's not a good fit for your requirements for reasons 1, 2, and 3. Consider UVW instead." Usually it's good advice. At the same time I will typically carry on the same conversation with other competing models, and that can really help avoid wasting time on faulty assumptions and terrible ideas.

ikerino 1y ago

How is that different than how any self-taught programmer learns? Dive into a too-big idea, try to make it work and learn from that experience. Repeat that a few hundred times and you'll have some strong intuitions and sensibilities.

pessimizer 1y ago
The self-taught programmer's idea was coded by someone who is no smarter than they are. It will never confuse them, because they understand how it was written. They will develop along with the projects they attempt. The junior dev who has agents write a program for them may not understand the code well enough to really touch it at all. They will make the wrong suggestions to fix problems caused by inexperienced assumptions, and will make the problems worse. i.e. it's because they're junior and not qualified to manage anybody yet. The LLMs are being thought of as something to replace juniors, not to assist them. It makes sense to me.
skydhash 1y ago
> Dive into a too-big idea, try to make it work and learn from that experience. Or... just pick up that book, watch a couple of videos on Youtube and avoid all that trying.

CamperBob2 1y ago

AI is the opposite, it will do what you prompt and never stop to tell you its a terrible idea That's not true at all, and hasn't been for a while. When using LLMs to tackle an unfamiliar problem, I always start by asking for a comparative review of possible strategies. In other words, I don't tell it, "Provide a C++ class that implements a 12-layer ABC model that does XYZ," I ask it, "What ML techniques are considered most effective for tasks similar to XYZ?" and drill down from there. I very frequently see answers like, "That's not a good fit for your requirements for reasons 1, 2, and 3. Consider UVW instead." Usually it's good advice. At the same time I will typically carry on the same conversation with other competing models, and that can really help avoid wasting time on faulty assumptions and terrible ideas.