As an educator and author, I feel Molly's pain (Anthropic owes me $60k for pirated books). My course and book sales have dropped significantly. I feel like much of the value in my book-based knowledge is harder to discover in AI agents. (One example might be inspecting SHAP values from XGBoost models and using that as feedback to feature engineering for a linear model (like Logistic Regression)). So I'm afraid more advanced techniques might go by the wayside in the name of delivery speed.
I use AI both in the cloud (I tend to avoid Anthropic...) and with local models.
I've probably written more code in the past year than in my whole career. I can now create what I desire (I just created a telemark skiing game over the weekend based on my Strava segments). I think this has helped my teaching as well. In a recent ML course, I created many interactive examples that in the past were just doodles on my whiteboard, but are now embedded in the notebooks I give students.
I've also taught a few AI courses to clients as well. However, with the inevitable rust/ASM-ification of everything, I'm not sure how long those skills will be valid. I do, however, think that having a human in the loop for ML and data analysis is still important.
The people reaping AI's benefits today generally developed their skills as juniors through old-fashioned struggling in the pre-ChatGPT era -- actually reading docs and articles, trial and error, puzzling over mysterious bugs, and generally doing lots of mental lifting.
How will skill building happen when LLMs and coding harnesses seemingly provide all the answers at one's fingertips?
> How will skill building happen when LLMs and coding harnesses seemingly provide all the answers at one's fingertips?
They don't provide all the answers. And are often wrong/mistaken. But they also produce stuff faster than you can consume it. This is great for when you know exactly what you want and can cheaply test the output and guide the LLM to the right solution. It is expensive (in developer hours) in other cases.
I have been building a new PL+VM this year and had to pause development on a few occasions because I have not had the time to go through the implementation because there are so many modules. But smaller tools (5-10KLOC), you can judge fairly quickly.
So, the answer to your question is: people need to discover the strengths and weaknesses of the tool on their own, decide what kind of expertise they want to attain, and if it is worth the effort.
__mharrison__ · · focus · HN ↗
I use AI both in the cloud (I tend to avoid Anthropic...) and with local models.
I've probably written more code in the past year than in my whole career. I can now create what I desire (I just created a telemark skiing game over the weekend based on my Strava segments). I think this has helped my teaching as well. In a recent ML course, I created many interactive examples that in the past were just doodles on my whiteboard, but are now embedded in the notebooks I give students.
I've also taught a few AI courses to clients as well. However, with the inevitable rust/ASM-ification of everything, I'm not sure how long those skills will be valid. I do, however, think that having a human in the loop for ML and data analysis is still important.
curt15 · · focus · HN ↗
How will skill building happen when LLMs and coding harnesses seemingly provide all the answers at one's fingertips?
sieve · · focus · HN ↗
They don't provide all the answers. And are often wrong/mistaken. But they also produce stuff faster than you can consume it. This is great for when you know exactly what you want and can cheaply test the output and guide the LLM to the right solution. It is expensive (in developer hours) in other cases.
I have been building a new PL+VM this year and had to pause development on a few occasions because I have not had the time to go through the implementation because there are so many modules. But smaller tools (5-10KLOC), you can judge fairly quickly.
So, the answer to your question is: people need to discover the strengths and weaknesses of the tool on their own, decide what kind of expertise they want to attain, and if it is worth the effort.