Exact Instructions Challenge - THIS is why my kids hate me. | Josh Darnit

What people said

srirangr · 2019-04 · Recommendation

1. Tell them what to do but do not tell them how to do it. (Just give a few pointers otherwise they'll be lost.) 2. Give them a playground to fail - Offload any non-business critical tasks and let them make mistakes. No one I know ever learnt programming without making any mistakes. Immediately tell them about best practices and how to avoid such mistakes in future. 3. Show them the impact of their work - There's nothing more motivating than seeing the impact of one's work. 4. Build curiosity - Answer as many questions as you can answer. Admit when you don't know any answers and start looking it up on the web right in front of them. People pick habits by looking at their superiors. If it's absolute beginning to programming, this video is a great way to show how difficult it is to teach a machine to do something and how clear instructions can help get things done: [VIDEO]

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zyang · 2017-02 · Recommendation

Have you seen the exact instruction challenge on youtube? I thought it was a great segue into technical thinking for young kids. [VIDEO]

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godelski · 2025-06 · Reference

As a ML researcher and a degree holding physicist, I'm really hesitant to use the words "understanding" and "describing" (much less hesitant) around these models. I don't find the language helpful and think it's mostly hateful tbh. The reason we use math in physics is because of its specificity. The same reason coding is so hard [0,1]. I think people aren't giving themselves enough credit here for how much they (you) understand about things. It is the nuances that really matter. There's so much detail here and we often forget how important they are because it is just normal to us. It's like forgetting about the ground you walk upon. I think something everyone should read about is Asimov's "Relativity of Wrong"[2]. This is what we want to see in these systems if we want to start claiming they understand things. We want to see them to deduction and abduction. To be able to refine concepts and ideas. To be able to discover things that are more than just a combination of things they've ingested. What's really difficult here is that we train these things on all human knowledge and just reciting back that knowledge doesn't demonstrate intelligence. It's very unlikely that they losslessly compress that knowledge into these model sizes, but without very deep investigation into that data and probing at this knowledge it is very hard to understand what it knows and what it memorizes. Really, this is a very poor way to go about trying to make intelligence[3], or at least making intellig

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