Raw LLM Responses
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@erwind917 Yeah, this species was always infatuated with shiny things. One could…
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Wont AI generate code quality that is average, given that it's based on a corpus…
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I am a writer. I have written over 100,000 words this year. I am disgusted with …
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That's what he said at the beginning: the AI is pulling from biased data sets an…
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You're more polite than me. I'm wondering if Stephen had the choice of 1. Huge w…
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current AI, Grog :"Q:how many strawberries are there in the word R? A:There are …
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What if the UBI is only enough for ONE thing on your list? Or worse yet: none of…
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I use generative AI a lot in in my work. It's important to tell it to be critica…
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Comment
I've had some better result by splitting a project in phases. Just like any other project, you need to plan your code for your requirements and make some design choices. LLM's can be pretty good at that, if you have your requirements clear, you need to be very specific when writing your prompts. Make it 'think' about design patterns and logical code separation so the codebase is split in logical chunks (think DDD and/or old fashioned UML). Then, when you have your architecture clear, you can start implementing function by function. And that's where the LLM can really speed up things, because you give it clear constraints and tasks. Just like splitting tasks between programmers in a team, you need to give the LLM very clear instructions.
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2026-01-20T10:1…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | none |
| Reasoning | unclear |
| Policy | none |
| Emotion | indifference |
| Coded at | 2026-04-27T06:24:59.937377 |
Raw LLM Response
[
{"id":"ytc_UgyD26D8k5eEuTOyKTB4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytc_UgzJfJcejIBD27TOe1d4AaABAg","responsibility":"user","reasoning":"consequentialist","policy":"none","emotion":"resignation"},
{"id":"ytc_UgzQNjSA0qEtpTid1Lx4AaABAg","responsibility":"user","reasoning":"virtue","policy":"none","emotion":"outrage"},
{"id":"ytc_UgylIqWuK_5kLDHxZ8B4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytc_UgzxKCP22-KfVIL-8Nx4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_UgxOt3h2QwsNc786jeB4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytc_Ugw9Uhz1GmldAMPdkAB4AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"indifference"},
{"id":"ytc_UgxRXznZpPNTy7M2BdR4AaABAg","responsibility":"user","reasoning":"deontological","policy":"none","emotion":"indifference"},
{"id":"ytc_Ugx0R57_oAVr4HkBrz94AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"outrage"},
{"id":"ytc_UgxEQiQSA81n5a_BREx4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"}
]