Raw LLM Responses
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in
This is a helpful way to explain the AI stack. LLMs think, RAG retrieves, Agents…
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Tyrone Muhammad, BSDA exactly. SO was built for a time when finding the right an…
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Strong framing. From a founder’s POV, the AI question is not just capability, it…
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Angharad Hurley Now that you point it out, I have a feeling that particular sent…
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Everybody keeps talking about chips. But chips alone do not solve: identity, tru…
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This is where most people actually get value- when they stop “learning Claude” a…
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Use of computers is not without consequences, you literally speak to yourself on…
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I agree. Most students are not trying to break the rules, they are trying to nav…
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Comment
Luís Rodrigues I think this has to start higher and has to go deeper. First AI is not equal AI. Anthropic and OpenAI don't share training data, prompts, weights and built in configuration. Anthrophic has 11 products that all have different limits and purposes. When using any of those everything starts with understanding the built in tools like read, webfetch. What you describe is a set of fancy over hyped key term. Behavioral patterns, known use cases. There dependencies. Guardrails, built in immutable prompts, those are the things that differentiate. An MCP an agent could be anything.. My skills in my workspace use API calls, run external judges, confirm semantically, review visually. Are those skills them agents? Can they overcome the char count limit of any built in tool?
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Coding Result
| Dimension | Value |
|---|---|
| Primary value | transparency |
| Secondary value | accountability |
| Alignment target | individual_users |
| Stance | critical |
| Emotion | outrage |
| Value justification | The speaker emphasizes the importance of understanding the built-in tools and limitations of AI systems, suggesting a desire for transparency in AI development and deployment. |
| Target justification | The speaker appears to be addressing individual users, such as themselves, who need to understand the capabilities and limitations of AI systems to use them effectively. |
| Coded at | 2026-06-11T08:12:11Z |
Raw LLM Response
```json
{
"value_primary": "transparency",
"value_secondary": "accountability",
"target": "individual_users",
"stance": "critical",
"emotion": "outrage",
"value_justification": "The speaker emphasizes the importance of understanding the built-in tools and limitations of AI systems, suggesting a desire for transparency in AI development and deployment.",
"target_justification": "The speaker appears to be addressing individual users, such as themselves, who need to understand the capabilities and limitations of AI systems to use them effectively."
}
```