Raw LLM Responses
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in
The 'who benefits' framing exposes the governance gap. I see companies rushing t…
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What’s emerging here is less about search evolution and more about a shift in wh…
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in
The checklist is solid. The honest caveat is that Day 3, building your voice fil…
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in
This is technologically impressive. But there is a profound difference between a…
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in
The real question is not what humans will do when AI does the work. It is what h…
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Many people know how to make clever points, but not everyone has the opportunity…
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in
The scary part about AI isn’t just job replacement. It’s that humans get meaning…
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Clara Hawking- " But more importantly, it frames schools not merely as places of…
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Comment
One big question that stopped me while learning AI/LLMs: Till now, I understood the basics of AI architecture, learning algorithms, and semantic weights. But what really fascinates me is this: How do large LLMs discover and adjust the “right” weights to generate accurate answers for completely new questions they’ve never seen before? I understand the basics of weights and training logic, but this is the point where my curiosity became much deeper than my understanding. Would love to hear insights from people working deeply in LLM training/research.
LinkedIn
AI Products & Tools
Full Stack Developer | JavaScript(ES6+), TypeSc…
2026-05-22T16:3…
Coding Result
| Dimension | Value |
|---|---|
| Primary value | transparency |
| Secondary value | none |
| Alignment target | individual_users |
| Stance | optimistic |
| Emotion | approval |
| Value justification | The speaker is seeking to understand how large LLMs work, specifically how they discover and adjust weights to generate accurate answers, which implies a desire for transparency in AI decision-making. |
| Target justification | The speaker is asking for insights from people working in LLM training/research, indicating that they are seeking to understand the technology for their own benefit as an individual user. |
| Coded at | 2026-06-11T08:00:15Z |
Raw LLM Response
```
{
"value_primary": "transparency",
"value_secondary": "none",
"target": "individual_users",
"stance": "optimistic",
"emotion": "approval",
"value_justification": "The speaker is seeking to understand how large LLMs work, specifically how they discover and adjust weights to generate accurate answers, which implies a desire for transparency in AI decision-making.",
"target_justification": "The speaker is asking for insights from people working in LLM training/research, indicating that they are seeking to understand the technology for their own benefit as an individual user."
}
```