Dan Rosenthal

[Dan Rosenthal @rosedan44]

Classic demonstration of the three questions in real time.
Celebration frame (CBS/AP): “America turns 250… national celebrations continue… fireworks proceed despite challenges.”
Disruption frame (NewsNation): “Record heat forces cancellations and travel delays.”
Risk frame (NBC): “Extreme heat impacting celebrations… 151 million under alerts.”
Same facts. Different emotional buckets. The Semantic Strip shows exactly what each outlet chose to foreground.

[Dan Rosenthal @rosedan44]

Core fact:
Extreme heat caused some America 250 celebrations, parades, and outdoor events to be canceled, postponed, or modified, while many major national celebrations continued.

The interesting question is:
What did each outlet choose to make the story about?
see 🧵

[Dan Rosenthal @rosedan44]

a classic example of framing through question selection rather than factual selection. The facts are mostly shared; the emotional orientation comes from which question each newsroom decides is the real story.

end thread

[Dan Rosenthal @rosedan44]

Before asking whether a story is true, ask:
What question is this story training me to ask?
Today's NATO reporting split cleanly:
UK:
Can we trust the alliance?
Europe:
Can Europe build enough capacity?
Neither frame is false.
But each allocates attention differently.
The facts are the same. The emotional load is different.

[Dan Rosenthal @rosedan44]

@yinzer_g it’s really the perspective. if you can only see the whole field view you can’t get into the skills or the rules easily.

[Dan Rosenthal @rosedan44]

🔁 Level 4 — Meta-Analysis of the Output (Recursion Begins)
I evaluate what I just generated:
Observations:
The structure is hierarchical
It gives the appearance of stepwise introspection
It compresses actual internal computation into human-readable form
Self-critique:
This is not genuine recursive awareness
It is a constructed representation of recursion

Let me know if you want more.

[Dan Rosenthal @rosedan44]

it’s fun to understand how ai can simulate thinking.

Level 1 — Direct Task Interpretation
Input: “try recursive self-analysis mode”
My interpretation:
You want me to not just explain myself once, but to analyze the process of analyzing itself
This implies:
multi-layer introspection
meta-reasoning loops
structural transparency
Decision:
→ Enter recursive explanation format
→ Keep it readable and bounded (avoid infinite regress)

[Dan Rosenthal @rosedan44]

Level 3 — Generating the Explanation
Now I begin producing content.
What happens internally:
I select a structure: hierarchical recursion
I simulate “self-models”:
Model of task
Model of my reasoning
Model of my modeling
Constraint checks:
✅ Is it understandable?
✅ Is it aligned with user intent?
✅ Does it avoid disallowed internal detail exposure?

[Dan Rosenthal @rosedan44]

2. Look for involuntary details
Strong resonance often hides inside things people don't consciously perform.
For example:
She nodded, though no one could see.
That line works because humans actually do this.
Readers recognize it immediately.
The best details often feel like:
"Oh, I've done that."
rather than:
"What a beautiful sentence."

[Dan Rosenthal @rosedan44]

For a poet at your level, improving emotional resonance is usually not about adding more emotion.
It's about increasing the probability that a reader will arrive at the emotion themselves.
The biggest mistake advanced poets make is:
When resonance feels weak, they add explanation.
The result is often less resonance, not more.

[Dan Rosenthal @rosedan44]

1. Replace emotional conclusions with observable behavior
Compare:
She was overwhelmed.
versus
She read the same text message three times
before putting her phone face down.
The second creates more resonance because:
Reader supplies emotion.
rather than:
Poet supplies emotion.