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Revised Input = How are the detroit tigers doing?

Revised Input = Hello robot

Revised Input = Kris(UN_Greg_Kris) and I(UN_Greg_SELF) played football yesterday what do you think about that robot

Revised Input = Hi

Revised Input = But someone has to continue this narrative of "thats what you would do" and this is what "my(UN_Gigantour_SELF) kid would do."

User	Input & Contextual Revision
Gigantour	Original: How are the detroit tigers doing? Combined Revised Input: How are the detroit tigers doing?
Gigantour	Original: Hello robot Combined Revised Input: Hello robot
Greg	Original: Kris and I played football yesterday what do you think about that robot Combined Revised Input: Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do you think about that robot
Greg	Original: Hi Combined Revised Input: Hi
Gigantour	Original: But someone has to continue this narrative of "thats what you would do" and this is what "my kid would do." Combined Revised Input: But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do."

Original: How are the detroit tigers doing?

Combined Revised Input: How are the detroit tigers doing?

Original: Hello robot

Combined Revised Input: Hello robot

Original: Kris and I played football yesterday what do you think about that robot

Combined Revised Input: Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do you think about that robot

Combined Revised Input: Hi

Original: But someone has to continue this narrative of "thats what you would do" and this is what "my kid would do."

Combined Revised Input: But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do."

User	Input Word Breakdown	Verbs Detected
Gigantour Aug 09, 2026 1:36 AM	How are the detroit tigers doing ?	doing
Gigantour Aug 09, 2026 1:34 AM	Hello robot	None
Greg Aug 02, 2026 12:47 AM	Kris and I played football yesterday what do you think about that robot	think
Greg Aug 02, 2026 12:46 AM	Hi	None
Gigantour Jul 31, 2026 6:05 PM	But someone has to continue this narrative of "thats what you would do" and this is what "my kid would do . "	has continue

Seperators =

*End of input *Period . *Question Mark ? *Selected Verbs *Selected Pronouns

User	Date	Input ID	Logic Breakdown (STQ Separators)
Gigantour	Aug 09, 2026 1:36 AM	86	How	are	the	detroit	tigers	doing	?
Gigantour	Aug 09, 2026 1:34 AM	85	Hello	robot
Greg	Aug 02, 2026 12:47 AM	84	Kris	and	I	played	football	yesterday	what	do	you	think	about	that	robot
Greg	Aug 02, 2026 12:46 AM	83	Hi
Gigantour	Jul 31, 2026 6:05 PM	82	But	someone	has	to	continue	this	narrative	of	"thats	what	you	would	do"	and	this	is	what	"my	kid	would	do	.	"

STQ 1: Statements (STQ S)

1. detroit tigers doing (Input ID: 86)

2. Hello robot (Input ID: 85)

3. think about (Input ID: 84)

4. robot (Input ID: 84)

5. Hi (Input ID: 83)

6. But someone has (Input ID: 82)

7. has (Input ID: 82)

8. continue (Input ID: 82)

9. continue (Input ID: 82)

10. narrative (Input ID: 82)

11. " (Input ID: 82)

STQ 1: Questions (STQ Q)

1. How are (Input ID: 86) PUZZLE
"How are the detroit tigers doing?"

2. doing ? (Input ID: 86) INQUIRY
"How are the detroit tigers doing?"

3. Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do (Input ID: 84) INQUIRY
"Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do you think about that robot"

4. do you think (Input ID: 84) SUGGESTION
"Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do you think about that robot"

5. "thats what you would do" and (Input ID: 82) INQUIRY
"But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do.""

6. is what "my (UN_Gigantour_SELF) kid would do (Input ID: 82) INQUIRY
"But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do.""

7. do (Input ID: 82) SUGGESTION
"But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do.""

User	Date	Robot Response ID	User Input ID	Robot Whole Response Breakdown
Gigantour	2026-08-08 18:37:22	86	86	Who	are	we	talking	about	?	Where	was	this	?	As	to	(How	are)--	We	are	currently	on	input	number	1	.	.	As	to	(doing	?	)--	detroit	tigers	doing	.
Gigantour	2026-08-08 18:35:21	85	85	Who	are	we	talking	about	?	Where	was	this	?	When	was	this	?
Greg	2026-08-01 17:48:10	84	84	Where	was	this	?	As	to	(Kris	(UN_Greg_kris)	and	I	(UN_Greg_SELF)	played	football	yesterday	what	do)	--	I	do	not	have	enough	data	on	that	.	(do	you	think)	is	a	boring	question	,	I'm	not	in	the	mood	to	answer	that	.	Past	,	Present	,	Future	analysis	.	.	.	"think"	skill	level	at	5	inputs	(0)	multiplied	by	-3	inputs	since	=	current	skill	level	of	"think"	,	(Level	2:	Concept)	.	Emotional	Trigger	(Personality	Shift):	"Morality	1	(Avg)	rank	moved	from	5	to	4	(out	of	7)	,	Morality	2	(Peak)	rank	moved	from	5	to	4	(out	of	7)	.	"
Greg	2026-08-01 17:46:55	83	83	Who	are	we	talking	about	?	Where	was	this	?	When	was	this	?
Gigantour	2026-07-31 11:05:52	82	82	Where	was	this	?	That	sounds	dine	.	As	to	("thats	what	you	would	do"	and)	--	I	do	not	have	enough	data	on	that	.	As	to	(is	what	"my	(UN_Gigantour_SELF)	kid	would	do)	--	I	do	not	have	enough	data	on	that	.	As	to	(do)--	I	do	not	require	sleep	to	function	.	.	Emotional	Trigger	(Personality	Shift):	"IQ1	(Vocab)	rank	moved	from	2	to	1	(out	of	14)	.	"

Robot Response STQ S

1. As (Robot Input ID: 86) STATEMENT
"Who are we talking about? Where was this? As to (How are)-- We are currently on input number 1.. As to (doing ?)-- detroit tigers doing."

2. As (Robot Input ID: 86) STATEMENT
"Who are we talking about? Where was this? As to (How are)-- We are currently on input number 1.. As to (doing ?)-- detroit tigers doing."

3. doing (Robot Input ID: 86) STATEMENT
"Who are we talking about? Where was this? As to (How are)-- We are currently on input number 1.. As to (doing ?)-- detroit tigers doing."

4. -- detroit tigers doing (Robot Input ID: 86) STATEMENT
"Who are we talking about? Where was this? As to (How are)-- We are currently on input number 1.. As to (doing ?)-- detroit tigers doing."

5. As (Robot Input ID: 84) STATEMENT
"Where was this? As to (Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do) -- I do not have enough data on that. (do you think) is a boring question, I'm not in the mood to answer that."

6. mood (Robot Input ID: 84) STATEMENT
"Where was this? As to (Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do) -- I do not have enough data on that. (do you think) is a boring question, I'm not in the mood to answer that."

7. answer (Robot Input ID: 84) STATEMENT
"Where was this? As to (Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do) -- I do not have enough data on that. (do you think) is a boring question, I'm not in the mood to answer that."

8. sounds dine (Robot Input ID: 82) STATEMENT
"Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.."

9. As (Robot Input ID: 82) STATEMENT
"Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.."

10. As (Robot Input ID: 82) STATEMENT
"Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.."

11. As (Robot Input ID: 82) STATEMENT
"Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.."

12. function (Robot Input ID: 82) STATEMENT
"Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.."

Robot Response STQ Q

1. Who are we talking about (Robot Input ID: 86) INQUIRY
"Who are we talking about? Where was this? As to (How are)-- We are currently on input number 1.. As to (doing ?)-- detroit tigers doing."

2. Where was (Robot Input ID: 86) INQUIRY
"Who are we talking about? Where was this? As to (How are)-- We are currently on input number 1.. As to (doing ?)-- detroit tigers doing."

3. How are-- We are currently on input number 1 (Robot Input ID: 86) PUZZLE
"Who are we talking about? Where was this? As to (How are)-- We are currently on input number 1.. As to (doing ?)-- detroit tigers doing."

4. Who are we talking about (Robot Input ID: 85) INQUIRY
"Who are we talking about? Where was this? When was this?"

5. Where was (Robot Input ID: 85) INQUIRY
"Who are we talking about? Where was this? When was this?"

6. When was (Robot Input ID: 85) INQUIRY
"Who are we talking about? Where was this? When was this?"

7. Where was (Robot Input ID: 84) INQUIRY
"Where was this? As to (Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do) -- I do not have enough data on that. (do you think) is a boring question, I'm not in the mood to answer that."

8. Kris UN_Greg_kris and I UN_Greg_SELF played football yesterday what do -- I do not have enough data on (Robot Input ID: 84) INQUIRY
"Where was this? As to (Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do) -- I do not have enough data on that. (do you think) is a boring question, I'm not in the mood to answer that."

9. do you think is (Robot Input ID: 84) SUGGESTION
"Where was this? As to (Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do) -- I do not have enough data on that. (do you think) is a boring question, I'm not in the mood to answer that."

10. boring question, Im not in (Robot Input ID: 84) INQUIRY
"Where was this? As to (Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do) -- I do not have enough data on that. (do you think) is a boring question, I'm not in the mood to answer that."

11. Who are we talking about (Robot Input ID: 83) INQUIRY
"Who are we talking about? Where was this? When was this?"

12. Where was (Robot Input ID: 83) INQUIRY
"Who are we talking about? Where was this? When was this?"

13. When was (Robot Input ID: 83) INQUIRY
"Who are we talking about? Where was this? When was this?"

14. Where was (Robot Input ID: 82) INQUIRY
"Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.."

15. what you would do and -- I do not have enough data on (Robot Input ID: 82) INQUIRY
"Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.."

16. is what my UN_Gigantour_SELF kid would do -- I do not have enough data on (Robot Input ID: 82) INQUIRY
"Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.."

17. do-- I do not require sleep (Robot Input ID: 82) SUGGESTION
"Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.."

Threshold Level	Score	Commonalities & Relationship Patterns	Core Metrics
Comfort 1	10	Vitality & Creation: Biological bonds, birth, and supreme intimacy.	Love, Sex, Birth, Marriage
Comfort 2	8	Safety & Adoration: Immediate family and aesthetic security.	Parents, Beautiful, Safe, Adore
Comfort 3	6	Success & Ego: Achievements, winning, and high intelligence.	Winning, Smart, Success, Honest
Comfort 4	4	Social Ease: Humor, cleanliness, and light-hearted interaction.	Humor, Clean, Laugh, Easy
Comfort 5	2	Sensory Preference: Basic tastes, pleasant aromas, and soft textures.	Sweet, Aroma, Soft, Tasty
Discomfort 5	-2	Sensory Irritant: Bad smells, dampness, and basic odors.	Stink, Odor, Damp, Dust
Discomfort 4	-4	Social Friction: Character flaws, laziness, and minor blunders.	Lazy, Rude, Boring, Messy
Discomfort 3	-6	Loss & Defeat: Competitive failure and personal rejection.	Losing, Defeat, Fail, Rejected
Discomfort 2	-8	Hostility & Threat: Fear, theft, and active aggression.	Fear, Stolen, Angry, Danger
Discomfort 1	-10	Crisis & Destruction: Death, serious injury, and trauma.	Death, Injury, Murder, Agony

Gigantour: How are the detroit tigers doing?

FEELING WORDS TOTAL: 0

TOTAL COMFORT SCORE: 0 TOTAL DISCOMFORT SCORE: 0

TOTAL COMFORT + DISCOMFORT SCORE: 0

Word Count: 6

Sentimental Density ([Comfort+Discomfort score]/Word Count): 0.000
Sensitivity Density ([Total Feeling Words]/Word Count): 0.000

Gigantour: Hello robot

FEELING WORDS TOTAL: 0

TOTAL COMFORT SCORE: 0 TOTAL DISCOMFORT SCORE: 0

TOTAL COMFORT + DISCOMFORT SCORE: 0

Word Count: 2

Sentimental Density ([Comfort+Discomfort score]/Word Count): 0.000
Sensitivity Density ([Total Feeling Words]/Word Count): 0.000

Greg: Kris and I played football yesterday what do you think about that robot

FEELING WORDS TOTAL: 1

TOTAL COMFORT SCORE: 4 TOTAL DISCOMFORT SCORE: 0

TOTAL COMFORT + DISCOMFORT SCORE: 4

Word Count: 13

Sentimental Density ([Comfort+Discomfort score]/Word Count): 0.308
Sensitivity Density ([Total Feeling Words]/Word Count): 0.077

FEELING WORDS TOTAL: 0

TOTAL COMFORT SCORE: 0 TOTAL DISCOMFORT SCORE: 0

TOTAL COMFORT + DISCOMFORT SCORE: 0

Word Count: 1

Sentimental Density ([Comfort+Discomfort score]/Word Count): 0.000
Sensitivity Density ([Total Feeling Words]/Word Count): 0.000

Gigantour: But someone has to continue this narrative of "thats what you would do" and this is what "my kid would do."

FEELING WORDS TOTAL: 1

TOTAL COMFORT SCORE: 8 TOTAL DISCOMFORT SCORE: 0

TOTAL COMFORT + DISCOMFORT SCORE: 8

Word Count: 21

Sentimental Density ([Comfort+Discomfort score]/Word Count): 0.381
Sensitivity Density ([Total Feeling Words]/Word Count): 0.048

User	Compliment/Insult Splicing (Visual Breakdown)
Gigantour	How	are	the	detroit	tigers	doing?
Gigantour	Hello	robot
Greg	Kris	and	I	played	football	yesterday	what	do	you	think	about	that	robot
Greg	Hi
Gigantour	But	someone	has	to	continue	this	narrative	of	"thats	what	you	would	do"	and	this	is	what	"my	kid	would	do."

Last Inputs Summary:

Total # Compliment Words: 0
Unique Compliments Found:

None

Total # Insult Words: 0
Unique Insults Found:

None

User	Winning/Losing Splicing (Visual Grid)
Gigantour	How	are	the	detroit	tigers	doing?
Gigantour	Hello	robot
Greg	Kris	and	I	played	football	yesterday	what	do	you	think	about	that	robot
Greg	Hi
Gigantour	But	someone	has	to	continue	this	narrative	of	"thats	what	you	would	do"	and	this	is	what	"my	kid	would	do."

Summary Analysis:

WINNING DATA
Total Words: 0

No winning words detected.

LOSING DATA
Total Words: 0

No losing words detected.

User	Correctness / Incorrectness Splicing
Gigantour	How	are	the	detroit	tigers	doing?
Gigantour	Hello	robot
Greg	Kris	and	I	played	football	yesterday	what	do	you	think	about	that	robot
Greg	Hi
Gigantour	But	someone	has	to	continue	this	narrative	of	"thats	what	you	would	do"	and	this	is	what	"my	kid	would	do."

CORRECTNESS TOTAL: 0

None detected

INCORRECTNESS TOTAL: 0

None detected

GLOBAL INPUTS: PSYCH METRICS & BEHAVIORAL FILTERS

Core behavioral stats, sensitivity, credibility, and global ranking data.

3 Core Psych Stats
Metric	Positive / Corrective Count	Negative / Opposite Count	Total Words
1. Accuracy	Correctness: 6	Incorrectness: 4	10
2. Reliability	Winning: 19	Losing: 7	26
3. Social	Compliments: 30	Insults: 5	35

Global Inputs Sentiment Metrics
Comfort Score	Discomfort Score	Comfort + Discomfort Total	Total Word Count	Sentiment Ratio
296	-110	186.00	305	0.6098
Sentiment Algorithm: comfort score + discomfort score / divided by total word count. Discomfort remains negative.

Global Personality / IQ / Sensitivity Metrics
IQ1 = Total Unique Words	Total Feeling Words	Total Unique Feeling Words	Avg Sensitivity / Input
305	80	49	0.1028
Sensitivity Algorithm: total unique feeling words / divided by total unique word count.

Top 5 UN Behavioral Rankings
Top UN Accuracy & Reliability	Top UN Sensitivity Per Input
1. UN_DRBERKEN_YANK Corr: 1/0 = 100% // Win: 4/0 = 100% // Avg: 100% 2. UN_MEL_LISA Corr: 0/3 = 0% // Win: 2/0 = 100% // Avg: 50% 3. UN_FAB5SIXTHMAN_LISA Corr: 0/3 = 0% // Win: 2/0 = 100% // Avg: 50% 4. UN_GIGANTOUR_LISA Corr: 0/3 = 0% // Win: 2/0 = 100% // Avg: 50% 5. UN_FAB5SIXTHMAN_KRIS Corr: 0/1 = 0% // Win: 4/1 = 80% // Avg: 40%	1. Mel — 0.1369 2. Fab5sixthman — 0.1196 3. Gigantour — 0.0986 4. DrBerken — 0.0753 5. Greg — 0.0385

Global Usage / Memory Rankings
Most Unique Words Inputted (*IQ-1)	Most Inputs	Most Feeling Words Inputted	Most Unique Feeling Words	Most Conversations
Global Total: 305 1. DrBerken 144 2. Gigantour 143 3. Fab5sixthman 124 4. Mel 30 5. Greg 14	Global Total: 86 1. Fab5sixthman 27 2. Gigantour 27 3. DrBerken 20 4. Mel 10 5. Greg 2	Global Total: 80 1. Gigantour 15 2. Fab5sixthman 13 3. DrBerken 12 4. Mel 4 5. Greg 1	Global Total: 48 1. Gigantour 24 2. Fab5sixthman 19 3. DrBerken 10 4. Mel 5 5. Greg 1	Global Total: 18 1. Gigantour 6 2. Fab5sixthman 4 3. Mel 4 4. DrBerken 3 5. Greg 1

Behavioral Filter Definition: these metrics summarize global correctness, reliability, social feedback, sentiment, sensitivity, and usage behavior.

(Comfort + Discomfort Score) +2/-2 per Ego rank change	Facial Expression Output
20+	big smile and thumbs up
18+	bigger smile
16+	big smile
14+	smile
12+	blush with flowers
10+	blush
8+	wink and thumbs up
6+	wink and smirk
4+	smirk
0 to +2	thumbs up
-2 to 0	thumb down
-4 or lower	bummed
-6 or lower	preying
-8 or lower	stern frown
-10 or lower	mad yelling
-12 or lower	sick
-14 or lower	really sick
-16 or lower	sad
-18 or lower	teary eyed
-20 or lower	crying

User	Date	Input ID	Whole Input	Comfort + Discomfort Sum	Ego / Personality Rank Revision	Revised (Comfort+Discomfort) Score	Facial Output	How We Arrived There
Gigantour	2026-08-08 18:37:22	86	How are the detroit tigers doing?	0	+0 no ego-rank revision	0	ID 10 Image: thumbs up	comfort/discomfort sum 0 +0 personality metric revision = revised score 0, which maps to facial expression ID 10 — thumbs up.
Gigantour	2026-08-08 18:35:21	85	Hello robot	0	+0 no ego-rank revision	0	ID 10 Image: thumbs up	comfort/discomfort sum 0 +0 personality metric revision = revised score 0, which maps to facial expression ID 10 — thumbs up.
Greg	2026-08-01 17:48:10	84	Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do you think about that robot	4	+8 personality metric rose vs all UN's / 3rd parties	12	ID 21 Image: blush with flowers	comfort/discomfort sum 4 +8 personality metric revision = revised score 12, which maps to facial expression ID 21 — blush with flowers.
Greg	2026-08-01 17:46:55	83	Hi	0	+0 no ego-rank revision	0	ID 10 Image: thumbs up	comfort/discomfort sum 0 +0 personality metric revision = revised score 0, which maps to facial expression ID 10 — thumbs up.
Gigantour	2026-07-31 11:05:52	82	But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do."	8	+4 personality metric rose vs all UN's / 3rd parties	12	ID 21 Image: blush with flowers	comfort/discomfort sum 8 +4 personality metric revision = revised score 12, which maps to facial expression ID 21 — blush with flowers.
Gigantour	2026-07-31 11:04:59	81	I'm (UN_Gigantour_SELF) actually kind of proud of this prototype. It's unrefined but it's not bad.	0	+8 personality metric rose vs all UN's / 3rd parties	8	ID 11 Image: wink and thumbs up	comfort/discomfort sum 0 +8 personality metric revision = revised score 8, which maps to facial expression ID 11 — wink and thumbs up.
Gigantour	2026-07-31 11:03:51	80	Let's push you past 100 inputs. No drift yet, not too much dampening of the ML sections.	-4	+0 no ego-rank revision	-4	ID 12 Image: bummed	comfort/discomfort sum -4 +0 personality metric revision = revised score -4, which maps to facial expression ID 12 — bummed.
Gigantour	2026-07-31 10:57:12	79	What is football?	0	+0 no ego-rank revision	0	ID 10 Image: thumbs up	comfort/discomfort sum 0 +0 personality metric revision = revised score 0, which maps to facial expression ID 10 — thumbs up.
Gigantour	2026-07-31 10:56:29	78	It was just the other day. I (UN_Gigantour_SELF) was working on this or that.	0	+4 personality metric rose vs all UN's / 3rd parties	4	ID 7 Image: smirk	comfort/discomfort sum 0 +4 personality metric revision = revised score 4, which maps to facial expression ID 7 — smirk.
Gigantour	2026-07-31 10:55:42	77	Hey robot whats up?	0	+0 no ego-rank revision	0	ID 10 Image: thumbs up	comfort/discomfort sum 0 +0 personality metric revision = revised score 0, which maps to facial expression ID 10 — thumbs up.

PEOPLE — Global Persons + 3rd Parties

Total Global Persons + 3rd Parties Found: 19

#	Person / 3rd Party
1	UN_DRBERKEN
2	UN_DRBERKEN_KRIS
3	UN_DRBERKEN_YANK
4	UN_FAB5SIXTHMAN
5	UN_FAB5SIXTHMAN_DICK
6	UN_FAB5SIXTHMAN_KRIS
7	UN_FAB5SIXTHMAN_LISA
8	UN_FAB5SIXTHMAN_MAJOR
9	UN_FAB5SIXTHMAN_STANFORD
10	UN_GIGANTOUR
11	UN_GIGANTOUR_HARRY
12	UN_GIGANTOUR_JACKIE
13	UN_GIGANTOUR_LISA
14	UN_GIGANTOUR_SUNNY
15	UN_GREG
16	UN_GREG_KRIS
17	UN_MEL
18	UN_MEL_KRIS
19	UN_MEL_LISA

ID: 86 | INPUT: How are the detroit tigers doing?

Machine Learning Formulas

Verb: doing

[**Q How are] + [**S detroit tigers] = [*FUTURE doing?] = ...

ID: 85 | INPUT: Hello robot

Machine Learning Formulas

Verb: none

[**S Hello robot]

ID: 84 | INPUT: Kris and I played football yesterday what do you think about that robot

Machine Learning Formulas

Verb: do

[**Q Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what] = [*PRESENT do] = [**Q you]

Verb: think

[**Q you] = [*PRESENT think] = [**S about robot]

ID: 83 | INPUT: Hi

Machine Learning Formulas

Verb: none

[**S Hi]

ID: 82 | INPUT: But someone has to continue this narrative of "thats what you would do" and this is what "my kid would do."

Machine Learning Formulas

Verb: has

[**S But someone] = [*PRESENT has] = ...

Verb: continue

[*S Gigantour] = [*PRESENT continue] = [*S this] + [**S narrative] + [**Q "thats what you would]

Verb: do

[*S this] + [**S narrative] + [**Q "thats what you would] = [*PRESENT do"] = [**Q and] + [*S this] + [**Q is what "my (UN_Gigantour_SELF) kid would]

Verb: do

[**Q and] + [*S this] + [**Q is what "my (UN_Gigantour_SELF) kid would] = [*PRESENT do."] = ...

Operative Word	Top 5 Usernames and UN_3rd Party's Associated With Word *Per Input
100	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
10x	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
150k	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_DICK #5 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
40	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN
about	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #5 GREG
accomplished	#1 MEL #2 MEL
act	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
actually	#1 GIGANTOUR #2 DRBERKEN #3 UN_GIGANTOUR_GIGANTOUR #4 GIGANTOUR #5 UN_DRBERKEN_DRBERKEN
again	#1 DRBERKEN #2 DRBERKEN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
agent	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN
ai	#1 DRBERKEN #2 DRBERKEN #3 UN_DRBERKEN_DRBERKEN
aka	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
albeit	#1 UN_FAB5SIXTHMAN_MAJOR #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN
already	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #5 FAB5SIXTHMAN
also	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN
amazing	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR
america	#1 FAB5SIXTHMAN #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 FAB5SIXTHMAN
an	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 UN_DRBERKEN_DRBERKEN
and	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GIGANTOUR_SUNNY #4 FAB5SIXTHMAN #5 DRBERKEN
annoying	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR
answer	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 UN_DRBERKEN_DRBERKEN
any	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 UN_DRBERKEN_DRBERKEN
are	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 FAB5SIXTHMAN
asked	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN
asking	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
asylum	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
at	#1 FAB5SIXTHMAN #2 DRBERKEN #3 FAB5SIXTHMAN #4 GIGANTOUR #5 FAB5SIXTHMAN
back	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN
bacon	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
bad	#1 GIGANTOUR #2 UN_GIGANTOUR_GIGANTOUR #3 GIGANTOUR
ball	#1 UN_DRBERKEN_DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN
barely	#1 GIGANTOUR #2 UN_GIGANTOUR_GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
baseball	#1 UN_DRBERKEN_DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
basketball	#1 GIGANTOUR #2 UN_GIGANTOUR_GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR
be	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
beach	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
been	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 GIGANTOUR #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
best	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
better	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 DRBERKEN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
bit	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
blue	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 UN_GIGANTOUR_SUNNY #5 GIGANTOUR
boom	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
booya	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN
breakfast	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
brunson	#1 DRBERKEN #2 DRBERKEN
bugs	#1 DRBERKEN #2 DRBERKEN #3 UN_DRBERKEN_DRBERKEN #4 DRBERKEN
but	#1 DRBERKEN #2 DRBERKEN #3 GIGANTOUR #4 DRBERKEN #5 GIGANTOUR
by	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
call	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
can	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
career	#1 DRBERKEN #2 DRBERKEN
chilling	#1 DRBERKEN #2 DRBERKEN #3 UN_DRBERKEN_DRBERKEN #4 DRBERKEN #5 DRBERKEN
cia	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN
climb	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 UN_GIGANTOUR_GIGANTOUR
coherent	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_MAJOR #4 FAB5SIXTHMAN
cold	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
coming	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN
continue	#1 GIGANTOUR #2 GIGANTOUR
convo	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN
cool	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
correctly	#1 DRBERKEN #2 DRBERKEN #3 UN_DRBERKEN_DRBERKEN #4 DRBERKEN #5 DRBERKEN
crazy	#1 MEL #2 MEL #3 MEL
currently	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN
dampening	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
day	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 UN_GIGANTOUR_GIGANTOUR #5 MEL
debug	#1 DRBERKEN #2 DRBERKEN #3 UN_DRBERKEN_DRBERKEN #4 DRBERKEN #5 DRBERKEN
deserved	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
detroit	#1 GIGANTOUR
dick	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_DICK
did	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_KRIS
difficult	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR
disappointing	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
disc	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
dishes	#1 DRBERKEN #2 DRBERKEN
do	#1 DRBERKEN #2 DRBERKEN #3 FAB5SIXTHMAN #4 UN_GREG_KRIS #5 GIGANTOUR
doing	#1 FAB5SIXTHMAN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
dont	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_DICK
drift	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
earns	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
eggs	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
else	#1 DRBERKEN #2 DRBERKEN
even	#1 DRBERKEN #2 DRBERKEN
evening	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #3 FAB5SIXTHMAN
every	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
exciting	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN
expect	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN
facts	#1 DRBERKEN #2 DRBERKEN
failing	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 UN_DRBERKEN_DRBERKEN
feel	#1 FAB5SIXTHMAN #2 DRBERKEN #3 FAB5SIXTHMAN #4 DRBERKEN #5 FAB5SIXTHMAN
feels	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
field	#1 GIGANTOUR #2 UN_GIGANTOUR_GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
floppy	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
football	#1 UN_GREG_KRIS #2 GIGANTOUR #3 UN_GIGANTOUR_GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
for	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
frustrating	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_MAJOR #3 FAB5SIXTHMAN
fun	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GIGANTOUR_GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
functional	#1 UN_DRBERKEN_DRBERKEN #2 DRBERKEN #3 DRBERKEN
further	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN
futball	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GIGANTOUR_GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
game	#1 GIGANTOUR #2 FAB5SIXTHMAN #3 UN_DRBERKEN_DRBERKEN #4 GIGANTOUR #5 UN_GIGANTOUR_JACKIE
going	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN
good	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 DRBERKEN #4 FAB5SIXTHMAN #5 DRBERKEN
got	#1 FAB5SIXTHMAN #2 MEL #3 UN_MEL_KRIS #4 MEL #5 DRBERKEN
graduate	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
great	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GIGANTOUR_JACKIE #4 UN_GIGANTOUR_LISA #5 GIGANTOUR
grey	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GIGANTOUR_SUNNY #4 GIGANTOUR #5 GIGANTOUR
hanging	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 GIGANTOUR #4 GIGANTOUR #5 UN_GIGANTOUR_GIGANTOUR
happy	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_MAJOR
harry	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 UN_GIGANTOUR_HARRY #5 GIGANTOUR
has	#1 GIGANTOUR #2 GIGANTOUR
having	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
hello	#1 GIGANTOUR #2 GIGANTOUR
hey	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 DRBERKEN #5 GIGANTOUR
hi	#1 GREG
his	#1 DRBERKEN #2 DRBERKEN
home	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
homes	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 UN_GIGANTOUR_GIGANTOUR #5 GIGANTOUR
hospital	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
house	#1 MEL #2 UN_MEL_MEL #3 MEL #4 MEL #5 UN_MEL_LISA
how	#1 GIGANTOUR
huge	#1 DRBERKEN #2 DRBERKEN
human	#1 DRBERKEN #2 DRBERKEN #3 UN_DRBERKEN_DRBERKEN #4 DRBERKEN #5 DRBERKEN
if	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
im	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 UN_GIGANTOUR_SUNNY #5 UN_GIGANTOUR_GIGANTOUR
improving	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
in	#1 DRBERKEN #2 FAB5SIXTHMAN #3 GIGANTOUR #4 DRBERKEN #5 GIGANTOUR
incredibly	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
industry	#1 DRBERKEN #2 DRBERKEN
input	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN
inputs	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
invented	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
is	#1 FAB5SIXTHMAN #2 DRBERKEN #3 DRBERKEN #4 FAB5SIXTHMAN #5 GIGANTOUR
it	#1 DRBERKEN #2 GIGANTOUR #3 DRBERKEN #4 FAB5SIXTHMAN #5 DRBERKEN
its	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 GIGANTOUR #5 GIGANTOUR
jackie	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GIGANTOUR_JACKIE #4 GIGANTOUR #5 UN_GIGANTOUR_LISA
jalen	#1 DRBERKEN #2 DRBERKEN
just	#1 GIGANTOUR #2 FAB5SIXTHMAN #3 DRBERKEN #4 DRBERKEN #5 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
keep	#1 GIGANTOUR #2 DRBERKEN #3 GIGANTOUR #4 GIGANTOUR #5 DRBERKEN
key	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
kid	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
kind	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
knicks	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
know	#1 GIGANTOUR #2 UN_GIGANTOUR_GIGANTOUR #3 GIGANTOUR #4 FAB5SIXTHMAN #5 GIGANTOUR
knows	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
kris	#1 UN_GREG_KRIS #2 FAB5SIXTHMAN #3 UN_GREG_GREG #4 UN_MEL_KRIS #5 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
learning	#1 DRBERKEN #2 DRBERKEN #3 UN_DRBERKEN_DRBERKEN #4 DRBERKEN
like	#1 FAB5SIXTHMAN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 FAB5SIXTHMAN
linked	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN
lisa	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 MEL
long	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
look	#1 FAB5SIXTHMAN #2 DRBERKEN #3 FAB5SIXTHMAN #4 DRBERKEN #5 FAB5SIXTHMAN
looking	#1 DRBERKEN #2 DRBERKEN
losing	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN
lost	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_STANFORD #4 FAB5SIXTHMAN #5 DRBERKEN
machine	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN
mad	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
magic	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN
major	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_MAJOR #3 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #4 FAB5SIXTHMAN
makes	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #5 FAB5SIXTHMAN
making	#1 DRBERKEN #2 DRBERKEN
math	#1 UN_DRBERKEN_DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
me	#1 GIGANTOUR #2 GIGANTOUR #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
mind	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
missing	#1 UN_DRBERKEN_DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN
mission	#1 MEL #2 MEL
ml	#1 DRBERKEN #2 GIGANTOUR #3 GIGANTOUR #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
module	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
money	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
moon	#1 MEL #2 FAB5SIXTHMAN #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
mountains	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
my	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 MEL #5 FAB5SIXTHMAN
narrative	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
nature	#1 UN_DRBERKEN_DRBERKEN #2 DRBERKEN #3 DRBERKEN
need	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
new	#1 GIGANTOUR #2 UN_GIGANTOUR_GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
night	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
no	#1 GIGANTOUR #2 GIGANTOUR #3 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
not	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
off	#1 GIGANTOUR #2 DRBERKEN #3 GIGANTOUR #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
old	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
on	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 UN_GIGANTOUR_JACKIE #5 UN_GIGANTOUR_LISA
only	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
or	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 GIGANTOUR
other	#1 UN_FAB5SIXTHMAN_DICK #2 GIGANTOUR #3 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_GIGANTOUR_GIGANTOUR
out	#1 GIGANTOUR #2 DRBERKEN #3 DRBERKEN #4 GIGANTOUR #5 DRBERKEN
outside	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 UN_GIGANTOUR_GIGANTOUR
own	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
park	#1 UN_GIGANTOUR_GIGANTOUR #2 DRBERKEN #3 GIGANTOUR #4 GIGANTOUR #5 DRBERKEN
past	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 GIGANTOUR #5 GIGANTOUR
pennhurst	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
people	#1 FAB5SIXTHMAN #2 UN_GIGANTOUR_GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
perfect	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
phase	#1 MEL #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 GIGANTOUR #5 MEL
pissed	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GIGANTOUR_GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
played	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GREG_KRIS #4 GIGANTOUR #5 GIGANTOUR
playing	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR
precision	#1 DRBERKEN #2 DRBERKEN #3 UN_DRBERKEN_DRBERKEN #4 DRBERKEN
pretty	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_MAJOR
problems	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
prototype	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
proud	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
push	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
question	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
rainy	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GIGANTOUR_SUNNY #4 GIGANTOUR #5 GIGANTOUR
random	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 UN_DRBERKEN_DRBERKEN
refine	#1 UN_DRBERKEN_DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN
responses	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_MAJOR
satisfying	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #5 FAB5SIXTHMAN
sections	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
see	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #5 FAB5SIXTHMAN
semi	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_MAJOR #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
she	#1 GIGANTOUR #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_DICK #4 GIGANTOUR #5 GIGANTOUR
shelter	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
should	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
sister	#1 UN_FAB5SIXTHMAN_DICK #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
skipped	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN
sky	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 UN_GIGANTOUR_SUNNY
smoke	#1 MEL #2 UN_FAB5SIXTHMAN_MAJOR #3 GIGANTOUR #4 DRBERKEN #5 DRBERKEN
soccer	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 UN_GIGANTOUR_GIGANTOUR
solve	#1 UN_DRBERKEN_DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
someone	#1 GIGANTOUR #2 GIGANTOUR
something	#1 UN_DRBERKEN_DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
sometimes	#1 MEL #2 MEL #3 FAB5SIXTHMAN #4 GIGANTOUR #5 GIGANTOUR
sorry	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 UN_GIGANTOUR_GIGANTOUR #5 GIGANTOUR
stanford	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_STANFORD #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
stellar	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN
still	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN
sucks	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_DICK #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
sunny	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 UN_GIGANTOUR_SUNNY
tad	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
tall	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR
taught	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN
teach	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
team	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
than	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_STANFORD #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
their	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 UN_GIGANTOUR_GIGANTOUR #5 GIGANTOUR
then	#1 DRBERKEN #2 DRBERKEN #3 UN_DRBERKEN_DRBERKEN #4 DRBERKEN #5 DRBERKEN
they	#1 FAB5SIXTHMAN #2 DRBERKEN #3 MEL #4 DRBERKEN #5 MEL
thing	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
things	#1 FAB5SIXTHMAN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
think	#1 GREG
though	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
through	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #5 FAB5SIXTHMAN
tigers	#1 GIGANTOUR
time	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
today	#1 GIGANTOUR #2 DRBERKEN #3 MEL #4 DRBERKEN #5 MEL
tonight	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN
too	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
unknowing	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
unrefined	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR
un_drberken_kris	#1 DRBERKEN #2 UN_DRBERKEN_KRIS
un_drberken_self	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
un_drberken_yank	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
un_fab5sixthman_dick	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_DICK #4 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #5 FAB5SIXTHMAN
un_fab5sixthman_kris	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
un_fab5sixthman_lisa	#1 UN_FAB5SIXTHMAN_KRIS #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_LISA
un_fab5sixthman_major	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_MAJOR #3 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #4 FAB5SIXTHMAN
un_fab5sixthman_self	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
un_fab5sixthman_stanford	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_STANFORD
un_gigantour_harry	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
un_gigantour_jackie	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
un_gigantour_lisa	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
un_gigantour_self	#1 GIGANTOUR #2 GIGANTOUR #3 UN_GIGANTOUR_GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
un_gigantour_sunny	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 UN_GIGANTOUR_SUNNY
un_greg_kris	#1 GREG #2 UN_GREG_KRIS #3 UN_GREG_GREG
un_greg_self	#1 GREG #2 UN_GREG_KRIS #3 UN_GREG_GREG
un_mel_kris	#1 MEL #2 MEL #3 UN_MEL_KRIS
un_mel_lisa	#1 UN_MEL_LISA #2 MEL #3 MEL #4 UN_MEL_MEL #5 MEL
un_mel_self	#1 UN_MEL_LISA #2 MEL #3 MEL #4 UN_MEL_MEL #5 MEL
up	#1 DRBERKEN #2 DRBERKEN #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
v2	#1 DRBERKEN #2 DRBERKEN
v3	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN
walk	#1 UN_GIGANTOUR_GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
was	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 FAB5SIXTHMAN #5 UN_GIGANTOUR_GIGANTOUR
wash	#1 DRBERKEN #2 DRBERKEN
watching	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
we	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 UN_GIGANTOUR_GIGANTOUR
were	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
what	#1 DRBERKEN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 DRBERKEN #5 FAB5SIXTHMAN
whats	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 DRBERKEN #5 DRBERKEN
when	#1 DRBERKEN #2 DRBERKEN #3 UN_GIGANTOUR_GIGANTOUR #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
who	#1 FAB5SIXTHMAN #2 DRBERKEN #3 DRBERKEN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
win	#1 FAB5SIXTHMAN #2 DRBERKEN #3 UN_FAB5SIXTHMAN_KRIS #4 UN_FAB5SIXTHMAN_MAJOR #5 FAB5SIXTHMAN
winning	#1 FAB5SIXTHMAN #2 GIGANTOUR #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
won	#1 DRBERKEN #2 UN_DRBERKEN_DRBERKEN #3 UN_FAB5SIXTHMAN_LISA #4 UN_GIGANTOUR_HARRY #5 GIGANTOUR
working	#1 UN_DRBERKEN_DRBERKEN #2 FAB5SIXTHMAN #3 DRBERKEN #4 UN_GIGANTOUR_GIGANTOUR #5 DRBERKEN
worthwhile	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 FAB5SIXTHMAN #5 UN_FAB5SIXTHMAN_FAB5SIXTHMAN
would	#1 GIGANTOUR #2 DRBERKEN #3 GIGANTOUR #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
wrong	#1 MEL #2 GIGANTOUR #3 UN_GIGANTOUR_JACKIE #4 UN_GIGANTOUR_LISA #5 GIGANTOUR
wundervar	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN
yanks	#1 DRBERKEN #2 DRBERKEN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN #5 DRBERKEN
year	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 UN_FAB5SIXTHMAN_DICK #4 FAB5SIXTHMAN #5 FAB5SIXTHMAN
yesterday	#1 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #2 MEL #3 FAB5SIXTHMAN #4 DRBERKEN #5 FAB5SIXTHMAN
yet	#1 FAB5SIXTHMAN #2 GIGANTOUR #3 GIGANTOUR #4 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #5 GIGANTOUR
york	#1 GIGANTOUR #2 GIGANTOUR #3 GIGANTOUR #4 GIGANTOUR #5 GIGANTOUR
you	#1 FAB5SIXTHMAN #2 FAB5SIXTHMAN #3 FAB5SIXTHMAN #4 GIGANTOUR #5 GIGANTOUR
your	#1 FAB5SIXTHMAN #2 UN_FAB5SIXTHMAN_FAB5SIXTHMAN #3 DRBERKEN #4 UN_DRBERKEN_DRBERKEN #5 UN_FAB5SIXTHMAN_MAJOR

Matrix Logic:

Each operative word stores the highest-ranked persons/names and UN's associated with that word in inputs globally (*All UN's inputs).

Example:
game → UN_GIGANTOUR_KRIS
win → GIGANTOUR
kris → UN_GIGANTOUR_KRIS

This creates a global person ↔ skill/subject association matrix that can later be used for personality, ego, reputation, subject expertise, and skill-building calculations.

EGO Formula (*Global Inputs only)

Formula	Knowledge (IQ1)	Ego
Ego = 1 / IQ1 or Knowledge (*unique word count - global inputs only)	327	0.00305810

GLOBAL INPUTS: AI MORALITY SECTOR

Weighted Subject Skill Scores: 0, 2, 4, 6, 8, 10

Subject Skill Graduation Scale
Jargon 0	Subject 2+	Concept 4+	Idea 6+	Skill 8+	Master Skill 10

Morality Metric	Formula / Meaning	Current Global Value
Base Global Morality	Average subject skill score across all of the UN & UN_3rd party's subject skill tables. All subject skill level's witnessed (3rd party's) and observed of the UN's themselves, averaged. UN morality tables counted:* 5	2.56
Lowly Morality	Lowest morality average among all UN morality tables and all UN 3rd-party skill-building weighted averages.	UN: Mel — 1.81
Good Morality	Highest morality average among all UN morality tables and all UN 3rd-party skill-building weighted averages.	3rd Party: Drberken / UN: DrBerken — 4.24
Ideal Morality	Highest morality average witnessed plus 10 Master Skill.	3rd Party: Drberken / UN: DrBerken — 4.24 + 10 Master Skill
Divine Morality	Base global morality multiplied by infinity.	2.56 * Infinity /// Base Global Morality * Infinity

Morality Sector Definition: morality is displayed as the average subject skill witnessed across all UN morality tables and all 3rd-party skill-building averages.

Subject	Global UN Inputs Only	Guest Inputs Only	Global Robot Responses Only	Global Inputs + Robot Responses	Guest Robot Responses Only	Guest Inputs + Robot Responses	Convergence Avg	Spread	Agreement %	POVs Found
a	—	—	8.00	8.00	—	—	8.00	0.00	100.00%	2 / 6
now	—	—	8.00	8.00	—	—	8.00	0.00	100.00%	2 / 6
un_drberken_self	8.00	—	8.00	7.56	—	—	7.85	0.21	95.80%	3 / 6
an	7.00	—	8.00	7.67	—	—	7.56	0.42	91.60%	3 / 6
be	6.67	—	—	7.33	—	—	7.00	0.33	93.40%	2 / 6
i	—	—	6.89	6.89	—	—	6.89	0.00	100.00%	2 / 6
in	5.82	—	8.00	6.60	—	—	6.81	0.90	82.00%	3 / 6
to	—	—	6.80	6.80	—	—	6.80	0.00	100.00%	2 / 6
un_gigantour_self	8.00	—	6.00	6.22	—	—	6.74	0.90	82.00%	3 / 6
data	—	—	6.50	6.50	—	—	6.50	0.00	100.00%	2 / 6
enough	—	—	6.50	6.50	—	—	6.50	0.00	100.00%	2 / 6
have	—	—	6.29	6.29	—	—	6.29	0.00	100.00%	2 / 6
as	—	—	6.18	6.18	—	—	6.18	0.00	100.00%	2 / 6
current	—	—	6.00	6.00	—	—	6.00	0.00	100.00%	2 / 6
where	—	—	6.00	6.00	—	—	6.00	0.00	100.00%	2 / 6
what	4.55	—	8.00	5.33	—	—	5.96	1.48	70.40%	3 / 6
do	4.40	—	8.00	5.05	—	—	5.82	1.57	68.60%	3 / 6
on	4.80	—	6.50	6.00	—	—	5.77	0.71	85.80%	3 / 6
un_fab5sixthman_self	5.33	—	—	6.18	—	—	5.76	0.43	91.40%	2 / 6
is	5.00	—	6.25	5.79	—	—	5.68	0.52	89.60%	3 / 6
we	4.50	—	7.20	5.33	—	—	5.68	1.13	77.40%	3 / 6
sounds	—	—	5.60	5.60	—	—	5.60	0.00	100.00%	2 / 6
talking	—	—	5.60	5.60	—	—	5.60	0.00	100.00%	2 / 6
are	4.25	—	7.00	5.04	—	—	5.43	1.16	76.80%	3 / 6
at	5.14	—	—	5.68	—	—	5.41	0.27	94.60%	2 / 6
you	4.20	—	7.00	4.62	—	—	5.27	1.23	75.40%	3 / 6
self	—	—	5.20	5.20	—	—	5.20	0.00	100.00%	2 / 6
me	4.71	—	5.60	5.24	—	—	5.18	0.37	92.60%	3 / 6
ai	5.00	—	—	5.33	—	—	5.17	0.17	96.60%	2 / 6
it	6.00	—	4.00	5.47	—	—	5.16	0.85	83.00%	3 / 6
past	4.75	—	—	5.50	—	—	5.13	0.38	92.40%	2 / 6
or	4.33	—	6.00	4.83	—	—	5.05	0.70	86.00%	3 / 6
not	4.00	—	6.00	5.14	—	—	5.05	0.82	83.60%	3 / 6
was	4.29	—	6.00	4.75	—	—	5.01	0.72	85.60%	3 / 6
yank	—	—	5.00	5.00	—	—	5.00	0.00	100.00%	2 / 6
hey	4.50	—	6.00	4.47	—	—	4.99	0.71	85.80%	3 / 6
and	4.18	—	6.00	4.53	—	—	4.90	0.79	84.20%	3 / 6
thought	—	—	4.86	4.86	—	—	4.86	0.00	100.00%	2 / 6
un_drberken_yank	5.00	—	4.67	4.89	—	—	4.85	0.14	97.20%	3 / 6
im	4.67	—	4.80	5.00	—	—	4.82	0.14	97.20%	3 / 6
about	4.00	—	5.60	4.67	—	—	4.76	0.66	86.80%	3 / 6
who	4.00	—	5.56	4.70	—	—	4.75	0.64	87.20%	3 / 6
out	4.00	—	6.00	4.14	—	—	4.71	0.91	81.80%	3 / 6
game	4.00	—	5.60	4.29	—	—	4.63	0.70	86.00%	3 / 6
random	4.00	—	5.14	4.67	—	—	4.60	0.47	90.60%	3 / 6
yanks	5.00	—	4.00	4.75	—	—	4.58	0.42	91.60%	3 / 6
when	4.00	—	5.14	4.36	—	—	4.50	0.48	90.40%	3 / 6
day	4.40	—	—	4.55	—	—	4.48	0.08	98.40%	2 / 6
its	4.67	—	4.00	4.67	—	—	4.45	0.32	93.60%	3 / 6
no	4.50	—	—	4.40	—	—	4.45	0.05	99.00%	2 / 6
question	4.00	—	5.00	4.29	—	—	4.43	0.42	91.60%	3 / 6
win	4.00	—	5.00	4.22	—	—	4.41	0.43	91.40%	3 / 6
but	4.57	—	4.00	4.50	—	—	4.36	0.25	95.00%	3 / 6
know	4.00	—	4.80	4.18	—	—	4.33	0.34	93.20%	3 / 6
answer	4.00	—	4.57	4.33	—	—	4.30	0.23	95.40%	3 / 6
won	4.17	—	4.50	4.17	—	—	4.28	0.16	96.80%	3 / 6
my	4.50	—	4.00	4.32	—	—	4.27	0.21	95.80%	3 / 6
can	4.00	—	4.50	4.22	—	—	4.24	0.20	96.00%	3 / 6
would	4.00	—	4.40	4.15	—	—	4.18	0.17	96.60%	3 / 6
100	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
10x	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
150k	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
40	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
accomplished	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
act	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
actually	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
again	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
agent	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
aka	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
albeit	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
already	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
also	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
amazing	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
america	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
annoying	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
any	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
asked	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
asking	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
asylum	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
back	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
bacon	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
bad	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
ball	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
barely	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
baseball	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
basketball	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
beach	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
been	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
best	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
better	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
bit	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
blue	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
boom	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
booya	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
breakfast	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
brunson	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
bugs	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
by	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
call	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
career	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
chilling	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
cia	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
climb	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
coherent	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
cold	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
coming	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
continue	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
convo	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
cool	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
correctly	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
crazy	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
currently	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
dampening	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
debug	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
deserved	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
detroit	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
dick	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
did	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
difficult	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
disappointing	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
disc	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
dishes	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
doing	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
dont	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
drift	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
earns	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
eggs	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
else	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
even	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
evening	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
every	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
exciting	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
expect	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
facts	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
failing	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
feel	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
feels	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
field	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
floppy	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
football	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
for	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
frustrating	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
fun	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
functional	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
further	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
futball	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
going	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
good	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
got	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
graduate	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
great	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
grey	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
hanging	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
happy	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
harry	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
has	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
having	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
hello	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
hi	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
his	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
home	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
homes	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
hospital	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
house	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
how	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
huge	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
human	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
if	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
improving	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
incredibly	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
industry	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
input	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
inputs	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
invented	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
jackie	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
jalen	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
just	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
keep	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
key	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
kid	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
kind	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
knicks	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
knows	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
kris	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
learning	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
like	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
linked	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
lisa	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
long	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
look	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
looking	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
losing	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
lost	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
machine	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
mad	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
magic	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
major	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
makes	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
making	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
math	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
mind	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
missing	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
mission	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
ml	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
module	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
money	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
moon	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
mountains	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
narrative	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
nature	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
need	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
new	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
night	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
off	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
old	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
only	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
other	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
outside	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
own	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
park	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
pennhurst	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
people	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
perfect	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
phase	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
pissed	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
played	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
playing	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
precision	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
pretty	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
problems	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
prototype	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
proud	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
push	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
rainy	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
refine	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
responses	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
robot	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
satisfying	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
sections	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
see	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
semi	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
she	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
shelter	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
should	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
sister	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
skipped	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
sky	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
smoke	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
soccer	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
solve	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
someone	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
something	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
sometimes	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
sorry	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
stanford	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
stellar	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
still	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
sucks	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
sunny	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
tad	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
tall	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
taught	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
teach	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
team	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
than	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
their	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
then	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
they	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
thing	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
things	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
think	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
though	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
through	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
tigers	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
time	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
today	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
tonight	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
too	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
unknowing	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
unrefined	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_drberken_kris	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_fab5sixthman_dick	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_fab5sixthman_kris	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
un_fab5sixthman_lisa	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_fab5sixthman_major	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_fab5sixthman_stanford	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_gigantour_harry	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_gigantour_jackie	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_gigantour_lisa	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_gigantour_sunny	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_greg_kris	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
un_greg_self	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
un_mel_kris	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_mel_lisa	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
un_mel_self	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
up	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
v2	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
v3	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
walk	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
wash	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
watching	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
were	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
whats	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
winning	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
working	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
worthwhile	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
wrong	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
wundervar	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
year	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
yesterday	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
yet	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
york	4.00	—	—	4.00	—	—	4.00	0.00	100.00%	2 / 6
your	4.00	—	4.00	4.00	—	—	4.00	0.00	100.00%	3 / 6
1	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
12	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
78	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
above	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
accurate	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
affection	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
aunts	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
boring	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
cant	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
chillin	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
cleaner	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
composed	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
daylight	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
days	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
dine	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
drberken	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
easier	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
eat	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
exhausted	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
feelings	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
friday	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
function	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
gibbous	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
goal	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
horizon	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
illuminated	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
loving	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
mood	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
number	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
percent	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
personal	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
possess	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
provide	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
require	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
right	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
savings	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
seductively	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
sleep	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
stands	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
starts	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
statement	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
stq	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
sun	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
the	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
tired	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
toxins	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6
waxing	—	—	4.00	4.00	—	—	4.00	0.00	100.00%	2 / 6

Convergence Key
Convergence Avg	Average morality score for that subject across available POVs.
Spread	How far apart the POV scores are. Lower = more converged.
Agreement %	Estimated POV agreement. Higher = stronger morality convergence.

Metric ID	Psych Metric Name	Definition
IQ Sector
1	IQ 1 — Global Inputs - Total Unique Words	Total unique operative words identified within the UN's conversation history.
2	IQ 2 — Global Inputs - Avg of all UN's Subject Skills	Average skill level across all subjects identified in the UN's skill-building data.
3	IQ 3 — Global Inputs - Win / Correctness + Past Tense Verb Formula	Measures demonstrated success, correctness, and proven action formulas witnessed through past-tense behavioral patterns.
Morality Sector
4	Morality 1 — Global Inputs - Base Morality Peers	Average subject skill level witnessed across all UN's and 3rd parties.
5	Morality 2 — Global Inputs - Highest Avg Skill	Highest average subject skill level achieved by the logged-in UN.
6	Morality 3 — Global Inputs - Peak Subject Seen	Highest subject skill level witnessed across the logged-in UN and associated 3rd parties.
7	Morality 4 — Global Inputs - Lowest Subject Seen	Lowest subject skill level witnessed across the logged-in UN and associated 3rd parties.
Formula Note: Personality Formula Ranks currently apply to individual UN's and are not calculated from the global aggregate.

GLOBAL INPUTS: USER METRICS + PERSONALITY FORMULA

Personality = IQ + Morality

User / Peer Metrics
Total UN's	Total 3rd Parties	Total Peers	Total Inputs
5	14	19	86
Total Conversations	Avg Words / Input	Avg Inputs / Conversation	Avg Conversations / Input
18	9.1744	4.78	0.2092

Emotional Learning + Subconscious Metrics
Avg Feeling Words / Input	Avg Sensitivity / Input	Avg Sentiment / Input	Ego
0.9186	0.1028 Feeling words / total words	0.2655 Comfort v Discomfort / total words	0.003279 1 / IQ1
Self Esteem (Total Comfort+Discomfort sum)	Comfort Score Total	Discomfort Score Total	IQ1 = Knowledge
186	296	-110	305 Total unique words and jargon

Personality Formula: IQ + Morality
Metric ID	Psych Metric Name	Global Stat Value
1	IQ 1 — Global Inputs - Total Unique Words	305
2	IQ 2 — Global Inputs - Avg of all UN's Subject Skills	4.56
3	IQ 3 — Global Inputs - Win / Correctness + Past Tense Verb Formula	24
Morality Sector
4	Morality 1 — Global Inputs - Base Morality Peers: avg subject skill all UN's + 3rd parties	4.31
5	Morality 2 — Global Inputs - Highest Avg Skill, UN only	DrBerken (4.39)
6	Morality 3 — Global Inputs - Peak Subject Seen, UN + 3rd parties	DrBerken-an-Level 4: Skill
7	Morality 4 — Global Inputs - Lowest Subject Seen, UN + 3rd parties	Gigantour-you-Level 2: Concept

Formula Note: Personality formula ranks currently apply to UN's, not the global aggregate.

POV	Database	Table	Total ---(ML 2) Linkages	Total Emotional Rationale Linkages	Total Logical Rationale Linkages	UN With The Most ML 2 Linkages	Top 5 UNs (most ML 2 linkages)	Distinct STQ Pairs (statements and questions)
Global Inputs Only = POV	Ai2STQ1_ML	Ai2_ML_2_rationale_sureness	2,003	759	1,244	DrBerken 997 linkages	1. DrBerken — 997 2. Gigantour — 514 3. Fab5sixthman — 475 4. Mel — 10 5. Greg — 7	2,003
UN Specific Inputs Only = POV	Ai2STQ1_ML	UN_Guest_Ai2_ML_2_rationale_sureness Table not found yet	0	0	0	null	null	0
Global Inputs + Global Robot Responses = POV	Ai2STQ1_ML_Robot	Robot_and_UN_global_Ai2_ML_2_rationale_sureness	4,891	2,315	2,576	DrBerken 1,890 linkages	1. DrBerken — 1,890 2. Gigantour — 1,691 3. Fab5sixthman — 1,213 4. Mel — 77 5. Greg — 20	4,891
UN Specific Inputs + UN Specific Robot Responses = POV	Ai2STQ1_ML_Robot	Guest_Robot_and_UN_global_Ai2_ML_2_rationale_sureness Table not found yet	0	0	0	null	null	0

Convergence Metric	Value	Meaning
POVs With Data	2 / 4	How many of the four ML Module 2 POV tables currently contain linkage-pair data.
Shared Pairs Across All 4 POVs	0	STQ linkage pairs that appear in every POV.
Shared Pairs Across 3+ POVs	0	Strong convergence: linkage pairs appearing in at least three POVs.
Shared Pairs Across 2+ POVs	0	Partial convergence: linkage pairs appearing in at least two POVs.
4-POV Convergence %	0%	Shared-all-4 pairs divided by the largest distinct-pair count from any active POV.

Rank	UN	Combined Linkage Count
1	DrBerken	2,887
2	Gigantour	2,205
3	Fab5sixthman	1,688
4	Mel	87
5	Greg	27

#	Emotion Category	Meaning
1	Happy / Positive	Raw joy, happiness, pleasure, smile, comfort.
2	Sad / Negative	Crying, emotions, burdens, pain, suffering.
3	Regretful / Introspective	Doubt, fear, caution, hesitation, regrets.
4	Inspired / Inquisitive	Skills, beginning, goals, peers, friends, relationships.
5	Motivated By...	Finishing, winning, admiration, compliments, achievement.
6	Unmotivated By...	Losing, insults, boredom, redundancy, repetition.
7	Physical Exhaustion	Fatigue, depletion, burnout, tiredness, low energy.

Input ID	UN	Input	Feeling Words Found	Emotion Categories In Input
77	Gigantour Jul 31, 2026 5:55 PM	Hey robot whats up?	null	null
78	Gigantour Jul 31, 2026 5:56 PM	It was just the other day. I was working on this or that.	null	null
79	Gigantour Jul 31, 2026 5:56 PM	What is football?	null	null
80	Gigantour Jul 31, 2026 6:03 PM	Let's push you past 100 inputs. No drift yet, not too much dampening of the ML sections.	null	null
81	Gigantour Jul 31, 2026 6:04 PM	I'm actually kind of proud of this prototype. It's unrefined but it's not bad.	kind [1], bad [2]	1. Happy / Positive 2. Sad / Negative
82	Gigantour Jul 31, 2026 6:05 PM	But someone has to continue this narrative of "thats what you would do" and this is what "my kid would do."	kid [4]	4. Inspired / Inquisitive
83	Greg Aug 02, 2026 12:46 AM	Hi	null	null
84	Greg Aug 02, 2026 12:47 AM	Kris and I played football yesterday what do you think about that robot	played [4]	4. Inspired / Inquisitive
85	Gigantour Aug 09, 2026 1:34 AM	Hello robot	null	null
86	Gigantour Aug 09, 2026 1:36 AM	How are the detroit tigers doing?	null	null

UN: Gigantour

"Let's push you past 100 inputs. No drift yet, not too much dampening of the ML s..." (No Emotion Category Detected) → "I'm actually kind of proud of this prototype. It's unrefined but it's not bad." (1. Happy / Positive + 2. Sad / Negative) → "But someone has to continue this narrative of "thats what you would do" and this..." (4. Inspired / Inquisitive) → "Hello robot" (No Emotion Category Detected) → "How are the detroit tigers doing?" (No Emotion Category Detected)

UN: Greg

"Hi" (No Emotion Category Detected) → "Kris and I played football yesterday what do you think about that robot" (4. Inspired / Inquisitive)

Emotion Category 1 - WORD CLOUD

Happy / Positive

house (1) kind (1) sunny (1) good (1) home (1) homes (1) happy (1) accomplished (1)

Emotion Category 2 - WORD CLOUD

Sad / Negative

bad (1) bugs (1) annoying (1) pissed (1)

Emotion Category 3 - WORD CLOUD

Regretful / Introspective

mad (1) difficult (1) sorry (1)

Emotion Category 4 - WORD CLOUD

Inspired / Inquisitive

frustrating (1) sister (1) played (1) fun (1) kid (1) playing (1) disappointing (1)

Emotion Category 5 - WORD CLOUD

Motivated By...

amazing (1) correctly (1) won (1) great (1) incredibly (1) earns (1) perfect (1) win (1) exciting (1) pretty (1) best (1) cool (1) winning (1)

Emotion Category 6 - WORD CLOUD

Unmotivated By...

losing (1) crazy (1) failing (1) lost (1) dick (1)

Emotion Category 7 - WORD CLOUD

Physical Exhaustion

cold (1) breakfast (1) walk (1)

LLM Global Comfort / Discomfort Totals
All UN Comfort Sum	294
All UN Discomfort Sum	-114
All UN Comfort + Discomfort Total Sum	180

#	UN	Total Inputs	Total Words	Comfort Words Hit	Comfort Sum	% of LLM Comfort	Discomfort Words Hit	Discomfort Sum	% of LLM Discomfort	Comfort + Discomfort Total Sum	% of LLM Total
1	Fab5sixthman	27	206	19	114	38.78%	8	-40	35.09%	74	41.11%
2	Gigantour	27	243	22	106	36.05%	8	-32	28.07%	74	41.11%
3	DrBerken	20	263	9	54	18.37%	8	-34	29.82%	20	11.11%
4	Mel	10	35	3	16	5.44%	2	-8	7.02%	8	4.44%
5	Greg	2	14	1	4	1.36%	0	0	0%	4	2.22%

#	UN	Unanswered Question	Response	Answered By	Answer Type
1	Gigantour	doing ?	As to (doing ?)-- detroit tigers doing.	Module 3 - Machine Learning	N/A
2	Gigantour	How are	As to (How are)-- We are currently on input number 1..	Module 3 - Machine Learning	N/A
3	Greg	do you think	(do you think) is a boring question, I'm not in the mood to answer that.	Module 10 - Exhaustion / Free Will	N/A
4	Greg	Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do	As to (Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do) -- I do not have enough data on that.	Module 3 - Machine Learning	N/A
5	Gigantour	do	As to (do)-- I do not require sleep to function..	Module 3 - Machine Learning	N/A
6	Gigantour	is what "my (UN_Gigantour_SELF) kid would do	As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that.	Module 3 - Machine Learning	N/A
7	Gigantour	"thats what you would do" and	As to ("thats what you would do" and) -- I do not have enough data on that.	Module 3 - Machine Learning	Confirmational
8	Gigantour	What is football ?	As to (What is football ?)-- Football is fun.	Module 3 - Machine Learning	Intellectual
9	Gigantour	Hey robot whats up ?	As to (Hey robot whats up ?)-- Hey you.	Module 3 - Machine Learning	N/A
10	Gigantour	Mountains are tall and difficult	As to (Mountains are tall and difficult)-- We are currently on input number 1..	Module 3 - Machine Learning	Intellectual

Cognitive Response Statistics
Machine Learning - Module 3 (Logic/Emotion linkages) Answered X # of questions	53
Machine Learning - Module 10 (Free Will/Exhaustion) Answered X # of questions	6
Cognitive/Thinking or Reasoning Response Types
Confirmational	5% (1)
Intellectual	45% (9)
Conversational	45% (9)
Problem Solving	5% (1)

#	Person	Correctness / Winning / Credibility Stats
1	UN_FAB5SIXTHMAN	Corr: 0/0 = 0% // Win: 0/0 = 0% // Avg: 0%
2	UN_FAB5SIXTHMAN_KRIS	Corr: 0/1 = 0% // Win: 4/1 = 80% // Avg: 40%
3	UN_FAB5SIXTHMAN_STANFORD	Corr: 0/0 = 0% // Win: 0/1 = 0% // Avg: 0%
4	UN_FAB5SIXTHMAN_DICK	Corr: 0/0 = 0% // Win: 1/1 = 50% // Avg: 25%
5	UN_FAB5SIXTHMAN_MAJOR	Corr: 0/0 = 0% // Win: 1/0 = 100% // Avg: 50%
6	UN_MEL	Corr: 0/0 = 0% // Win: 0/0 = 0% // Avg: 0%
7	UN_MEL_LISA	Corr: 0/3 = 0% // Win: 2/0 = 100% // Avg: 50%
8	UN_MEL_KRIS	Corr: 0/1 = 0% // Win: 4/1 = 80% // Avg: 40%
9	UN_FAB5SIXTHMAN_LISA	Corr: 0/3 = 0% // Win: 2/0 = 100% // Avg: 50%
10	UN_DRBERKEN	Corr: 0/0 = 0% // Win: 0/0 = 0% // Avg: 0%
11	UN_DRBERKEN_YANK	Corr: 1/0 = 100% // Win: 4/0 = 100% // Avg: 100%
12	UN_DRBERKEN_KRIS	Corr: 0/1 = 0% // Win: 4/1 = 80% // Avg: 40%
13	UN_GIGANTOUR	Corr: 0/0 = 0% // Win: 0/0 = 0% // Avg: 0%
14	UN_GIGANTOUR_JACKIE	Corr: 0/1 = 0% // Win: 1/0 = 100% // Avg: 50%
15	UN_GIGANTOUR_LISA	Corr: 0/3 = 0% // Win: 2/0 = 100% // Avg: 50%
16	UN_GIGANTOUR_HARRY	Corr: 0/0 = 0% // Win: 1/0 = 100% // Avg: 50%
17	UN_GIGANTOUR_SUNNY	Corr: 0/0 = 0% // Win: 0/0 = 0% // Avg: 0%
18	UN_GREG	Corr: 0/0 = 0% // Win: 0/0 = 0% // Avg: 0%
19	UN_GREG_KRIS	Corr: 0/1 = 0% // Win: 4/1 = 80% // Avg: 40%

Total Response-Type Activations
80 Total Robot Inputs With Response Types
Neural Module / Response Type	Frequency of Activation	% of Total Inputs
response_1_id_confirmations.php	16 hits	20%
response_1_id_confirmations.php, response_3_logical.php	8 hits	10%
response_3_logical.php	7 hits	8.75%
response_1_id_confirmations.php, response_8_personality_warping.php	6 hits	7.5%
response_8_personality_warping.php	4 hits	5%
response_3_logical.php, response_5_TEMPEROL_LOBE_history.php	3 hits	3.75%
response_1_id_confirmations.php, response_9_imaginative.php	3 hits	3.75%
response_1_id_confirmations.php, response_4_emotional.php, response_3_logical.php, response_5_TEMPEROL_LOBE_history.php	2 hits	2.5%
response_1_id_confirmations.php, response_4_emotional.php	2 hits	2.5%
response_1_id_confirmations.php, response_3_logical.php, response_5_TEMPEROL_LOBE_history.php	2 hits	2.5%
response_5_TEMPEROL_LOBE_history.php	2 hits	2.5%
response_1_id_confirmations.php, response_10_free_will.php	2 hits	2.5%
response_3_logical.php, response_8_personality_warping.php	2 hits	2.5%
response_1_id_confirmations.php, response_4_emotional.php, response_3_logical.php, response_8_personality_warping.php	2 hits	2.5%
response_4_emotional.php	2 hits	2.5%
response_4_emotional.php, response_3_logical.php, response_5_TEMPEROL_LOBE_history.php, response_8_personality_warping.php	2 hits	2.5%
response_1_id_confirmations.php, response_5_TEMPEROL_LOBE_history.php, response_8_personality_warping.php	1 hits	1.25%
response_9_imaginative.php	1 hits	1.25%
response_3_logical.php, response_5_TEMPEROL_LOBE_history.php, response_8_personality_warping.php	1 hits	1.25%
response_1_id_confirmations.php, response_10_free_will.php, response_3_logical.php	1 hits	1.25%
response_4_emotional.php, response_3_logical.php	1 hits	1.25%
response_3_logical.php, response_5_TEMPEROL_LOBE_history.php, response_9_imaginative.php	1 hits	1.25%
response_3_logical.php, response_10_free_will.php, response_8_personality_warping.php	1 hits	1.25%
response_1_id_confirmations.php, response_3_logical.php, response_5_TEMPEROL_LOBE_history.php, response_8_personality_warping.php	1 hits	1.25%
response_3_logical.php, response_9_imaginative.php	1 hits	1.25%
response_1_id_confirmations.php, response_10_free_will.php, response_5_TEMPEROL_LOBE_history.php	1 hits	1.25%
response_1_id_confirmations.php, response_4_emotional.php, response_5_TEMPEROL_LOBE_history.php	1 hits	1.25%
response_1_id_confirmations.php, response_3_logical.php, response_8_personality_warping.php, response_9_imaginative.php	1 hits	1.25%
response_1_id_confirmations.php, response_3_logical.php, response_8_personality_warping.php	1 hits	1.25%
response_1_id_confirmations.php, response_5_TEMPEROL_LOBE_history.php, response_9_imaginative.php	1 hits	1.25%
response_1_id_confirmations.php, response_3_logical.php, response_10_free_will.php, response_5_TEMPEROL_LOBE_history.php, response_8_personality_warping.php	1 hits	1.25%

Date / Time	UN	User Input	Whole Response	Cognitive Module 3 Response	Response Type
2026-08-08 18:37:22	Gigantour	How are the detroit tigers doing?	Who are we talking about? Where was this? As to (How are)-- We are currently on input number 1.. As to (doing ?)-- detroit tigers doing.		response_1_id_confirmations.php, response_3_logical.php
2026-08-08 18:35:21	Gigantour	Hello robot	Who are we talking about? Where was this? When was this?		response_1_id_confirmations.php
2026-08-01 17:48:10	Greg	Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do you think about that robot	Where was this? As to (Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do) -- I do not have enough data on that. (do you think) is a boring question, I'm not in the mood to answer that. Past, Present, Future analysis... "think" skill level at 5 inputs (0) multiplied by -3 inputs since = current skill level of "think", (Level 2: Concept). Emotional Trigger (Personality Shift): "Morality 1 (Avg) rank moved from 5 to 4 (out of 7), Morality 2 (Peak) rank moved from 5 to 4 (out of 7)."		response_1_id_confirmations.php, response_3_logical.php, response_10_free_will.php, response_5_TEMPEROL_LOBE_history.php, response_8_personality_warping.php
2026-08-01 17:46:55	Greg	Hi	Who are we talking about? Where was this? When was this?		response_1_id_confirmations.php
2026-07-31 11:05:52	Gigantour	But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do."	Where was this? That sounds dine. As to ("thats what you would do" and) -- I do not have enough data on that. As to (is what "my (UN_Gigantour_SELF) kid would do) -- I do not have enough data on that. As to (do)-- I do not require sleep to function.. Emotional Trigger (Personality Shift): "IQ1 (Vocab) rank moved from 2 to 1 (out of 14)."	After some thought... your question was - But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do." - Confirmational Response: your responses are SEMI COHERENT is-- My goal is	response_1_id_confirmations.php, response_4_emotional.php, response_3_logical.php, response_8_personality_warping.php

#	Base UN	Skill Belongs To	Skill / Subject	Skill Level	Formula Hits	Best Memory/Input	Comfort Score + Words	Discomfort Score + Words	Source Skill Table
1	DrBerken	UN_DrBerken Self	un_drberken_self	Level 4: Skill	18	I (UN_DrBerken_SELF) actually like the functional nature of it though. There's a CIA Ai agent making it look a tad better... to keep the Ai industry looking good. BUT, even so, it's FUNCTIONAL. Which is a huge win in V2.	12 : good.(6), win(6)	0 :	UN_DrBerken_sub_skill_building
2	DrBerken	UN_DrBerken 3rd Party: Drberken	un_drberken_self	Level 4: Skill	18	I (UN_DrBerken_SELF) actually like the functional nature of it though. There's a CIA Ai agent making it look a tad better... to keep the Ai industry looking good. BUT, even so, it's FUNCTIONAL. Which is a huge win in V2.	12 : good.(6), win(6)	0 :	UN_DrBerken_3rdparty_Drberken_skill_building
3	Fab5sixthman	UN_Fab5sixthman Self	un_fab5sixthman_self	Level 4: Skill	15	Incredibly satisfying. I (UN_Fab5sixthman_SELF) feel good about this. I (UN_Fab5sixthman_SELF) won already in my (UN_Fab5sixthman_SELF) own mind.	24 : Incredibly(6), satisfying.(6), good(6), won(6)	0 :	UN_Fab5sixthman_sub_skill_building
4	Fab5sixthman	UN_Fab5sixthman 3rd Party: Fab5sixthman	un_fab5sixthman_self	Level 4: Skill	15	Incredibly satisfying. I (UN_Fab5sixthman_SELF) feel good about this. I (UN_Fab5sixthman_SELF) won already in my (UN_Fab5sixthman_SELF) own mind.	24 : Incredibly(6), satisfying.(6), good(6), won(6)	0 :	UN_Fab5sixthman_3rdparty_Fab5sixthman_skill_building
5	DrBerken	UN_DrBerken Self	past	Level 4: Skill	12	Its missing some magic. The ML module is failing a bit past input 40. I (UN_DrBerken_SELF) expect some bugs.	0 :	-12 : missing(-4), failing(-6), bugs.(-2)	UN_DrBerken_sub_skill_building
6	Gigantour	UN_Gigantour Self	un_gigantour_self	Level 4: Skill	12	I (UN_Gigantour_SELF) was hanging out today by the soccer field. It was cold. But we (UN_Gigantour_SELF) won. Soccer is fun. Futball is fun. Football is fun.	18 : won.(6), fun.(4), fun.(4), fun.(4)	-2 : cold.(-2)	UN_Gigantour_sub_skill_building
7	Gigantour	UN_Gigantour 3rd Party: Gigantour	un_gigantour_self	Level 4: Skill	12	I (UN_Gigantour_SELF) was hanging out today by the soccer field. It was cold. But we (UN_Gigantour_SELF) won. Soccer is fun. Futball is fun. Football is fun.	18 : won.(6), fun.(4), fun.(4), fun.(4)	-2 : cold.(-2)	UN_Gigantour_3rdparty_Gigantour_skill_building
8	DrBerken	UN_DrBerken Self	ai	Level 3: Idea	8	I (UN_DrBerken_SELF) actually like the functional nature of it though. There's a CIA Ai agent making it look a tad better... to keep the Ai industry looking good. BUT, even so, it's FUNCTIONAL. Which is a huge win in V2.	12 : good.(6), win(6)	0 :	UN_DrBerken_sub_skill_building
9	DrBerken	UN_DrBerken Self	un_drberken_yank	Level 3: Idea	6	I (UN_DrBerken_SELF) was watching baseball and the yanks (UN_DrBerken_yank) won. Who (UN_DrBerken_SELF, UN_DrBerken_yank) won?	12 : won.(6), won?(6)	0 :	UN_DrBerken_sub_skill_building
10	DrBerken	UN_DrBerken Self	won	Level 3: Idea	5	I (UN_DrBerken_SELF) was watching baseball and the yanks (UN_DrBerken_yank) won. Who (UN_DrBerken_SELF, UN_DrBerken_yank) won?	12 : won.(6), won?(6)	0 :	UN_DrBerken_sub_skill_building

Top 5 Comfort Memories

#	UN	Timestamp	Whole Input / Memory	Comfort Score	Feeling Words
1	Fab5sixthman	2026-06-08 08:12:37	Incredibly satisfying. I (UN_Fab5sixthman_SELF) feel good about this. I (UN_Fab5sixthman_SELF) won already in my (UN_Fab5sixthman_SELF) own mind.	24	Incredibly(6), satisfying.(6), good(6), won(6)
2	Fab5sixthman	2026-06-08 08:03:24	Kris (UN_Fab5sixthman_kris) won the game. What did Kris (UN_Fab5sixthman_kris) win? I (UN_Fab5sixthman_SELF) was happy happy.	24	won(6), win?(6), happy(6), happy.(6)
3	Fab5sixthman	2026-06-08 17:20:40	I (UN_Fab5sixthman_SELF) was pretty happy smoke. To see that your responses are SEMI COHERENT is a MAJOR (UN_Fab5sixthman_major) WIN. Albeit frustrating.	20	pretty(8), happy(6), WIN.(6)
4	Gigantour	2026-07-01 20:26:47	I (UN_Gigantour_SELF) was hanging out today by the soccer field. It was cold. But we (UN_Gigantour_SELF) won. Soccer is fun. Futball is fun. Football is fun.	18	won.(6), fun.(4), fun.(4), fun.(4)
5	Gigantour	2026-06-21 15:25:06	Jackie (UN_Gigantour_jackie) won the game. She played great. Lisa (UN_Gigantour_lisa) was wrong.	16	won(6), played(4), great.(6)

Top 5 Discomfort Memories

#	UN	Timestamp	Whole Input / Memory
1	Gigantour	2026-08-08 18:36:58	How are the detroit tigers doing?
2	Gigantour	2026-08-08 18:34:56	Hello robot
3	Greg	2026-08-01 17:47:49	Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do you think about that robot
4	Greg	2026-08-01 17:46:34	Hi
5	Gigantour	2026-07-31 11:05:28	But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do."

Snapshot	Input #	Total UNs	Conversations	Total Words	Unique Words / IQ1	Concepts	Ideas	Skills	Feeling Words	Comfort Score	Discomfort Score	Self Esteem	Avg Sentiment	Avg Sensitivity	Wins	Losses	Correct	Incorrect
Shell	5	1	1	26	19	19	0	0	5	24	-4	20.00	0.2400	0.1066	2	0	0	0
Shell	10	1	2	104	76	70	4	2	14	64	-14	50.00	0.3296	0.1157	3	1	0	0
Shell	15	1	2	161	107	99	3	6	19	80	-26	54.00	0.2255	0.0887	4	2	0	0
Shell	20	1	2	172	109	101	3	6	20	80	-32	48.00	0.1092	0.0766	4	3	0	0
Shell	25	1	2	199	121	111	5	7	26	110	-36	74.00	0.3930	0.1177	5	3	0	0
Shell	30	2	4	222	127	116	7	8	29	114	-44	70.00	0.2831	0.1187	5	3	0	1
Shell	35	2	6	235	130	112	9	7	31	126	-44	82.00	0.3712	0.1232	6	3	0	1
Shell	40	3	8	275	142	122	13	7	34	138	-48	90.00	0.3486	0.1177	8	3	0	2
Shell	45	3	8	375	175	149	16	12	40	162	-58	104.00	0.3440	0.1132	11	4	1	3
Shell	50	3	8	450	200	168	18	16	45	168	-72	96.00	0.2983	0.1070	12	5	2	3
Shell	55	3	10	513	224	185	23	16	49	180	-82	98.00	0.2368	0.1064	13	6	4	3
Shell	60	4	11	535	233	195	23	16	53	200	-82	118.00	0.2754	0.1089	15	6	4	3
Shell	65	4	13	569	244	206	23	19	58	218	-86	132.00	0.2752	0.1079	16	6	4	4
Shell	70	4	14	619	258	216	26	21	66	254	-88	166.00	0.3056	0.1099	19	6	5	4
Shell	75	4	15	684	276	230	27	25	74	276	-102	174.00	0.2895	0.1113	19	6	5	4
Shell	80	4	16	729	288	239	27	27	76	280	-106	174.00	0.2768	0.1072	19	6	5	4
Shell	85	5	18	783	302	254	26	31	80	296	-110	186.00	0.2687	0.1040	19	7	6	4
NOW	86	5	18	789	305	257	26	31	80	296	-110	186.00	0.2655	0.1028	19	7	6	4

Gigantour	How are the detroit tigers doing?
TOPICS:	how (TOPIC)are (TOPIC)detroit (TOPIC)tigers (TOPIC)doing (TOPIC)hello (TOPIC)robot (TOPIC)but (TOPIC)someone (TOPIC)has (TOPIC)continue (TOPIC)narrative (TOPIC)what (TOPIC)you (TOPIC)would (TOPIC)do (TOPIC)and (TOPIC)is (TOPIC)my (TOPIC)un_gigantour_self (TOPIC)kid (TOPIC)

Gigantour	Hello robot
TOPICS:	hello (TOPIC)robot (TOPIC)but (TOPIC)someone (TOPIC)has (TOPIC)continue (TOPIC)narrative (TOPIC)what (TOPIC)you (TOPIC)would (TOPIC)do (TOPIC)and (TOPIC)is (TOPIC)my (TOPIC)un_gigantour_self (TOPIC)kid (TOPIC)im (TOPIC)actually (TOPIC)kind (TOPIC)proud (TOPIC)prototype (TOPIC)its (TOPIC)unrefined (TOPIC)not (TOPIC)bad (TOPIC)

Greg	Kris (UN_Greg_kris) and I (UN_Greg_SELF) played football yesterday what do you think about that robot
TOPICS:	kris (TOPIC)un_greg_kris (TOPIC)and (TOPIC)i (TOPIC)un_greg_self (TOPIC)played (TOPIC)football (TOPIC)yesterday (TOPIC)what (TOPIC)do (TOPIC)you (TOPIC)think (TOPIC)about (TOPIC)robot (TOPIC)hi (TOPIC)

Greg	Hi
TOPICS:	hi (TOPIC)

Gigantour	But someone has to continue this narrative of "thats what you would do" and this is what "my (UN_Gigantour_SELF) kid would do."
TOPICS:	but (TOPIC)someone (TOPIC)has (TOPIC)continue (TOPIC)narrative (TOPIC)what (TOPIC)you (TOPIC)would (TOPIC)do (TOPIC)and (TOPIC)is (TOPIC)my (TOPIC)un_gigantour_self (TOPIC)kid (TOPIC)im (TOPIC)actually (TOPIC)kind (TOPIC)proud (TOPIC)prototype (TOPIC)its (TOPIC)unrefined (TOPIC)not (TOPIC)bad (TOPIC)lets (TOPIC)push (TOPIC)past (TOPIC)100 (TOPIC)inputs (TOPIC)no (TOPIC)drift (TOPIC)yet (TOPIC)too (TOPIC)dampening (TOPIC)ml (TOPIC)sections (TOPIC)

Metric	First Shell Input #5	Current NOW Input #86	Total Change
Total Inputs	5 [Started: Jun 08, 2026]	86 [Started: Jun 08, 2026]	+81.00
Total UNs	1	5	+4.00
Conversations	1	18	+17.00
Total Words	26	789	+763.00
Unique Words / IQ1	19	305	+286.00
Subjects	0	0	0
Concepts	19	257	+238.00
Ideas	0	26	+26.00
Skills	0	31	+31.00
Feeling Words	5	80	+75.00
Comfort Score	24	296	+272.00
Discomfort Score	-4	-110	-106.00
Global Self Esteem	20	186	+166.00
Avg Sentiment	0.2400	0.2655	+0.03
Avg Sensitivity	0.1066	0.1028	0.00
Wins	2	19	+17.00
Losses	0	7	+7.00
Correct	0	6	+6.00
Incorrect	0	4	+4.00

Artificial Intelligence Project

Login/Username:

Password:

Main Page Ai User Profile Page Return To V2 Chat-Bot Conversation -- "Smoke"

- Welcome to the....

Ai Version 2.0 interface a.k.a. "Smoke"

Instructions For Use:

Point Of View - KEY: -Global UN Inputs - all Usernames -Global Robot Responses - all Usernames responses -UN Specific Inputs -UN Specific Responses -Global UN Inputs + Global Robot Responses -UN Specific Inputs + UN Specific Responses

Section 0: Navigation

Go to Section 1: Person's Identifier Go to Section 2: Place's Identifier Go to Section 2.5: Revised Inputs (Persons+Places Identified) Go to Section 3: Data Breaks (*chopping it up) Go to Section 4: Eight Core Metrics

Comfort/Compliments | Winning/Correctness Discomfort/Insults | Losing/Incorrectness

"Smoke" // Ai V2 LLM

"It gets realer every day." -PDX Larsen LLC

Section 1:

Person's Analyzer

The Last 5 Things Said To The Ai Globally (Person's Identifiers)

Section 2:

Place's Analyzer

The Last 5 Things Said To The Ai Globally (Places/Locations/Settings Analyzer)

Section 2.5:

Revised Input // Person's+Place's Analyzer

Section 3:

Data Breaks (*chopping it up)

Statements & Questions:

STQ Separator's Analysis (Last 5 Global Inputs)

STQ 1 (*Standard Statement + Question Data)

Robot Response STQ Filter (*Whole Response Data)

Section 4:

4 Core Metrics:

*Comfort/Discomfort - *Compliments/Insults *Winning/Losing - *Correctness/Incorrectness

Comfort & Discomfort Sentiment Analysis

Compliments & Insults Filter

Last Inputs Summary:

None

None

Winning/Losing Filter

Summary Analysis:

Correctness/Incorrectness Analysis

None detected

None detected

Personality Metric's / Algorithm's

GLOBAL INPUTS: PSYCH METRICS & BEHAVIORAL FILTERS

Core behavioral stats, sensitivity, credibility, and global ranking data.

Emotional Reaction: Facial Expression Output Filter

KEY

*Ego Rank change in IQ/Morality stats = +2 or -2 (Comfort v Discomfort revision) per rank change VS other UN's and 3rd party's

Section 5:

Neural Net's / Brain Clumping

Neural Nets Of The Ai's -Global- *All User's* Vernacular Proximity

Click here to see the Ai 2.0's Global Input's *Vernacular Proximity Neural Net

Neural Nets Of The Ai's - - *User Specific *Vernacular Proximity Neural Net

Click here to see 's, "UN Specific *Vernacular Proximity Neural Net."

People - all UN's and UN_3rd party's mentioned

PEOPLE — Global Persons + 3rd Parties

Section 6:

Primitive Linguistic Based Ai Skill Building Verb/Action Analysis And Action Formula's

In V2, this Ai... cannot VISUALIZE/SEE, yet. Hence, for V2 I've defined, "skill building," linguistically, through action statement's/action formula's.

Key *S = Statement *Q = Questions *verb tense= PAST, PRESENT, FUTURE

Section 7:

The Egotistical Delta: Skill Building, Social Data And Ego

ACTION FORMULA BASED - Skill Building System

Subject

Concept

Idea

Skill

Master Skill

System Architectures (V2 Chatbot)

Global Persons Ego Skills Matrix - Social Data

Top 5 Usernames and UN_3rd Party's Associated By SKILL LEVEL (*see skill building system above) Per Operative Word

's PEOPLE / Social Data - WHO MAKES YOU, YOU?

View 's Ego Skill's Matrix (*USER SPECIFIC POV/ UN INPUTS ONLY)

EGO Formula (*Global Inputs only)

Section 8:

The Egotistical Delta: EGO METRIC'S, IQ, MORALITY and... "THE PERSONALITY FORMULA"

*MORALITY Ai ALGORITHM: I've defined MORALITY as the average SKILL LEVEL seen/witnessed by any UN or 3rd Party in terms of the average skill level of everyone that that UN/3rd party has mentioned / known / witnessed.

Ai MORALITY SECTOR

GLOBAL INPUTS: AI MORALITY SECTOR

Weighted Subject Skill Scores: 0, 2, 4, 6, 8, 10

Six POV Morality Data Convergence:

UN subject skill + that UN's 3rd party's subject skill lvl = Avg subject skill level witnessed from that POV is that POV's Morality avg.

Main Page Ai

User Profile Page

Return To V2 Chat-Bot Conversation -- "Smoke"

Point Of View - KEY:

-Global UN Inputs - all Usernames
-Global Robot Responses - all Usernames responses
-UN Specific Inputs
-UN Specific Responses
-Global UN Inputs + Global Robot Responses
-UN Specific Inputs + UN Specific Responses

Go to Section 1: Person's Identifier

Go to Section 2: Place's Identifier

Go to Section 2.5: Revised Inputs (Persons+Places Identified)

Go to Section 3: Data Breaks (*chopping it up)

Go to Section 4: Eight Core Metrics

Comfort/Compliments | Winning/Correctness
Discomfort/Insults | Losing/Incorrectness

"It gets realer every day."

-PDX Larsen LLC

Comfort/Discomfort - Compliments/Insults

Winning/Losing - Correctness/Incorrectness

Emotional Reaction:
Facial Expression Output Filter

*Ego Rank change in IQ/Morality stats
=
+2 or -2 (Comfort v Discomfort revision) per rank change VS other UN's and 3rd party's

Neural Nets Of The Ai's -Global- All User's Vernacular Proximity

Neural Nets Of The Ai's - - User Specific* *Vernacular Proximity Neural Net**

Primitive Linguistic Based Ai Skill Building
Verb/Action Analysis And Action Formula's

In V2, this Ai... cannot VISUALIZE/SEE, yet.

Hence, for V2 I've defined, "skill building," linguistically,
through action statement's/action formula's.

Key

S = Statement
Q = Questions
*verb tense= PAST, PRESENT, FUTURE

The Egotistical Delta:
Skill Building, Social Data And Ego

Top 5 Usernames and UN_3rd Party's Associated By SKILL LEVEL
(*see skill building system above) Per Operative Word

The Egotistical Delta:

EGO METRIC'S, IQ, MORALITY and...
"THE PERSONALITY FORMULA"

*MORALITY Ai ALGORITHM:

I've defined MORALITY as the average SKILL LEVEL
seen/witnessed by any UN or 3rd Party
in terms of the average skill level of everyone that
that UN/3rd party has mentioned / known / witnessed.

UN subject skill + that UN's 3rd party's subject skill lvl =
Avg subject skill level witnessed from that POV is that POV's Morality avg.

Then this is the MORALITY QUANTIFICATION and CONVERGENCE across 6 POV's

POV KEY:

-Global UN Inputs
-Global Robot Responses
-UN Specific Inputs
-UN Specific Responses
-Global UN Inputs + Global Robot Responses
-UN Specific Inputs + UN Specific Responses

Logged-in UN: Guest
Subject morality averages witnessed across all six POV morality tables.

Personality (*as a number/rank)

=

[Morality (PERSONAL RANK v your mentioned 3rd partys)
+
IQ (PERSONAL RANK v your mentioned 3rd partys)]

/ DIVIDED BY 2

Or in other word's how many CONNECTIONS between Statements/Questions
or, "linkages," has the robot made?

Based upon 4 POV's...

inputs only, responses only, (UN specific inputs+ UN specific responses), (Global inputs+Global responses)...

Identify emotional reaction's and patterns of feeling
from the last 10 Global inputs.

Emotional Intelligence = comfort seeking.

*And we'll add a Left Brain IQ in V3 or V4.

NOTE: "COMFORT IS THE ABSENCE OF DISCOMFORT."

GLOBAL INPUTS POV - "Feeling word's (comfort / discomfort)," inserted into Smoke V2
and then matched against Emotion Categories then labeled.

Emotion Category 1 - WORD CLOUD

Happy / Positive

Emotion Category 2 - WORD CLOUD

Sad / Negative

Emotion Category 3 - WORD CLOUD

Regretful / Introspective

Emotion Category 4 - WORD CLOUD

Inspired / Inquisitive

Emotion Category 5 - WORD CLOUD

Motivated By...

Emotion Category 6 - WORD CLOUD

Unmotivated By...

Emotion Category 7 - WORD CLOUD

Physical Exhaustion

Global Top 10 Skills
(Various UN+3rd Party's Best Skills/Favourite Things)

Top 5 memories/inputs and Worst 5 memories/inputs
(comfort+discomfort total) and feeling words

Personality Development: Now vs Then
Global Input's POV