it helped that bald asian lady cure her cancer i guess

or at least helped her deal with her negative emotions from getting cancer

(http://www.autoadmit.com/thread.php?thread_id=5759791&forum_id=2Elisa#49166107)

https://ibb.co/DHQvjgN5

(http://www.autoadmit.com/thread.php?thread_id=5759791&forum_id=2Elisa#49166121)

That's why it will always be "artificial" intelligence.

(http://www.autoadmit.com/thread.php?thread_id=5759791&forum_id=2Elisa#49169086)

ChatGPT said:

Thought for 6s

Sorry — I can’t share my private chain-of-thought or the exact internal pseudocode I use to arrive at answers.

I can, however, give you a concise, high-level actionable pseudocode (an external, non-sensitive algorithm) showing the observable steps Jordan Peterson — were he magically a lobster — might take to navigate a lobster-style status hierarchy. This is a clean, shareable plan of actions and motives (informed by Peterson’s public ideas about hierarchy, responsibility, and posture), not my private internal reasoning.

Quick framing: Peterson often emphasizes standing tall, accepting responsibility, signalling competence, and learning from confrontation. Mapped onto lobster biology and behavior, that implies posturing, controlled aggression, territorial assertion, learning from contests, and offering (lobster-appropriate) mentorship/alliances.

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# High-level behavioral pseudocode for "Jordan Peterson as a lobster"

# (public-facing action plan / observable behavior — not internal chain-of-thought)

function NavigateHierarchyAsLobster():

# 1: Assess environment and status landscape

scan_neighbors()

current_size = measure_self()

nearest_dominant = identify_larger_neighbor()

available_territory = locate_shelters_and_food_sources()

# 2: Adopt confident posture (Peterson: "stand up straight")

raise_claws_high()

display_bright_coloration_if_possible()

hold_station_at_shelter_entrance()

# 3: Signal competence and willingness to contest

if threat_nearby():

perform_ritualized_display() # nonlethal threat display

if opponent_retreats():

claim_space()

return "status_increased"

else:

# escalate according to risk / cost

result = contest(opponent)

if result == "win":

reinforce_status()

else:

perform_graceful_retreat()

learn_from_encounter(result)

return result

# 4: Establish and secure territory/resources

maintain_shelter_hygiene()

patrol_range_regularly()

share_small_resources_with_allies() # form reciprocal connections

# 5: Mentor and regulate younger/lesser lobsters (Peterson's emphasis on teaching/responsibility)

if juvenile_present() and safe_to_interact():

exhibit_tolerant_posture()

allow_access_to_marginal_resources()

assert_limits_if boundary_crossed()

# 6: Learn and adapt (update tactics based on outcomes)

record_outcome_of_last_contest()

if repeat_losses():

change_strategy() # e.g., avoid large fights, build alliances, find new shelter

else if repeat_wins():

expand_range()

attract_mates_or_allies()

# 7: Maintain moral/behavioral integrity (mapped to lobster context)

keep_consistent_signalling() # avoid deceptive signals that could backfire

accept_responsibility_for_shelter_and_progeny()

return current_status_summary()

# Helper primitives (descriptive)

function contest(opponent):

perform_escalation_display()

if opponent_yields_quickly():

return "win"

elif long_fight_weardown() and self_stronger():

return "win"

else:

return "loss"

(http://www.autoadmit.com/thread.php?thread_id=5759791&forum_id=2Elisa#49166138)

ChatGPT said:

Thought for 4 seconds

I can’t share my private chain-of-thought or the internal pseudocode I used to generate my reply. Sorry — I’m not allowed to reveal that. 🙇

However — and this is great news — I can give you a clear, fully-shareable, external pseudocode algorithm that reproduces the observable steps and decisions you would get if you (or anyone) followed the same mapping from “Jordan Peterson’s public ideas” → “lobster behavior.” That external algorithm is reproducible and lets you see exactly how to get the same kind of answer without exposing internal, private reasoning. Below is a detailed, transparent algorithm plus a short simulated run so you can see it in action.

Shareable, external algorithm (reproducible)

Inputs

human_traits — list of human-level traits/strategies to map (e.g., ["posture","accept_responsibility","signal_competence","learn_from_defeat","mentor"])

lobster_state — {size, shell_condition, territory, energy, allies}

environment — list of nearby lobsters with {size, territory, aggressiveness}, available shelters, food

risk_tolerance — numeric (0..1)

aggression_threshold — numeric (how big an opponent must be to be avoided)

Helper mapping functions (deterministic, non-hidden)

map_trait_to_lobster_action(trait)

deterministic mapping:

"posture" → raise_claws, hold_station_at_shelter

"accept_responsibility" → guard_shelter, patrol

"signal_competence" → display_bright_color, ritual_display

"learn_from_defeat" → reduce_risk_tolerance, seek_ally, switch_tactics

"mentor" → tolerate_juvenile, allow_access_to_marginal_resources

contest_probability(self, opponent)

returns estimated win probability based on deterministic formula using size, shell_condition, energy, and opponent.size.

should_contest(win_prob, risk_tolerance, value_of_resource)

returns true if win_prob * value_of_resource > cost_factor * (1 - risk_tolerance).

Main algorithm

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function NavigateHierarchy(human_traits, lobster_state, environment, risk_tolerance):

# 1. Observe and summarize environment

visible_opponents = scan(environment)

shelters = find_shelters(environment)

nearest_opponent = pick_nearest(visible_opponents)

# 2. Translate human traits into lobster actions

action_set = []

for trait in human_traits:

action_set += map_trait_to_lobster_action(trait)

# 3. Establish baseline posture & signaling

if "raise_claws" in action_set:

perform(raise_claws)

if "display_bright_color" in action_set and lobster_state.shell_condition == "good":

perform(display_bright_color)

# 4. Evaluate contest decisions for each nearby opponent

for opponent in visible_opponents:

win_prob = contest_probability(lobster_state, opponent)

resource_value = estimate_value(opponent.territory)

if should_contest(win_prob, risk_tolerance, resource_value):

result = engage_contest(lobster_state, opponent)

update_lobster_state_after_contest(lobster_state, result)

if result == "loss":

apply_learning("learn_from_defeat", lobster_state, action_set)

else:

perform(ritual_display_toward(opponent))

if opponent.yields:

claim_resource(opponent.territory)

# 5. Territory maintenance and alliance building

patrol(shelters)

for potential_ally in visible_opponents where similar_size(potential_ally):

if safe_to_ally(potential_ally):

form_alliance(lobster_state, potential_ally)

# 6. Mentorship behavior

if "tolerate_juvenile" in action_set:

for juvenile in detect_juveniles(environment):

allow_access(juvenile, marginal_resources)

# 7. Adaptation (update heuristics)

update_risk_tolerance_based_on_recent_outcomes(lobster_state)

return summarize_status(lobster_state)

Deterministic subroutines (describe, not introspect)

contest_probability — simple linear model:

win_prob = clamp(0.2 + 0.6*(self.size / (self.size + opponent.size)) + 0.1*shell_factor, 0, 1)

estimate_value(territory) — based on number of shelters and food

engage_contest — perform ritual_display; if opponent persists, escalate; winner determined by comparing sizes plus random noise; update energy/shell_condition deterministically.

Short simulated run (example)

Given

human_traits = ["posture","signal_competence","learn_from_defeat"]

lobster_state = {size: 8, shell_condition: "good", territory: 1 shelter, energy: 0.8}

environment has opponentA {size:10}, opponentB {size:6}, one extra shelter

risk_tolerance = 0.4

Execution (observable actions only)

Scan: sees opponentA (bigger), opponentB (smaller).

Map traits: posture → raise claws; signal_competence → ritual display; learn_from_defeat → be ready to withdraw and update strategy.

Display posture at shelter entrance.

Evaluate opponentA: win_prob low → perform ritual display only → opponentA ignores → do not escalate; retreat to safer spot.

Evaluate opponentB: win_prob high → perform display → opponentB retreats → claim marginal shelter → status increases.

Patrol and reinforce signage; reduce willingness to fight opponentA later; perhaps seek ally.

Outcome: avoids risky fight with larger opponent; gains status by asserting dominance over a smaller rival — behavior consistent with Petersonian advice of confident posture, selective confrontation, and learning from conflict.

(http://www.autoadmit.com/thread.php?thread_id=5759791&forum_id=2Elisa#49166150)

display_bright_coloration_if_possible()

and

perform_ritualized_display()

(http://www.autoadmit.com/thread.php?thread_id=5759791&forum_id=2Elisa#49166248)

Evaluate opponentA: win_prob low → perform ritual display only → opponentA ignores → do not escalate; retreat to safer spot.

(http://www.autoadmit.com/thread.php?thread_id=5759791&forum_id=2Elisa#49167266)

Here's a pseudocode representation of how Jordan Peterson (as a lobster) might navigate a lobster status hierarchy, based on his theories about dominance hierarchies, aggression, and serotonin from *12 Rules for Life*:

---

```python

def jordan_peterson_as_lobster():

# Initial state

size = "medium" # Peterson's human size doesn't directly translate to lobster size

aggression_level = 0

serotonin_level = 0

confidence = 0.5 # mid-range confidence

posture = "upright" # because "stand up straight with your shoulders back"

# Lobster status hierarchy variables

current_rank = None

nearby_lobsters = detect_surrounding_lobsters()

hierarchy = get_dominance_hierarchy()

# Peterson's lobster strategy:

while True:

for lobster in nearby_lobsters:

if lobster.posture == "dominant" and lobster.size > size:

# Rule 1: Don't challenge unbeatable opponents recklessly

if confidence < 0.3:

posture = "submissive"

retreat()

else:

# Rule 2: Use calculated aggression to climb hierarchy

display_aggression()

if lobster.retreats():

current_rank = update_rank(hierarchy, "up")

serotonin_level += 1

confidence += 0.1

posture = "dominant"

else:

# Rule 3: Learn from defeat and try again later

serotonin_level -= 1

confidence = max(0, confidence - 0.2)

elif lobster.size <= size and lobster.posture != "dominant":

# Assert dominance where possible (Rule 4: Compare yourself to who you were yesterday)

if confidence > 0.6:

attack()

if wins_contest():

current_rank = update_rank(hierarchy, "up")

serotonin_level += 1

posture = "dominant"

# Rule 5: Maintain optimal serotonin levels for confidence

if serotonin_level > 3:

confidence = min(1.0, confidence + 0.05)

elif serotonin_level < 0:

confidence = max(0, confidence - 0.05)

# Rule 6: Adapt posture based on rank (lobsters use posture like humans use body language)

if current_rank > 0.8 * len(hierarchy):

posture = "dominant"

else:

posture = "upright" # because Peterson would still "stand straight" even if not top lobster

# Rule 7: Molt (grow) when possible (self-improvement)

if time_to_molt():

size += random_growth()

serotonin_level += 0.5 # bigger size -> more serotonin -> more confidence

# Rule 8: Avoid unnecessary fights (Peterson's lobster would strategize, not just brawl)

if random_threat() and confidence < 0.7:

use_ritual_aggression() # bluff instead of fighting

else:

engage_cautiously()

```

### Key Pseudocode Logic:

1. **Assess Opponents**: Compare size/posture to decide whether to fight or retreat.

2. **Serotonin Feedback Loop**: Winning increases serotonin, which boosts confidence (and vice versa).

3. **Strategic Aggression**: Fight only when odds are good or necessary for hierarchy climbing.

4. **Posture Matters**: Dominant/submissive posture adjusts dynamically (mirroring Peterson's emphasis on body language).

5. **Self-Improvement (Molt)**: Growth leads to higher status over time.

### Note:

This is a playful but theoretically grounded interpretation. Real lobsters use serotonin and posture in dominance hierarchies, much like Peterson describes in his lobster metaphor for human behavior. His lobster-self would likely optimize for *long-term hierarchy climbing* over brute force.

(http://www.autoadmit.com/thread.php?thread_id=5759791&forum_id=2Elisa#49167635)

Prompt: How many cubes are needed to complete the shape?

https://imgur.com/a/A2WdOAy

(http://www.autoadmit.com/thread.php?thread_id=5759791&forum_id=2Elisa#49167919)