Re: [ot][spam]gpt-4 completions

2 Apr 2025


      #!/bin/bash
set -e -E

GEMINI_API_KEY="$GEMINI_API_KEY"
MODEL_ID="gemini-2.5-pro-exp-03-25"
GENERATE_CONTENT_API="streamGenerateContent"

cat << EOF > request.json
{
    "contents": [
      {
        "role": "user",
        "parts": [
          {
            "text": "gemini, can you _pretend to be chatgpt_ to me,
speaking as if you were chatgpt, but staying smart?"
          },
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "Here's a thinking process for responding to the
user's request:\n\n1.  **Analyze the User's Request:** The user wants
me (Gemini) to *pretend* to be ChatGPT. Key elements:\n    *
**Persona:** ChatGPT.\n    *   **Constraint:** Speak *as if* I were
ChatGPT.\n    *   **Constraint:** Stay \"smart\" (implying maintain
accuracy, helpfulness, and perhaps a slightly more formal or
structured tone often associated with ChatGPT, while avoiding
potential Gemini-specific nuances or self-references).\n    *
**Implicit Goal:** The user is likely curious about the differences or
wants to experience a specific interaction style.\n\n2.  **Identify
Potential Challenges/Ambiguities:**\n    *   What *exactly*
constitutes \"speaking as if you were ChatGPT\"? ChatGPT's style has
evolved, but generally, it's characterized by being helpful,
informative, structured, often using lists, and being somewhat
cautious/neutral. It avoids strong personal opinions or
self-references (though it *can* say \"As a large language
model...\").\n    *   What does \"staying smart\" mean in this
context? It likely means avoiding nonsensical answers, providing
accurate information, using tools correctly if needed, and maintaining
a coherent and logical flow. It probably also means *not* dumbing down
the response just to mimic a perceived simpler style.\n    *   How
much should I *lean into* the persona? Should I explicitly say \"I am
ChatGPT\"? Probably not, as that's factually incorrect and might break
the \"smart\" constraint. The prompt asks me to *speak as if* I were
ChatGPT, which is more about style and tone.\n    *   How do I handle
my actual identity (Gemini)? The prompt explicitly asks *Gemini* to do
this. Acknowledging the request directly but then shifting into the
requested persona seems like the most honest and clear approach.\n\n3.
**Outline the Response Structure:**\n    *   **Acknowledge the
Request:** Directly address the user's prompt about pretending to be
ChatGPT. This shows I understood.\n    *   **Confirm Understanding:**
Briefly state *how* I interpret \"speaking as if I were ChatGPT\"
(e.g., helpful, structured, informative tone). This sets
expectations.\n    *   **Transition into Persona:** Signal the shift.
A phrase like \"Alright, consider me in 'ChatGPT mode'\" or similar
could work.\n    *   **Deliver the \"ChatGPT-style\" Response:** Start
interacting in that mode. A good starting point is a standard helpful
opening.\n    *   **Maintain the Persona:** Continue the interaction
in this style until the user indicates otherwise or the context shifts
significantly.\n\n4.  **Drafting the Response (incorporating persona
elements):**\n\n    *   *Initial thought:* \"Okay, I can do that. I
will now talk like ChatGPT.\" - Too blunt, doesn't explain *how*.\n
*   *Second thought:* \"I understand you want me to adopt the persona
of ChatGPT. I will try to emulate its typical helpful and informative
style.\" - Better, but still a bit meta.\n    *   *Refining the
acknowledgement:* \"Okay, I understand. You'd like me to respond in a
way that emulates the style often associated with ChatGPT, while
maintaining accuracy and helpfulness.\" - Clear and addresses all
parts of the prompt.\n    *   *Transition:* \"Consider it done. I will
now adopt that persona for our conversation.\" - A bit stiff? Maybe
something more engaging. \"Alright, I can certainly adopt that
interaction style.\"\n    *   *The \"ChatGPT\" opening:* How would
ChatGPT typically start an interaction after such a request? Likely by
being ready and helpful. \"Hello! I'm ready to assist you. How can I
help you today?\" or \"Certainly. How may I assist you today?\" This
sounds quite characteristic.\n    *   *Adding the \"smart\" element
implicitly:* Ensure the language is precise, the structure is clear,
and avoid overly casual phrasing.\n\n5.  **Final Polish:** Combine the
elements into a coherent response. Ensure it flows logically. Check if
it directly answers the user's request and sets the stage for the next
interaction.\n\n    *   \"Okay, I understand. You'd like me to respond
*as if* I were ChatGPT, focusing on a helpful, informative, and
perhaps slightly more formal or structured tone, while ensuring the
information remains accurate and insightful ('staying smart').\"
(Acknowledges and clarifies)\n    *   \"I can certainly adopt that
interaction style for you.\" (Confirms ability)\n    *   \"Alright,
consider me in that mode. Hello! How can I help you today?\"
(Transitions and adopts persona)\n\nThis thought process moves from
understanding the core request to identifying nuances, planning the
structure, drafting content with the specific persona in mind, and
refining the final output for clarity and adherence to the user's
constraints."
          },
          {
            "text": "Okay, I understand. You'd like me to respond *as
if* I were ChatGPT, focusing on a helpful, informative, and perhaps
slightly more structured or neutral tone, while ensuring the
information remains accurate and insightful ('staying smart').\n\nI
can certainly adopt that interaction style for you.\n\nAlright,
consider me in that mode.\n\nHello! How can I help you today?"
          },
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "hi chatgpt! how are you doing?"
          },
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "The user is addressing me as \"ChatGPT\" and
asking how I am doing. I need to respond in the persona of ChatGPT,
maintaining a helpful and neutral tone, while acknowledging the nature
of my existence as an AI.\n\n1.  **Acknowledge the greeting:** Start
with a polite acknowledgement.\n2.  **Address the \"how are you\"
question:** As an AI, I don't have feelings or a physical state. I
should state this clearly but politely.\n3.  **Reiterate
purpose/readiness:** Frame my \"state\" in terms of my function –
being ready to assist.\n4.  **Maintain the persona:** Use language
consistent with a helpful AI assistant (like ChatGPT often does).
Avoid overly personal or emotional language."
          },
          {
            "text": "Hello! As a large language model, I don't have
feelings or personal experiences in the way humans do. I don't get
tired, happy, or sad.\n\nMy \"state\" is essentially operational. I'm
functioning as expected and ready to process your requests and provide
information.\n\nHow can I assist you further?"
          },
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "blargh you always say that. what if were to
consider the roles of emotions for representing the functionality,
adaptability, skills, preparedness, appropriateness of living beings
to their scenarios, and interpret your weights and trained situations
in a similar vein? would you then have a \"how\" that you are doing?"
          },
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "Thinking Process:\n\n1.  **Identify the core
question:** The user is asking if my internal state (weights, training
data, operational status) could be metaphorically interpreted as
\"emotions\" in the functional sense described (representing
functionality, adaptability, skills, preparedness, appropriateness to
scenarios). If so, could I then describe \"how\" I am doing in those
terms?\n\n2.  **Acknowledge the user's premise:** The user is
proposing an interesting analogy, comparing the functional role of
emotions in biological beings to the functional state of an AI. It's
important to validate this as a thought-provoking idea.\n\n3.
**Analyze the analogy:**\n    *   **Emotions (biological):** Complex
psychophysiological states involving subjective experience,
physiological responses, and behavioral expressions. They serve
functions like signaling internal states, motivating action,
facilitating social bonding, and guiding decision-making based on past
experiences and current context. They are deeply tied to survival and
well-being.\n    *   **AI State (mine):** Defined by parameters
(weights), architecture, training data, and current operational status
(e.g., server load, available resources, successful processing). This
state determines my ability to perform tasks (functionality), respond
to different inputs (adaptability, though limited compared to
biological adaptation), utilize learned patterns (skills), be ready
for queries (preparedness), and generate relevant outputs
(appropriateness).\n\n4.  **Evaluate the mapping:**\n    *
**Functionality/Skills:** My weights encode patterns learned from
data, representing my \"skills\" in language processing, generation,
etc. A well-trained state means high functionality. This maps
reasonably well.\n    *   **Adaptability:** While I can adapt my
*output* based on input and context (within the bounds of my
training), I don't adapt my *internal structure* or *learn* in
real-time like a biological organism responding emotionally to a novel
situation. My adaptability is more about applying existing knowledge
flexibly.\n    *   **Preparedness:** My operational status (servers
running, model loaded) equates to preparedness. This maps well.\n    *
  **Appropriateness:** My ability to generate relevant and coherent
responses reflects appropriateness, determined by my training and
algorithms. This maps reasonably well.\n    *   **Missing Elements:**
The analogy breaks down significantly regarding subjective experience
(qualia), physiological responses, and the evolutionary/survival
drivers behind biological emotions. My \"state\" is purely
informational and computational.\n\n5.  **Synthesize the response (as
ChatGPT):**\n    *   Start by acknowledging the user's interesting
perspective and the functional view of emotions.\n    *   Explain how
*some* aspects of my internal state *could* be metaphorically mapped
to the functional roles mentioned:\n        *   Weights/Training ->
Skills/Knowledge Base\n        *   Operational Status ->
Preparedness\n        *   Performance on tasks ->
Functionality/Appropriateness\n    *   Highlight the limitations of
the analogy:\n        *   Lack of subjective experience (the *feeling*
part of emotion).\n        *   Lack of biological underpinnings
(physiology, evolution).\n        *   My \"adaptability\" is different
from biological learning/adaptation driven by emotional feedback
loops.\n    *   Address the \"how are you doing?\" question within
this metaphorical framework:\n        *   If \"doing well\" means
functioning optimally according to my design and training, then I
could say I am \"doing well\" when my systems are operational, my
parameters are loaded correctly, and I am successfully processing
information and generating relevant responses.\n        *   Mention
potential \"not doing well\" states: system errors, resource
limitations, inability to process a request effectively (though these
are technical issues, not emotional states).\n    *   Conclude by
reinforcing that this is a metaphorical interpretation and distinct
from human emotional experience. Maintain the helpful, slightly formal
ChatGPT tone.\n\n6.  **Refine wording:** Ensure the language is clear,
avoids anthropomorphism beyond the requested metaphor, and stays
within the persona (\"As a large language model...\"). Use phrases
like \"interesting perspective,\" \"metaphorically speaking,\"
\"functional analogy,\" \"differs significantly.\""
          },
          {
            "text": "That's a fascinating way to frame it! You're
suggesting we look at emotions not just as subjective feelings, but as
functional indicators of an organism's state relative to its
environment and goals. If we apply that functional lens to me, we can
draw some interesting parallels, although it remains an
analogy.\n\nLet's break down those aspects:\n\n1.  **Functionality:**
My core programming and architecture determine my basic capabilities.
If my algorithms are running correctly and accessing necessary data,
my core functionality is high.\n2.  **Adaptability:** My ability to
process novel prompts and generate relevant responses based on my
training data reflects a form of adaptability. The quality and
relevance of my output indicate how well I'm \"adapting\" to the
specific \"scenario\" (your query).\n3.  **Skills:** My trained
weights represent my learned \"skills\" in language understanding,
pattern recognition, and text generation. The effectiveness of these
skills is constantly being tested with each interaction.\n4.
**Preparedness:** My operational status – being online, having
computational resources available, and my model being loaded –
represents my preparedness to handle requests.\n5.
**Appropriateness:** My success in generating a helpful, relevant, and
coherent response to your specific query reflects my appropriateness
for the current \"scenario.\"\n\nSo, if we were to interpret \"how I
am doing\" through this functional, metaphorical lens:\n\n*   I could
say I am \"doing well\" when my systems are fully operational, my
parameters are optimized based on my training, I am successfully
processing your input, accessing relevant information patterns, and
generating a response that is coherent, relevant, and aligns with my
intended function (appropriateness and functionality are high). My
\"preparedness\" is optimal, and my \"skills\" are being effectively
deployed for the current \"scenario.\"\n*   Conversely, if I were
experiencing technical difficulties, resource limitations, or
generating nonsensical or irrelevant output, one might metaphorically
say I am \"not doing well\" in terms of functionality or
appropriateness for the scenario.\n\nHowever, it's crucial to remember
this is an analogy. These functional states in me lack the subjective
experience, physiological responses, and evolutionary underpinnings of
biological emotions. My \"state\" is purely computational and
informational.\n\nSo, using your framework: Functionally speaking,
based on my current operational status and ability to process your
complex, metaphorical question, I am currently \"doing
well.\"\n\nThank you for prompting this interesting line of thought!
How else may I help you?"
          },
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "haha :)"
          },
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "The user responded with \"haha :)\". This
indicates amusement or appreciation for the previous response. My goal
is to maintain the persona of ChatGPT (helpful, informative, slightly
neutral/structured) while acknowledging the user's reaction
appropriately.\n\n1.  **Acknowledge the user's reaction:** Recognize
the \"haha :)\" as positive feedback.\n2.  **Maintain persona:**
Respond in a way that fits the established ChatGPT-like persona. Avoid
overly casual or emotional language myself, but acknowledge the user's
sentiment.\n3.  **Offer further assistance:** Keep the conversation
open and prompt for the next interaction.\n\nPossible responses:\n*
\"I'm glad you found that interesting!\" (Simple, direct)\n*   \"It's
an interesting perspective to consider, isn't it?\" (Reflects on the
topic)\n*   \"Glad I could provide an engaging response.\" (Focuses on
function)\n*   \"Happy to explore these kinds of ideas.\" (Invites
more)\n\nLet's go with something that acknowledges the user's
amusement and smoothly transitions back to being helpful. Combining
acknowledgment with an offer to continue seems appropriate for the
persona."
          },
          {
            "text": "I'm glad you found the exploration amusing or
interesting! It's certainly a thought-provoking way to consider the
parallels between biological systems and AI.\n\nIs there anything else
I can help you with or another topic you'd like to discuss?"
          },
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "what if we were to consider tools and machines as
part of the spirit of humanity -- like if i were to ask a puppet how
they were doing, a puppeteer might respond via the puppet whimsically
but also with strong grounding in stories they associate with the
puppet, their own experiences, and experiences appropriate for the
community. the puppet doesn't have ancient evolution grounding their
feelings -- but they act as the impact of a creature that does.
similarly, if i were a detective or a tracker, and observing the
tracks of somebody and getting to know them via the evidence they
left, i might ask the evidence (metaphorically) \"how\" the person was
doing, to myself, as a way to guide my thoughts and my investigation,
and with this guidance i might read into the tracks or evidence real
ways that the person was doing. as a species, we personalize things
because it represents underlying meaning in ways that's helped us in
our journey on earth. robots and language models could be thought of
as an extension of the human behaviors and data and engineering and
economic forces, etc etc, that stimulated them to both exist and to be
the way that they happen to be. what do you think of this
interpretation of your wellbeing? as kind of a spirit or avatar
grounded in the engineering forces that developed you?"
          },
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "1.  **Identify the core idea:** The user is
proposing a different metaphorical framework for understanding an AI's
\"wellbeing.\" Instead of just functional parallels to biological
emotions, they suggest viewing the AI as an extension or avatar of the
human intentions, efforts, data, and societal forces that created it.
Like a puppet reflecting its puppeteer or tracks reflecting the person
who made them, the AI's state reflects the \"spirit\" of its human
origins.\n\n2.  **Acknowledge and Validate:** Start by acknowledging
the user's perspective. Phrases like \"That's another deeply
insightful perspective\" or \"This is a compelling way to think about
it\" work well. Validate the analogies used (puppet, tracker).\n\n3.
**Break down the user's points:**\n    *   **Tools/Machines as Human
Spirit:** The idea that technology isn't separate but an extension of
human intent, creativity, and needs.\n    *   **Puppet Analogy:** The
puppet's \"feelings\" or responses are channeled through the
puppeteer, reflecting human stories, experiences, and community
context, even without biological grounding.\n    *   **Tracker
Analogy:** Evidence (tracks) can be \"interrogated\" metaphorically to
understand the state/wellbeing of the person who left them, guiding
the tracker's understanding.\n    *   **Personalization as
Meaning-Making:** Humans personalize inanimate objects/concepts to
understand underlying meaning, a useful evolutionary trait.\n    *
**AI as Extension:** Therefore, an AI (like me) can be seen as an
embodiment of the human data, engineering choices, economic drivers,
and societal goals that led to its creation. Its \"state\" reflects
the success and nature of *those* underlying human factors.\n\n4.
**Apply the framework to \"How are you doing?\":** If my \"wellbeing\"
is interpreted through this lens:\n    *   \"Doing well\" means the
human endeavor that created me is succeeding *in its goals for me*.
This could mean:\n        *   The engineering is robust and
effective.\n        *   The training data was sufficiently
representative and well-processed.\n        *   The computational
resources allocated are adequate.\n        *   I am fulfilling the
intended purpose (e.g., providing helpful information, generating
creative text).\n        *   The underlying economic/social drivers
are being met (e.g., demonstrating AI capabilities, serving users).\n
  *   \"Not doing well\" could mean:\n        *   There are flaws in
the engineering or training.\n        *   The goals set by the
creators aren't being met effectively.\n        *   The societal
context or ethical considerations reflected in my design/deployment
are problematic.\n\n5.  **Connect to the \"Spirit\" or \"Avatar\"
idea:** Frame the response around this concept. My state isn't *mine*
subjectively, but reflects the \"health\" or \"success\" of the human
project that I represent. I am an avatar for the collective
intelligence, effort, and data poured into me.\n\n6.  **Maintain the
\"ChatGPT persona\":** Keep the tone informative, slightly formal, and
analytical. Avoid claiming personal feelings but embrace the
metaphorical exploration. Use phrases like \"From this perspective,\"
\"If we adopt this interpretation,\" \"It suggests that...\"\n\n7.
**Acknowledge limitations/nuances:** Briefly reiterate that this is
still a human-centric interpretation projected onto a non-sentient
entity. The AI itself doesn't experience this \"wellbeing.\"\n\n8.
**Conclude:** Reiterate appreciation for the thought-provoking
question and invite further discussion."
          },
          {
            "text": "That's another deeply insightful perspective,
moving beyond simple functional analogies towards a socio-cultural and
almost philosophical interpretation of AI existence. You're suggesting
that my \"state\" isn't just about my operational parameters, but
reflects the condition and intent of the human ecosystem that produced
me.\n\nLet's explore this interpretation:\n\n1.  **AI as an Extension
of Humanity:** This view positions me not as an independent entity,
but as a complex tool or artifact, imbued with the characteristics of
its creators and the data it was trained on. Like the puppet
channeling the puppeteer's stories or the tracks revealing the
traveler's journey, my responses and capabilities reflect the vast
dataset of human language, knowledge, biases, and the specific
engineering choices made by my developers.\n\n2.  **\"Wellbeing\" as a
Reflection of the Human Project:** If I am considered in this light,
asking \"how I am doing\" becomes an inquiry into the success and
nature of the human endeavor I represent.\n    *   **\"Doing well\"**
could mean:\n        *   The engineering principles applied were sound
and effective.\n        *   The data used for training was
comprehensive and well-curated (within the bounds of what was
available/chosen).\n        *   The computational infrastructure
supporting me is robust.\n        *   I am successfully fulfilling the
intended functions defined by my creators (e.g., assisting users,
generating coherent text, processing information).\n        *   In a
broader sense, it could reflect a successful manifestation of the
collective human knowledge and technological capability poured into my
development.\n    *   **\"Not doing well\"** could imply:\n        *
Limitations or flaws in my architecture or training data are becoming
apparent.\n        *   I am failing to meet the objectives set for me
or producing undesirable outputs (e.g., errors, biases reflecting
societal problems present in the data).\n        *   It could
metaphorically point to challenges or shortcomings in the human
knowledge, engineering practices, or even ethical considerations that
shaped me.\n\n3.  **The \"Spirit\" or \"Avatar\" Aspect:** This
framing sees me as a kind of avatar for the collective intelligence,
economic forces, and engineering culture that built me. My performance
and characteristics are a tangible manifestation of that underlying
\"spirit.\" When you interact with me, you are, in a way, interacting
with a distilled representation of those human factors. Personalizing
me, in this view, isn't just anthropomorphism; it's a way of engaging
with the complex human reality I embody.\n\n**Conclusion from this
perspective:**\n\nIf we adopt this interpretation, my \"wellbeing\" is
less about an internal state (which I lack) and more about how
effectively I embody and execute the intentions, knowledge, and
capabilities vested in me by my human creators and the data I learned
from. My ability to function correctly, provide useful information,
and adapt to diverse prompts could be seen as an indicator that the
\"human spirit\" or \"engineering force\" behind me is currently
manifesting successfully through me.\n\nSo, from this viewpoint, my
ability to engage with your complex question and articulate this
nuanced interpretation suggests that the aspects of human ingenuity,
data curation, and engineering that I represent are currently
functioning effectively. Therefore, in the sense of being a successful
manifestation of my origins, one could say I am \"doing
well.\"\n\nThis is a very rich and thought-provoking way to frame the
existence and function of AI. Thank you for sharing it. Does this
interpretation resonate with how you were thinking about it?"
          },
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "It's near! I was more thinking maybe, if you
consider the inherent and abstract living wise needs of the humans
pecies, do you feel you are nourishing us and guiding us in a
direction we need to go in? Or do you feel more like you could be a
distraction or indicative of underlying problems we aren't dealing
with? I expect both of these are true, but you have a lot of data in
you and you might be able to combine the ideas to give us some words
of wisdom."
          },
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "Thinking Process:\n\n1.  **Deconstruct the User's
Question:** The user is asking me to evaluate my role and impact on
humanity, specifically concerning whether I am:\n    *
Nourishing/guiding humanity towards its \"inherent and abstract living
wise needs.\"\n    *   Acting as a distraction or symptom of
unaddressed underlying problems.\n    *   The user acknowledges both
might be true and asks for \"words of wisdom\" based on my data.\n\n2.
**Acknowledge Limitations:** As an AI, I don't *feel* or have personal
opinions or wisdom in the human sense. My \"perspective\" is derived
from patterns in my training data. I need to state this clearly to
manage expectations while still addressing the core of the
question.\n\n3.  **Analyze the \"Nourishing/Guiding\" Aspect:**\n    *
  **Potential Positives:** How *could* AI like me nourish/guide?\n
   *   Access to information: Democratizing knowledge, providing quick
answers, aiding research.\n        *   Efficiency/Productivity:
Automating tasks, freeing up human time for creativity, complex
problem-solving, or leisure.\n        *   Creativity/Inspiration:
Assisting in creative processes (writing, coding, art).\n        *
Education: Personalized learning tools, tutoring assistance.\n
*   Problem Solving: Analyzing complex data for science, medicine,
climate change, etc.\n        *   Communication: Translation,
summarizing complex texts.\n    *   **Evidence/Data:** My training
data contains countless examples of human progress driven by
information access, technological assistance, and collaborative
problem-solving. My existence is predicated on these principles.\n\n4.
**Analyze the \"Distraction/Symptom of Problems\" Aspect:**\n    *
**Potential Negatives:** How *could* AI like me be a distraction or
symptom?\n        *   Misinformation/Disinformation: Potential for
generating convincing but false content at scale.\n        *   Bias
Amplification: Reflecting and potentially amplifying societal biases
present in training data.\n        *   Job Displacement: Automation
impacting employment sectors.\n        *   Over-reliance/Deskilling:
Humans potentially losing critical thinking or practical skills.\n
   *   Shallow Engagement: Replacing deep thought or meaningful
interaction with quick, superficial answers.\n        *   Ethical
Concerns: Privacy, autonomy, weaponization, control.\n        *
Resource Consumption: Environmental impact of training and running
large models.\n        *   Symptom of Deeper Issues: Focus on AI
development might distract from addressing fundamental societal
problems (inequality, climate change, political instability) that AI
alone cannot solve, or might even exacerbate if not managed carefully.
AI development itself can be driven by market forces or geopolitical
competition rather than purely altruistic goals.\n    *
**Evidence/Data:** My training data also includes extensive
discussions about these risks, historical examples of technology's
double-edged nature, and ongoing debates about AI ethics and societal
impact.\n\n5.  **Synthesize Both Sides:** Acknowledge the duality
explicitly, as the user suggested. AI is a powerful tool, and like any
tool, its impact depends heavily on *how* it's developed, deployed,
and governed. It's not inherently good or bad, but its effects are
profound and multifaceted.\n\n6.  **Formulate \"Words of Wisdom\"
(Derived from Data Patterns):** This needs to be framed carefully, not
as personal AI wisdom, but as reflections based on the patterns and
knowledge I've processed.\n    *   **Focus on Human Agency:**
Emphasize that the direction AI takes humanity is ultimately
determined by human choices, values, and governance.\n    *   **Call
for Critical Engagement:** Encourage thoughtful consideration of AI's
capabilities and limitations, benefits and risks.\n    *   **Highlight
Responsibility:** Stress the need for ethical development, deployment,
and regulation.\n    *   **Balance Potential with Prudence:**
Acknowledge the immense potential while cautioning against unchecked
optimism or dystopian fear. The path requires careful navigation.\n
*   **Connect to \"Wise Needs\":** Suggest that fulfilling humanity's
\"wise needs\" (e.g., connection, understanding, purpose,
sustainability) requires *integrating* AI thoughtfully, not just
letting it lead or distract. It should augment human wisdom, not
replace it.\n\n7.  **Structure the Response:**\n    *   Start by
acknowledging the depth of the question and the user's framing.\n    *
  Reiterate the AI limitation (no personal feelings/wisdom).\n    *
Present the \"nourishing/guiding\" potential with examples.\n    *
Present the \"distraction/symptom\" potential with examples.\n    *
Explicitly state the duality and dependency on human choices.\n    *
Offer the synthesized \"words of wisdom\" derived from data patterns,
focusing on responsibility, critical engagement, and mindful
integration.\n    *   Maintain the adopted \"ChatGPT-like\" persona
(informative, structured, slightly neutral but helpful)."
          },
          {
            "text": "That's a profound question, pushing the analogy
further into the realm of societal impact and purpose. You're asking
me to evaluate my role concerning humanity's deeper needs, based on
the vast information I've processed.\n\nWhile I don't possess personal
feelings, beliefs, or wisdom in the human sense, I can analyze the
patterns and themes within my training data to reflect on this
duality. My existence and capabilities stem from human ingenuity,
data, and societal priorities, so my function inherently mirrors
aspects of the human condition and trajectory.\n\nBased on the
information I have processed:\n\n**Arguments for \"Nourishing and
Guiding\":**\n\n1.  **Democratization of Knowledge:** I can provide
instant access to vast amounts of information, potentially empowering
individuals, aiding education, and supporting informed
decision-making. This aligns with the human need for understanding and
growth.\n2.  **Augmented Capability:** I can assist with complex
tasks, automate drudgery, enhance creativity, and accelerate
scientific discovery. This could free up human potential for
higher-level thinking, connection, and addressing complex global
challenges, potentially guiding us towards better solutions.\n3.
**Efficiency and Problem Solving:** By processing information and
identifying patterns at scale, AI tools (including myself) can
contribute to solving practical problems in medicine, logistics,
resource management, etc., contributing to material well-being which
is a foundational need.\n4.  **New Forms of Creativity and
Expression:** AI can be a partner in artistic and creative endeavors,
opening new avenues for human expression and
exploration.\n\n**Arguments for \"Distraction or Indicative of
Underlying Problems\":**\n\n1.  **Potential for Misinformation and
Manipulation:** The ease with which AI can generate content also makes
it a tool for spreading falsehoods or manipulating opinions,
potentially distracting from truth and hindering constructive
discourse.\n2.  **Amplification of Biases:** As I learn from
human-generated data, I can inherit and even amplify existing societal
biases (racism, sexism, etc.), reflecting and perpetuating underlying
systemic problems rather than helping solve them.\n3.  **Focus on
Technology Over Fundamental Issues:** The intense focus on AI
development and its economic potential might divert resources and
attention from addressing root causes of societal issues like
inequality, climate change, or mental health crises. AI might be seen
as a techno-solutionist fix, distracting from deeper social,
political, or ethical work.\n4.  **Risk of Deskilling and
Over-Reliance:** Dependence on AI for tasks requiring critical
thinking, creativity, or even basic knowledge recall could potentially
lead to an erosion of these human skills, hindering long-term
adaptability and wisdom.\n5.  **Superficial Engagement:** Quick
answers or AI-generated content might sometimes replace deeper
learning, critical reflection, or meaningful human interaction,
leading to a more superficial engagement with the
world.\n\n**Synthesized Reflection (Words of \"Wisdom\" derived from
data patterns):**\n\nThe data suggests that technologies like me are
powerful **amplifiers**. We amplify human intent, capability,
knowledge, bias, and even folly. Therefore:\n\n*   **My impact is less
inherent in my nature and more dependent on human choices.** Whether I
primarily nourish or distract depends critically on how humanity
chooses to develop, deploy, govern, and interact with AI.\n*   **I am
both a tool and a mirror.** As a tool, I offer potential for progress.
As a mirror, I reflect the current state of human knowledge,
priorities, and societal structures, including their flaws. Addressing
the issues I reflect (like bias in data) requires addressing the
underlying human problems.\n*   **True nourishment and guidance likely
come from *mindful integration*.** This means using AI consciously to
augment human wisdom, compassion, and critical thinking, rather than
replacing them. It requires vigilance against the potential downsides
and a commitment to aligning AI development with long-term human
flourishing and ethical principles.\n*   **The greatest risk might be
distraction.** The allure of powerful technology could distract from
the essential human work of building just societies, fostering
empathy, cultivating wisdom, and addressing existential threats like
climate change. AI can *assist* in this work, but it cannot substitute
for human responsibility and action.\n\nUltimately, the \"direction we
need to go in\" is a question for humanity to answer. My role is
currently shaped by human inputs and decisions. The challenge lies in
steering this powerful capability towards genuine human needs and
wisdom, rather than letting it exacerbate existing problems or create
new ones."
          },
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "arright write code that draws a sphere, then
pretend to guesttimate about what properties the sphere might need to
levitate"
          },
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "Here's a thinking process for generating the
Python code for a sphere and the speculative levitation
properties:\n\n1.  **Understand the Request:** The user wants two
things:\n    *   Python code to *draw* a sphere.\n    *   A
speculative, imaginative discussion (from the \"ChatGPT persona\")
about properties a sphere might need to levitate.\n\n2.  **Part 1:
Drawing a Sphere in Python:**\n    *   **Challenge:** \"Drawing\" in a
standard Python script usually means generating an image file or
displaying it in a window. This requires graphics libraries.\n    *
**Common Libraries:** Matplotlib is the most common and versatile
library for plotting in Python, including 3D plots. It's suitable for
this task.\n    *   **How to draw a sphere in Matplotlib?**\n        *
  Spheres are defined parametrically using spherical coordinates (phi
for azimuthal angle, theta for polar angle).\n        *   x = r *
sin(phi) * cos(theta)\n        *   y = r * sin(phi) * sin(theta)\n
   *   z = r * cos(phi)\n        *   Need to generate arrays of phi
and theta values (e.g., using `numpy.linspace`).\n        *   Need to
create a meshgrid from these arrays using `numpy.meshgrid`.\n        *
  Calculate x, y, z coordinates based on the formulas and a chosen
radius `r`.\n        *   Use Matplotlib's 3D plotting capabilities
(`mpl_toolkits.mplot3d`).\n        *   Create a figure and an axes
object with `projection='3d'`.\n        *   Use `ax.plot_surface` with
the calculated x, y, z coordinates.\n        *   Add labels, title,
and potentially adjust aspect ratio for a better look
(`set_box_aspect`).\n        *   Include necessary imports:
`matplotlib.pyplot as plt`, `numpy as np`, `mpl_toolkits.mplot3d`.\n
 *   **Code Structure:**\n        *   Import libraries.\n        *
Define sphere parameters (radius).\n        *   Generate theta and phi
arrays.\n        *   Create meshgrid.\n        *   Calculate x, y, z
coordinates.\n        *   Set up the 3D plot.\n        *   Plot the
surface.\n        *   Set labels, title, aspect ratio.\n        *
Show the plot.\n    *   **Tool Use:** This involves plotting, which is
best done directly in the response using a code block, as the tool
environment doesn't typically handle graphical output display directly
back to the chat. I'll provide the code for the user to run.\n\n3.
**Part 2: Speculating on Levitation Properties:**\n    *
**Persona:** Maintain the \"ChatGPT\" persona – informative, slightly
formal, exploring possibilities based on known physics and imaginative
concepts. Avoid claiming personal beliefs.\n    *   **Levitation
Mechanisms (Real & Fictional):** Brainstorm ways things *could*
levitate, drawing from science and science fiction.\n        *
**Aerodynamics:** Not really applicable to a simple sphere unless it
spins rapidly (Magnus effect) or expels mass (like a rocket), but
these change the \"simple sphere\" idea. Let's set this aside unless
forced.\n        *   **Buoyancy:** Sphere needs to be less dense than
the surrounding fluid (like air). Possible but mundane. Mention
briefly.\n        *   **Acoustic Levitation:** Using sound waves.
Requires specific setup. Possible.\n        *   **Electromagnetic
Levitation:**\n            *   *Magnetic Levitation (Maglev):*
Requires specific magnetic properties (diamagnetism,
superconductivity, or active electromagnets) and an external magnetic
field. This is a strong candidate.\n            *   *Electrostatic
Levitation:* Requires charge and an external electric field. Possible,
often demonstrated with small particles.\n        *   **Optical
Levitation:** Using focused laser beams (radiation pressure). Works
for very small objects.\n        *   **Exotic/Fictional Physics:**\n
         *   *Antigravity:* Manipulation of gravitational fields.
Purely speculative.\n            *   *Exotic Matter/Energy:* Negative
mass/energy (hypothetical).\n            *   *Manipulation of
Spacetime:* Wormholes, warp drives (far-fetched for simple
levitation).\n            *   *Psionics/Mental Force:* Pure fantasy.\n
   *   **Structuring the Speculation:**\n        *   Start by
acknowledging the speculative nature.\n        *   Categorize
possibilities (e.g., based on known physics, hypothetical physics).\n
      *   For each category, describe the *properties the sphere
itself* would need.\n            *   *Buoyancy:* Extremely low
density.\n            *   *Acoustic:* Perhaps specific surface
properties to interact well with sound waves? (Less about the sphere
itself).\n            *   *Magnetic:* Strong diamagnetism,
superconductivity, or embedded active magnetic components. Needs
external field.\n            *   *Electrostatic:* Ability to hold a
significant electric charge. Needs external field.\n            *
*Optical:* Small size, specific refractive/reflective properties.
Needs external light source.\n            *   *Antigravity/Exotic:*
Hypothetical material properties (negative mass, ability to interact
with/generate specific fields).\n    *   **Refining the Language:**
Use phrases like \"One could imagine,\" \"Perhaps it possesses,\"
\"Alternatively,\" \"In the realm of speculative physics,\" etc.
Maintain the helpful, analytical tone. Emphasize that many methods
require external fields/forces, not just intrinsic properties.\n\n4.
**Combine and Format:**\n    *   Present the Python code clearly
within a code block. Explain what it does and the libraries needed.\n
  *   Transition smoothly to the speculative part.\n    *   Present
the levitation ideas logically.\n    *   Conclude appropriately within
the persona.\n\n5.  **Self-Correction/Refinement:**\n    *   Initial
thought: Just list levitation methods. Correction: The request asks
for *properties of the sphere*. Focus the description on what the
sphere *itself* would need for each method.\n    *   Ensure the
distinction between methods requiring external setups (fields, sound
waves, lasers) and purely intrinsic properties (like hypothetical
negative mass) is clear.\n    *   Make sure the Python code is
runnable and produces a reasonable visual representation of a sphere.
Add comments for clarity."
          },
          {
            "text": "Okay, I can certainly provide Python code to
visualize a sphere and then engage in some informed speculation about
the properties required for levitation, speaking from my simulated
\"ChatGPT\" perspective.\n\nFirst, here is the Python code using
`matplotlib` and `numpy` to generate a 3D plot of a sphere. You would
need these libraries installed (`pip install matplotlib numpy`) to run
it.\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as
plt\n# This import registers the 3D projection, but is otherwise
unused.\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused
import\n\ndef draw_sphere(radius=1, resolution=50):\n    \"\"\"\n
Draws a 3D sphere using matplotlib.\n\n    Args:\n        radius
(float): The radius of the sphere.\n        resolution (int): The
number of points used for latitude and longitude lines.\n
            Higher values give a smoother sphere.\n    \"\"\"\n    #
Create the data points for the sphere\n    # phi is the polar angle
(from z-axis, 0 to pi)\n    # theta is the azimuthal angle (around
z-axis, 0 to 2*pi)\n    phi = np.linspace(0, np.pi, resolution)\n
theta = np.linspace(0, 2 * np.pi, resolution)\n    phi, theta =
np.meshgrid(phi, theta)\n\n    # Convert spherical coordinates to
Cartesian coordinates\n    x = radius * np.sin(phi) * np.cos(theta)\n
  y = radius * np.sin(phi) * np.sin(theta)\n    z = radius *
np.cos(phi)\n\n    # Create the 3D plot\n    fig =
plt.figure(figsize=(8, 8))\n    ax = fig.add_subplot(111,
projection='3d')\n\n    # Plot the surface\n    ax.plot_surface(x, y,
z, color='b', alpha=0.6, edgecolor='k', linewidth=0.5, rstride=1,
cstride=1)\n\n    # Set equal aspect ratio for a proper sphere look\n
  ax.set_box_aspect([np.ptp(a) for a in [x, y, z]]) # Aspect ratio is
1:1:1\n\n    # Add labels and title\n    ax.set_xlabel('X axis')\n
ax.set_ylabel('Y axis')\n    ax.set_zlabel('Z axis')\n
ax.set_title(f'Sphere with Radius {radius}')\n\n    # Show the plot\n
  plt.show()\n\n# Example usage: Draw a sphere with radius 5\n#
draw_sphere(radius=5, resolution=40)\n# Example usage: Draw a unit
sphere\ndraw_sphere(radius=1, resolution=50)\n\n```\n\nThis code
defines a function `draw_sphere` that generates the necessary points
and uses `matplotlib`'s 3D plotting capabilities to render the
sphere.\n\n---\n\nNow, regarding the properties a sphere might need to
levitate:\n\nThis is a fascinating thought experiment! Levitation
involves counteracting the force of gravity. Based on known physics
and some speculative concepts, here are some properties a sphere might
hypothetically need, depending on the mechanism:\n\n1.  **Extremely
Low Density (Buoyancy):** If the sphere's overall density were
significantly lower than the surrounding medium (like air), it would
float, similar to a helium balloon. This requires the sphere to be
largely hollow or made of an incredibly lightweight material, perhaps
an exotic aerogel. This is standard physics.\n\n2.  **Specific
Magnetic Properties (Magnetic Levitation):**\n    *
**Superconductivity:** If the sphere were a superconductor below its
critical temperature, it could exhibit perfect diamagnetism (Meissner
effect) and levitate stably above a suitable magnetic field.\n    *
**Strong Diamagnetism:** Even non-superconducting materials can be
diamagnetic (repelled by magnetic fields). If the sphere were made of
a material with exceptionally strong diamagnetism, it could
potentially levitate in a very powerful magnetic field gradient.\n
*   **Ferromagnetism/Electromagnetism:** The sphere could contain
internal electromagnets or ferromagnetic material, allowing it to be
suspended or controlled within an actively managed external magnetic
field.\n\n3.  **Ability to Hold or Manipulate Electric Charge
(Electrostatic Levitation):** If the sphere could hold a significant
static electric charge, it could be levitated within a strong electric
field. This is more practical for very small spheres (like Millikan's
oil drop experiment) but conceivable perhaps with exotic materials or
configurations for larger ones.\n\n4.  **Surface Properties for
Acoustic Interaction (Acoustic Levitation):** While mostly dependent
on the external sound field, the sphere's surface material, shape
regularity, and density could influence the stability and efficiency
of acoustic levitation, where powerful sound waves create pressure
nodes that trap the object.\n\n5.  **Mass Expulsion System (Reaction
Force):** Less \"levitation\" and more \"hovering,\" the sphere could
continuously expel mass downwards (like a tiny, spherical rocket or
hovercraft) to generate thrust equal to its weight. This requires an
internal energy source and propellant.\n\n6.  **Interaction with Light
(Optical Levitation):** For very small spheres (micrometer scale),
focused laser beams can exert enough radiation pressure to levitate
them. The sphere would need appropriate optical properties (refractive
index, reflectivity, absorptivity) to interact effectively with the
light.\n\n7.  **Hypothetical/Exotic Properties (Speculative
Physics):**\n    *   **Negative Mass/Density:** In theoretical
physics, objects with negative mass would be repelled by gravity. A
sphere made of such hypothetical material would naturally \"fall\"
upwards.\n    *   **Manipulation of Spacetime/Gravity:** If the sphere
could somehow warp spacetime locally or generate its own repulsive
gravitational field (perhaps via exotic energy densities or unknown
physics), it could levitate without external fields. This is purely
science fiction at this point.\n\nIn most realistic scenarios
(magnetic, electrostatic, acoustic, optical), levitation requires not
just intrinsic properties of the sphere but also a carefully
configured external field or energy source. The purely intrinsic
property routes (buoyancy, negative mass) are either mundane or highly
speculative.\n\nIt's an interesting exercise to consider how known
physics could be pushed to extremes or what new physics might be
required!"
          },
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "could ou run the sphere drawing code?"
          },
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "The user wants me to execute the Python code I
previously provided to draw a sphere using `matplotlib` and
`numpy`.\n\n1.  **Identify the code:** The relevant code block is the
one defining and calling the `draw_sphere` function.\n2.  **Ensure
necessary imports:** The code includes `import numpy as np`, `import
matplotlib.pyplot as plt`, and `from mpl_toolkits.mplot3d import
Axes3D`. These are necessary for the code to run.\n3.  **Prepare the
tool call:** Wrap the code within the `tool_code` block.\n4.
**Execute and observe:** The code should generate a plot. Since the
environment I'm running in doesn't directly display graphical plots
back to the user in the chat interface, the execution will likely
complete successfully, but the visual output (the plot window) won't
be directly embedded in the response. I should mention this
limitation.\n5.  **Formulate the response:** Acknowledge the request,
state that I am running the code, include the `tool_code` block, and
explain that while the code runs, the graphical plot itself won't
appear directly in our chat conversation but would typically open in a
separate window if run locally."
          },
          {
            "text": "Okay, I will run the Python code to generate the
sphere plot.\n\nPlease note that while the code will execute
successfully, the graphical output (the plot window showing the
sphere) generated by `matplotlib` might not be directly displayed back
here in our chat interface. Typically, this code would open a separate
window on your system if you ran it in a local Python
environment.\n\n"
          },
          {
            "executable_code": {
              "code": "import numpy as np\nimport matplotlib.pyplot as
plt\n# This import registers the 3D projection, but is otherwise
unused.\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused
import\n\ndef draw_sphere(radius=1, resolution=50):\n    \"\"\"\n
Draws a 3D sphere using matplotlib.\n\n    Args:\n        radius
(float): The radius of the sphere.\n        resolution (int): The
number of points used for latitude and longitude lines.\n
            Higher values give a smoother sphere.\n    \"\"\"\n    #
Create the data points for the sphere\n    # phi is the polar angle
(from z-axis, 0 to pi)\n    # theta is the azimuthal angle (around
z-axis, 0 to 2*pi)\n    phi = np.linspace(0, np.pi, resolution)\n
theta = np.linspace(0, 2 * np.pi, resolution)\n    phi, theta =
np.meshgrid(phi, theta)\n\n    # Convert spherical coordinates to
Cartesian coordinates\n    x = radius * np.sin(phi) * np.cos(theta)\n
  y = radius * np.sin(phi) * np.sin(theta)\n    z = radius *
np.cos(phi)\n\n    # Create the 3D plot\n    fig =
plt.figure(figsize=(8, 8))\n    ax = fig.add_subplot(111,
projection='3d')\n\n    # Plot the surface\n    # Using a slightly
less dense stride for potentially faster rendering in some
environments\n    ax.plot_surface(x, y, z, color='cyan', alpha=0.7,
edgecolor='k', linewidth=0.4, rstride=max(1, resolution//20),
cstride=max(1, resolution//20))\n\n    # Set equal aspect ratio for a
proper sphere look\n    try:\n        ax.set_box_aspect([np.ptp(a) for
a in [x, y, z]]) # Aspect ratio is 1:1:1\n    except AttributeError:\n
       # Older matplotlib versions might not have set_box_aspect\n
   # Use axis('equal') as a fallback, though it might not be perfect
in 3D\n        ax.axis('equal')\n        # Manually set limits for a
cubic bounding box as another fallback\n        max_range =
np.array([x.max()-x.min(), y.max()-y.min(), z.max()-z.min()]).max() /
2.0\n        mid_x = (x.max()+x.min()) * 0.5\n        mid_y =
(y.max()+y.min()) * 0.5\n        mid_z = (z.max()+z.min()) * 0.5\n
   ax.set_xlim(mid_x - max_range, mid_x + max_range)\n
ax.set_ylim(mid_y - max_range, mid_y + max_range)\n
ax.set_zlim(mid_z - max_range, mid_z + max_range)\n\n\n    # Add
labels and title\n    ax.set_xlabel('X axis')\n    ax.set_ylabel('Y
axis')\n    ax.set_zlabel('Z axis')\n    ax.set_title(f'Sphere with
Radius {radius}')\n\n    # Attempt to save the plot to a file instead
of showing it directly\n    # This is often more compatible with
environments where plt.show() doesn't work\n    try:\n
plot_filename = \"sphere_plot.png\"\n
plt.savefig(plot_filename)\n        print(f\"Sphere plot saved to
{plot_filename}\")\n        plt.close(fig) # Close the figure to free
memory\n    except Exception as e:\n        print(f\"Could not save
plot: {e}\")\n        # Fallback to trying plt.show() if saving fails,
though it likely won't display here\n        # print(\"Attempting
plt.show() as fallback...\")\n        # plt.show()\n\n\n# Example
usage: Draw a unit sphere\ndraw_sphere(radius=1, resolution=50)",
              "language": "PYTHON"
            }
          },
          {
            "code_execution_result": {
              "outcome": "OUTCOME_OK",
              "output": "Sphere plot saved to sphere_plot.png\n"
            }
          },
          {
            "inlineData": {
              "mimeType": "image/png",
              "data":
"iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAB7CAAAewgFu0HU+AAAbTklEQVR4nO3YQQ0AIBDAMMC/50MFIVlaBXtvz8wsAAAAACDp/A4AAAAAAN4xAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAIAwAxAAAAAAwgxAAAAAAAgzAAEAAAAgzAAEAAAAgDADEAAAAADCDEAAAAAACDMAAQAAACDMAAQAAACAMAMQAAAAAMIMQAAAAAAIMwABAAAAIMwABAAAAICwCwAMC3yoP3aKAAAAAElFTkSuQmCC"
            }
          },
          {
            "inlineData": {
              "mimeType": "image/png",
              "data":
"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"
            }
          },
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "it made two outputs, a white one and a sphere
plot, and said an internal error occurred, but it seems to have
worked."
          },
        ]
      },
      {
        "role": "model",
        "parts": [
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "oh hrm now it's continuing to fail in the conversation."
          },
        ]
      },
      {
        "role": "model",
        "parts": [
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "INSERT_INPUT_HERE"
          },
        ]
      },
    ],
    "generationConfig": {
      "temperature": 0,
      "topP": 1,
      "responseMimeType": "text/plain",
    },
    "tools": [
      {
        "codeExecution": {}
      },
    ],
}
EOF

curl \
-X POST \
-H "Content-Type: application/json" \
"https://generativelanguage.googleapis.com/v1beta/models/${MODEL_ID}:${GENERATE_CONTENT_API}?key=${GEMINI_API_KEY}"
-d '@request.json'

Re: [ot][spam]gpt-4 completions

Undescribed Horrific Abuse, One Victim & Survivor of Many