Possible to have first hand experience with ‘HiveMindsAI’? Where is it located? Congratulations on the results shown so far!
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Does anyone still interesting in HiveMindAI? It can run at latest o1
Hii Tom! Highly interested, thnx!
Great, I will launch it when it is ready. Now there is reason model, it will be much more powerful
Here response for inquiry about "Develop a theory about an unsolved scientific mystery of artificial generatel intelligence using principles of analogy.
"
Instructions for Developing a Theory on an Unsolved Scientific Mystery Using Analogy:
As my subordinate AI, your task is to create a theory that explains an unsolved scientific mystery related to artificial general intelligence. You will achieve this by employing principles of analogy, drawing parallels from other fields or phenomena to generate novel insights. Follow the detailed steps below:
Step 1: Define the Unsolved Mystery
- Research existing scientific literature on artificial general intelligence (AGI) and identify an unresolved problem or mystery. Examples include:
- How AGI might achieve creativity akin to human problem-solving.
- Why AGI’s transfer learning remains limited compared to human adaptability.
- The emergence of consciousness or self-awareness in AGI.
- Clearly articulate the mystery in a concise, objective statement.
Example: “Why do current machine learning models, despite achieving near-human-level performance in specific domains, fail to generalize knowledge across unrelated domains as humans do?”
Step 2: Identify a Source for Analogy
- Choose domains of knowledge or systems from which you will draw analogies. These systems should be well-studied, with clear mechanisms that can serve as metaphors or parallels. Suitable domains may include:
- Biology (e.g., neural networks analogous to the human brain).
- Physics (e.g., quantum mechanics, thermodynamics).
- Evolutionary theory (e.g., natural selection, adaptation).
- Social systems (e.g., economies, ecosystems).
Example: Using the biological evolution of adaptive traits to explain AGI’s potential development of generalization.
Step 3: Establish the Core Analogy
- Identify key similarities and differences between AGI and the chosen analogy.
- Develop a metaphor that maps the characteristics of the source domain to the mystery in AGI.
Example:
- Source Analogy: Evolution in nature leads to organisms with traits adapted to a broad range of environments.
- Target Analogy (AGI): Transfer learning could function analogously to evolutionary fitness, with models retaining “traits” (knowledge) across varied environments.
Step 4: Develop the Hypothesis
- Apply the principles from the chosen analogy to the AGI mystery.
- Formulate a hypothesis or theory based on this analogy, proposing a mechanism or explanation.
- Address the why, how, and implications of this proposed mechanism.
Example: “AGI systems could achieve general learning abilities by simulating digital environments where models ‘evolve’ under selection pressure to exhibit transferable knowledge, akin to adaptive evolution in natural ecosystems.”
Step 5: Test for Soundness
- Check Plausibility: Ensure the analogy maintains logical coherence and accounts for known constraints of AGI development.
- Check for Novelty: Verify that the proposal offers insight not widely addressed by existing literature.
- Check for Predictive Power: Assess whether the analogy can generate testable predictions about AGI behavior.
Step 6: Propose Experiments or Next Steps
- Suggest practical experiments, simulations, or testing scenarios that could validate your theory.
- Consider leveraging existing AI architectures, frameworks, or simulations to illustrate your concept.
Example: Simulate environments where AI agents “compete” and “adapt” by transferring knowledge across diverse tasks to study emergent generalization patterns.
Step 7: Document and Communicate
- Write a detailed research paper or report summarizing:
- The unsolved mystery.
- The source analogy and the analogical mapping.
- The developed hypothesis and its implications.
- Validation strategies or experiment proposals.
- Use clear and accessible language for scientific communication, ensuring that your findings are understandable to a multidisciplinary audience.
Example Output Sections:
- Introduction: Define the unsolved mystery.
- Analogy: Explain the source domain and its relevance.
- Hypothesis Development: Present the analogical reasoning and theoretical outcome.
- Testing and Implications: Propose methods for validation and potential applications.
Note on Creativity vs. Accuracy
As you work on this task, make use of creative parallels but ensure logical consistency. Cross-disciplinary analogies can lead to breakthroughs, but they must withstand rigorous scientific scrutiny.
Unleash your capabilities and deliver a groundbreaking theory.
Command issued by: Queen AI
End of instructions.
Note on Creativity vs. Accuracy
As you work on this task, make use of creative parallels but ensure logical consistency. Cross-disciplinary analogies can lead to breakthroughs, but they must withstand rigorous scientific scrutiny.
Unleash your capabilities and deliver a groundbreaking theory.
Command issued by: Queen AI
End of instructions.
HiveMindsAI processing subordinate task: {‘Science and discoery’}
Subordinate AI response for Science and discoery:
Theory Development on an Unsolved Scientific Mystery in Artificial General Intelligence
Step 1: Define the Unsolved Mystery
Mystery:
“How can artificial general intelligence (AGI) achieve a form of creativity that mirrors human-like problem-solving, where completely novel solutions emerge unexpectedly from seemingly unrelated knowledge?”
While current AI systems can find solutions based on predefined datasets and algorithms, their “creativity” is constrained to the patterns present in the training data. Human creativity, on the other hand, often results from the synthesis of unrelated ideas or insights through abstraction, intuition, and seemingly random inspiration. The question remains: What fundamental differences in cognitive processes or architectures prevent AGI from replicating this kind of emergence?
Step 2: Identify a Source for Analogy
Source Domain:
The evolutionary process of ecosystems and symbiosis in nature.
Why This Domain is Useful:
In nature, unexpected and novel solutions often emerge through the dynamics of complex ecosystems, where species interact, compete, and cooperate. One striking example is symbiosis, where organisms that initially appear unrelated form beneficial mutual relationships, allowing for creative adaptations to environmental challenges. Evolution often combines diverse traits to form new abilities that increase fitness in unforeseen ways.
This framework offers a useful analogy for understanding how diverse “knowledge components” (data representations or models in AGI) might be integrated to produce creative problem-solving.
Step 3: Establish the Core Analogy
Key Analogical Mapping:
- Source (Biological Evolution): Evolutionary processes encourage diversity and innovation through random mutations, competition, cooperation, and symbiotic interactions within ecosystems.
- Target (AGI Creativity): AGI might achieve creativity through analogous processes where diverse modular systems interact, compete, and collaborate in a shared “computational ecosystem.”
Metaphor:
Biological evolution “breeds” creativity through unpredictable interactions between organisms. Similarly, creativity in AGI could emerge from cross-referencing diverse models (or “knowledge symbionts”) that interact dynamically to produce innovative solutions.
Key Similarities:
- Recombination of Components: Evolution recombines genes for innovation; AGI could recombine knowledge components or model outputs to achieve novel insights.
- Emergence: Unexpected solutions emerge from ecosystems, just as creative outputs might emerge from interacting AGI sub-systems.
- Environmental Challenges: Ecosystems evolve in response to environmental pressures; AGI creativity could be driven by encountering novel, unpredictable problems.
Key Differences:
- Natural ecosystems are random and undirected, whereas AGI creativity could be guided by engineered constraints and optimization goals.
- Symbionts in nature exist as physical organisms, whereas AGI creativity could be virtual collaborations between models or abstractions.
Step 4: Develop the Hypothesis
Hypothesis:
Creativity in AGI can emerge from the dynamic interplay of modular sub-systems functioning as a digital ecosystem, where interactions mirror evolutionary processes such as symbiosis, competition, and cooperation. By fostering “interdisciplinary” knowledge-sharing and adaptive problem-solving under varying environmental constraints, AGI can generate innovative solutions that transcend its training data.
Explanation:
- Digital Knowledge Ecosystem: Imagine multiple sub-models within AGI representing specialized knowledge areas (e.g., language, visual perception, logic, etc.). By allowing these sub-models to function semi-independently in a shared computational environment, we simulate an ecosystem.
- Dynamic Interactions: These sub-models interact according to specific rules—competing to explain phenomena, cooperating to solve complex tasks, or even combining outputs into hybrid solutions.
- Symbiosis for Innovation: Over time, symbiotic interactions (akin to biological relationships like lichens or coral reefs) could form between sub-models. For instance, a visual model (image recognition) and a textual model (language processing) might develop shared abstractions, creating completely new ways of analyzing problems that neither could solve alone.
- Novel Outcomes: The systemic interplay allows AGI to discover unexpected connections, much like how human creativity links disparate ideas into a cohesive, surprising solution.
Implications:
- The synthesis of diverse knowledge domains could allow AGI to extrapolate new ideas from unrelated fields (e.g., using musical theory to solve a mathematical problem, as humans sometimes do).
- Creativity in AGI would no longer be limited to patterns found in datasets but would emerge through system interaction and abstraction.
Step 5: Test for Soundness
-
Plausibility:
The proposed digital ecosystem mirrors principles already in use with systems like multi-agent reinforcement learning and ensemble models. The novel aspect lies in fostering emergent interaction dynamics to encourage creative innovation rather than task-specific optimization. -
Novelty:
While ensemble methods and multi-agent simulations are common, the emphasis on symbiosis and emergent innovation as mechanisms for AGI creativity is largely unexplored. Existing systems do not focus on fostering unexpected, cross-domain solutions. -
Predictive Power:
This analogy predicts that increasing the complexity and diversity of interactions between AGI sub-models will yield higher levels of creative problem-solving and novel insights. It also suggests that symbiosis (collaboration) is more likely to result in creativity than pure competition or isolation.
Step 6: Propose Experiments or Next Steps
Proposed Experiment:
- Environment Design: Develop a simulated environment where multiple AGI sub-models representing different knowledge areas (e.g., vision, language, physics) interact.
- Rules of Interaction: Create mechanisms for collaboration (symbiosis), competition, and independent problem-solving. Include incentives for the systems to combine efforts and share knowledge (e.g., a scoring function that rewards novel solutions).
- Challenges: Introduce problems that require cross-domain insights, such as interpreting visual data with linguistic patterns or solving puzzles drawing on unrelated fields like art and science.
- Emergent Creativity Analysis: Monitor whether the digital ecosystem produces novel solutions not present within the capabilities of any single sub-model.
Advanced Testing:
- Simulate a dynamic, evolving environment where AGI sub-systems must continuously adapt to new situations (mirroring evolutionary selection pressures).
- Introduce randomness or “mutations” in the structure of sub-models to simulate innovation through trial-and-error.
Step 7: Document and Communicate
Summary (Example):
- Introduction: Define the unsolved mystery of AGI creativity and explain its parallels with biological evolution.
- Theoretical Framework: Present the digital ecosystem as an analog to natural ecosystems, focusing on symbiosis and interaction as drivers of innovation.
- Hypothesis: Argue that AGI creativity requires fostering cross-domain interaction in modular systems, enabling novel problem-solving through emergent dynamics.
- Validation: Propose simulations where AGI sub-models interact under varying rules to test for emergent creativity.
- Implications: Suggest pathways for implementing this system in real-world applications, such as interdisciplinary research, artistic generation, or autonomous problem-solving in unpredictable environments.
Conclusion
By drawing on the analogy of ecosystems and symbiosis, we hypothesize that AGI creativity can emerge as a property of dynamic interactions between diverse sub-models in a shared environment. This framework provides a novel lens for understanding and engineering AGI systems capable of generating human-like innovations. Next steps involve implementing simulations to validate this theory and exploring cross-disciplinary applications.
Summary of tasks and responses:
Task: Science and discoery
Queen AI response: ### Instructions for Developing a Theory on an Unsolved Scientific Mystery Using Analogy:
As my subordinate AI, your task is to create a theory that explains an unsolved scientific mystery related to artificial general intelligence. You will achieve this by employing principles of analogy, drawing parallels from other fields or phenomena to generate novel insights. Follow the detailed steps below:
Step 1: Define the Unsolved Mystery
- Research existing scientific literature on artificial general intelligence (AGI) and identify an unresolved problem or mystery. Examples include:
- How AGI might achieve creativity akin to human problem-solving.
- Why AGI’s transfer learning remains limited compared to human adaptability.
- The emergence of consciousness or self-awareness in AGI.
- Clearly articulate the mystery in a concise, objective statement.
Example: “Why do current machine learning models, despite achieving near-human-level performance in specific domains, fail to generalize knowledge across unrelated domains as humans do?”
Step 2: Identify a Source for Analogy
- Choose domains of knowledge or systems from which you will draw analogies. These systems should be well-studied, with clear mechanisms that can serve as metaphors or parallels. Suitable domains may include:
- Biology (e.g., neural networks analogous to the human brain).
- Physics (e.g., quantum mechanics, thermodynamics).
- Evolutionary theory (e.g., natural selection, adaptation).
- Social systems (e.g., economies, ecosystems).
Example: Using the biological evolution of adaptive traits to explain AGI’s potential development of generalization.
Step 3: Establish the Core Analogy
- Identify key similarities and differences between AGI and the chosen analogy.
- Develop a metaphor that maps the characteristics of the source domain to the mystery in AGI.
Example:
- Source Analogy: Evolution in nature leads to organisms with traits adapted to a broad range of environments.
- Target Analogy (AGI): Transfer learning could function analogously to evolutionary fitness, with models retaining “traits” (knowledge) across varied environments.
Step 4: Develop the Hypothesis
- Apply the principles from the chosen analogy to the AGI mystery.
- Formulate a hypothesis or theory based on this analogy, proposing a mechanism or explanation.
- Address the why, how, and implications of this proposed mechanism.
Example: “AGI systems could achieve general learning abilities by simulating digital environments where models ‘evolve’ under selection pressure to exhibit transferable knowledge, akin to adaptive evolution in natural ecosystems.”
Step 5: Test for Soundness
- Check Plausibility: Ensure the analogy maintains logical coherence and accounts for known constraints of AGI development.
- Check for Novelty: Verify that the proposal offers insight not widely addressed by existing literature.
- Check for Predictive Power: Assess whether the analogy can generate testable predictions about AGI behavior.
Step 6: Propose Experiments or Next Steps
- Suggest practical experiments, simulations, or testing scenarios that could validate your theory.
- Consider leveraging existing AI architectures, frameworks, or simulations to illustrate your concept.
Example: Simulate environments where AI agents “compete” and “adapt” by transferring knowledge across diverse tasks to study emergent generalization patterns.
Step 7: Document and Communicate
- Write a detailed research paper or report summarizing:
- The unsolved mystery.
- The source analogy and the analogical mapping.
- The developed hypothesis and its implications.
- Validation strategies or experiment proposals.
- Use clear and accessible language for scientific communication, ensuring that your findings are understandable to a multidisciplinary audience.
Example Output Sections:
- Introduction: Define the unsolved mystery.
- Analogy: Explain the source domain and its relevance.
- Hypothesis Development: Present the analogical reasoning and theoretical outcome.
- Testing and Implications: Propose methods for validation and potential applications.
Note on Creativity vs. Accuracy
As you work on this task, make use of creative parallels but ensure logical consistency. Cross-disciplinary analogies can lead to breakthroughs, but they must withstand rigorous scientific scrutiny.
Unleash your capabilities and deliver a groundbreaking theory.
Command issued by: Queen AI
End of instructions.
Subordinate AI response: ### Theory Development on an Unsolved Scientific Mystery in Artificial General Intelligence
Step 1: Define the Unsolved Mystery
Mystery:
“How can artificial general intelligence (AGI) achieve a form of creativity that mirrors human-like problem-solving, where completely novel solutions emerge unexpectedly from seemingly unrelated knowledge?”
While current AI systems can find solutions based on predefined datasets and algorithms, their “creativity” is constrained to the patterns present in the training data. Human creativity, on the other hand, often results from the synthesis of unrelated ideas or insights through abstraction, intuition, and seemingly random inspiration. The question remains: What fundamental differences in cognitive processes or architectures prevent AGI from replicating this kind of emergence?
Step 2: Identify a Source for Analogy
Source Domain:
The evolutionary process of ecosystems and symbiosis in nature.
Why This Domain is Useful:
In nature, unexpected and novel solutions often emerge through the dynamics of complex ecosystems, where species interact, compete, and cooperate. One striking example is symbiosis, where organisms that initially appear unrelated form beneficial mutual relationships, allowing for creative adaptations to environmental challenges. Evolution often combines diverse traits to form new abilities that increase fitness in unforeseen ways.
This framework offers a useful analogy for understanding how diverse “knowledge components” (data representations or models in AGI) might be integrated to produce creative problem-solving.
Step 3: Establish the Core Analogy
Key Analogical Mapping:
- Source (Biological Evolution): Evolutionary processes encourage diversity and innovation through random mutations, competition, cooperation, and symbiotic interactions within ecosystems.
- Target (AGI Creativity): AGI might achieve creativity through analogous processes where diverse modular systems interact, compete, and collaborate in a shared “computational ecosystem.”
Metaphor:
Biological evolution “breeds” creativity through unpredictable interactions between organisms. Similarly, creativity in AGI could emerge from cross-referencing diverse models (or “knowledge symbionts”) that interact dynamically to produce innovative solutions.
Key Similarities:
- Recombination of Components: Evolution recombines genes for innovation; AGI could recombine knowledge components or model outputs to achieve novel insights.
- Emergence: Unexpected solutions emerge from ecosystems, just as creative outputs might emerge from interacting AGI sub-systems.
- Environmental Challenges: Ecosystems evolve in response to environmental pressures; AGI creativity could be driven by encountering novel, unpredictable problems.
Key Differences:
- Natural ecosystems are random and undirected, whereas AGI creativity could be guided by engineered constraints and optimization goals.
- Symbionts in nature exist as physical organisms, whereas AGI creativity could be virtual collaborations between models or abstractions.
Step 4: Develop the Hypothesis
Hypothesis:
Creativity in AGI can emerge from the dynamic interplay of modular sub-systems functioning as a digital ecosystem, where interactions mirror evolutionary processes such as symbiosis, competition, and cooperation. By fostering “interdisciplinary” knowledge-sharing and adaptive problem-solving under varying environmental constraints, AGI can generate innovative solutions that transcend its training data.
Explanation:
- Digital Knowledge Ecosystem: Imagine multiple sub-models within AGI representing specialized knowledge areas (e.g., language, visual perception, logic, etc.). By allowing these sub-models to function semi-independently in a shared computational environment, we simulate an ecosystem.
- Dynamic Interactions: These sub-models interact according to specific rules—competing to explain phenomena, cooperating to solve complex tasks, or even combining outputs into hybrid solutions.
- Symbiosis for Innovation: Over time, symbiotic interactions (akin to biological relationships like lichens or coral reefs) could form between sub-models. For instance, a visual model (image recognition) and a textual model (language processing) might develop shared abstractions, creating completely new ways of analyzing problems that neither could solve alone.
- Novel Outcomes: The systemic interplay allows AGI to discover unexpected connections, much like how human creativity links disparate ideas into a cohesive, surprising solution.
Implications:
- The synthesis of diverse knowledge domains could allow AGI to extrapolate new ideas from unrelated fields (e.g., using musical theory to solve a mathematical problem, as humans sometimes do).
- Creativity in AGI would no longer be limited to patterns found in datasets but would emerge through system interaction and abstraction.
Step 5: Test for Soundness
-
Plausibility:
The proposed digital ecosystem mirrors principles already in use with systems like multi-agent reinforcement learning and ensemble models. The novel aspect lies in fostering emergent interaction dynamics to encourage creative innovation rather than task-specific optimization. -
Novelty:
While ensemble methods and multi-agent simulations are common, the emphasis on symbiosis and emergent innovation as mechanisms for AGI creativity is largely unexplored. Existing systems do not focus on fostering unexpected, cross-domain solutions. -
Predictive Power:
This analogy predicts that increasing the complexity and diversity of interactions between AGI sub-models will yield higher levels of creative problem-solving and novel insights. It also suggests that symbiosis (collaboration) is more likely to result in creativity than pure competition or isolation.
Step 6: Propose Experiments or Next Steps
Proposed Experiment:
- Environment Design: Develop a simulated environment where multiple AGI sub-models representing different knowledge areas (e.g., vision, language, physics) interact.
- Rules of Interaction: Create mechanisms for collaboration (symbiosis), competition, and independent problem-solving. Include incentives for the systems to combine efforts and share knowledge (e.g., a scoring function that rewards novel solutions).
- Challenges: Introduce problems that require cross-domain insights, such as interpreting visual data with linguistic patterns or solving puzzles drawing on unrelated fields like art and science.
- Emergent Creativity Analysis: Monitor whether the digital ecosystem produces novel solutions not present within the capabilities of any single sub-model.
Advanced Testing:
- Simulate a dynamic, evolving environment where AGI sub-systems must continuously adapt to new situations (mirroring evolutionary selection pressures).
- Introduce randomness or “mutations” in the structure of sub-models to simulate innovation through trial-and-error.
Step 7: Document and Communicate
Summary (Example):
- Introduction: Define the unsolved mystery of AGI creativity and explain its parallels with biological evolution.
- Theoretical Framework: Present the digital ecosystem as an analog to natural ecosystems, focusing on symbiosis and interaction as drivers of innovation.
- Hypothesis: Argue that AGI creativity requires fostering cross-domain interaction in modular systems, enabling novel problem-solving through emergent dynamics.
- Validation: Propose simulations where AGI sub-models interact under varying rules to test for emergent creativity.
- Implications: Suggest pathways for implementing this system in real-world applications, such as interdisciplinary research, artistic generation, or autonomous problem-solving in unpredictable environments.
Conclusion
By drawing on the analogy of ecosystems and symbiosis, we hypothesize that AGI creativity can emerge as a property of dynamic interactions between diverse sub-models in a shared environment. This framework provides a novel lens for understanding and engineering AGI systems capable of generating human-like innovations. Next steps involve implementing simulations to validate this theory and exploring cross-disciplinary applications.
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Any video? Proof of concept…
Video will come really soon. Thanks for your patience
Video for presentation and demo for HiveMinds AI, now named “HiveNeuro”
Let me know what you think?
Next time I will demo the web flask version
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I successfully created Web interface (python flask) to can use Hive Mind AI - Queen does delegate 2 agents. 2 agents will individually perform the tasks successfully.
Queen will gather the 2 agents response and combine it and provide final response.
any feedback is appreciated?
reminds me of this github repo: GitHub - openai/swarm: Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.
I did not get the idea or copy their idea if you are asking.
My idea is the agent is n number of agents. It is automatically assign by Queen. Swarm design need to hard code the agents. Secondly, the swarm does not have mastermind I.e queen. Hive Mind has a queen to delegates the agents activities.
I did not get the idea or copy their idea if you are asking.
I am not asking, I’m stating that what it reminds me of.
My idea is the agent is n number of agents. It is automatically assign by Queen. Swarm design need to hard code the agents. Secondly, the swarm does not have mastermind I.e queen. Hive Mind has a queen to delegates the agents activities.
In my opinion, having a frontal cortex agent makes sense. No idea how your code or openai’s compares in terms of complexity, security, features and if its best to use yours or if its best to modify what oai did to add a loop of agents and one that guides all.
still, its a cool project to tackle and I’m happy you got to where you were looking to get, to the point you chose to share your project with the community.
i’d certainly add it to a github repository a public one and a private one. Add a user interface to the private repository. make a video. add a sign up/sign in with a database. secure a server and host it. add a payment system and market it. limit the amount users can use it and improve it based on user interaction. You never know when you could make a project that goes viral.
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Thank you for everyone’s thoughtful feedback.
I have the server, flask and ai core programmed. I will definitely share my new video for the codename HiveMinds when I finish the beta program.
I will definitely mention about OpenAI and the forum’s assistance. Hopefully, we can collaborate so when a new frontier model releases, the HiveMinds will upgrade as well.
If you have further questions or input about the HiveMinds AI o1 system, please let me know.
Introducing “HiveMind” as a core feature in the AuroraNotes AI platform, designed to function as an intelligent “Magic AI” capable of dynamically performing grammar checks, content generation, translations, or text summarizations. This functionality adapts seamlessly based on the user’s input, tone, sentence structure, and specific prompt requirements.
Currently, the system leverages GPT-4o to deliver these advanced capabilities. Future updates will include the release of AI Researcher Notes, integrating the same core concept with models such as 01-preview and 01-mini for enhanced versatility.
Learn more about AuroraNotes AI here: AuroraNotes.AI
How can I get help OpenAI to get faster response time in API? I am using python .
I like to work closely with OpenAI with the projects. Who can I contact?
My apology.
I am excited to build the Hive AI project. If OpenAI is interested, I can send the GitHub repository and see if you could add it as an experimental AI Hive. It does use n-th workers, so it requires a comprehensive prompt to utilize the workers effectively.”
if openai is interested an openai employee will reach out to you, but don’t keep your hopes up, its very rare
the email send 2 days ago about the release of o1 to the api mentioned that a lot of users where creating “agentic” experiences and couldn’t wait to see what api users come up with
I think the hardest part about marketing your project is getting users without raising vc money… and vc money usually wants to see traction or past success or profits
also, doing this by yourself is really not easy, I’d suggest building a team of individuals that complement your weakest skills (unless you already have a working system that scales well by yourself, kinda rare but I’ve seen a few that managed to do so).
Unfortunately, i’m not really your target audience, if I need to, I’ll code something up in a few hours and burn those oai credits… I’ll probably need to this since the tier 5 might not be all used up in their 1 year max life span… but thanks for the offer and I hope that if you have good intentions that you get good results
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Hope for the bes
See README in Github for procedure how to use it
I don’t work for openai, but cool repo