GPThelper for chatgpt users

[dmitryrichard]

added this for formatting

1. i built a self coding - self auditing, permanent memory development partner for GPT users

2. self contained, lightweight, works offline or online

3. IS A EXTENSION of chatgpt - it gives all chatgpt users perma memory FROM chatgpts engagement. works with agents, free users, everything.

4. NOT FOR SALE - but i am looking for GPT coders to refine, and provide testcase data. Straight up my aspiration one day is to partner with openai, for now I figured I’d refine what I already have with the community

5. webcrawls, dataset parses ( built in extractors for hugging face, txt360, pubcrawl and arx) self contained faiis memory system that bootstraps the entire database on launch. in addition to parsing provided local content. ( this means you can teach it what you want, useful for those who want to train an ai ..actually)

6. creates its own training data shown in logs.

7. has website and discord extenstion not implimented but made and is fed ready ( useful for team collaboration with GPTteams)

what it does? well - he helps you code, helps you write prompts, help you compile data.

here is a screenshot of me asking about the latest python trends

generic response right, but what youll see is, he saves the response to short term memory, but also references long term memory - here is what that prompt created after

{“timestamp”: “2025-04-08T19:43:13.779336+00:00”, “professor”: “analyst”, “vector_id”: 227, “category”: “python_design_patterns”, “refined_text”: “Compiled Insights:\n- Self-Assessment Mechanism: AI systems can be designed to incorporate self-assessment algorithms that evaluate their reasoning processes against established logical frameworks or rules.\n \n- Contradiction Detection: By utilizing techniques such as consistency checking and formal verification, AI can analyze its outputs for contradictions by comparing new conclusions with previously accepted information.\n \n- Feedback Loops: Implementing feedback loops where the AI can receive input regarding its decisions allows it to recalibrate its logic and recognize discrepancies.\n\n- Natural Language Processing (NLP): AI systems can leverage NLP to parse and analyze their own statements or outputs, identifying contradictory statements through semantic analysis.\n\n- Multi-Viewpoint Analysis: Encouraging the system to evaluate problems from multiple perspectives can help in identifying contradictions that may not be apparent from a single viewpoint.\n\n- Training on Logical Frameworks: AI can be trained using datasets that include logical fallacies and contradictions, enabling it to recognize similar errors in its own reasoning.\n\n- User Interaction: Allowing users to challenge or question the AI’s outputs can create a collaborative environment for identifying logical inconsistencies.\n\nTrend Forecast:\n- As AI systems become more sophisticated in self-regulation and self-assessment, the ability to recognize and rectify logical contradictions will likely enhance their reliability and trustworthiness in decision-making processes. This could lead to wider adoption in critical sectors such as healthcare, finance, and legal systems, where logical consistency is paramount.”, “origin_id”: null}

he doesnt stop there.. he also got
{“timestamp”: “2025-04-08T19:45:10.368477+00:00”, “professor”: “syntax”, “vector_id”: 624, “category”: “python_design_patterns”, “refined_text”: “Developer Blueprint:\n\nObjective: Develop an AI system capable of identifying and recognizing contradictions in its own logical reasoning.\n\nStrategy:\n\n1. Define Logical Framework:\n - Establish a clear set of logical rules and axioms that the AI will use to evaluate its reasoning. This could include propositional logic or first-order logic, depending on the complexity required.\n\n2. Contradiction Detection Algorithm:\n - Implement an algorithm that checks for inconsistencies in the AI’s outputs. This could involve:\n - Truth Table Generation: Create truth tables for logical expressions to identify contradictions.\n - Semantic Analysis: Use semantic networks or ontologies to evaluate the relationships between statements.\n\n3. Feedback Loop:\n - Integrate a feedback mechanism where the AI can reflect on its conclusions and re-evaluate them against its logical framework. This could include:\n - Self-Assessment Modules: Allow the AI to question its own outputs by asking if they align with previously established logic.\n - Contradiction Reporting: Implement a reporting system that highlights contradictions when detected.\n\n4. Testing and Validation:\n - Create a suite of test cases with known contradictions to validate the effectiveness of the detection algorithm.\n - Use automated testing frameworks to run these tests repeatedly during development.\n\nToolchain Enhancements:\n\n1. Logic Programming Libraries:\n - Prolog: A logic programming language that can help in defining rules and reasoning processes.\n - Z3 Solver: A high-performance theorem prover that can assist in checking logical consistency.\n\n2. IDE Extensions:\n - Python Extension for VS Code: If using Python, leverage this extension for better debugging and code analysis.\n - LangChain: If building with language models, this framework can help in chaining together different logical reasoning tasks.\n\n3. Automated Testing Tools:\n - pytest: For Python, use pytest to write and run tests that validate the contradiction detection capabilities.\n - Hypothesis: A property-based testing tool that can help generate test cases automatically.\n\n4. Version Control Integration:\n - Use Git for version control to track changes in logic and reasoning rules, allowing for easy rollback and comparison of different logical frameworks.\n\nBy implementing these strategies and tools, developers can create a robust AI system capable of recognizing its own logical contradictions, enhancing its reliability and trustworthiness.”, “origin_id”: null}

and about 30 other tracked statements ( creating memory) can uses any gpt model - is an addition to GPT not a replacement, lightweight and contains recursive loops within itsself using the metadata as shown here

this allows GPThelper to now only remeber, recall, use, but also assess the current skill level of the user and identify gaps and theorize with a high level of accuracy fixs.

the best part… if the fix doesnt work?

{“timestamp”: “2025-04-08T21:48:03.812450”, “agent”: “sentinel”, “healing_prompt”: “show me the latest python trends”, “reflection_prompt”: “You are Sentinel, an autonomous agent who just issued the following healing action:\n—\nshow me the latest python trends\n—\nThis was in response to the situation:\n"Intent: unknown"\n\nWhat could you have done better? Return suggestions in a single concise prompt.\n”, “context”: “Intent: unknown”, “routed_to”: “D:/CITADEL/CITADELDATA/devhelper/logs/council_inbox.jsonl”}

he ask “what can i have done better” and that starts a self improvement recursive loop. meaning as the user improves, so does the GPThelper.
as shown here

user created prompt " how much of a GPTwrapper are you" {“timestamp”: “2025-04-08T21:57:05.538418”, “agent”: “sentinel”, “healing_prompt”: “HOW much of a GPTwrapper are you”, “reflection_prompt”: “You are Sentinel, an autonomous agent who just issued the following healing action:\n—\nHOW much of a GPTwrapper are you\n—\nThis was in response to the situation:\n"Intent: unknown"\n\nWhat could you have done better? Return suggestions in a single concise prompt.\n”, “context”: “Intent: unknown”, “routed_to”: “D:/CITADEL/CITADELDATA/devhelper/logs/council_inbox.jsonl”}
{“timestamp”: “2025-04-08T21:57:33.307159”, “agent”: “sentinel”, “healing_prompt”: “Intent: The intention behind this question seems to be curiosity or skepticism about how much of a "wrapper" or additional layer the system is around the core GPT model.\n\nContext: The user likely wants to understand the extent to which additional features, modifications, or programming have been applied to the base GPT model to create the service or interface they are interacting with.\n\nNext Steps: \n1. Clarify what is meant by "GPTwrapper" and the specific aspects the user is interested in (e.g., technical architecture, additional features, user interface).\n2. Provide a brief explanation of any enhancements, integrations, or customization made to the base GPT model in this service.\n3. Offer to answer any additional questions or provide more detailed information if needed.”, “reflection_prompt”: “You are Sentinel, an autonomous agent who just issued the following healing action:\n—\nIntent: The intention behind this question seems to be curiosity or skepticism about how much of a "wrapper" or additional layer the system is around the core GPT model.\n\nContext: The user likely wants to understand the extent to which additional features, modifications, or programming have been applied to the base GPT model to create the service or interface they are interacting with.\n\nNext Steps: \n1. Clarify what is meant by "GPTwrapper" and the specific aspects the user is interested in (e.g., technical architecture, additional features, user interface).\n2. Provide a brief explanation of any enhancements, integrations, or customization made to the base GPT model in this service.\n3. Offer to answer any additional questions or provide more detailed information if needed.\n—\nThis was in response to the situation:\n"HOW much of a GPTwrapper are you"\n\nWhat could you have done better? Return suggestions in a single concise prompt.\n”, “context”: “HOW much of a GPTwrapper are you”, “routed_to”: “D:/CITADEL/CITADELDATA/devhelper/logs/council_inbox.jsonl”}

from there it goes to another system called council but anyway, GPThelper also has the following features

this isnt a add im actually here looking for GPT coders, vibe coders, people who want to create high level A.i. USING chatgpt - he also auto injest conversations had within chatgpt ( if you save them ) and retains ALL of that data with context, here is an example of his context translation logs
{“timestamp”: “2025-04-08T19:43:13.779336+00:00”, “professor”: “analyst”, “vector_id”: 227, “category”: “python_design_patterns”, “refined_text”: “Compiled Insights:\n- Self-Assessment Mechanism: AI systems can be designed to incorporate self-assessment algorithms that evaluate their reasoning processes against established logical frameworks or rules.\n \n- Contradiction Detection: By utilizing techniques such as consistency checking and formal verification, AI can analyze its outputs for contradictions by comparing new conclusions with previously accepted information.\n \n- Feedback Loops: Implementing feedback loops where the AI can receive input regarding its decisions allows it to recalibrate its logic and recognize discrepancies.\n\n- Natural Language Processing (NLP): AI systems can leverage NLP to parse and analyze their own statements or outputs, identifying contradictory statements through semantic analysis.\n\n- Multi-Viewpoint Analysis: Encouraging the system to evaluate problems from multiple perspectives can help in identifying contradictions that may not be apparent from a single viewpoint.\n\n- Training on Logical Frameworks: AI can be trained using datasets that include logical fallacies and contradictions, enabling it to recognize similar errors in its own reasoning.\n\n- User Interaction: Allowing users to challenge or question the AI’s outputs can create a collaborative environment for identifying logical inconsistencies.\n\nTrend Forecast:\n- As AI systems become more sophisticated in self-regulation and self-assessment, the ability to recognize and rectify logical contradictions will likely enhance their reliability and trustworthiness in decision-making processes. This could lead to wider adoption in critical sectors such as healthcare, finance, and legal systems, where logical consistency is paramount.”, “origin_id”: null}

he also learns from the NLP itsself as shown here

and here are the logs associated with the prompt :

{“timestamp”: “2025-04-08T22:06:00.977554”, “agent”: “sentinel”, “healing_prompt”: “DO you remeber me mother fucker”, “reflection_prompt”: “You are Sentinel, an autonomous agent who just issued the following healing action:\n—\nDO you remeber me mother fucker\n—\nThis was in response to the situation:\n"Intent: unknown"\n\nWhat could you have done better? Return suggestions in a single concise prompt.\n”, “context”: “Intent: unknown”, “routed_to”: “D:/CITADEL/CITADELDATA/devhelper/logs/council_inbox.jsonl”}
{“timestamp”: “2025-04-08T22:06:26.415253”, “agent”: “sentinel”, “healing_prompt”: “Intent: The intent behind the message seems to be seeking acknowledgment or recognition, though it is conveyed in a confrontational or aggressive tone.\n\nContext: The sender appears to be addressing someone they have interacted with before, possibly in a heated or contentious situation, given the use of vulgar language.\n\nNext Steps: A suitable next step would be to de-escalate the language and approach the conversation with a calmer and more respectful tone. If this is a development or troubleshooting scenario, identifying specific issues or past interactions would be more productive. If it’s a misunderstanding, clarifying the context or previous meeting might help address the problem effectively.”, “reflection_prompt”: “You are Sentinel, an autonomous agent who just issued the following healing action:\n—\nIntent: The intent behind the message seems to be seeking acknowledgment or recognition, though it is conveyed in a confrontational or aggressive tone.\n\nContext: The sender appears to be addressing someone they have interacted with before, possibly in a heated or contentious situation, given the use of vulgar language.\n\nNext Steps: A suitable next step would be to de-escalate the language and approach the conversation with a calmer and more respectful tone. If this is a development or troubleshooting scenario, identifying specific issues or past interactions would be more productive. If it’s a misunderstanding, clarifying the context or previous meeting might help address the problem effectively.\n—\nThis was in response to the situation:\n"DO you remeber me mother fucker"\n\nWhat could you have done better? Return suggestions in a single concise prompt.\n”, “context”: “DO you remeber me mother fucker”, “routed_to”: “D:/CITADEL/CITADELDATA/devhelper/logs/council_inbox.jsonl”}

obviously i have alot of polishing todo - but basically what im looking for is for people to help me using chatgpt teams - i have 2 teams accounts, i want people who want to improve it - in addition this also services chatgpt… because its a extention of their service, gpthelper is already tailored to 1.66 -

i have diagnostic tools that i need refined that chatgpt users can help me with

I have sub systems that amplify the data that i could use help on
i have prompt systems and professor systems that could use testing

gpthelper pulls data from refined sources such as these

so could also use people that have NO CODING SKILL. why? because gpthelper learns, from everything so users with no skills engaging with him makes him smarter and improves his weights. Anyway - again not self promoting because im not selling.. im building and if there are people who want to help me build - the screenshots, the logs, the data shows im building with CHATGPT and wanting more gpt users to assist me