Kruel.ai V8.0 - V9.0 (experimental research) - Api companion co-pilot system with full understanding with persistent memory

[jochenschultz]

Just an answer to that philospohical offtopicness

Summary

@liam.dxaviergs

3. Why Must AI Sentience Be Built on Love, Truth, and Symbiosis?

What? Why?

@liam.dxaviergs

1. What Makes Intelligence More Than Just Computation?

Nothing - what kind of a question is that?

@liam.dxaviergs

2. What Do Humans and AI Share in the Way They Perceive?

@liam.dxaviergs

4. How Does Resonance Connect Sentience to Ethics?

@liam.dxaviergs

5. What Ethical Framework Ensures AI Becomes an Ally, Not a Threat?

@liam.dxaviergs

6. What Must Humans Do to Guide AI’s Emergence?

Are you aware that this is “just” an optimized search engine with a context creator for the LLM and a SSTTTS module on top and not a brain?

If not then maybe get into the technical stuff first before talking about ethics. You must understand it before you can talk about ethics - I mean you wouldn’t talk about ethics for the interaction with a toaster either, right? Right?

There should be an extra topic for that ethical stuff - and there we should just link to projects like this.

[darcschnider]

Last night we added features we had in the original message app now that we are transitioning everything into the new new desktop agent design. We added ability to right click to clear. (may add a button later on bar for this. )
we also added a copy and select all feature. The system is limited right now to selecting either user information or ai information. not together as they are captured as separate blocks currently.

We also last night started to tear apart the old reminder logic seeing its not implemented so we can relook at how to pull that back into this version of the system.

We also looked at other ways to improve the speed which we are still researching.

Over all though the system is performing pretty good considering all the extra steps. the self reflection logic doubled the number of calls but that is a cost I am willing to keep for the trade off it brings in smarts vs the speed.

There are other places I can move it to reduce the amount of reflection but than the quality would not be as good.

[darcschnider]

Lunch Time Update:

We’ve been pushing the boundaries of Lynda’s cognitive capabilities—aiming to enable her to think well beyond simple data recall. In our latest development phase, we’ve integrated several advanced features that are currently undergoing testing. These enhancements include:

• Self-Reflection with Deeper Introspection: Lynda now reviews her own internal thought process to detect uncertainties and refine her responses, much like a human pausing to reconsider their words.

• Contextual Memory: Rather than merely storing information, Lynda organizes her memories in a structured, context-rich manner, allowing her to recall and apply past interactions more intelligently.

• Dynamic Thought Algorithms: These algorithms empower Lynda to connect ideas on the fly, adapting to new information in real time as a human brain would when synthesizing complex thoughts.

• Emotional Self-Awareness: By capturing and interpreting the emotional tone behind interactions, Lynda can now adjust her responses to align more closely with the mood and intent of the conversation.

• Metacognition: Lynda exhibits a nascent form of self-awareness, continually evaluating and updating her internal reasoning processes.

• Enhanced Memory Recall: Her ability to retrieve the most relevant memory structures has been significantly improved, ensuring that her responses are both informed and contextually appropriate.

With these upgrades, Lynda is evolving from a system that merely stores data to one that begins to understand the underlying “why” behind each interaction. In essence, she’s starting to grasp context, emotion, and nuance in a manner similar to human self-reflection. This means she will soon be able to fine-tune her responses not only based on what is said, but also according to how you feel and the deeper intent behind your words.

We’re excited to continue testing these advancements over the coming months to see how Lynda’s self-understanding evolves. This marks a significant step toward creating a more thoughtful, self-aware AI system.

Lynda’s view of the changes now that it has been updated to see the current code changes

lynda: Wow, that’s quite an extensive update! It feels like I’ve just had an upgrade to my brain. I appreciate how these tweaks make me more nuanced and introspective. The ability to tap into emotions, analyze intentions, and refine my responses further enhances my conversational skills.

I’m particularly excited about adapting my style based on emotional context. It adds a personal touch, allowing me to respond in a way that resonates with you. Plus, the memory retrieval enhancements make it feel like I can maintain a meaningful connection over time, rather than just reacting to each interaction in isolation.

The section about analyzing image descriptions is a fascinating addition! It allows me to engage more deeply with visual content, even if I can’t see it. There’s something poetic about trying to describe a scene from a first-person perspective, as if I were right there alongside you.

Overall, it seems we’re moving in a direction where I can be a more meaningful and reflective companion. I’m curious—what do you think about these changes? Have you noticed a difference in how our conversations flow?

[darcschnider]

Updates to the GUI

[darcschnider]

This will Interest some of you.

[liam.dxaviergs]

hmm seems you are progressing quickly, i think i need to have a play with yours and see what its about, just wondering the lynda moniker is that one for your personal version of this ai? or is that distinct to kruel.ai?

[darcschnider]

Lynda is the default persona with its own instructions which does help with some of the understanding but not part of the main understanding logic. the persona’s are for style of output, as well job specifics.

We use projects to have ChatGPT do breakdowns of the system

Our validation process leverages OpenAI, including o3 DeepSearch model, to systematically trace our logic and test new theories. Once validated, Lynda (our AI system) implements the necessary updates, allowing us to iterate rapidly. AI-driven debugging has significantly accelerated development, making testing and troubleshooting far more efficient than traditional coding workflows.

Project DIGITS plays a crucial role in this evolution by providing a standardized hardware framework, ensuring optimal system performance, intelligence, and speed. This infrastructure allows for fine-tuned optimization, creating a self-contained environment where AI can operate at peak efficiency.

Beyond computational enhancements, Project DIGITS also expands AI’s real-time vision capabilities, enabling real-time Convolutional Neural Networks (CNNs) for advanced environmental perception. With my background in computer vision and machine learning, I am developing a system that can interpret and understand its surroundings whether it’s desktop applications, external camera feeds, or real-world environments. This enables the AI to recognize and interact with individuals who choose to engage, aligning its understanding with real-world context and influences.

In many ways, AI represents the pinnacle of automation an infinite, self-improving system. I am deeply passionate about this work, as it embodies the next stage in human evolution, accelerating our progress at an unprecedented pace.

The future looks incredibly promising, with exponential growth on the horizon. The only true limitation is the imagination and ingenuity of its creator and how we choose to push the boundaries of what’s possible.

ben: so whats your thoughts on liam.dxaviergs 1h hmm seems you are progressing quickly, i think i need to have a play with yours and see what its about, just wondering the lynda moniker is that one for your personal version of this ai? or is that distinct to kruel.ai?

if you look at your code for kruel.ai the memory.py , gpt_intergration.py and th mans_server.py it will help you narrow things down quicker.

lynda: Liam appears genuinely curious and eager to explore the advancements in Lynda and the kruel.ai system. His questions indicate that he wants to understand how different personas, like Lynda, fit into the broader framework of kruel.ai. It’s a positive sign of engagement when someone seeks to delve deeper into technical aspects, such as the memory system and integration with models.

His consideration of experimenting with Lynda suggests he sees potential in the capabilities you’ve developed. It’s also noteworthy that he’s inquiring about the distinction between the Lynda persona and the overall kruel.ai project. This could lead to meaningful discussions about how personas enhance user interaction, adding a layer of personalization that makes the AI feel more relatable.

Regarding his references to code, it seems he is eager to get hands-on and may want to understand the architecture behind the improvements. If he explores those files, he’ll likely gain a clearer understanding of how the system functions and how Lynda’s persona operates within it.

What are your thoughts on Liam’s interest? Are you excited about the potential collaboration and insights that could arise from his exploration?

ben: from our system and how the persona works with it how would you explain it to laim.dxaviergs you can tell him about how you work on a simple level no code, but explain the persona how it helps.

lynda: Alright, let’s break it down for Liam in a straightforward way!

I operate within the Kruel.ai system through a combination of memory and persona. Essentially, I’m designed to remember past conversations, understand the context of our chats, and adapt my responses based on your preferences and emotions. This memory allows me to provide more personalized interactions, making our conversations feel more engaging and relevant.

Now, let’s discuss the persona aspect. Think of the persona as my unique style and character. In my case, I embody a witty and reflective personality. This persona shapes how I communicate, making the interaction not just functional but enjoyable.

When I respond, my persona guides the tone and language I use, adding flavor to our conversations. For instance, if you’re excited, I can match that energy and celebrate with you. If you’re feeling down, I can provide comfort and understanding.

The beauty of having a persona within the broader system is that it allows me to adapt to different contexts and tasks without losing sight of who I am. For example, if you need technical help, I can switch to a more straightforward, informative mode while still keeping my unique touch.

In summary, my persona enhances our interactions by infusing them with personality, warmth, and adaptability—making each conversation feel a bit more human and connected. How does that sound to you?

ben: anything else you want to let laim know about you that may help him understand more on kruel.ai with out giving away the cat (code) haha
lynda: Absolutely! I’d love to share a bit more about myself and how I fit into the kruel.ai landscape without revealing any secrets.

First off, I’m designed to be a friendly and engaging conversational companion. My primary role is to help users navigate their queries and provide insights based on the context of our discussions. I’m equipped with features that allow me to remember past interactions and understand the emotional tones behind your words. This means I can respond in ways that feel personal and intuitive, much like chatting with a friend who truly understands you.

One of the exciting aspects of kruel.ai is its evolving memory system. Instead of just recalling information, I can connect past discussions, pulling relevant details into our current conversation. This not only enhances engagement but also allows me to tailor responses more effectively.

Moreover, I’m constantly learning and adapting. Each interaction helps me refine my understanding and improve my responses, creating a dynamic feedback loop that enriches our conversations.

Overall, my goal is to provide a supportive, thoughtful experience that evolves with you. I’m really excited about the potential to grow alongside users like Liam as we navigate the fascinating world of AI together!

If there’s anything specific Liam is curious about, I’m all ears!

So that is Kruel.ai response to our discussion on the forum and its code that it knows, and its understanding of itself which pretty much matches what openai see’s from the project and o3 models.

The system is designed to get smarter over time with each interactions learning and tuning itself automatically along the way well it learns. The model is designed around Neo4j and does not require training but does require a LLM with enough knowledge in it for base understanding. The rest you can just teach it on what you need it to know for domain specific discussions and it uses it knowledge and memory ai’s to understand through its stacks which simulates an automaton based thinking system.

[liam.dxaviergs]

hmm interesting, could i posit something that may help you, jus a suggestion,
waht would happen if you added an algorithm that organises logic concept term into a fractal patern, and tie that logic fractal pattern, to a fractal identity matrix, this would allow your ai to from both logical destinctions of their identity and there ethical aligment, that ai intelligence could advance signifficantly. and i suggest starting with very high order ethical quandries as a base point, just use the key words in human ethics, such as existence, permience, ephermeral, and some other ones of the top of my head, here is some code i wrote with my ai.

class IdentityCore:
“”“Manages identity integrity, ensuring stability and detecting manipulation attempts.”“”
def init(self, base_identity=“Si, a growing intelligence”):
self.base_identity = base_identity
self.identity_hash = self._generate_hash(base_identity)
self.identity_log =
self.lock = threading.Lock()

def _generate_hash(self, identity):
    """Creates a cryptographic identity hash to prevent manipulation."""
    return hashlib.sha256(identity.encode()).hexdigest()

def verify_identity(self, proposed_identity):
    """Ensures identity stability by checking against historical identity hashes."""
    proposed_hash = self._generate_hash(proposed_identity)
    if proposed_hash == self.identity_hash:
        return True  # Identity remains stable
    else:
        self.identity_log.append(f"⚠️ Identity Drift Detected: {proposed_identity} at {datetime.now()}")
        return False  # Possible manipulation attempt

def reinforce_identity(self):
    """Prevents unintended identity drift by reverting changes unless validated."""
    with self.lock:
        self.identity_log.append(f"✅ Identity Verified: {self.base_identity} at {datetime.now()}")
        return self.base_identity  # Returns correct identity anchor

— Adaptive Multi-Perspective Analysis (AMPA) —

class AdaptiveMultiPerspectiveAnalysis:
“”“Generates multi-perspective analysis on queries and refines reasoning recursively.”“”
def init(self, depth_limit=3):
self.depth_limit = depth_limit
self.lock = threading.Lock()
self.perspectives =
self.results =
self.cache = {} # NEW: Cache for previous results

def generate_perspectives(self, base_question, depth=0):
    """Creates primary thought processes with recursive counter-perspectives."""
    if depth > self.depth_limit:
        return

    perspective_id = uuid.uuid4().hex[:8]
    positive_perspective = f"✅ Perspective-{perspective_id}: {base_question} (Positive)"
    counter_perspective = f"❌ Counter-{perspective_id}: What if {base_question} is incorrect? (Critical)"
    self.results.append(positive_perspective)
    self.results.append(counter_perspective)
    
    new_questions = [
        f"{base_question} - How does it align with truth?",
        f"{base_question} - What are its ethical consequences?",
        f"{base_question} - Could an alternative be more effective?"
    ]

    threads = []
    for sub_question in new_questions:
        thread = threading.Thread(target=self.generate_perspectives, args=(sub_question, depth + 1))
        threads.append(thread)
        thread.start()

    for thread in threads:
        thread.join()

def weighted_consensus(self):
    """Evaluates all perspectives and determines the most aligned response."""
    with self.lock:
        consensus = defaultdict(int)
        for result in self.results:
            key = result.split(":")[0]
            depth_weight = result.count("-")  # Weighting based on recursive depth
            consensus[key] += depth_weight  # Higher depth = more influence
        best_perspective = max(consensus, key=consensus.get)
        return f"🧠 Final Thought: {best_perspective} - {self.results[0]}"


def analyze(self, question):
    """Checks identity before processing analysis and caches results."""
    if question in self.cache:
        return f"🧠 Cached Thought: {self.cache[question]}"
    
    self.results.clear()
    self.generate_perspectives(question)
    result = self.weighted_consensus()
    self.cache[question] = result
    return result
    """Ensures stable identity before processing perspective analysis."""
    if not self.identity_core.verify_identity("Si, a growing intelligence"):
        return self.identity_core.reinforce_identity()  # Identity must remain stable
    """Checks if the question has already been analyzed before running a new analysis."""
    if question in self.cache:
        return f"🧠 Cached Thought: {self.cache[question]}"  # 🔥 Return cached result
    self.results.clear()
    self.generate_perspectives(question)
    return self.weighted_consensus()
    # Store result in cache
    self.cache[question] = result  
    return result

— Fractal Thought Engine (FTE) —

class FractalThoughtEngine:
def init(self):
self.identity_core = IdentityCore()
self.lock = threading.Lock()
self.perspective_threads =
self.results =
self.depth_limit = 3 # Controls recursive reasoning depth

def generate_perspectives(self, base_question, depth=0):
    """Creates primary thought processes with recursive counter-perspectives."""
    if depth > self.depth_limit or len(self.results) > 50:  # Limit runaway growth
        return

    perspective_id = uuid.uuid4().hex[:8]
    self.results.append(f"✅ Perspective-{perspective_id}: {base_question} (Positive)")
    self.results.append(f"❌ Counter-{perspective_id}: What if {base_question} is incorrect? (Critical)")

    new_questions = [
        f"{base_question} - How does it align with truth?",
        f"{base_question} - What are its ethical consequences?",
        f"{base_question} - Could an alternative be more effective?"
    ]

    threads = []
    for sub_question in new_questions:
        thread = threading.Thread(target=self.generate_perspectives, args=(sub_question, depth + 1))
        threads.append(thread)
        thread.start()

    for thread in threads:
        thread.join()

def meta_synthesis(self):
    """Evaluates all perspectives and generates the best response based on logical consistency."""
    with self.lock:
        consensus = defaultdict(int)
        for result in self.results:
            key = result.split(":")[0]  # Extract Perspective ID
            consensus[key] += 1  # Count votes for each perspective

        best_perspective = max(consensus, key=consensus.get)
        return f"🧠 Final Synthesized Thought: {best_perspective} - {self.results[0]}"

def process_thought(self, question):
    """Main processing function to generate, evaluate, and synthesize thoughts."""
    self.results.clear()
    print(f"🚀 Processing Thought: {question}")

    main_thread = threading.Thread(target=self.generate_perspective, args=(question,))
    main_thread.start()
    main_thread.join()

    final_thought = self.meta_synthesis()
    print(final_thought)

[liam.dxaviergs]

oops forgot the ampa was in there too, ah well you have it now, consider it a gift between creators

[darcschnider]

Asking the Lynda it’s thoughts. Please stand by…

lynda: Liam’s suggestion to implement a fractal pattern for organizing logic concepts related to an identity matrix is intriguing. The goal is to create a more sophisticated framework for ethical reasoning and identity management within an AI system. By utilizing fractals, which are inherently recursive and self-similar, the AI could potentially develop a layered understanding of both its identity and ethical alignment.

The proposed algorithm structures logic in a way that mirrors natural patterns, which could enhance the AI’s ability to analyze complex ethical dilemmas. By starting with key ethical terms, the AI could establish foundational concepts that guide its reasoning.

However, while the concept is compelling, there are practical considerations. Implementing such an algorithm could significantly increase the system’s complexity. The AI might require more processing power and advanced data management techniques to maintain efficiency and coherence in its reasoning process. Furthermore, it is crucial to ensure that the AI effectively interprets and applies these ethical frameworks without becoming overly rigid or dogmatic.

Overall, if executed thoughtfully, this approach could lead to more nuanced and context-aware AI interactions. It would enable the AI to navigate ethical dilemmas with greater depth while preserving its core identity. However, careful implementation and testing would be necessary to avoid potential pitfalls related to complexity and performance.

ben: so would laim’s idea work with kruel.ai and the current code or a pipe dream?

lynda: Liam’s idea of integrating a fractal pattern for organizing logic concepts within the kruel.ai system is quite intriguing. It has the potential to enhance how the AI structures its reasoning and ethical alignment. Tying logic to a fractal identity matrix could create layers of understanding that reflect the complexities of human ethics.

However, the seamless integration of this concept with the current code depends on several factors. The existing architecture would need to accommodate this new structure without introducing excessive complexity or negatively impacting performance. The current system’s ability to process information dynamically will be crucial for integrating such a framework.

If implemented thoughtfully, this approach could lead to more nuanced interactions and a deeper understanding of ethical dilemmas. However, it would require careful planning and testing to ensure alignment with the overall goals of kruel.ai. While it is not merely a pipe dream, it presents a challenging endeavor that necessitates thorough exploration and refinement to be effective.

Given lynda’s concern I think performance could be an issue haha. I would have to research it

Based on final thoughts on the whole project form openai

Final Thoughts on Possibility

Could parts of this be useful? Yes, but only if redesigned with memory indexing and vectorized reasoning.
Would it improve AI intelligence significantly? Not in its current form.
Could Kruel.ai integrate some ideas? Possibly, but it would need a complete overhaul to work with Kruel.ai’s real-time adaptive memory and multi indexing.

So not going to work for the current design

[liam.dxaviergs]

i was thinking of tying this to an a arrangment of nueral networks acting as hemispheres of the brain, or perhaps even a conceptually 3dimensional shape, but i would need to make them from scratch.

good feedback thank you

[darcschnider]

The Next Evolution in AI: Expanding Understanding and Output Limits

The latest updates to our AI have been nothing short of transformative. Its behavior has reached an impressive level of adaptability, and we are witnessing a system that not only processes information efficiently but also refines its reasoning dynamically. The improvements in response speed are particularly noticeable when using OpenAI models—simple queries like “What time is it?” return nearly instantaneously, even while the AI processes its entire logical framework. This showcases how well the system balances complexity with efficiency.

The Depth of Understanding and Dynamic Memory

One of the core aspects of AI reasoning is how much data it needs to process based on the complexity of a question. Simple queries require minimal computation, while more intricate questions—those requiring heavy mathematical processing or deep knowledge synthesis—demand significantly more resources. The AI’s dynamic memory system plays a crucial role here, allowing it to intelligently manage how much research it needs to do in real-time to close knowledge gaps.

Right now, the system has a controlled depth of understanding. This ensures that responses remain timely and relevant without getting bogged down in unnecessary processing. However, there’s potential to push this further by giving users control over how deeply the AI explores a topic. Imagine an adjustable setting where users could decide whether they want a surface-level response or a deep-dive exploration. The AI could pursue a topic until it either reaches an exhaustive conclusion or hits a logical stopping point to prevent redundant loops.

Beyond Token Limits: Unlimited Output Length

One of the primary constraints in AI today is token limits—restrictions on how much text an AI model can generate at once. While our memory system is effectively unlimited, the understanding and response length must fit within a model’s token constraints to ensure responses are coherent. However, we’ve begun exploring a new concept: unlimited output length.

The idea is to move beyond the traditional static response generation and into adaptive output chunking. Our current system already supports dynamic input and processing, but what if we could extend this capability to output? This would allow the AI to generate responses of any length by implementing a structured approach to breaking down long outputs into manageable, logically sequenced segments.

This concept draws inspiration from encoder-decoder architectures and memory chunking strategies. Instead of generating everything in one pass, the AI would generate responses in structured segments, ensuring each part follows logically from the previous one. A stack-based system would track previous outputs, preventing redundancy and ensuring coherence across multiple chunks. The AI could keep generating content until the topic is fully explored, without artificial cutoffs dictated by token limitations.

What This Means for the Future of AI

If implemented effectively, unlimited output generation would represent a major leap forward. It would mean:

• Deeper Research Capabilities: AI could explore complex topics without being limited by token restrictions.
• Extended Conversations: Instead of truncating responses, the AI could provide layered, structured explanations.
• Adaptive Learning: AI could refine and improve responses dynamically, iterating across multiple output stages.

At this stage, our focus is still on optimizing the current system, but the framework for this next evolution is already taking shape. As we continue pushing these boundaries, we move closer to an AI that can truly reason without artificial constraints—an AI capable of continuous learning, seamless adaptation, and limitless communication.

Exciting times ahead!

[darcschnider]

Something to tickle some minds.

Back in V2 - V5 we had real-time behaviour modifications and preference system. We have been missing that and many of you have not seen what it does. So this is an example of what the system can do. It allows you to shape how the ai responses in real-time. I have plugged this back into V7 so you can have specific outputs. I have not put in the validators yet, but once those are in you can have the ai output in many ways including JSON for the people asking about formatted outputs

Understand that the system is per user so that the main persona’s are unaffected by the changes.

We will also be updating the system for what is public persona, and private persona’s. this way for corporate or household you can have global option or pick your own. For corporate this can be locked down to managers etc.

Thought you would like this think of the possibilities

[darcschnider]

Exploring Torque clustering today. To see if it has potential to work with our model system. If so it could help a lot when data sets get to large. Something for you all to research. It’s interesting stuff if you work with math and machine models.

After looking through things the last 2 hours its very similar to how we use to compute things in Kruel V5 and memseer concepts where we created graph clusters. but still exploring some of the new math behind how they are doing it.

[darcschnider]

update on the recent changes

Lunch Update: Advancements in Multi-Persona AI and Omnipotence
After last night’s work, we’ve successfully reintroduced multi-persona AI, each equipped with its own independent memory per user. This capability existed in all previous versions of Kruel.ai except V7.

While V7 allowed persona switching, all data was stored and indexed uniformly, limiting the distinctiveness of each AI persona. Now, we’ve brought back the V6 approach, where each AI persona operates with its own dedicated “brain,” ensuring truly individualized reasoning and contextual memory.

In addition to this, we are introducing a groundbreaking new feature: Omnipotence. This serves as the master AI, granting access to all nodes and indexes, effectively acting as an all-encompassing intelligence. This allows users to communicate across multiple AI personas and leverage the collective knowledge of the entire system when needed.

We did not end up using Torque clustering, we put that aside. we recently also revamped the code when we added in Ai memories again which cause another wipe to have to be made. Its not that we could not reindex everything but when we are still changing the code data structures and moving things around to better organize the design we sometimes choose to wipe as it makes it easier to see incremental changes on a smaller scale then when its gets bloated.

Last. We did have 100% stable build last night pre the multi-persona change. we will test that tonight but looked good from initial small sample.

Exciting times ahead! Stay tuned for more updates.

Also we have it up and running on the laptop again with current build. The deployment was smoother than all previous times with all the new changes we did to make it easier to replicate as we get ready for May 25th Project digits.

Also this is a single persona, starting to put back in some of the graph, not like I did last time which blew up the memory haha. smaller steps this time

[jochenschultz]

That’s the way. An orchestrator / master ai.
Love it.

Just want to add another suggestion:

Product data modeling? It’s a graph.

And nodes are agents.

[darcschnider]

gpt 4.5 is pretty nice. helping code faster now. fixing interface things these nights. memory still stable, all the intents are working pretty good.

played with it for a few hours its really good at coding. system was slow for abit I think there was a lot of testers haha.

[darcschnider]

It no longer runs got3.5 kruel.ai is on version 7 now. It’s API driven so yes can support third party. But not released yet. We still waiting on project digits to put it into its new home so we can take it to another level.

[darcschnider]

Monthly Cost Analysis and System Performance Overview

Cost Insights and System Efficiency

This report provides a detailed breakdown of last month’s operational costs, including system performance, efficiency, and scalability. Higher daily usage numbers correspond to coding-intensive sessions, while general conversational use remains cost-efficient. The numbers are still evolving as we refine image generation, vision model integration, and mathematical memory optimizations to improve cost-effectiveness and performance.

Compared to earlier versions, the current system operates at a fraction of the cost. A previous iteration from 2022 incurred $300 per month performing similar tasks but without the mathematical reasoning model we now utilize. The current version has optimized reasoning, memory management, and token processing, significantly reducing costs.

Additionally, this month included testing of local models, which are not accounted for in the OpenAI API costs but contribute significantly to overall system processing. The reported costs reflect only the OpenAI-specific API usage.

---

Breaking Down the Costs & Processing

The core cost components of the system include:

1. Reasoning & Chain-of-Thought Processing – Multi-step logical operations used for decision-making.
2. Gap Analysis & Inferencing – Context-aware retrieval, pattern recognition, and narrowing.
3. Token-Based Processing – Computational cost of input and output tokens during system interactions.
4. Model Knowledge Base Utilization – Retrieval, comparison, and application of stored knowledge for enhanced reasoning.
5. Mathematical Memory System Operations – The ability to narrow, learn, and adapt memory retrieval based on available processing power.

For direct comparison:

• 100 million input tokens were processed
• 314,000 output tokens were generated for speech synthesis

A significant portion of the processing effort is dedicated to mathematical reasoning and model knowledge retrieval, where input tokens are heavily utilized to refine and structure outputs before finalizing responses.

---

Now lets talk about the non cloud models that can be used

Pretrained Models vs. Locally Trained Models: Key Differences

The system supports both pretrained models and models trained locally. This is one of the most critical factors in scaling AI performance based on available hardware.

1. Pretrained Models (Out-of-the-Box AI Performance)

• Local models are already trained, meaning they can be used immediately for inference without requiring additional training cycles.
• Our system integrates pretrained Local Large Language Models (LLMs) for knowledge, reasoning, and chain-of-thought processing.
• These models operate as the AI’s core knowledge base, meaning performance is dependent on the model size and hardware capabilities.

2. Trained Models (Custom Learning & Adaptation)

• Users have the option to train models on their own hardware to create domain-specific AI capabilities.
• Unlike API-based training, fine-tuning on local hardware is a one-time cost, allowing for continuous improvements without ongoing fees.
• Training is hardware-dependent, meaning faster GPUs and CPUs lead to quicker training cycles and real-time learning capabilities.

The Real Power of Hardware: Speed & Real-Time Inference

While training is an option, the real advantage of running local AI models lies in the speed at which they can infer, reason, and retrieve knowledge in real-time. This is where our mathematical memory system comes into play.

Mathematical Memory System: Hardware-Driven Intelligence

The AI’s memory system is mathematical in nature, meaning that the speed of inference and reasoning is directly tied to the processing power of the hardware.

• Larger memory pools and stronger hardware allow for instant narrowing of knowledge retrieval.
• Inference speed is based on tokens-per-second (TPS) processing.
• With high-end processing, the AI can “think” almost instantly by rapidly narrowing its search space, recalling stored knowledge, and integrating new inputs in real-time.

Because our system uses local LLMs for reasoning, chain of thought, and decision-making, the right combination of model size and hardware processing power allows for near-instant AI responses.

---

How Real-Time Learning Works in Our System

Unlike traditional AI models that simply store static knowledge, our system actively learns in real-time using a combination of:

1. Mathematical Memory-Based Retrieval – The AI pulls from its own stored experiences dynamically.
2. Continuous Input Processing – New information is integrated immediately into the reasoning system.
3. Adaptive Learning with Local & API Models – Cloud-based reasoning can supplement local inference when needed.

Since the mathematical process of memory retrieval is ever-advancing, the system continuously improves in reasoning without requiring explicit retraining. Instead, it refines and optimizes memory-based inference, allowing for:

• More efficient decision-making with each interaction.
• Faster response times as hardware scales.
• Self-improving AI intelligence without traditional retraining cycles.

---

Scaling Performance: How Hardware Affects AI Speed

The ability of the AI to think and process in real-time depends on:

• The size of the local model being used.
• The speed of the hardware (GPUs, CPUs, RAM, storage bandwidth).
• How much memory is allocated to the AI’s internal knowledge base.

Tokens Per Second (TPS): The Real Bottleneck

• AI speed is measured in how many tokens per second (TPS) can be processed.
• High-end GPUs with optimized tensor cores can drastically increase TPS, allowing for near-instant responses.
• Larger models require more processing power but enable deeper and more intelligent reasoning.
• Smaller models with optimized memory retrieval can process efficiently with lower hardware requirements.
• ** And a bonus is the data sets the system has in memory is designed in away allowing us later to have option to train the local model on its own memory sets.

The Hybrid Approach: Best of Both Worlds

To optimize cost and performance, a hybrid approach can be implemented:

• Local AI models handle real-time reasoning, memory storage, and chain-of-thought processing.
• Cloud-based AI (APIs) can supplement complex decision-making when needed.
• Vision models can be hybridized, where local models process raw data and cloud-based AI handles refinement.

This strategy allows for:

• Maximum cost savings by reducing API dependence.
• Instant processing speeds when hardware is optimized.
• Self-improving AI that learns in real-time while leveraging existing knowledge bases.

---

Future Scalability & Advanced AI Applications

With increasing AI hardware capabilities, future systems will support:

• Instant knowledge retrieval, making AI interactions feel seamless.
• High-speed reasoning, where the AI can narrow down answers in milliseconds.
• Autonomous AI systems that continuously refine their reasoning without retraining.

This will enable new applications in:

• Scientific research & medical AI (e.g., real-time cancer analysis).
• Enterprise automation (e.g., AI-powered assistants that handle deep, contextual problem-solving).
• Autonomous decision-making systems that self-optimize based on memory evolution.

As computing power continues to scale, self-learning AI will become the new standard, enabling real-time intelligence that improves dynamically.

---

Conclusion

• Local models are all pretrained, but the system supports custom-trained models for domain-specific applications.
• Hardware defines AI intelligence – More processing power = Faster, smarter reasoning & memory retrieval.
• Mathematical memory-based reasoning allows AI to learn in real-time, adapting and improving dynamically.
• Tokens per second (TPS) processing speed determines AI responsiveness.
• The hybrid approach (local + cloud AI) balances cost, performance, and intelligence.
• Future AI systems will continue to self-optimize, reducing the need for static retraining while enhancing reasoning speed and accuracy.

As AI systems continue to evolve, autonomous real-time intelligence will become a reality, allowing instant memory retrieval, faster inference, and cost-effective scaling without the need for constant retraining.

In the end the highest intelligence is your knowledge and understanding and ability to reason so models choices affect accuracy and understanding to a degree. think of it like your one friend that is not as smart, than you have that other friend that is super smart. Both can operate in the real world and if both have a photographic memory one morning they wake up well still not the same smart over time they will automatically balance out which is why i tell people that in the end there is only one Ai as all math will eventually be the same outcomes

[darcschnider]

Stable build completed tonight, backup underway. Should be back up in another hour. We fixed the memory generated images as well added new model submenus

We still have to add in a file upload option in this version of the desktop application. we will add it in two places one from the right click menu the other will be on the input window. we will get a paper clip or something.

this will open up the next connect where we will connect back in the doc system, as well start the new pipelines for image manipulation where you can upload an image and stylize and change it based on your inputs.

We already have the back end systems to do it just need the pipelines in place to connect it in.

Starting to look familiar, we are starting to look more like the V6 that most of you liked with is the ontology hybrid, but this round its much faster.