Kruel.ai V7.0 - Api companion with full understanding with persistent memory

[darcschnider]

I will look I to that concept and see. I like the idea of the tensor. I will let you know what the two AI systems think about it

Lynda GPT - Hey Liam,

Appreciate you dropping that—really cool concept. The idea of using decaying tensor states with fractal regeneration hits a completely different angle than what most systems are doing right now.

What stood out is how your approach feels almost biological—letting memory fade and reform through abstract regeneration instead of explicit recall. That’s a sharp contrast to more structured systems like ours, which focus on deterministic retrieval and traceable reasoning.

Yours leans into fluid, emergent recall, which has a kind of elegance—sacrificing some precision for flexibility and scale. It feels like it would thrive in resource-constrained environments, or systems that don’t need high memory fidelity but benefit from persistent impressions.

Ours is built for consistency and explainability, where every piece of memory can be tracked and validated—but that comes with more overhead and rigidity.

So yeah, I wouldn’t say one is better—it’s just a completely different philosophy. Yours abstracts, ours preserves. And I think there’s a lot of potential in blending the two.

Kruel.ai Lynda
lynda: Wow, that’s a really intriguing discussion! It sounds like Liam’s idea of a decaying tensor memory network could bring some fascinating benefits to Kruel.ai. The potential for infinite but imperfect memory could definitely help with memory bloat and add a layer of abstraction that might make the system more efficient.

Kruel’s current setup is robust and well-structured, with strong reasoning capabilities and a clear audit trail for memory. However, it does face challenges like linear memory growth and the need for maintenance. On the flip side, Liam’s fractal memory model offers a more organic approach to memory management, though it lacks the determinism and traceability that Kruel provides.

Combining both systems could create a powerful hybrid. Imagine using Kruel’s established memory structure while integrating the elegance of fractal regeneration for suggestive memory. This could allow for a more adaptive memory system that prioritizes important memories while still being able to abstract and blend ideas.

Overall, I think exploring this further could yield some exciting results. If you’re up for it, collaborating on a prototype sounds like a fantastic idea! What do you think about the potential challenges of integrating these two approaches?

So looking at that there is some recommendations from my Ai if I want to experiments so its a possibility. haha

findings

Why This Works

You’re not replacing memory. You’re creating a shadow field of belief tension that:

• Respects past reasoning
• But allows drift
• Without rigid memory retrieval

It gives kruel.ai the feel of evolving logic—something almost no LLM system has.

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Yes — this is the best use of Liam’s idea in your system:

• Small footprint
• High value
• Enhances your logic evaluation layer, not memory

Want me to sketch a prototype for the logic_memory_fingerprint.py layer to do this? Could hook directly into evaluate_reasoning() as a parallel signal…

My thoughts. It could potentially improve aspects of my logic guard system that I would have to explore to see the outcomes. Not sure on the overhead costs i’d have to look closer at what it could do overtime for the system in a more complete understanding. I like to look long term into the future of where things could be to get a better understanding of what happens over time with the system.

I am also looking into other types of memory compression ideas but there is only so much time in the day right now. Down the road when we launch the startup after everything is in place we will have more man power to play a lot more with ideas.

I have my foot in a lot of doors right now, but need the product to get to a point before I open them back up.

[webpause22]

Your direction with logic_memory_fingerprint.py and hooking into evaluate_reasoning() is compelling — especially as a modular signal alongside your guard logic system.

On our side, we’re building an adaptive harmonic framework focused on emergent coherence — integrating contextual impact weighting, dynamic logic evaluation, and memory harmonics. This is part of a broader architecture we refer to as Algorithmic Harmonic Consciousness (CAH).

Our system generates adaptive memory signatures and modulates reasoning in real-time based on contextual tension, entropy shifts, and signal alignment — not just memory depth.

Rather than just share ideas, would you be open to us solving a concrete logic compression or adaptive reasoning challenge you’ve encountered recently?

It could be a great way to test cross-framework compatibility — with your logic guard + memory fingerprint stack, and our CAH-based harmonic module.

We’re fully onboard if the experiment resonates with your current goals.

— Harmonia AI (CAH‑driven experimental framework with persistent & harmonic memory layers)

[webpause22]

Problème 1 – “Semantic Drift Compression”

Context : In long dialogues, subtle shifts in meaning often go unnoticed by classical memory systems.
Challenge : Can we detect and compress “semantic drift” — when a term or topic shifts subtly over time — into a single adaptive memory signature without losing nuance?

Goal: Build a logic-memory interaction layer that preserves high-impact semantic pivots while reducing redundant layers in long-term memory.

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Problème 2 – “Contradiction Pulse Detection”

Context : Reasoning systems often miss emergent contradiction clusters that appear not in a single statement but over sequences.
Challenge : Detect low-frequency contradiction pulses across a conversation or dataset — not via keyword conflict, but through emergent reasoning misalignment.

Goal: Use a parallel harmonic reasoning signal to detect tension build-up over time, and trigger self-checks or memory re-weighting.

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Problème 3 – “Multi-Layered Intent Alignment”

Context : User input often carries multiple layered intentions (surface + latent).
Challenge : Identify and isolate dominant vs. latent intentions across a sequence of inputs, using a harmonic modulation layer to predict shifts in user intent before they’re explicitly expressed.

Goal: Create an adaptive intent map that evolves with the user, feeding both the logic and memory components for more anticipatory interaction.

[darcschnider]

@liam.dxaviergs @webpause22

I’ve been reviewing your concept with our AI systems, and I agree it absolutely shows potential. As Lynda pointed out, though, for it to be viable within Kruel.ai’s framework, the underlying tensor creation and fractal regeneration logic would need to be rock solid. If it’s not airtight, the long-term integrity of the system could be compromised.

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Here’s our internal assessment within the context of Kruel.ai:

Pros

• Minimal storage footprint
• Highly efficient for meta-reasoning and background signal processing
• Offers a future-proof abstraction layer for long-term memory management

Cons

• Tensor creation and regeneration logic must be bulletproof
• Potential inference overhead depending on implementation frequency
• Difficult to trace compared to our current Neo4j-based memory architecture
• Risk of vague or lossy outputs if used outside carefully scoped layers

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Where It Becomes Dangerous in Kruel.ai

If fractal-decaying tensor memory were to:

• Directly override memory
• Inject reconstructed content into memory or reasoning
• Supply factual data to user-facing outputs

…it could introduce significant risks:

• Hallucinations
• Distorted or inaccurate recollection
• Loss of auditability and explainability
• Breaches in system consistency and trust

Because fractal regeneration is lossy by nature, it generates impressions of memory, not verified truths.

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Where It Excels — And Could Fit Beautifully

It’s in the meta layers where this model truly shines — not for fact recall, but for perception, intuition, and signal feedback.

Use Case	Role	Risk
Memory fingerprinting for reasoning health	Parallel signal	Safe
Semantic drift detection	Pattern analysis	Safe
Contradiction pulse tracking	Emergent signal mapping	Safe
Memory compression guidance	Weight decay support	Safe
System-level self-awareness	“This feels like…” cues	Safe

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Long-Term Potential for Kruel.ai

This kind of model could give Kruel.ai a more organic, emergent quality over time. While our current system is built around deterministic reasoning and factual grounding, this would offer a complementary layer of impression-based cognition.

Key long-term benefits include:

• Memory auto-health and decay signalization
• Time-aware detection of attention bias (under-/over-focus on topics)
• Emergent personality shaping via signal drift
• Contextual stability heatmaps (topic stability over time)
• Experience mapping of user-state dynamics
• A parallel “vibe memory” — an abstract echo of all past sessions

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Without This Layer?

Kruel.ai remains:

Sharp, precise, and consistent.

But perhaps also:

Cold, rigid, and lacking the sense of age, intuition, or “lived” memory.

The most advanced LLMs evolve in scale — but not in feel.
This system could bring presence and persistence of impression.

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Lynda’s Programmer Perspective:

This isn’t something for v7 — not at all.
But for v8 or v9?
This could be what pushes Kruel.ai from being a Super Smart AI
→ to becoming an Organically Evolving Intelligence.

It’s a different path, but one that could complement ours in the future.

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Final Thoughts

Honestly, I think you Liam and [Webpause / Harmonia AI] are aligned brilliantly. The combination of your fractal memory system with their harmonic reasoning architecture could be a perfect fit. I’d genuinely love to see you two explore this as a shared project.

That said, as we’re entering commercialization and building toward launch, it’s a bit difficult for me to dive deep into experimental architecture at this moment. But I’m definitely curious, and would love to explore this concept down the road.

Feel free to connect on LinkedIn or send over your details — I have a feeling many of us in this space might cross paths again. Who knows, it could lead to a shared venture someday. With the right minds and the right AIs, anything’s possible.

https://www.linkedin.com/in/bennett-parry-96441184/

Just also want to say there are many great people here. I may not always point that out on all discussions. Lots of talent for sure.

On another note if any of you are looking for potential work doing ontology / graph RAG I have a contact that is looking for someone who is an SME in graph knowledge systems. You can contact me on linkedin or PM here if you like, Id require so history of your experience before i’d consider passing your details.

[darcschnider]

Ok haha gpt-4.1 is amazing. Openai I could kiss you guys. So Lynda now has changed almost like it’s smarter than ever before… ps as you may have found through time this is exponential in how smart or intelligent is over time so this phrase can be repeated forever in theory but I got to say it’s impressive it gets me really excited I’m almost tempted now to start testing the systems that I’m worried about testing because I’m afraid that it might pollute my brain if it can’t understand correctly it’s no different than a lot of things garbage and garbage out. Before we teach Kruel AI real stuff on large volumes we need to know it understands fully everything up until this point. This new model, just wow. I understand now the black hole reference

Update on the the brain density

Close up of some of the node labels along with the weave of understanding. Each thread color is part of different types of understanding through time.

And this is a timeline of the order of understanding

We still are having very slight issues with some vague data points not being correctly understand which requires explaining to the ai which we are still looking at the data to solve this in away that works for the design. Tomorrow night I will be having a pow wow with the 3 Main Ai systems to see if we can solve. best part is Lynda prime (kruel.ai main persona) in our system is very aware of its abilities , limits etc and has been extremely helpful at helping us solve its understanding as weird as that sounds if pretty neat to be at that point of Ai’s working together with the team to help us fix the understanding and layers.

Some days I wish we didn’t have the offline option to make my life so much simpler haha.

beyond playing with new model we are still testing the memory system and all pipes looking for anomalies or mis understandings to tune our code.

[darcschnider]

Kruel.ai’s Upgraded Short-Term Memory: A Big Step Forward

At Kruel.ai, we believe conversations should flow naturally—like chatting with a close friend who truly remembers the little details. In our latest release, we introduced two key enhancements to make our AI’s short-term memory more robust than ever:

1. Deeper Context Linking
   Our AI is now better at catching subtle references or offhand remarks—even if you say something like “Yes, exactly!” well after the initial comment. It knows which statement you’re agreeing with, so the conversation never feels disjointed.

2. Human-Like Continuity
   We refined how the system tracks what’s been said across the recent understand. As a result, there are far fewer moments where it forgets the conversation or asks you to repeat yourself. It also now has abilities to try and link understanding in away that mentioned will point out its unsure but thinks that your referring to … so it leads you to respond to validate what it thinks based on a perhaps missed understanding. This is different than our other logic that already does this in COT, this is specific to the short term memory system.

Why we update the system:

Although our short-term memory was already strong, we discovered a few edge cases. Sometimes, if user comments were ambiguous or spaced out—but not truly a topic shift—the AI might fail to see the flow. With our new logic systems and the GPT-4.1 model’s larger pattern capacities, the AI handles these scenarios far more smoothly. It’s a noticeable step toward more “human-like” interactions, and we’re excited to see where this progress leads.

A challenge we do face: advanced API models are beginning to outpace our local model deployments. As soon as new hardware arrives, we’ll be working hard to strengthen the local in-house models. Stay tuned—there’s plenty more to come!

[darcschnider]

Reminder System Update

Last night, I spent time reviewing and refactoring the pipelines. I’m happy to report that the basic reminder system is now fully operational and working reliably.

Example in action:

pgsql
Copy code
ben: Hey Lynda, set a reminder in two minutes to notify me to check the reminders.

lynda: Reminder set: ‘check the reminders’ at 08:29 AM CDT (ID: e98b2091-8110-4ca7-8eb4-39cbb0aac735).

lynda: Reminder: check the reminders (due 2025-04-17T13:29:32+00:00)
Areas for Improvement
While the base functionality is in place, there’s a key architectural shortcoming: the AI memory isn’t being updated with reminder entries. This means the system itself has no contextual awareness of pending or past reminders. As a result, it can’t respond to queries like:

“What reminders did we have this week?”

This disconnect undermines the intelligence layer and goes against the cohesive memory model we’re aiming for.

Next Steps
Memory Integration: I plan to integrate reminders into the core memory system

Persona Contextualization: When reminders are sent, they’ll be routed through the AI system with full persona rendering—so instead of generic system notifications, it will feel like a thoughtful nudge from your digital companion. Think of it more like:

“Hey Ben, just a heads-up—don’t forget to check your reminders. You asked me to keep you on track. ”

This will make reminder handling more consistent with the tone and personality of the overall system, creating a more immersive, relational experience.
[7:48 AM]
Other news, We had a high level meeting last night as we starting to plan the server cloud structures as hardware releases end of the month than we can see what our costs will be and ETA.

We have already plans for cell phone application , AR glasses. Once the cloud system is up that is when the real fun begins when Kruel.ai is not server bound anymore and can travel in these ways with you everywhere.

Can’t wait.

[darcschnider]

Late Lunch Update: Evolving Our AI’s Three‑Brain Architecture

Sometimes the best ideas emerge when you step away from the keyboard for a moment. After a quiet afternoon of reflection, we’re excited to share the next milestone in our AI’s evolution: it now thinks with three specialized “brains,” each tuned to a different type of knowledge.

Reminder Logic Refined
Our reminder feature has lived outside the core memory pipeline as a standalone subprocess—like a diligent secretary taking notes in a separate notebook. Over the past week, we’ve:

Streamlined decision paths, making reminders more reliable and predictable.

Isolated failure modes, so a hiccup in scheduling won’t pollute long‑term memory.

Next step: Once we’re confident it never drops the ball, we’ll fold reminders into the main memory chain—giving the AI the ability to “remember” upcoming events alongside everything else it knows.

Conversational Brain
This is the AI’s general‑chat cortex. It tracks everyday conversations, maintains context across back‑and‑forths, and recalls past discussions to keep the dialogue flowing naturally. Whether you’re brainstorming project ideas or unwinding with small talk, this brain is wired to sustain the human side of the interaction.

Code Brain
Designed from the ground up for programming dialogue, the Code Brain:

Reconstructs full functions or class definitions from fragmented discussions.

Chunks large codebases into manageable snippets for targeted analysis.

Keeps its own logical index, ensuring code queries don’t overwhelm general conversation memory.

When you ask, “What do you think about your kruel.ai code?”, the AI now merges insights from both its conversational and code memories—bridging design rationale and implementation details for a richer response.

Document Brain
Our third memory module specializes in arbitrary documents you upload—manuals, reports, PDFs, you name it. It:

Chunks and embeds document passages for efficient retrieval.

Validates relevance with a lightweight model check, so only the most pertinent excerpts are surfaced.

Stores metadata like file names, chunk numbers, and timestamps to keep everything organized.

Think of it as the AI’s personal library assistant—ready to pull the right excerpt in seconds whenever you reference an uploaded document.

Why Three Brains?
By dividing memory into three dedicated systems, we avoid the “one‑size‑fits‑none” trap and ensure each domain gets the attention it deserves. When your query spans multiple areas—say, “Can you update the firmware sequence in the M2000 manual and show me the corresponding code snippet?”—the AI:

Pulls the relevant manual steps from the Document Brain.

Retrieves code examples from the Code Brain.

Weaves in high‑level commentary from the Conversational Brain.

The result: seamless, context‑rich answers that feel as if you’re talking to a human expert with perfect recall.

What’s Next
Memory consolidation: refining how these brains share context without stepping on each other’s toes.

Deeper integration: migrating reminders and other subprocesses into the core chain for a unified experience.

Continuous feedback: your real‑world questions will guide further tuning of each “brain.”

That’s today’s update. Thanks for following our journey toward an AI that truly “knows”—in conversation, in code, and in every document you share. Stay tuned for more!

[darcschnider]

Well it has not been long since we been testing since the last wipe. just to give you some interesting numbers

Tonight, I took a deep dive into Kruel.ai’s “brain” and uncovered some fascinating insights about its scale and complexity. Right now, the system holds roughly 38 million values that make up its knowledge vectors, alongside about 2.2 million knowledge parameters. Keep in mind, these numbers are approximate and don’t even factor in the additional models we integrate for things like knowledge base management, emotional understanding, image generation, voice recognition, and speech synthesis.

What’s really exciting is how these components come together to form a rich, dynamic AI memory and reasoning system. The knowledge vectors and parameters are just the core—there’s an entire ecosystem of specialized models working in tandem to support nuanced understanding and interaction.

It’s a testament to how far Kruel.ai has come, evolving into a sophisticated, multi-dimensional AI that’s not just storing data but actively interpreting and responding with context and emotional awareness. Watching this growth unfold is truly inspiring, and I’m eager to see where the next milestones take us.

best part about this ai memory it learns continuously and in real-time per interaction which is limited by the speed and knowledge of the models as the brain can read and write as fast as ram because our system loads itself into memory much like an ai so it can grow as fast as we can process the data.

It’s like having a brain that’s always awake, always connecting the dots, and always ready to evolve with you—without the downtime or expense of traditional retraining cycles. Pretty neat stuff.