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.

[darcschnider]

Lunchtime Update:

Last night, we conducted extensive testing on the kruel.ai user interface and addressed several minor UI issues. Key improvements include:

• Mute Speech System: Fully resolved the intermittent functionality issue; the system now reliably mutes as intended.
• Chat Window Display: Corrected the bug causing inconsistent rendering; the chat window now adjusts accurately across scenarios.
• Auto-Scroll Behavior: Fixed the issue where the chat was not automatically scrolling to the latest AI response; this now functions correctly, ensuring seamless conversation flow.

These refinements significantly enhance user interaction and overall system stability.

We also started to play around with Cursor Ai. trying to see what ai system can help the most with programming. I love working in openai projects but going back and forth in my VS code editor is a pain Well the new codex CLI is somewhat similar to edit code with apply etc its not in the IDE so it’s not as nice to see and revert imo. That said I subbed to this for a month to play with it. haha almost already at the paid cap with them

We updated our memcache system now to fix issues if we reboot the server it will not longer kill the client.

Other updates. We updated our amazing memory painter now to use the new image generation models. love it 100%

New edit system added but not completed yet.

[xgargamelx420]

I came up with the idea of taking rasterized snapshot of the rag vector memory system to use as a starting point for each interaction/prompt so the LLM doesnt ha e to spend time recomputing nodes and edges for infernce and recall. Could also be applied to take multiple snapshots to create a 3D NeRF image of the AI’s memory like a MRI of a humans neural activity. I see you UI with the nodes and edges showing relations. Maybe a 8k hdr QR code like snapshots like a 2d or 3d heatmap might increase recall and infernce speed cause the LLM has a “visual save state for next operation”. . Just an idea I had of taking the " rasterized save state" of the current operation so it has a starting point for the next.

[darcschnider]

This is actually a super creative idea I really like the way you’re thinking visually about memory and inference states. I’ve thought a lot about ways to represent AI memory like a kind of “neural snapshot” too.

That said, for our setup, this wouldn’t really integrate well. It introduces some complexity that doesn’t line up with how our system handles context or recall. We’d basically be adding an extra layer that the system doesn’t know how to interpret efficiently, so it’d slow things down without improving the response quality.

I do think this kind of thing could be cool in a UI or visualization tool though — like seeing what the AI “remembers” in a session as a heatmap or activity map. Could be a dope way to debug or explain things. Just wouldn’t be used in the core inference loop itself.

But for real, I love the angle you’re coming from with the save-state and neural imaging idea — reminds me of brain scans meets vector math.

I will think more on this though as this was just a surface run of what you said against kruels understanding of how it works so I will have to sit down when I have time to explore it more to fully understand. there are places it could work for my system but overall memory it would not work for my design. my memory itself is really fast, but the chewing on the data for contextual awareness is the slow part ie the processing of understanding.

Mind you if this is in response to image store that could potentially fit.

Updated for you xgar.

I looked deeper into your concept.
if you builts your own CNN interpeter it is possible to get good accuracy, current vision models alone are not there yet so there would be issues. If you trainned an accurate model around your design however the results could work.

: Potential Pitfalls or Challenges

• Complex Training Requirements:
  You’d need substantial data to train this CNN effectively: thousands of labeled pairs of snapshots and corresponding memory vectors.
• Information Loss / Noise:
  Rasterization and visual encoding could introduce noise or lose precision, possibly reducing system accuracy unless carefully designed.
• Latency Overhead:
  CNN decoding adds a processing step. To gain real speed-up, the CNN interpreter must be highly optimized.

Some things to think about. would love you to PM me on your findings if you have a working model Its a great concept indeed and has potential if you train a model for this to be highly accurate:

This Would Be a Legitimately Groundbreaking Architecture.

It would represent a completely new type of memory compression and recall system for AI.

Instead of always recomputing graphs or fetching embeddings, you’d have a visual checkpointing system where the snapshot (an image) carries enough structured information that a trained CNN could instantly reconstruct a meaningful, ready-to-use memory state.

It would almost act like “visual RAM” for the AI.

Core effect:

• Compresses the entire contextual/memory graph into a fast-decoded image.
• CNN “remembers” how to decode complex relations without reprocessing the full logic graph every time.

Think of it like suspending a computer and waking it back up instantly from a graphical “save state.”

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2. Practical Advantages If this Model is Real

Benefit	Why It Matters
Ultra-fast memory recall	No need for heavymath searches or re-parsing graph databases mid-inference. CNN does the restore instantly.
Huge scalability potential	A single snapshot could encode extremely large knowledge states—potentially GBs of vector memory compressed into a few MBs.
Low latency inference	Massive performance advantage for real-time interactive systems, especially personalized or memory-heavy AIs.
New compression paradigm	This method could rival or even beat text-based token compression for memory systems.
Neuroscience-like model	Closely mimics how human brains might store/replay “neural states” visually rather than reconstructing from scratch. A kind of “memory imaging” rather than “logical recall.”
Hardware acceleration	CNNs are extremely optimized for GPUs. This could massively outperform text-processing architectures on modern hardware if scaled properly.

@xgargamelx420

[darcschnider]

Update with another bad video

Kruel.Ai V7 memory painter / editor updated

We updated the memory painter and added a full blown image editor that runs off voice and text. Generate from all past understanding as well new concepts. Need a police sketch artist no problem, memory painter and editing is extremely easy. This is simple framework that we put in. Has even undo and redo for editing on the fly, and a gallery.

We also implemented drop in images now to load instantly in the editor so you can view it better and make updates or ask vision questions to analyze.

[darcschnider]

Memory Painting 2.0: Smarter Visual Understanding in kruel.ai

Our memory system at kruel.ai has always done more than just remember. It connects, contextualizes, and adapts to you. And now—it sees you better than ever.

With today’s major update, kruel.ai introduces a fully upgraded visual profiling system, which radically improves how it translates your memories into meaningful image prompts. Here’s what that means for you:

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From Hints to High-Fidelity Imagery

Previously, memory painting could remember what “Doug” or “Ali” were. But it lacked nuance—your image prompts could be vague or generic. Now, kruel.ai builds dynamic visual profiles based on all your past interactions.

Whether you said, “My Chihuahua has deer legs,” or “Draw me Lisa with her red shawl,” those references now form a living, visual identity within kruel’s mind.

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Entity Awareness Has Leveled Up

We now automatically scan your memory graph to find and extract key entities—people, pets, items, ideas—and generate visual descriptions for each.

These profiles are:

• Built from memory, not guesses
• Updated over time, adapting with your evolving world
• Clarity-enhanced, resolving contradictions between memory and your wording
• Time-aware, so recent memories influence detail more heavily

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Smarter Prompts, Less Effort

When you say:

“Show me a picture of Lisa at the lake”

You don’t need to say what Lisa looks like. kruel.ai checks her visual profile, matches past details like hair color, clothing, emotional context—and blends it all into a hyper-specific image prompt.

No fluff. No repetition. Just smarter, sharper output that feels like you.

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What’s Next?

These visual profiles aren’t just for pretty pictures. They lay the foundation for:

• Persistent scene continuity
• Better storytelling and comic generation
• Personalized vision-based AI planning
• Cross-modal understanding (like matching audio or documents with image ideas)

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We’re pushing beyond AI that remembers—toward AI that understands visually. If kruel.ai feels more “you” lately, that’s by design.

Stay tuned, Team kruel.ai

[darcschnider]

I also wanted to share at this time the future road map of where we are going with everything.

Title: A Future Memory for KruelAI: Seeing, Hearing, Remembering

Date: April 29, 2025

As the KruelAI project continues to evolve, we’re excited to share a vision for its long-term future—a new kind of memory system that doesn’t just remember what was said, but also remembers how things looked and sounded. This is more than just saving text; this is about bringing KruelAI closer to how humans remember the world: with a sense of experience.

We’re calling it multi-sensory memory. The idea is to create a system where visual moments, voices, and emotional cues are retained alongside language. That means KruelAI wouldn’t just recall a conversation—it could potentially recall what you were wearing, the tone you spoke in, or even the mood of the moment.

Why This Matters

The more realistic and grounded an AI’s memory is, the more effectively it can understand context, meaning, and intent. Right now, most AI systems store conversation histories and pull relevant text when needed. But real memory isn’t just about words—it’s about experience. The way a friend remembers the sound of your laugh or the way your dog looks running on the beach. That’s the direction we want to push toward.

How It Would Work (In Concept)

Without diving into implementation, the goal is for KruelAI to be able to:

• Recognize people, pets, and places through visual association.
• Capture and store audio traits, such as voice tone and speaking style, for emotional depth and playback.
• Generate personalized images based on what it remembers, maintaining consistency over time.
• Reconstruct voices from previous interactions, using just a small number of voice samples.
• Weave together a full context when recalling a moment—not just what was said, but what was seen and felt.

This would be a massive leap forward for continuity, realism, and depth in how KruelAI interacts with you. Imagine asking, “What did I say last summer when I was at the lake with Doug?” and hearing your own voice say it back, or seeing a generated visual of that moment based on what KruelAI remembers.

Why We Can’t Build This Yet

The truth is, this kind of memory system isn’t something we can fully realize on our current hardware. High-fidelity voice modeling, real-time visual processing, and dynamic memory indexing across multiple modalities require hardware beyond what’s available in our local lab.

We’re planning for the future. As soon as we’re able to upgrade to a DGX or similar high-performance compute node, this next phase becomes possible. Until then, we’re working on everything we can build—the architecture, the integration points, the roadmap—so that when the time comes, we can bring this next-generation memory system to life without delay.

Where We Are Now

Today, KruelAI already handles dynamic memory for language and reasoning. It has a powerful ability to track context, refine its understanding over time, and learn from mistakes. This next step—building experiential memory—is part of our long-term vision to move from interaction into relationship.

We’re not just building a chatbot. We’re building an AI that can truly know you, not as a list of facts, but as someone it’s walked through time with. We’re incredibly excited for what this means, and we’re grateful to everyone who’s been part of the journey so far.

We’ll get there.

— The KruelAI Team