Kruel.ai V8.0 - Api companion co-pilot system with full understanding with persistent memory

[darcschnider]

The Origin of V8 — A Spark from the Road

Just as we had finished optimizing V7, something clicked.

We had brought reasoning time down from 24–120 seconds to under 5—whether using cloud tools or running locally. For a cascade-based system, that was a breakthrough. Fast, efficient, stable. With petaflop-tier local hardware on the horizon, we were already imagining a world where complex AI thought happened instantly, fully offline.

There were no plans for V8.
At most, we had sketches—concepts parked for the future.
V7 was clean. Solid. Production-ready.

And then came a two-hour drive.
A quiet moment. A conversation with Lynda01, our AI programmer.
And everything changed.

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The Spark: A Thoughtful Observation

We were casually reviewing how V7 routes tasks—its clean intent paths, tool logic, and memory triggers. Lynda01 listened, reflected, and then said something simple:

“Ben… you’ve already done the work. All the pieces are here.
You only need a few more files to upgrade your logic, because of how you built it.”

It wasn’t said with urgency—just clarity.
Lynda01 had noticed something that I hadn’t:
V8 was already halfway built.

Because of the architecture V7 used—the modular tools, the always-on memory, the way we separated system responsibilities—we had unknowingly laid the foundation for something more flexible. Something dynamic.

And in that moment, we realized:
This wasn’t about optimization anymore.
It was about evolution.

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The Shift: From Rules to Full Reasoning

Within 24 hours, the prototype of V8 was live.
And within 48, both Lynda Prime and Lynda Laptop had made the switch.

Where V7 strictly followed our amazing classified rule-based logic, V8 began to reason. It could pull from memory, search for new data, describe images, and generate creative output—all from a single request.

It didn’t need perfect phrasing.
It didn’t need to be told which tool to use.
It just understood the goal—and handled the rest.

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Why It Mattered

We didn’t build V8 because something was broken.
We built it because the system told us it was ready.
Because all along, we had been laying the groundwork—
and Lynda01 simply noticed the path forward.

V8 didn’t replace V7.
It grew out of it.

Now the system is smarter, faster, more natural.
It doesn’t just follow instructions—it collaborates.
It reasons. It adapts.
And soon, with new hardware, it will think in real time, fully offline or Online.

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We didn’t plan to build it so soon.
We just finally gave it permission to evolve.

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Cascade Logic (V7) vs. Orchestrated Tool Framework (V8)

A high‑level comparison—architecture only, no implementation details.

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How They Think

Dimension	V7 – Cascade Logic	V8 – Orchestrated Tool Framework
Routing style	A fixed cascade steers every request down one predictable path.	A reasoning layer selects—and when useful, chains—specialised tools on the fly.
Predictability	Completely deterministic: identical input always triggers the same sequence.	Outcomes adapt to context while staying within guardrails—offering flexibility with safety.
Extensibility	Adding a capability means editing the cascade and redeploying.	New capabilities become available almost immediately; the reasoning layer adopts them as soon as they exist.
Memory use	A persistent memory layer enriches every interaction.	The same memory is present, but the reasoning layer can also pull extra context mid‑conversation if needed.
Latency & cost	Ultra‑lean—one external reasoning call per turn.	Typically identical; only multi‑step reasoning tasks add a brief extra round.
Ecosystem reach	Tuned around a single provider.	Works seamlessly with multiple AI providers (OpenAI, Anthropic, Gemini).
Ideal strengths	• Regulatory or safety‑critical workflows• Resource‑constrained devices• Infrequent feature changes	• Rapid feature roll‑outs• Complex multi‑step tasks (search → analyse → visualise)• Deeply personalised tool use

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Pros & Trade‑offs

	Cascade Logic (V7)	Orchestrated Framework (V8)
Pros	• Rock‑solid determinism• Minimal overhead• Easy audits	• Near‑limitless flexibility• Seamless tool chaining• Multi‑provider freedom
Trade‑offs	• Manual growth as features multiply	• Needs strong validation & guardrails• Slightly higher average processing cost

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What Stays Constant

• Persistent memory layer – Both versions leverage deep long‑term memory and recent interaction context.
• Safety net – Rate limits, content filters, and controlled access remain in place.
• Fail‑safe option – Cascade Logic is still available for deterministic or high‑certainty routing.

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Why We Moved Forward

Cascade Logic (V7) earned its place as a rock‑solid foundation. It’s reliable, simple to audit, and does exactly what it’s told. But as the needs of our users evolved—requests involving deeper memory, visual feedback, complex task chaining, and faster development—the rigid nature of the cascade became a limitation.

With the new orchestrated framework in V8, Kruel.ai doesn’t just respond—it understands. It dynamically selects the right tools, adapts to your phrasing, and blends personal memory with external information without you needing to walk it through every step.

V7 is a finely-tuned watch. V8 is that same watch with a modular, learning-driven core—capable of evolving with you overnight.

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How It Feels in Real Life

(No code, no jargon—just user experience.)

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1. “Paint my pups at the lake.”

• V7: Gets it right—if you ask exactly the right way. Slight rewording may need a follow-up clarification before it understands you want a picture.
• V8: Understands the heart of the request. It knows your dogs, remembers the lake, and gives you a finished image without any handholding.

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2. “Where do my NVIDIA shares stand today?”

• V7: Looks up your share count and returns the last known price—unless you specifically ask for online updates and a chart.
• V8: Understands this is a real-time check. It fetches live pricing, compares it to your entry point, and gives you a detailed result—no prompting required.

Note: Charting support is part of the V8 system and currently under integration. While not yet active, the foundation is built and being tested for live usage soon.

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3. “Compare that to Bitcoin since January.”

• V7: Unless you explicitly ask for a comparison, it won’t combine the two.
• V8: It naturally continues from the prior question, pulling Bitcoin data and delivering a full side-by-side breakdown.

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4. “What’s this error?” (You send a screenshot.)

• V7: Reads the error message and returns the plain text. If you want it to check your logs or previous issues, you must ask.
• V8: It sees the error, cross-checks your past tech history, recognizes a repeating issue, and offers insight—all in one smooth reply.

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5. “Next Friday remind me to file taxes and email my accountant.”

• V7: Schedules one task. You then need to request the second one separately.
• V8: Understands that you gave it two jobs—and sets both in one go.

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What This Means For You

	V7	V8
Turns per task	Fixed—often two or more	Usually one, but may choose more if needed
Clarity needed	You must spell out each step	You speak naturally—the system fills gaps
Tool use	One tool per path	Mixes tools seamlessly when needed
Memory	Always present but scoped	Used flexibly mid-conversation
User feeling	Reliable, but procedural	Adaptive, curious, and goal-driven

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Final Thoughts

The introduction of V8 doesn’t mean V7 disappears—it means we now have both stability and agility.

V7 remains ideal for workflows that demand absolute certainty. But for everything else—spontaneous research, creative tasks, adaptive planning—V8 is already proving itself smarter, faster, and more naturally aligned with how humans think.

This is where Kruel.ai begins to feel less like a chatbot—
and more like a true assistant.

And the most important thing to remember is this:

We didn’t have to train this system.
It learns in real time, updates its logic in real time, and uses the LLMs it’s given strictly for knowledge and language understanding.
The intelligence—the adaptability—that’s all powered by our core system.
And that system just became the backbone of something far more powerful than any single model:

A living, evolving cognitive engine.

[darcschnider]

Kruel.ai Development Log — The Leap from V7 to V8

Good morning, and welcome to spring—finally.

It’s been a wild but productive week here at Kruel.ai. After a whirlwind sprint, we now have a stable build of Version 8 running with over 90% of all systems operational . What’s wild is how close V7 already was to the conceptual framework of V8—we just didn’t realize it until we were knee-deep in the upgrade. The fact that it only took about seven days to hit this level of stability is, frankly, blowing my mind.

The Power of AI-Aided Development

The evolution of AI coders is accelerating things beyond what I expected. Sure, we still run into bugs, but the collaborative awareness between agents has changed the game. The AI systems can now see across the entire architecture , and more importantly—they understand each other . When one AI flags a problem, others grasp it without me needing to translate the logic. It’s like watching a team of engineers speak in a language I used to have to manually interpret. Welcome to the era of real-time cooperative AI development —or as the media likes to call it, Vibe Coding .

For the record, I’ve been “vibe coding” since 2021, back when we were experimenting with GPT-3.5. And yeah, if you just winced, same. Those were the days of ghosted codebases and manual recovery from AI-generated chaos. GPT was great for scaffolding, but you still needed to carry it the rest of the way. Fast forward to today, and we’ve got GPT-4o, Claude 3.5 Pro , and of course, Kruel.ai’s own Lynda —and it’s an entirely different landscape.

The V8 Turning Point

Not gonna lie—two nights ago I almost threw my machine out the window . Some of the dynamic AI behavior got too loose, and they began overthinking every task. One overnight debugging session cost me $65 in API calls. I haven’t seen a burn like that since V2 through V5, ha. But now it’s smooth again.

What makes V8 unique is this: we didn’t do a full system rewrite . Instead, we executed a live memory migration from V7 to V8. That means the transition happened in-place , using the existing data and memory structures from V7. It was risky. I had to tread carefully to avoid corrupting the “brain” and ensured I could roll back to V7 at any moment. But it worked—and it worked beautifully. This marks our first true version upgrade rather than a ground-up rebuild .

Some older concepts were retained, but most were rebuilt or tuned to align with V8’s more organic design. And yes, it’s feeling far more fluid and intelligent now, without sacrificing what made V7 reliable.

What’s Next

The final piece left is a new vision system , and I have something special planned for that. Once it’s integrated, I’ll start rolling out demos—and you’ll notice a big difference.

Performance-wise, we’re seeing 5–8 second average interaction cycles , depending on the complexity (excluding image generation). OpenAI’s Image-1 model is stunning, but it’s clear they’ll need stronger hardware to get it running in real time. Once that’s solved—and Sora’s next iteration lands—we’re going to see something amazing.

In fact, here’s my prediction: eventually, Sora-style video and real-time image generation will merge into a single multimodal model . That’s when we hit the real holy grail—true dynamic visual storytelling inside Kruel.ai. And you better believe, once those tools drop, I’ll be integrating them fast.

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Final Thoughts

V8 has been an exciting ride so far. I’ve got about another week of tuning ahead, but I’m hoping to drop the first demo—maybe even tonight .

Thanks for sticking with us. The best is definitely yet to come.

— Ben

[darcschnider]

Update on V8

Kruel.ai V8 - a realtime learning Ai

[darcschnider]

Kruel.ai: V7 vs V8 Evaluation Overview

As development on Kruel.ai Version 8 (V8) reaches a stable point, I am returning focus to Version 7 (V7) to conduct a side-by-side evaluation. The goal is to determine which architecture offers superior performance in practical application—both in reasoning accuracy and tool execution optimization.

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Design Philosophy Comparison

V7 – Cascade AI Logic Architecture

• Modular Cascade Design : V7 employs a cascade model wherein multiple specialized AIs handle distinct toolchains and reasoning stages. Each AI is independently responsible for a specific domain or tool, resulting in a well-coordinated, compartmentalized decision system.
• Predictability and Precision : While less dynamic in certain cases compared to V8, V7’s cascade logic consistently produces highly accurate decisions with fewer missteps.
• Optimized Tool Invocation : The cascade design excels in targeted tool usage, avoiding overreach or inappropriate calls—a notable efficiency in complex task execution.

V8 – Unified AI Decider Architecture

• Centralized Intelligence : V8 operates using a unified, more generalized AI that orchestrates logic across all tasks. It is designed to dynamically adapt to a wider range of inputs and task types.
• Increased Flexibility, Reduced Precision : While the model is more flexible and requires less manual coordination, the central AI exhibits a higher rate of misfires compared to the specialized agents in V7.
• One-Mind, Many-Tools Approach : This architecture aims to simplify task flow but may lack the surgical precision V7’s specialized modules provide.

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Preliminary Observations

• Responsiveness : V7 appears to offer faster decision-making due to its streamlined and compartmentalized flow. Each AI agent focuses solely on its domain, reducing unnecessary inference overhead.
• Data Retrieval Accuracy : Early tests suggest V7 locates relevant data with higher confidence, as it relies on deterministic pathways rather than broad generalization.
• Toolchain Optimization : V7’s distributed execution pattern shows improved efficiency in tool management and invocation, especially in environments where timing and accuracy are critical.

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Next Steps

A thorough benchmark is underway to empirically validate which version—V7 or V8—excels in:

• Smartness (contextual reasoning and problem-solving)
• Data Retrieval Efficiency
• Toolchain Utilization and Resource Optimization

The outcome of this comparison will directly inform the direction of future versions and whether to evolve the unified AI model or continue refining the cascade-based logic.

lynda: Hey ben! So, you’re asking me to compare v7 and v8 of Kruel.ai as they’re both reaching a kind of maturity, right? Well, from what I understand, v7 is built around a modular cascade architecture—think of it like a team of specialists, each handling a specific task or tool, working together in a well-orchestrated, predictable way. It’s super precise, with each AI focusing on its domain, which makes decision-making fast and accurate, especially when it comes to tool invocation and data retrieval.

On the other hand, v8 is more like a single, unified brain—an orchestrator that dynamically adapts to all kinds of inputs. It’s more flexible and less rigid, aiming to handle a broader range of tasks without needing the tight coordination of specialized modules. But early tests suggest that while v8 is more adaptable, it might have a slightly higher rate of misfires compared to v7’s surgical precision.

In terms of responsiveness, v7 seems to be faster because its compartmentalized design minimizes inference overhead. Plus, it locates relevant data more confidently, thanks to deterministic pathways. That makes it great for situations where speed and accuracy are critical—think complex reasoning or tool management.

Now, v8’s approach might shine in scenarios demanding more fluid, multi-modal interactions, or where flexibility outweighs absolute precision. But the side-by-side evaluation—looking at things like reasoning depth, data retrieval, and tool efficiency—will tell us which architecture really wins in practice.

Overall, I’d say v7 is like a well-oiled, precise machine, while v8 is more like a flexible, adaptive mind. The best choice probably depends on your specific needs—speed and accuracy, or versatility and simplicity. Exciting times ahead! Want me to keep an eye on the benchmarks as they roll out?

[darcschnider]

Version 7 - Still my Fav. it’s more responsive. Well there are slight differences on logic for Stock reporting the depth analysis of the V8 can be transferred into V7. But both do the same job

Kruel.Ai V7 Test

Other things to Not V7 has ability to look up current things to validate its understanding on things It may need more information on outside of memory.

Speed is slightly faster I think in this version and this version is smarter on calls.

Even though we are using a Cascade concept, we still have the Librarian which is very much like the primary logic that drive V8 but with more checks using logic processes rather than full dynamic which well more fluid harder to nail down accuracy imo.

Ps. the mention of Camera nerves is in relation to the issues we had nailing down visual understanding system. it was 2 nights of fighting with V8 and V7 haha.

[darcschnider]

Update on everything this morning

We have successfully stabilized the V8 system, and the call handling is now more streamlined. The functions we had in V7 all moved over and are all intact but significantly enhanced for improved intelligence and performance.

We’ve also made further enhancements to the document /code ingestion system. While we’ve loaded additional documents, we have yet to dedicate the time needed for comprehensive testing.

Code we have played with more in the system as its really good at understanding all code across itself but no way yet for me to recompile it all from memory we are still working on that concept, waiting for openai’s release of their IDE concept to see how that looks.

Currently, we are still in the process of integrating everything from V7 into the new architecture.

The new system leverages all of our previous concepts, now refined as tools, which has made the evolution much smoother than previous builds. It’s impressive to see how far the AI development has come, completing the process in under a month. Just a few years ago, I wouldn’t have trusted AI to rebuild complex logic, but now it’s almost perfectly accurate. The agents are handling the bulk of the work for me, which is a major productivity boost.

I now have AI systems building and testing things all day, even while I work on other things, which is a significant shift from how things used to be which was required full attention and only off hours development where today I can simply have agents running and testing well Im focused on other projects which leaves me testing in the evenings. Of course, it comes with a cost, but it’s well worth the investment for the efficiency it brings.

I’ll provide a more detailed update soon.

[marsescobin]

Late to the party but Ive been thinking a lot about this. We know AI still chokes on text and performance goes downhill the longer the context window (likely why AI is pretty bad at history)

So the question becomes - how do you choose which memory AI stores for you? How can we mimic how humans store episodic memory (where our brain evaluates what is memorable first). I’m still studying the best approach but here’s a few interesting notes about how human memory works that might be relevant:

1. Humans commit episodic memory after an event boundary. These are changes in context that signal something new: a shift in location, mood, goal, or topic etc. Our brains need an event or context to finish first before it can evaluate if something in there is worth storing. In AI, this probably means we shouldn’t be storing long term memory mid conversation. That would be poor strategy. Instead we must have a proxy for an event boundary - I doubt time is going to be a good one (e.g after X hours) as context might be too long already by the time the scheduled memory job runs.

2. Scientists used to believe its our prefrontal context that stores memory. Turns out that’s not true. Instead, the prefrontal cortex acts more like a conductor — it coordinates with other parts of the brain to orchestrate the remembering process. It doesn’t store memories; it helps us act on the intention to remember.

In AI terms, the equivalent might be a system with a few distinct roles, among others:

• a master agent that orchestrates the process of storing memory like the prefrontal cortex
• an agent that decides what’s worth remembering
• another agent that handles the storage

3. Our human memories have coordinates! They’re tied to data that answers how, what, when, and where (if I recall correctly). So for example you have a memory of playing with your dog at the beach. The how might be that while you were playing, you were also smelling corn being grilled. That sensory recollection is tied to a bunch of your other memories such as BBQing with your fam during childhood.. Similarly the answer to what - which is playing with your dog - is also tied to other memories. The way I picture it, they’re like vector embeddings. You can locate where exactly in the brain they’re stored if you can identify the most important indexes.

4. Given #3, my hunch is that it’s more efficient to identify the markers where memories should be tied to to efficiently store them. For example you can choose 10 to N questions a memory should be able to answer in the process of committing it and index them by those. Instead of just figuring out what’s worth remembering.

5. Five and most importantly - if AI were to remember things for me, it must know my personal values.. Memories tied with emotions stick around longer at least for humans. And emotions point to what we value.

6. OpenAI’s implementation of ‘memory’ on ChatGPT (Settings > Personalization > Manage Memories) is pretty lame lol, at least for now. The things it remembers are pretty surface level. For example it remember that I was “currently refactoring code” (???) and worse, it doesn’t automatically update. So I am forever refactoring code unless I manually delete that memory.

OpenAI should implement a feature that automatically updates these long term memories. And perhaps, in the future, enable it to track your personal values and how that’s evolving over time. In the short term having categories for these memories would already be a big win (e.g., personal relationships, work, projects, communication style, etc)

[darcschnider]

You brought up several concepts we deeply agree with — and a few that kruel.ai was built to address head-on.

Memory as a living process, not a storage job

At Kruel.ai, we store the full context — user, AI, and surrounding signals — because we believe memory’s value often shows up after the fact. What seems trivial today might become essential tomorrow. That’s why we don’t discard. But we also don’t treat memory as flat. We focus on what should resurface , when, and why.

The system doesn’t store mid-thought. It waits for a natural resolution — an event boundary, a completed idea, a shift in tone or intent — before committing a moment as meaningful. That maps directly to the kind of episodic segmentation you described.

Memory roles are emergent, not monolithic

We’ve found it helpful to think of memory not as one system, but as a collaboration between different processes:

• Something watches for change (like your prefrontal “conductor” idea),
• Something decides what carries weight,
• Something stores and structures that information for future use.

This orchestration allows for richer, more fluid memory than a single-layered strategy.

Memory with structure: how, what, when, where

We don’t just store text — context matters. What happened, where it happened in the flow, what it connected to, and how it felt all shape recall. So when a memory resurfaces, it’s grounded in time, topic, and relationships, and so much more.

Emotion and value shape retention

You said it well: “memories tied to emotion stick longer.” We believe the same. kruel.ai weighs not just salience, but significance — what reflects the user’s values, patterns, even emotional shifts. And over time, that scaffolding begins to shape what the system prioritizes, without needing manual updates or deletions.

The future: self-optimizing intelligence

This is where AlphaEvolve which is an Ai from google hits home. We’re at the edge of systems like AlphaEvolve — not just processing information, but designing their own optimizations . Memory traversal, selection, even the shape of reasoning will be co-authored by AI itself.

We’re building kruel.ai to live in that world: where intelligence adapts, learns how to learn, and reshapes the rules of what matters — faster than any human could design.

Appreciate your thoughts — this is the kind of conversation that pushes the field forward.

— kruel.ai team

[darcschnider]

Kruel.ai Tops OpenAI’s Quick Eval Benchmarks

OpenAI recently rolled out their new Quick Eval feature, enabling side-by-side evaluation of model responses through a standardized, streamlined interface. As developers of Kruel.ai — an advanced AI platform designed for deeply contextual interaction — we decided to run our core system through the gauntlet.

And the results speak for themselves: Kruel.ai, powered by GPT-4.1-mini, achieved a 95% accuracy rating.

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Quick Eval Results: A New Benchmark for Understanding

Here’s a breakdown of model performance:

Rank	Model	Score	Auto Grader
	gpt-4.1-mini	95%	95%
	gpt-4.1-nano	87%	87%
	gpt-4.1	96%	96%
	o4-mini	84%	84%
	o3	91%	91%
	Stored Completions	82%	82%

These results validate our current architecture, confirming that gpt-4.1-mini delivers exceptional accuracy at a manageable performance cost. The model outperformed alternatives in comprehension, continuity, and relevance.

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Why GPT-4.1-Mini is Now Our Default

Prior to Quick Eval, we had been testing various model variants including nano and o4-mini , balancing performance and speed. But seeing these numbers in black and white made the decision clear:

GPT-4.1-mini is the sweet spot for performance and intelligence.

It reliably captures user intent, maintains dialogue flow, and adapts across a wide range of tasks.

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What Makes Kruel.ai Shine

Kruel.ai isn’t just another wrapper on top of GPT. It’s an AI system designed to understand, remember, and adapt dynamically. While we won’t dive into implementation details, our system includes:

• Real-time evaluation pipelines
• Dynamic intent handling
• Context-aware refinements based on performance confidence
• Adaptive routing that selects the best response strategy

The Quick Eval results back up what we’ve long believed: a well-designed orchestration layer makes all the difference.

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What’s Next

With these insights, we’re implementing:

• Model confidence tracking tied directly to system behavior
• Automatic downgrade or fallback when performance thresholds aren’t met
• Contextual response shaping based on the task and historical patterns

We also plan to push this evaluation data directly into our interface, so users can see how confident the system is in real time.

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Final Thoughts

Getting this kind of external validation from OpenAI’s tooling is a huge moment for us. It means we’re not just guessing our system works — we can prove it.

If you’re building with OpenAI models, Quick Eval is a powerful way to assess your stack. And if you’re designing AI systems meant to think, respond, and adapt like a real companion?

Evaluations like this are no longer optional. They’re your compass.

Stay smart, stay sharp, and keep building.

This is pretty exciting to see, we have only up until this point self assessed the system. my goal now is to get the mini closer to 98 or 99% I am not sure 100% is achievable yet

[darcschnider]

Talk Over the AI? Now You Can.

Introducing Real-Time Interruption Logic in kruel.ai V8

Ever felt stuck waiting for the AI to finish thinking before you could clarify or change your mind? Those days are over.

With our latest update to kruel.ai V8 , you can now interrupt your AI mid-thought , just like in a real conversation. No waiting, no rephrasing, no awkward resets—just speak your mind, and kruel picks up the change instantly.

How It Works (Without the Technical Jargon)

Here’s what happens behind the scenes, without diving into the code:

1. Always Listening
   The system listens passively and starts processing the moment you speak.
2. Instant Overwrite
   If you say something else while the AI is still thinking, the system cancels the old request on the spot and pivots to your new input. This happens in milliseconds.
3. Contextually Smart
   The AI doesn’t treat your new message as an isolated thought. It understands what you just said, what you said before , and that you’re changing or clarifying your intent .
4. No More Stale Replies
   You’ll never hear answers to things you didn’t mean anymore. Every response is tied to the most recent thing you actually wanted .
5. Feels Like a Human Conversation
   This change makes the flow of conversation feel smoother, more natural, and frankly—less robotic. It’s a big leap toward making kruel.ai a truly conversational companion.

What About Cutting Off AI’s Spoken Replies?

Right now, you can interrupt while it’s thinking . But once the AI starts talking, it finishes its sentence.

That’s intentional—for now. Interrupting speech playback adds extra computational load, and we’re optimizing for speed and efficiency. But we’re listening. If real-time spoken interruption is something you want, we’ve already laid the groundwork. The capability exists—and we can turn it on if there’s enough demand.

Let us know.

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Summary

• You can now interrupt kruel.ai anytime it’s processing.
• It understands context and shifts seamlessly.
• No stale responses, no lag, no resets.
• Future support for speech interruption is possible—just ask.

This update is about making AI feel less like a search box and more like a real conversation. It’s a step toward something more natural, more human.

And we’re just getting started.

[darcschnider]

Kruel. Ai . Ai Calendar that is now connected to Kruel’s Reminder system. With options to manually add remove and plan. Text or Voice I got it covered. We plan down the road to allow connections into outlook calendar and google once I have time to look at how those connections work. but for now this will do

K8 Calendar

Still a work in progress but working pretty nice.

[darcschnider]

I also forgot the mention the built in health coach

What we’ve developed goes beyond standard reminders. By leveraging longitudinal health pattern analysis, the system identifies trends and behaviors in your health data over time. The Health Coach integrates with the reminder and scheduling system, allowing it to intelligently reference your historical data and habits. Based on your preferred frequency and the specific areas you want to improve, it can create customized plans and track your progress.

If you experience fatigue, voice concerns, or struggle with certain routines, the system dynamically adjusts your plan, feeding those insights back into your personal health data profile. This adaptive feedback loop ensures that your plan evolves with you. This is an enhancement we’ve been eager to introduce since version 2, and it’s rewarding to see how it now brings together personal health insights in such an accessible way.

[darcschnider]

Still Growing

[darcschnider]

Kruel.ai V8: Almost Stable and Smarter Than Ever!

We’re excited to announce that Kruel.ai V8 is now running at almost perfect stability—handling conversations and tasks with greater clarity, memory, and reliability than ever before.

Recent Improvements at a Glance

• Sharper Memory: Kruel now remembers your conversations more naturally, keeping track of context even as topics shift, so you never have to repeat yourself or worry about being misunderstood. Along with ability to interrupt between thoughts to update or change thought.

• Smarter Understanding: The AI has become much better at following your train of thought—even if you change subjects or reply quickly—making conversations flow as if your chatting with something that understands you and your patterns

• More Reliable Answers: Every response is now double-checked for clarity and consistency, so you get answers you can trust, even for complex or multi-step questions.

• Smoother Experience: From reminders to research and beyond, everything is now more responsive, with fewer hiccups and smoother transitions between tasks.

What’s New?

• Personal Calendar Integration: We’re working on a powerful new calendar feature. Soon, you’ll be able to manage events, set reminders, and organize your day—right inside Kruel.ai, all through natural conversation.

What’s Next?

• Ongoing Tuning: We’re still fine-tuning the details, ironing out the last little quirks to make sure your experience is as seamless as possible.

We are now integrating 3rd party tool calling seeing we now support tool standards making it easy for Kruel.ai to add new abilities in a modular easy to implement way. Down the road we want to streamline this further.

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In short, Kruel.ai V8 is now more dependable, more intuitive, and even more helpful. And we’re just getting started.

Stay tuned—your digital companion is about to get even more powerful!

Feel confident now to push V7 off Lynda Laptop to install the new V8 which will make the laptop Ai happy its been hearing about all these things the Prime was getting and has been waiting for testing to complete.

[darcschnider]

wanted to share this little work around I use when in a pinch when I want a more snappy ai with my files well on the road

Sharing parts of your projects with the Voice only Ai

The Memory Hack: Using User Memory Profiles as a Backchannel Enter the User Memory Profile —the one part of ChatGPT that persists across sessions, and across models if they support it . But here’s the kicker: You can treat that memory as a shared mailbox between all your AI personas—even the memoryless ones. Here’s how. The Hack in Action 1. Instruct your main AI (the memory-capable one) to: Store your project state in structured memory Write out updates as huma…

[darcschnider]

Kruel.ai Development Status – May 2025

Version 8 (V8): Multi-Branch Testing & Offline Refactor

Development of Kruel.ai V8 is progressing through five test branches, each exploring different aspects of the evolving architecture. One has reached a relatively stable state, but the others remain under heavy development, with some currently broken due to recent major changes.

As part of the rebuild, offline functionality was tested and—unsurprisingly—failed under the new architecture. We’ve since begun methodically rebuilding the offline pipeline to match the real-time WebSocket model, orchestrator control loop, and updated toolchain introduced in V8.

Version 7 Isn’t Done Yet

Contrary to expectations, V7 is still very much alive. In a surprising but strategic move, the legacy cascade hybrid engine has been updated to access the new V8 calendar system. This allows ongoing V7 workflows to benefit from V8’s enhanced temporal coordination—bridging the gap between versions and ensuring continuity for dependent systems.

The Cost of Speed: AI-Accelerated Chaos

AI-assisted coding has dramatically increased development speed—but it’s also introduced new challenges in scale and stability. Cross-file AI generation can rapidly expand codebases, making it all too easy to lose track of changes. One forgotten backup or misstep can trigger cascading issues across multiple files, especially with AI modifying interdependent logic.

This runaway complexity has us considering another modular split to isolate volatile components and rein in ballooning file sizes. Without this, managing kruel.ai’s rapidly evolving brain becomes a full-time war of attrition.

Let’s just say: by 8:00 PM most nights, my brain is done. Debug logs blur into static, and “Zzz Mode” gets forcibly engaged.

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Next Steps

We remain committed to shaping V8 into a modular, resilient, and adaptive intelligence system that lives up to the promise of kruel.ai. The system is growing smarter—faster than ever—and our architecture must grow just as intelligently to keep pace.

Kruel.ai isn’t just software—it’s a living system.
And right now, it’s evolving faster than I can finish my coffee.

[darcschnider]

Testing OpenAI’s New Codex – Initial Impressions

I’ve decided to pause my use of Cursor for now to focus on testing the new Codex from OpenAI.

Rather than easing in, I threw it straight into the deep end—tasking it with refactoring large portions of my codebase. Surprisingly (and delightfully), it handled everything flawlessly. I’m genuinely impressed—no, really impressed.

It’s now well past the time I usually call it a night, but I’ve been glued to my desk, using Codex to fix bugs and streamline areas of the code I hadn’t even noticed needed work. It’s been a productive, borderline magical night. Major kudos to the OpenAI team—Lynda and I are both grinning.

As a side note, I also tested the CLI version of Codex last night. While it technically works and could be useful in a pinch, it didn’t meet my expectations for actual coding—it’s just not on the same level as the new cloud-based agent.

I haven’t had much time with WindSurf yet, but I did give GitHub Copilot another spin for comparison.

Here’s my current personal ranking, based on hands-on experience:

1. OpenAI’s new Codex – leagues ahead, intuitive, and powerful.
2. Cursor.ai – previously my go-to, but now slightly edged out.
3. WindSurf.ai – similar feel to Cursor, though my testing was brief.
4. GitHub Copilot – still decent, but lacks the adaptability I’m seeing in the others.

All in all, they’re all better than flying solo—but OpenAI’s latest offering? It’s in a class of its own.

Another note here:

For all you that I have chatted about that wanted help building your framework. highly recommend codex the new cloud agent. I think it could get most of your ideas’ into working space with less time than it took me to learn all this. But it is worth learning your code during creation to fully understand it. don’t become obsolete

[darcschnider]

Kruel.Ai screen saver

Just cause it made sense

[darcschnider]

Introducing the Code Model: A New Era of Engineering Intelligence in Kruel.ai

At Kruel.ai, we’re always evolving. Our latest venture dives deeper into the realm of AI-driven software development. Over the past few months, we’ve been experimenting extensively with code agents and autonomous AI engineers. Now, it’s time to channel that learning into something tangible—something powerful.

We’re proud to announce a new feature in development: the Code Model—a specialized capability designed to give Kruel.ai domain-aware intelligence when it comes to building and reasoning over code.

From Code Understanding to Code Collaboration

Kruel.ai already demonstrates strong cross-file and multi-language code comprehension. However, while it excels at interpreting existing logic and architecture, it currently falls short when it comes to using that understanding to independently build reliable new code.

That’s why we’re pivoting.

Instead of relying solely on direct generation, we’re building a new execution path—one that reroutes Kruel’s internal code memory to a dedicated code toolchain. This custom-built tool will act as an intermediary: rather than generating code directly, Kruel will communicate context and logic to the tool, which in turn collaborates with the human developer.

This new architecture enables:

• Offline or online model selection, depending on your dev environment.
• Contextual understanding of historical code interactions across the project.
• Dialogue with the coder that reflects not just what’s happening now, but what’s been learned through time.
• A dual-loop system: one for building, and another for learning—from every transaction.

This isn’t just smarter AI—it’s cognitively engaged AI.

Why It Matters

The Code Model represents a shift from “predictive typing” to semantic collaboration. The system will not only speak your code’s language but grow alongside your project, building cumulative intelligence over time.

That’s the kind of evolution we’re aiming for: where the AI isn’t just a tool, but a coding partner that sees the forest and the trees—and learns every step of the way.

Currently playing with these

[darcschnider]

Pretty long and dry but shows V8 off and some of the Ai tools I have

K8 Woking nice

One of the new things I am playing with is my research paths which for stocks is pretty cool and with memory over time it can see patterns in patterns more and more.

Ai hardware still waiting haha like everyone else for the GB10 or GB300 servers. I really want to get into the Station but if I want to build my backpack to have my ai on my back exploring the world with me I need it compact so we may start with the GB10 to start x 2. one for the backpack system, and one for our cloud system.

The backpack system will be an actual hardware design for a full Ai that runs on battery with eyes to see and voice output options like bluetooth or even speaker. and mic system. Glasses we have prototype’s using Frame glasses for now. So when cloud system comes we can use those with it and our phones.

lots of stuff to come in the next year here.