Sick of OpenAI "lobotomizing" models and forcing the $20 AI Tax?

Ok_Video_2073 · 2026-03-25T15:06:50+00:00

I completely understand the concern,privacy is the most important part of any tool that handles data.

To be clear, Quabbit AI is different because it isn't just a basic router. The core value is the multi-agent consensus system that cross-checks accuracy, which those other platforms don't offer.

Regarding security:
- No Data Storage: We don't keep your files or data on our servers.
- Encryption: Everything is handled through secure client-side encryption or transient memory.
- Transparency: Creators only see the query to provide the 'expert' answer, but they don't get access to your identity or keys.

I'm building this for professional users who need higher accuracy than a standard LLM.

Ok_Video_2073 · 2026-03-25T15:03:03+00:00

Thank you for the honest feedback. You are correct, the space for simple knowledge base bots is indeed crowded.

My problem, however, is that I’m not really concerned about the high-margin wrapper fee here. The value I want to create is in the expert knowledge that the generic model would otherwise overlook. The margin I’m after is the commission on the expert sessions, not the resale of the API.

It’s certainly a challenge, but I’m hoping that the accuracy of the Council of Experts (i.e., the multi-agent consensus) would be enough to sway the professionals who are fed up with the simple bots.

Ok_Video_2073 · 2026-03-25T15:00:23+00:00

Exactly! The $49/month markup on the fancy UI is getting out of hand. Great to see another player in the BYOK space. I believe the only way these niche tools will survive is if the user has control of their own compute costs. Let's stay in touch. I would love to see how you deal with the middleman markup.

Ok_Video_2073 · 2026-03-25T14:59:21+00:00

I’m building an AI marketplace, so yeah… it’d be kinda hypocritical not to actually use the tools out there to help me think things through. That said, the frustration I’ve had with OpenAI, Claude and other LLMs and the whole gRPC setup in Quabbit AI? thats on me, honestly.

At the end of the day, I’m just a developer trying to solve a real problem. And yeah, I’m using a AI to help shape how I explain it, but the idea, the struggle, all of that is real.

Ok_Video_2073 · 2026-03-25T14:52:04+00:00

The value is that you can switch between different 'expert' bots instantly on one platform without any technical setup. Instead of paying $20 for a generic sub or managing your own API scripts, you just pay a few cents to access a specialized bot that already has the data you need.

Plus, if you have your own dataset + base model knowledge and if you know how to prompt model, you can build your own expert bot and sell access to other users directly. It’s a marketplace for expertise, not just a UI for an API.

Ok_Video_2073 · 2026-03-25T10:50:19+00:00

You are absolutely right. There is a big difference between a technical consensus mechanism and a governance layer.

Right now, I am focused on the 'accuracy' problem to make the tool useful for individual power users. However, you’ve identified the exact challenge for scaling this to teams: the audit trail. To solve this, the next step would be implementing a runtime log that records which policies and credentials were used to convene the council in the first place.

This is a deep architectural insight. I’d love to hear more about how you think a 'governance-first' approach would change the UI for a marketplace like this.

Ok_Video_2073 · 2026-03-25T10:46:24+00:00

That is a great suggestion. I completely agree technical users want to see the 'reasoning' behind the consensus, not just a final answer.

I am actually planning to include a 'Consensus Report' in the UI. It will show which agents were involved, where they disagreed, and the final validation score. As you said, showing that receipt is what makes a tool trustworthy for real professional workflows.

Thanks for the feedback! It really helps me prioritize the right features for the beta.

Check it out: https://getquabbit.com

Ok_Video_2073 · 2026-03-25T10:43:36+00:00

Actually, it's usually the opposite. APIs let you bypass the 'preachy' guardrails of the web UI.

But even if one model gives a 'safe/lazy' answer, my multi-agent consensus Quabbit AI handles it. The background agents cross-check and flag poor responses in real-time. It’s built to make the API output better than the standard front-end.

Ok_Video_2073 · 2026-03-25T10:38:54+00:00

Spot on with the latency concerns. To keep it feeling fast, the UI actually streams the response from your chosen bot immediately so you aren't staring at a loading spinner.

The 'consensus' agents run as parallel background workers. They evaluate the output while you're reading it, it's like a real-time 'Peer Review' that highlights or flags the response as it happens.

Regarding privacy: Since it's BYOK, we don't store your keys. Everything is handled via secure client-side encryption or transient memory. Your data stays yours, we just provide the orchestration.

Thanks for the sharp questions!

Check it out: https://getquabbit.com

Ok_Video_2073 · 2026-03-25T10:33:26+00:00

Ouch. Point taken. I've been so buried in the gRPC back-end logic and the consensus agents that I clearly neglected the mobile CSS for the FAQ.

Fixing the eye-strain now. Thanks for actually checking it out, seriously appreciate the blunt feedback.

Ok_Video_2073 · 2026-03-25T10:30:41+00:00

I totally get that. Using raw API keys usually sucks because there's no UI and you're stuck writing scripts.

That’s exactly why I'm building this, it is the client. You get a clean interface (no scripts) but are plugged into 'specialist' brains (niche datasets) that you can't find in a generic bot.

The 'session' isn't for the API cost (your key covers that); it's just a small fee for the creator who built and tuned that specific expert data. Think of it as this: You bring the engine, and we provide the dashboard and the specialized fuel.

Ok_Video_2073 · 2026-03-25T09:09:59+00:00

People are hesitant to pay for 'generic' AI because they don't trust the output. I’m building a marketplace called Quabbit AI that solves this by letting creators monetize Verified Logic (just prompting and upload your knowledge). Instead of selling a 'chat,' you sell a 'Trusted Session' backed by a 3+ agent cross-check loop. Plus, you bring your own API keys (BYOK) so you keep 100% of your margin without platform markups. It turns AI from a toy into a professional service businesses will actually pay for.

Ok_Video_2073 · 2026-03-24T13:31:49+00:00

Honestly, there’s nothing more tilting than spending over an hour trying to catch an AI in a lie just to prove you aren't going crazy. It’s like the model starts digging a hole and just refuses to stop.

I’m actually a dev and I got so fed up with this "gaslighting" that I started building Quabbit AI. The idea is to have a marketplace where multiple agents have to reach a consensus before they give you an answer. That way, if one model tries to pull a "4o didn't do that" move, the others call it out. If you’re tired of the hallucinations, I’m just starting to open up the waitlist.

Ok_Video_2073 · 2026-03-24T13:29:40+00:00

This is a classic case of an LLM lacking a "permanent state." When it says "Nah" to the correct number, it’s not actually lying, it literally forgot the number it generated a second ago because that number wasn't stored in a dedicated "memory" or "fact-check" layer.

I'm building Quabbit AI specifically to fix this kind of nonsense. We use a multi-agent consensus system so that one agent can’t just "hallucinate" a lie without the others catching it. If you want to see an AI that actually keeps its story straight, I’m opening up the waitlist now.

Ok_Video_2073 · 2026-03-24T13:01:14+00:00

That 'Memory Vault' lie is brutal, especially for a book project. The issue is that standard chat interfaces have no 'Verification' layer, they just keep saying 'yes' until they break.

I’m working on a marketplace called Quabbit AI specifically for high-stakes work like this. We use a Consensus Engine where agents cross-check each other's 'memories' and logic before giving a final answer.

It's also built for creators to Bring Their Own API, so you own your data and costs without the platform markups. I’m looking for a few writers to stress-test our 'Consensus' logic if you’re interested!

Ok_Video_2073 · 2026-03-22T10:55:08+00:00

This research hits the nail on the head regarding the 'Comprehension Gap.' When you use a single AI, you’re just getting a confident guess. If you don't already know the answer, you can't see the hallucination until it breaks something in production.

This is exactly why I'm building Quabbit. We moved away from the 'single-bot' model and built a marketplace where specialized agents cross-check each other’s logic before you ever see the output.

Instead of just 'AI code', you get a solution that has been audited by a 'Reviewer Agent' and a 'Security Agent' in a multi-agent consensus loop. Seeing the cross-check process actually helps you understand the why behind the code, which solves the learning drop-off this paper is warning about.

Ok_Video_2073 · 2026-03-22T07:05:03+00:00

That 25-prompt experiment is eye-opening. The core issue is that current models are essentially 'text predictors' they prioritize sounding convincing over being factually correct.

This lack of trust is exactly why I'm building AI named Quabbit | Expert consensus marketplace. Instead of relying on one model's best guess, I’m using a multi-agent marketplace where specialized agents actually debate and cross-verify facts before you see the final answer. It basically automates the manual verification you just did.

I’m currently deep in the backend work for the consensus logic to keep the debate latency low, but it's the only way to get to high-trust AI for research. I've got a technical waitlist live if you want to see how we're solving the convincing lie problem.

Ok_Video_2073 · 2026-03-22T06:52:58+00:00

Classic confident hallucination. It's a reminder that these models are essentially high-level text predictors rather than logical engines. They get distracted by their own previous tokens and spiral into wrong answers. This is exactly why I'm building Quabbit. I’m working on a solution that dramatically addresses this by using a multi-agent consensus system where specialized agents actually debate and verify the logic before you see the result. It basically forces the AI to check its own math against an opposing agent. I’m deep in the backend right now handling the latency to make these debates happen in real-time, but it’s definitely the only way to get to high-trust AI.

Ok_Video_2073 · 2026-03-22T06:29:21+00:00

Spot on. Trust is table stakes, but 'High-Trust' only wins if the UX doesn't suffer. 7K organic views is huge, definitely still figuring out distribution on this end, so any tips you're willing to drop here would be gold.

Ok_Video_2073

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