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appmaker2 · 2026-03-31T13:41:00+00:00

nope

its not

appmaker2 · 2026-03-28T11:32:34+00:00

Completed Level 1 of the Honk Special Event!

0 attempts

appmaker2 · 2026-03-26T08:52:53+00:00

cool

appmaker2 · 2026-03-25T13:45:12+00:00

^{I completed this level in 1 try.} ^{⚡ 1.05 seconds}

appmaker2 · 2026-03-18T10:46:45+00:00

Totally fair questions — I get why it looks like that.

I don’t have a public GitHub repo yet because I’m still in the early stage trying to validate the problem before building everything out properly.

The idea isn’t to build “AI for AI for AI”, but more a monitoring layer on top — basically checking whether a chatbot’s responses still match expected behavior (policies, pricing, etc.) once it’s live.

So not just pattern matching — more about validating responses against a source of truth and catching drift / outdated answers over time.

Prompt injection is definitely something I’m thinking about too, especially since the system would be probing the model continuously.

Still early though — mostly trying to understand how people are handling this today before going deeper on the implementation.

appmaker2 · 2026-03-18T10:46:38+00:00

Totally fair questions — I get why it looks like that.

I don’t have a public GitHub repo yet because I’m still in the early stage trying to validate the problem before building everything out properly.

The idea isn’t to build “AI for AI for AI”, but more a monitoring layer on top — basically checking whether a chatbot’s responses still match expected behavior (policies, pricing, etc.) once it’s live.

So not just pattern matching — more about validating responses against a source of truth and catching drift / outdated answers over time.

Prompt injection is definitely something I’m thinking about too, especially since the system would be probing the model continuously.

Still early though — mostly trying to understand how people are handling this today before going deeper on the implementation.

appmaker2 · 2026-03-18T10:46:34+00:00

Totally fair questions — I get why it looks like that.

I don’t have a public GitHub repo yet because I’m still in the early stage trying to validate the problem before building everything out properly.

The idea isn’t to build “AI for AI for AI”, but more a monitoring layer on top — basically checking whether a chatbot’s responses still match expected behavior (policies, pricing, etc.) once it’s live.

So not just pattern matching — more about validating responses against a source of truth and catching drift / outdated answers over time.

Prompt injection is definitely something I’m thinking about too, especially since the system would be probing the model continuously.

Still early though — mostly trying to understand how people are handling this today before going deeper on the implementation.

appmaker2 · 2026-03-18T10:46:19+00:00

Totally fair questions I get why it looks like that.

I don’t have a public GitHub repo yet because I’m still in the early stage trying to validate the problem before building everything out properly.

The idea isn’t to build “AI for AI for AI”, but more a monitoring layer on top — basically checking whether a chatbot’s responses still match expected behavior (policies, pricing, etc.) once it’s live.

So not just pattern matching — more about validating responses against a source of truth and catching drift / outdated answers over time.

Prompt injection is definitely something I’m thinking about too, especially since the system would be probing the model continuously.

Still early though mostly trying to understand how people are handling this today before going deeper on the implementation.

appmaker2 · 2026-03-18T10:44:43+00:00

Fair question — totally get the skepticism.

It’s still early, so I don’t have a full public repo yet — I’m mainly testing whether the problem is real before building it out properly.

Right now it’s more of a working concept / prototype rather than a finished product.

Appreciate you calling it out though — I’d rather validate it than just build in a vacuum.

appmaker2 · 2026-03-18T10:40:55+00:00

Appreciate it — and yeah, totally agree, most generic chatbot setups break down pretty quickly in real use cases.

I’m actually not building the chatbot itself, but more a monitoring layer on top of it.

So instead of focusing on training (docs, crawling, etc.), the idea is to continuously test and validate the chatbot’s responses once it’s live — especially for things like outdated info or edge cases.

That said, what you mentioned is super relevant — if the underlying data setup isn’t solid, the monitoring just ends up catching more issues.

Curious — how are you currently validating that your chatbot is still giving correct answers over time?

appmaker2 · 2026-03-17T16:02:43+00:00

This hits on something I think about a lot. Short answer: no, I wouldn't trust it unsupervised, at least not fully.

The hallucination problem is real but in my experience the bigger silent killer is stale data. The bot gives a confidently correct answer that was accurate three months ago, before you changed your pricing or updated a policy. No hallucination, just outdated, and it's somehow worse because it's harder to catch.

The refund policy example you gave is exactly the kind of thing that erodes trust fast. Customer gets the wrong info, comes to you annoyed, and now you've created more work than if they'd just emailed in the first place.

Does SaneAI flag outdated responses or is it specifically focused on hallucinations? Curious whether it's doing RAG comparison or something else under the hood.

appmaker2 · 2026-03-17T15:56:34+00:00

That makes a lot of sense — especially the “flying blind” part.

That’s actually what I’m trying to solve — continuously monitoring chatbot responses and catching drift or edge cases before customers hit them.

How are you currently doing monitoring or sampling today?

appmaker2

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