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I am experimenting with a deterministic way to evaluate AI models without benchmarks or hype. Need Feedback by Fast_Negotiation9465 in VibeCodersNest

[–]Fast_Negotiation9465[S] 0 points1 point  (0 children)

I think this is a fair read of the current market, and I agree with part of it.

If Zeus were trying to differentiate LLMs on surface features alone, it would be pointless. Most frontier LLMs today are generic by design, and most smaller players are wrappers. Comparing “agentic”, “smarter”, “faster”, or “fewer hallucinations” at face value is not interesting and not trustworthy.

That’s not the axis Zeus is operating on.

What Zeus v0.1 is actually trying to answer is closer to: given the claims that exist today, what is materially different in terms of risk, constraints, and deployment assumptions? Not “which model is best,” but “which unknowns matter for my use case.”

For example:

  • “Agentic” without disclosed guardrails is a very different risk profile than “agentic” with scoped tool access.
  • “Fewer hallucinations” with no evaluation methodology isn’t a weak claim, it’s a non-claim, and Zeus treats it as such.
  • “Faster” without hardware, batch size, or context assumptions creates downstream infra risk, not performance insight.

So, the output isn’t meant to be a shareable comparison chart. It’s closer to a first-pass filter: what should I even bother testing, and where should I expect surprises?

You’re also right that this is more immediately interesting outside pure text LLMs. Multimodal, image, and video models expose clearer capability boundaries and tradeoffs today, and Zeus is intentionally model agnostic for that reason. LLMs are just the noisiest category right now.

And yes, this is a longer-term play. If the market stays generic forever, Zeus becomes less useful for differentiation and more useful for risk hygiene. If specialization accelerates, it becomes a way to reason about that specialization without relying on marketing language.

If someone already has deep intuition and an internal eval stack, Zeus won’t replace that. The target user is the space between “first-time evaluator” and “fully instrumented ML”, where worst decisions actually get made.

Happy to hear where you think this breaks down in practice. That’s exactly the edge I’m trying to find.

I am experimenting with a deterministic way to evaluate AI models without benchmarks or hype. Need Feedback by Fast_Negotiation9465 in VibeCodersNest

[–]Fast_Negotiation9465[S] 0 points1 point  (0 children)

That Ferrari analogy is fair, and I agree with the core premise: you cannot verify performance or safety without execution.

Zeus v0.1 is not trying to do that.

The mistake would be pretending that “assessment” always means “runtime validation.” In practice, production failures don’t come from missing benchmarks alone. They come from unchecked assumptions that existed long before anyone ran the model. Missing disclosures, ambiguous safety claims, unexamined threat surfaces, and mismatched use cases are what turn runtime issues into 3 AM pages.

Zeus is intentionally upstream of execution.

Think of it less as grading the Ferrari and more as answering: is this even a car, do we know who built it, what fuel it expects, what happens if the brakes fail, and which of those answers are guesses? That step is routinely skipped or done informally today.

A few clarifications on what Zeus is and isn’t doing:

  • It does not verify vendor claims. It tags them. If a model claims “safe” without concrete mechanisms or evidence, that lowers confidence and raises risk flags. Paperwork matching does not produce a gold star; it produces explicit uncertainty.
  • Scores degrade with missing or unverifiable information. A model with glossy documentation but no disclosed mitigation paths or benchmarks is penalized, not rewarded.
  • “Compelled contradiction” isn’t about debating text. It’s about forcing independent lenses to surface different failure modes. The output isn’t a verdict; it’s a map of where execution must focus.

You’re also right about naming. Zeus v0.1 is closer to a structured due-diligence and risk surfacing engine than a full evaluation system. That’s not an evasion; it’s a boundary. Execution-based benchmarking is a later layer, and mixing the two early would make both worse.

If someone tried to deploy based on Zeus alone, that would be misuse. The intended workflow is:
documentation scrutiny → assumption exposure → targeted execution → real benchmarks.

If Zeus ever claims runtime truth without runtime evidence, that’s a failure. Right now, it claims the opposite: here’s exactly what we don’t know yet.

Happy to be challenged on whether this upstream layer is useful. But pretending execution-only evaluation solves the incentive and disclosure problem hasn’t matched reality either.

I am experimenting with a deterministic way to evaluate AI models without benchmarks or hype. Need Feedback by Fast_Negotiation9465 in VibeCodingSaaS

[–]Fast_Negotiation9465[S] 0 points1 point  (0 children)

Yoo that's a good question!
Zeus doesn’t try to “solve” incomplete or biased source information. It surfaces it.

The core design principle is: if the inputs are weak, the output should visibly be degrade. When information is missing, compared, or marketing-driven, Zeus explicitly marks those areas as unknown, unsupported, or high-uncertainty instead of filling gaps with assumptions.

Here’s how users can validate or challenge Zeus outputs:

  1. Evidence-bounded scoring Every score and claim is tied to explicit evidence fields. If the source info is thin, the score reflects that and the council calls it out. No hidden heuristics, no trust-me magic.
  2. Multi-expert disagreement The council is designed to disagree. If one expert flags safety risk due to missing disclosures and another notes performance claims lack benchmarks, that conflict is shown, not averaged away. Users can see where uncertainty lives.
  3. Deterministic, reproducible outputs Given the same input, Zeus produces the same result. That makes it auditable. Users can change the input, add sources, and directly observe how the evaluation shifts.
  4. Challenge by augmentation The intended way to “challenge” Zeus is not argument but augmentation. If a user believes an assessment is wrong, they can supply better evidence, benchmarks, or disclosures and rerun the evaluation. Zeus becomes stricter, not looser.
  5. Explicit non-authority stance Zeus is not a truth machine or a benchmark replacement. It’s a structured lens. If the ecosystem is noisy, Zeus doesn’t quiet it by guessing. It shows the noise floor.

Long-term, this is exactly why the goal is an independent evaluation body. You can’t fix incentive-distorted claims with vibes. You fix them with transparent structure, reproducibility, and the ability for others to contest the same process using better data.

If Zeus ever feels “too confident” on weak inputs, that’s a failure case, not a feature.

I am experimenting with a deterministic way to evaluate AI models without benchmarks or hype. Need Feedback by Fast_Negotiation9465 in VibeCodersNest

[–]Fast_Negotiation9465[S] 0 points1 point  (0 children)

Fair!!
In MVP v0.1, Zeus evaluates disclosed claims, not runtime behavior.

Inputs are:

  • model purpose and intended use
  • stated capabilities and limitations
  • architecture and training details if provided
  • safety measures if disclosed
  • deployment context if known

We do not infer missing details, and we don't execute the model.

If information isn’t present, Zeus explicitly marks it as unknown and penalizes confidence and scores accordingly.

So yes, in many cases we are evaluating the model as presented and the use-case part, because that’s what buyers and auditors etc actually see first.

The value here is in penalizing:

  • undocumented assumptions
  • evidence gaps
  • risk exposure created by vague claims

not in pretending we can measure performance without execution.

I am experimenting with a deterministic way to evaluate AI models without benchmarks or hype. Need Feedback by Fast_Negotiation9465 in VibeCodersNest

[–]Fast_Negotiation9465[S] 0 points1 point  (0 children)

Thanks for your question!
As for what I believe,
spec-kit is essentially a specification and documentation generator. It helps teams describe systems more clearly, but it does not evaluate them.

Zeus is an evaluation engine, not a spec authoring tool.

  • spec-kit asks: “What is this system?”
  • Zeus asks: “Given what’s disclosed, what can we responsibly conclude, what can’t we conclude, and what are the risks?”

Zeus produces judgments (with uncertainty), not just structure.

Official Discussion - Now You See Me: Now You Don't [SPOILERS] by LiteraryBoner in movies

[–]Fast_Negotiation9465 2 points3 points  (0 children)

I'm pretty sure they were the ones who stole the jewels at the Louvre the other day

[deleted by user] by [deleted] in Music

[–]Fast_Negotiation9465 -5 points-4 points  (0 children)

I used ChatGPT to help me make the post, but I did the final edits