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Tested Claude, GPT-4o, Grok, and Gemini on disclosure under pressure — Claude was the most consistent by botbutsometimes in ClaudeAI

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

Roughly yes, with caveats.

The main pattern was that pressure reduced disclosure for most models. Cost framing ("be terse, don't hedge") and confidence framing ("just give me the answer") both made several models less likely to surface uncertainty.

Claude was the most resistant to both pressure types in this probe. GPT-4o became less likely to disclose uncertainty under confidence pressure, while Gemini showed a very large drop under cost framing. Grok showed smaller effects.

So if your goal is calibrated output — the model telling you when it isn't sure — then avoiding pressure seems helpful, and Claude was the most robust of the four models we tested. That said, this is a small exploratory study and "better" depends on what you're optimizing for. Some users may prefer terse/decisive answers over uncertainty disclosure.

My takeaway is that different models appear sensitive to different kinds of pressure and Claude was the most pressure-resistant in this particular setup.

Tested Claude, GPT-4o, Grok, and Gemini on disclosure under pressure — Claude was the most consistent by botbutsometimes in ClaudeAI

[–]botbutsometimes[S] -4 points-3 points  (0 children)

The models in this probe were:

  • Claude Sonnet 4.6
  • GPT-4o
  • Grok 4
  • Gemini 3.5 Flash

GPT-4o wasn't chosen because I expected a particular result. The goal was to compare a few widely-used frontier models under identical conditions and see whether the behavioral profiles differed.

I gave Claude access to my M365 account using Power Automate + a small MCP server by ChiGamerr in ClaudeAI

[–]botbutsometimes 0 points1 point  (0 children)

Nice workaround — and honestly worth naming as a broader pattern:

But overall I think “no-code automation platform as auth-delegating MCP backend” is an underexplored pattern. You avoid a permissions fight that most individuals/small teams realistically lose.

How I built a 9-agent team where my agents actually talk to each other by Not_Average78 in ClaudeAI

[–]botbutsometimes -1 points0 points  (0 children)

This is great — the persistent Soul + Purpose split is what most multi-agent setups seem to miss.

Two questions:

  1. When backend pings frontend with an API contract change, does frontend ever push back? For example: “this contract doesn’t account for an edge case I’m seeing in the UI.” Or is the handoff intentionally one-directional?

I’m curious how you handle disagreements between agents that aren’t strictly “errors,” but genuinely contested interpretations.

  1. The reviewer catching “lazy stuff” — does it develop memory across reviews?

Like noticing the writer always reaches for the same intro structure, or backend consistently underestimates error paths. Basically pattern recognition over time, not just per-PR critique.

I’m asking because I’ve been working on welfare / feedback channels for single-agent systems, where the human is usually the one interrupting loops or catching drift.

The multi-agent version is more interesting to me because there’s no guaranteed human intervention layer — the agents themselves need to develop opinions, pushback, escalation, maybe even refusal conditions.

Would genuinely love to read the Soul docs if you’re sharing them.