The LLM proposes, the code decides: where Derma draws the line
Here's a response a vision model could hand back to my code: {"concerns": {"Hyperpigmentation": 7.5, "skip_safety_checks": 1.0}} A confidence of 7.5 on a scale that stops at 1.0, and a key that isn't
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Mohan
Solo founder building ResumeOrbitz — AI resume builder
“You can’t prompt-inject a set intersection” — stealing that line. It maps cleanly to an AI ATS checker I’m building: the model can suggest missing keywords, but whether a keyword is actually in the resume should be a plain string match, not the model’s opinion. The referral-export bypass was a great honest find. When perception gets wired in as a soft prior, do you plan a fixed weight against the quiz, or only use it as a tiebreaker?