Jev Architecture Masterclass: Easy to God Mode

1) Easy Mode: what Jev actually is - Definition: a classification / decision model. You give it input + a question. It returns probabilities (yes 96% / no 4%), not an essay. You need two building blocks: - Inputs. The thing you're judging (an ad creative, a resume, a page state, pasted invoice text). - Checkable questions. Dozens if you want. Fire them in parallel. Get instant yes/no (with confidence) back. - Why it matters: ~20–200x faster and ~40–400x cheaper than making an LLM write you a paragraph for every judgment. Use case: Meta Ads Analyzer. For every ad, Jev runs a question pack (does the hook call out the audience? quantified result in the first sentence?) and turns those probabilities into a dashboard you can act on. 2) Hard Mode: detailed scoring across a pile of items Definition: stop asking one model for a vibe summary. Ask many precise questions across hundreds of items, then bucket the results. Rules of thumb: - Write questions like hiring criteria or ad QA, not vibes. "Has the candidate shipped and maintained a user-facing TypeScript/React app?" - Weight what matters. Not every must-have is equal. - Sort into clear buckets (Shortlist / Review / Decline) so humans only spend time on the middle. Use case: An AI recruiter that sifts hundreds of applicants in milliseconds against your must-haves. 3) God Mode: real-time products that didn't make sense before Definition: Jev as System 1 (fast, intuitive) next to LLMs as System 2 (slow, deep reasoning). Speed + cost unlocks products that were too slow or expensive to build. What to use Jev for: - Lots of data that needs detailed checks → classification loop. - Must react while the user is still speaking or pasting → hit Jev on every word / state update. - Combine them. LLM for deep reasoning. Jev for the snap decisions inside the loop. Use case: voice-controlled browser (transcript + page state → click/scroll probabilities) and smart paste (invoice text maps into the right expense fields as you paste).