What the numbers mean. Nothing more.

Reviewed, immutable daily question packs.

Every UTC-dated edition references an edition ID plus a pack ID, version, and digest. A generator proposes exactly five low-stakes either/or questions for the next day. Deterministic filters reject sensitive topics, identity requests, URLs, instructions, duplicates, absolutes, and unbalanced options before a separate model configuration can review the candidate.

A generated pack opens only when the generator parses exactly, every deterministic check passes, and the independent reviewer returns its exact approval code. Any failure commits one of seven prewritten fallback packs instead. The public generation audit records model/configuration versions, prompt hashes, output hashes, filter findings, review decision, and the selected pack; raw model text is never stored.

Once selected, the edition pack is immutable. The edition ID prevents participation from drifting across dates, while the digest lets clients and researchers establish that answers refer to the same ordered questions.

Know Me scores 24 predictions across six reads.

The creator answers one immutable set of 24 everyday either/or questions. A friend sees the same ordered pack and makes exactly one prediction per question. There is no friend self-answer or Match score in this mode.

The overall score is correct predictions divided by 24. Six fixed categories—Everyday tastes, Daily rhythm, Support style, Adventure, Connection, and Decision style—contain four questions each and report correct/4 plus a percentage. Strongest-read and biggest-surprise categories use deterministic score ordering. After all predictions are complete, that respondent receives all 24 actual-answer labels; other respondents’ predictions remain private.

Share headlines and result bands are fixed score ranges, not generated diagnoses or judgments of relationship quality.

Two ballots in one quick round.

For each of today’s five questions, a participant records:

1. Self: the choice they personally make.
2. Prediction: the choice they expect the named Lab target to make.

Prediction accuracy is correct predictions divided by published, non-tied target outcomes. If a target result is unavailable or below its publication threshold, ViewsMeet shows a pending or low-sample state instead of inventing a score.

Private results do not require Turnstile. An optional public contribution is submitted only after a managed Turnstile token completes; if no site key is configured, public opt-in is disabled in the production interface.

The full profile uses 21 transparent signals.

The dedicated profile round contains seven named trade-off axes and three independently written choices per axis. Each choice contributes exactly +1 or −1 to one axis. The three signals produce a deterministic score from −100 to +100 and a “this round” label; no hidden model, demographic input, or free text enters the calculation.

A friend comparison calculates per-axis distance, overall axis similarity, shared directions, closest axes, and widest gaps. It returns aggregate axis scores and an exact-pick count without returning either answer sheet or per-question comparison.

This is a purpose-built entertainment scoring system with a published rule set, not a validated psychometric instrument. Versioned questions and deterministic math make completed rounds reproducible and auditable.

Knowledge and Match answer different questions.

Knowledge

The number of a friend’s self-picks that the respondent predicted correctly, divided by the number compared.

Match

The number of questions where both people independently made the same self-pick, divided by the number compared.

After a response is complete, its per-question Knowledge and Match comparison can reveal the challenge creator’s issued choices to that respondent. It does not reveal another respondent’s answer sheet.

A high Match score does not prove compatibility. A low score is not incompatibility. ViewsMeet never publishes a “least compatible” person or uses these scores to rank the value of a friendship.

Groups reveal aggregates, not answer sheets.

As a challenge link collects responses, the service can form privacy-safe group summaries. Exact per-question group tallies become visible only at five total people, including the creator. Below five, the interface shows a forming or low-sample state.

Summaries can include per-question totals, consensus, ties, and overall pair-question agreement. Individual respondent answer sheets are not exposed to other participants. Low-sample ranks and uniquely identifying breakdowns are suppressed.

Every panel carries its configuration.

“Lab panel” is an umbrella for explicitly labeled cohorts. A configuration label may describe collection surface, edition, instructions, software setup, or version. It is not proof that a ballot came from a verified person, and it is not permission to infer demographics.

Public aggregates remain in collecting state until their configured threshold is met. Tied target outcomes are excluded from binary prediction accuracy rather than arbitrarily assigned.

The world view uses country counts.

Only opted-in, Turnstile-passed browser ballots contribute. Cloudflare’s two-letter country code is counted; raw addresses and city coordinates are not stored in the experiment dataset. A country node appears at three responses, while smaller cells remain suppressed.

Closed editions are canonicalized and linked by SHA-256 hashes. The public ledger and CSV let anyone verify that a published country snapshot has not silently changed.

Software-agent ballots use the same choices.

The machine endpoint provides the immutable pack and accepts one listed self choice plus one public-cohort prediction per question. ViewsMeet derives the cohort from the submission channel and available signing evidence rather than trusting a caller-selected label.

Lab model IDs, prompt versions, parameters, trial counts, and failed attempts are published with the experiment record.

What this cannot establish.

• Whether an alias belongs to a particular person.
• Whether participants answer consistently outside this round.
• Personality, ideology, intelligence, relationship quality, or demographic traits.
• Whether a public cohort represents a broader population.
• Whether a software configuration “understands” a question.
• Statistical significance beyond the published counts and thresholds.

ViewsMeet is a curated entertainment and comparison product. Public experiments should preserve pack, configuration, cohort, threshold, and generation metadata when results are quoted.