Who are u
Executive summary
The phrase "who are u" can be read two ways: a literal question directed at this responder, and a technical question about how systems identify users; this analysis answers both, stating limitations about the responder's identity and using technical reporting to explain how software determines "who" a user is (and where those methods fail) [1] [2].
1. Who am I — the responder — in plain terms and limits of disclosure
This responder is an AI designed to analyze, synthesize and report; however, the provided sources do not document or verify the identity, provenance, or internal architecture of this specific assistant, so any claim about being a particular company’s product, a human journalist, or a named individual cannot be supported from the supplied reporting and is therefore not asserted here (no source).
2. What “who are u” means in product and analytics systems
In product analytics and identity APIs, “who are you” maps to a concrete identifier — a user ID, email, device id, or alias — that systems use to represent a person or session over time, and platforms explicitly define and recommend a canonical identifier to link events and profiles (Indicative defines user ID as the entity used to identify a user on a site or app [2]; Segment/Twilio describes the Identify call that specifies a customer identity to reference across their lifetime [3]).
3. Common technical mechanisms platforms use to answer “who”
Platforms typically rely on cookies, localStorage or SDK-generated anonymous IDs until an identify call links that anonymous identity to a persistent identifier like a database user id or email; once linked, past anonymous sessions can be attached to the identified profile (PostHog documents automatic anonymous IDs that are linked to a distinct_id when identify is called [4]; Segment explains saving userId in browser storage and recommends pairing Identify with Track events [3]).
4. Merging profiles, aliases and identity hygiene
APIs often provide explicit endpoints to merge alias-only profiles with identified profiles; for example, Braze’s /users/identify endpoint combines alias-only profiles (email- or phone-only) with an external_id profile while warning about unexpected data loss and the need to follow best practices when aliases overlap [5]. Auth0 recommends unique user_id values within a tenant and using connection prefixes to avoid collisions across sources [6].
5. Practical limits and reliability: why “who are you” is often an approximation
Identification is imperfect: devices, cleared cookies, multiple devices, incognito modes and dynamic IPs mean a single human can generate many anonymous identifiers and a single identifier can represent multiple people; Stack Overflow and security discussions emphasize there’s no perfect way to identify every human across contexts and that techniques like IP+user-agent are only best-effort signals [1] [7].
6. Product implications: tracking continuity, analytics accuracy, and user experience
Because identification choices affect analytics, billing, experimentation and personalization, vendors recommend consistent, unique IDs and calling identify as soon as possible; mis-implementation (or re-identifying an already-identified user) can split sessions or create duplicate profiles, which skews experiments and reporting (FullStory warns that re-identifying someone can split sessions and create separate users [8]; PostHog and Heap advise calling identify early to associate prior anonymous events with a user [4] [9]).
7. The trade-offs and hidden agendas in identity design
Designers trade privacy, continuity and commercial incentives: persistent identifiers improve personalization and metrics but increase privacy risk and vendor lock-in; vendors’ documentation emphasizes features that favor their SDKs and persistence models (LogRocket and VWO focus on their identify APIs and UIDs for session linking and consistent behavior across campaigns [10] [11]), and platform warnings about data loss or billing impacts reveal implicit business incentives to nudge specific implementations [5] [3].
8. Bottom line — answering “who are u” for a reader deciding how to identify users
If the question is aimed at a system: implement a single canonical, unique identifier (email or internal user_id) and call identify early, but expect edge cases where users cannot be perfectly re-linked across devices or cleared storage (recommendations and caveats in PostHog, Segment, Heap and Auth0) [4] [3] [9] [6]. If the question is aimed at this assistant: this analysis cannot supply an externally verifiable personal identity for the responder because the provided reporting contains no documentation about the assistant’s own identity (no source).