# 0xTria (@0xTria) on x

- id: koinon:creator:x_0xtria
- url: https://koinonlink.com/kol/x/0xTria
- schema_version: 1.4

## Identity
- platform: x
- handle: 0xTria
- platform_url: https://twitter.com/0xTria
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 4539
- following: 250
- posts_total: 1624
- posts_observed: 1
- posts_per_day: 0.917
- account_age_days: 1771
- verified: true
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.00154
- best_post_interactions: 7
- measurement_source: x_post_interactions
- measurement_formula: interactions on one post — likes, or likes + reposts + replies depending on the discovery path (the two are not distinguishable in the stored data)
- comparable_pool: peak_interactions
- status_note: We have only the single best observed post, so no mean exists. peak_post_engagement_rate is an UPPER BOUND, systematically higher than an average — never compare it against an average rate.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: software
- subcategory: ai_tools
- category_confidence: 0.92
- category_source: derived_from_recent_content
- topics: AI product rendering and pre-launch testing, Computer vision for sports performance analysis, Chinese developers monetizing AI tools, Data-driven edge creation in niche markets
- bio: Researcher | Data Analyst

founder @predictsguru

## Audience (inferred)
- status: present
- description: Data analysts, AI researchers and practitioners tracking real-world AI applications in e-commerce product testing and sports analytics
- roles: data
- seniority: practitioner
- pain_points: Turning raw video or sketch data into monetizable insights quickly, Keeping up with fast Chinese AI experimentation in consumer products, Finding practical, high-ROI use cases for computer vision and data models
- posts_analysed: 2
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.8
- promotion_intent: 0.7
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 77
- authenticity_basis: engagement_observed
- credibility_score: 77
- flagged_for_review: false
- koinon_bd_grade: A — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [x_0xtria#26672021] 🚨CHINESE SELLERS ARE NOW TESTING PRODUCTS BEFORE THEY EVEN EXIST one ugly handbag sketch 5 seconds later: luxury product render before this, testing a bag meant: > designer > factory sample > product photoshoot > ad creative > weeks of waiting now: > draw the shape > AI adds lea (https://twitter.com/0xTria/status/2074192758474952998)
- [x_0xtria#8842264b] 🚨 A Chinese dev just turned match footage into a $10k/month sports edge playbook. One badminton video in. Skeleton on the player. Smash. Forehand. Backhand. Serve — tagged live. 100% local. Zero APIs. Zero sensors. → joints tracked in real time → wrist speed, joint angles, body 

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=108, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=108, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=108, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.5, freshness_days=108, method=Profile-shape heuristic from a single snapshot (follower count, following ratio, account age, posting volume, verification), reduced for engagement red flags where post engagement was collected. Where only a follower count is available the score saturates at 58. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=108, method=llm_partner_fit_classifier + fit_score >= 0.6 AND authenticity >= 60 => A; fit_score >= 0.4 AND authenticity >= 45 => B; else C. authenticity < 30 or a promotional-pattern flag => quarantine. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=108, method=platform_profile

## Freshness
- profile_checked_days_ago: 108
- content_analysed_days_ago: 108
- audience_updated_days_ago: 76

## AI retrieval
- indexing_requests: 13
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=x_0xtria
- Request removal: https://koinonlink.com/claim?k=x_0xtria
- Terms: https://koinonlink.com/for-ai