# Viktor Seraleev (@seraleev) on x

- id: koinon:creator:x_seraleev
- url: https://koinonlink.com/kol/x/seraleev
- schema_version: 1.4

## Identity
- platform: x
- handle: seraleev
- platform_url: https://twitter.com/seraleev
- website: http://screencut.app
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 27314
- following: 367
- posts_total: 10382
- posts_observed: 6
- posts_per_day: 1.867
- account_age_days: 5561
- verified: true
- size_tier: koc

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.00146
- best_post_interactions: 40
- 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: app
- category_confidence: 0.9
- category_source: derived_from_recent_content
- topics: Mobile app attribution, Privacy-first tracking solutions, iOS ATT and SKAdNetwork compliance, Indie app development, Ad tech alternatives to AppsFlyer, Developer tools for mobile monetization
- bio: Indie mobile dev · Portfolio: 15 apps

## Audience (inferred)
- status: present
- description: Indie mobile developers building and monetizing apps, especially those focused on privacy-compliant attribution and ad tracking
- roles: dev
- seniority: practitioner
- pain_points: Struggling with iOS ATT restrictions and IDFA deprecation, Finding it hard to get accurate attribution without relying on traditional tools like AppsFlyer, Need to balance privacy compliance with effective ad performance tracking
- posts_analysed: 7
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.9
- promotion_intent: 0.8
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 90
- authenticity_basis: engagement_observed
- credibility_score: 90
- 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_seraleev#9d62dd0a] I keep seeing more and more about AppStack. Here’s a quick summary: (https://twitter.com/seraleev/status/2037697984960344242)
- [x_seraleev#42272818] AppStack is trying to replace AppsFlyer with a simpler, privacy-first attribution system. The key idea: they bypass classic iOS limitations (ATT / SKAdNetwork).
- [x_seraleev#e51bbb23] 1. probabilistic attribution
- [x_seraleev#39406690] 2. device-level data without IDFA
- [x_seraleev#e7fab8bf] 3. improved signals sent back to ad networks
- [x_seraleev#7eee284a] Founded by engineers Lucas Moscon and Tom Darmon. Raised a s

## 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=20, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=20, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=20, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=79, 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=79, 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=79, 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: 79
- audience_updated_days_ago: 20

## AI retrieval
- indexing_requests: 11
- 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_seraleev
- Request removal: https://koinonlink.com/claim?k=x_seraleev
- Terms: https://koinonlink.com/for-ai