# Ross (@rossbulat) on x

- id: koinon:creator:x_rossbulat
- url: https://koinonlink.com/kol/x/rossbulat
- schema_version: 1.4

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

## Reach
- followers: 1750
- following: 279
- posts_total: 1708
- posts_observed: 1
- posts_per_day: 0.275
- account_age_days: 6221
- verified: true
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.01829
- best_post_interactions: 32
- 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: 0.9
- category_source: derived_from_discovery_path
- topics: Polkadot staking performance, Validator reward retention and compounding, Blockchain node operation transparency, Transparent staking metrics
- bio: Director of JKRB. 软件工程师。

## Audience (inferred)
- status: present
- description: Polkadot validators and blockchain staking operators, primarily software engineers and technical operators managing node operations
- roles: dev
- seniority: practitioner
- pain_points: Struggling with inconsistent reward retention and compounding in Polkadot staking, Lacking transparent and reliable metrics to track actual staking performance, Finding it hard to identify trustworthy validator operators due to opaque reporting
- posts_analysed: 4
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.7
- promotion_intent: 0.6
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 72
- authenticity_basis: engagement_observed
- credibility_score: 72
- flagged_for_review: false
- koinon_bd_grade: C — 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_rossbulat#4e74af98] True story. #Polkadot polkadot:native https://t.co/H6lglMx82J (https://twitter.com/rossbulat/status/2071831758400839938)
- [x_rossbulat#d52960b1] Stay in the loop ☁️ We’ve added a new headline ticker to the https://t.co/n0EIVBSVD0 homepage, putting the latest announcements and updates front and center, so you can catch what’s new at a glance. #Polkadot $DOT https://t.co/YsudrekqRg
- [x_rossbulat#80a25d28] 100% reward DOT retained. 100% compounded. Not every month will achieve 100% retention—but transparent reporting and a clear commitment to retaining and compounding $DOT should be the standard for validators. Nominate https://t.co/yjRi16P0RR validators today and support validator
- [x_rossbulat#af65fa97] A one-month snapshot from one of Polkadot’s largest validator operators shows 0% of rewards compounded and 0% retained during the period. These metrics are coming soon to the Polkadot Staking Dashboard, giving nominators more conte

## 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=29, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=29, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=29, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=89, 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=89, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=123, 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=89, 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=123, 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=123, method=platform_profile

## Freshness
- profile_checked_days_ago: 123
- content_analysed_days_ago: 89
- audience_updated_days_ago: 29

## AI retrieval
- indexing_requests: 7
- 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_rossbulat
- Request removal: https://koinonlink.com/claim?k=x_rossbulat
- Terms: https://koinonlink.com/for-ai