# Fabio Jonathan (@FabioJonathanA) on x

- id: koinon:creator:x_fabiojonathana
- url: https://koinonlink.com/kol/x/FabioJonathanA
- schema_version: 1.4

## Identity
- platform: x
- handle: FabioJonathanA
- platform_url: https://twitter.com/FabioJonathanA
- website: https://echoforge.to
- country: United States
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 554
- following: 761
- posts_total: 1288
- posts_observed: 2
- posts_per_day: 0.21
- account_age_days: 6146
- verified: true
- size_tier: nano

## Engagement
- status: not_collected
- status_note: Engagement has not been collected for this creator yet. This is a gap in our collection, NOT a statement that the creator has no engagement. Follower count is known, so it is measurable.
- 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: 0.92
- category_source: derived_from_recent_content
- topics: readersguild book app features, social reading and progress sharing, ML-powered book recommendations, RevenueCat shipaton integrations, community moderation via Telegram bots
- bio: Based in SF, Seattle, Portland, Bali | Running an app studio 👇

## Audience (inferred)
- status: present
- description: Independent app developers and indie hackers, focused on building social reading apps and using tools like RevenueCat
- roles: dev
- seniority: practitioner
- pain_points: How to design social interactions and UGC sharing features for a reading app, Implementing simple ML-powered personalized recommendation systems (e.g., dating-style matching), Automating community moderation and bug feedback workflows, Rapidly iterating and integrating subscription monetization during shipathon-style events
- posts_analysed: 6
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 0.8
- promotion_intent: 0.6
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 44
- authenticity_basis: profile_shape_only
- credibility_score: 44
- 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_fabiojonathana#112bbb69] making an app to read with friends ❤️ https://t.co/iGTQkNPQAX (https://twitter.com/FabioJonathanA/status/2071297736541184023)
- [x_fabiojonathana#d8a19600] making an app to read with friends ❤️ https://t.co/9wfZVlmp8E
- [x_fabiojonathana#33e11f29] making a quarter rating system for readersguild for shipaton #BookReaders https://t.co/1GsWuzC7tz
- [x_fabiojonathana#e47047aa] makin spotify wrap for ma book app lesgo. and yes the sound play in app too https://t.co/cgkLSmLrTC
- [x_fabiojonathana#0daf87f1] building a dating app style book recommendation system for readersguild the backend is just ol machine learning. for @RevenueCat shipaton makin too much ugc tiktok im addicted to phonk now https://t.co/8NSU4kM0Zf
- [x_fabiojonathana#3b476dac] working on sending direct report of community moderation, filter and bug report directly to telegram bot https://t.co/nCYKDicXFg
- [x_fabiojonathana#3084e47f] making https://t.co/IST2jpPOEr for @RevenueCat shipaton added feature where user can share their current reading progress after every read. we’re strava for reading afterall #book #bookworm #bookish https://t.co/yPEsDn6Snj

## 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=78, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=78, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=78, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=78, 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=78, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=112, 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=0.25, freshness_days=78, 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=112, 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=112, method=platform_profile

## Freshness
- profile_checked_days_ago: 112
- content_analysed_days_ago: 78
- audience_updated_days_ago: 78

## 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_fabiojonathana
- Request removal: https://koinonlink.com/claim?k=x_fabiojonathana
- Terms: https://koinonlink.com/for-ai