# Adolfo Builes (@abuiles) on x

- id: koinon:creator:x_abuiles
- url: https://koinonlink.com/kol/x/abuiles
- schema_version: 1.4

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

## Reach
- followers: 1727
- following: 639
- posts_total: 546
- posts_observed: 19
- posts_per_day: 0.08
- account_age_days: 6814
- verified: true
- size_tier: nano

## Engagement
- status: peak_post_only
- peak_post_engagement_rate: 0.0029
- best_post_interactions: 5
- 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: devtools
- category_confidence: 0.85
- category_source: derived_from_recent_content
- topics: AI-powered Shopify app development, Developer tooling and workflow automation, Shopify app architecture and scalability, Vibe coding pitfalls, Git and version control in app development
- bio: Building @tenderprompt: Use AI to build your own Shopify custom apps

Ex: @getrecharge: my team built Bundles (acquired), customer portals, and page builders.

## Audience (inferred)
- status: present
- description: Developers building custom Shopify apps with AI, mainly full-stack engineers and indie hackers focused on automation and tooling
- roles: dev
- seniority: practitioner
- pain_points: Struggling with the hidden costs and technical debt of 'vibe coding' Shopify apps, Frustrated by the lack of adoption of tools like GitButler despite their clear value, Confused by Shopify App Store review team's unpredictable naming and approval patterns
- posts_analysed: 5
- 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: 62
- authenticity_basis: engagement_observed
- credibility_score: 62
- 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_abuiles#12f1f32a] Shopify’s App Store review team coming up with some creative names in their test stores. Is it good news to see Gabriel coming after Lucifer? https://t.co/l6yPVmzBeB (https://twitter.com/abuiles/status/2068687322405548102)
- [x_abuiles#44c09906] When Dynamic Workers, Facets, Shell, and other cool tech came out at Cloudflare, it was obvious to me what kind of things this unlocked. It was also adjacent to some problems I've been thinking about for a while. I took a bet on it, left my dream job, and jumped all in. Excited t
- [x_abuiles#52855626] This week there has been a lot of talk about vibe coding Shopify apps. I just wrote a post about the hidden cost of doing it. With cameo mentions of @OisinO, @celispj, and @codyplof https://t.co/ShZlErKBy9
- [x_abuiles#ccdb724c] https://t.co/eiiek9NMnB
- [x_abuiles#2da03b2e] Why hasn’t GitButler caught on yet? Instead of forcing your cod

## 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=80, 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=80, 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=80, 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: 80
- audience_updated_days_ago: 20

## AI retrieval
- indexing_requests: 6
- 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_abuiles
- Request removal: https://koinonlink.com/claim?k=x_abuiles
- Terms: https://koinonlink.com/for-ai