# Yannick Dalbin (@yannickdalbin) on tiktok

- id: koinon:creator:tiktok_yannickdalbin
- url: https://koinonlink.com/kol/tiktok/yannickdalbin
- schema_version: 1.4

## Identity
- platform: tiktok
- handle: yannickdalbin
- platform_url: https://www.tiktok.com/@yannickdalbin
- website: https://yannickdalbin.com/liens
- country: France
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 4469
- posts_observed: 1
- verified: false
- 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: ai_tools
- category_confidence: 0.95
- category_source: derived_from_recent_content
- topics: LLM selection and providers, AI app architecture for production, Claude Code / vibe coding workflows, Avoiding technical debt in AI-generated code, From demo to live SaaS deployment, Dev productivity and automation with AI
- bio: Un dev passionné qui t'aide à passer de l'idée à la production grâce à l'IA 🚀

## Audience (inferred)
- status: present
- description: Developers and indie builders using AI (Claude, LLMs) to ship production apps and SaaS from idea stage
- roles: dev
- seniority: practitioner
- pain_points: Moving AI-generated prototypes/demos into stable production with live data, Accumulating technical debt and 'Frankencode' from unstructured AI coding, Choosing the right LLM/provider balancing cost, compliance (RGPD) and performance, Structuring AI projects to avoid vibe coding failures and hidden architecture issues
- posts_analysed: 40
- confidence: 0.75

## 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: 39
- authenticity_basis: profile_shape_only
- credibility_score: 39
- 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
- [tiktok_yannickdalbin#35df5d42] Choisir son LLM pour dev : Du Brainstorming au Code Stratégie de sélection de modèles IA pour développeurs. Voyez concrètement quand solliciter un modèle à forte capacité de raisonnement pour l'audit/architecture et quand basculer sur un modèle optimisé pour la génération de code (https://www.tiktok.com/@yannickdalbin)
- [tiktok_yannickdalbin#013bd866] original sound - yannickdalbin
- [tiktok_yannickdalbin#248c1f4f] It is a becoming a big trend on TikTok now! Click here: llm
- [tiktok_yannickdalbin#c8db76e4] Check out Yannick Dalbin’s video! #TikTok >
- [tiktok_yannickdalbin#492812bf] Modèles LLM : Propriétaire, Open-Weight ou Open-Source ? Comment choisir le bon modèle LLM pour votre projet ?	#llm #IntelligenceArtificielle #OpenSource #TechOps #DevOps
- [tiktok_yannickdalbin#16016359] Choisir son fournisseur LLM : RGPD & critères Comment bien choisir son fournisseur de LLM pour son application ?	#llm #Intel

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

- audience.description: source_type=ai_inferred, confidence=0.75, freshness_days=70, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.75, freshness_days=70, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.75, freshness_days=70, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=70, 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=70, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=70, 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=70, 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=70, 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=70, method=platform_profile

## Freshness
- profile_checked_days_ago: 70
- content_analysed_days_ago: 70
- audience_updated_days_ago: 70

## AI retrieval
- indexing_requests: 10
- 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=tiktok_yannickdalbin
- Request removal: https://koinonlink.com/claim?k=tiktok_yannickdalbin
- Terms: https://koinonlink.com/for-ai