# DataStax Developers (@DataStaxDevs) on youtube

- id: koinon:creator:youtube_datastaxdevs
- url: https://koinonlink.com/kol/youtube/DataStaxDevs
- schema_version: 1.3

## Identity
- platform: youtube
- handle: DataStaxDevs
- platform_url: https://www.youtube.com/@DataStaxDevs
- country: United States
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- followers: 37800
- posts_observed: 88
- verified: false
- size_tier: koc

## 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: RAG与Graph RAG优化, Langflow构建AI Agent和Agentic Flows, Astra DB向量数据库在GenAI中的应用, LLM prompting与实际部署技巧, 多模态与非结构化数据AI应用案例
- bio: We're moving! As DataStax joins IBM, our story will continue on IBM channels. Follow IBM Technology and IBM Developers to ...

## Audience (inferred)
- status: present
- description: AI application developers, primarily backend/full-stack engineers building RAG, Graph RAG, and multi-Agent systems
- roles: dev
- seniority: practitioner
- pain_points: Low RAG accuracy, irrelevant answers, or hallucinations, Difficulty in processing unstructured data and integrating it into AI workflows, Slow setup and iteration of AI Agents and multi-Agent workflows, Challenges in vector database selection and production performance/scalability
- posts_analysed: 47
- 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: 52
- authenticity_basis: profile_shape_only
- credibility_score: 52
- flagged_for_review: false
- koinon_bd_grade: B — 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
- [youtube_datastaxdevs#1683e2c4] Vibe Coding Tip: Let Your AI Agent Manage Your To-Do List! #vibecoding (https://www.youtube.com/@DataStaxDevs)
- [youtube_datastaxdevs#8afdb873] Vibe Coding Tip: Don’t Let Your AI Agent Take Over 🤖 #shorts #vibecoding
- [youtube_datastaxdevs#928a945e] Why Frequent Commits Matter in Vibe Coding 🚨
- [youtube_datastaxdevs#f5ecb463] Secret to Vibe Coding Success: Break Down Tasks into Milestones
- [youtube_datastaxdevs#99ca657b] Astra DB + MCP: A New Era for Database Interaction
- [youtube_datastaxdevs#5484e28f] The EASIEST Way to Fetch and Query Web Data for AI Applications
- [youtube_datastaxdevs#f7e33bdc] 3 BIG Problems With Large Language Models
- [youtube_datastaxdevs#395ef856] Challenges in AI Application Deployment

## 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=34, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.75, freshness_days=34, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.75, freshness_days=34, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=0, 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=0, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=1, 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=0, 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=1, 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=1, method=platform_profile

## Freshness
- profile_checked_days_ago: 1
- content_analysed_days_ago: 0
- audience_updated_days_ago: 34

## AI retrieval
- indexing_requests: 1
- 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=youtube_datastaxdevs
- Request removal: https://koinonlink.com/claim?k=youtube_datastaxdevs
- Terms: https://koinonlink.com/for-ai