How does the Free Open Beta and Founding Member Pass work?

During early development, RYUN is 100% free for all users during the Open Beta. You can also choose to support early development with a Founding Backer Pass: Tier 1 (€12 one-time for 1 full year of RYUN Suite), Tier 2 (€25 for 3 full years + Patron flair + VIP voting), or Tier 3 (€50 one-time, strictly limited to 50 backers for Lifetime Access to current and all future RYUN products forever).

Why is RYUN so affordable compared to €30/mo career or language tools?

Most platforms add a 500%–1000% markup on top of AI tokens. RYUN uses a Bring Your Own Key (BYOK) architecture: you connect your own OpenAI, Google Gemini, Anthropic Claude, or Groq key (or free tier keys) and pay only pennies directly to the model provider. You only pay RYUN a modest annual platform fee (€12/yr single app or €18/yr for the full Suite) for our high-performance UI and workflow engines.

What happens after the Open Beta period ends?

When RYUN reaches full general release, it transitions to its standard annual subscription model (€12/yr for Ravina, €12/yr for Nativra, or €18/yr for the complete RYUN Suite Bundle). All new users will receive a 3-day unrestricted free trial. Founding members who backed during the beta will automatically have their full prepaid duration (1 year, 3 years, or Lifetime) activated with zero interruptions.

How do Ravina and Nativra connect under one account?

RYUN uses a unified Single Sign-On (SSO) architecture. Logging in once grants immediate access to both Ravina (career strategy) and Nativra (language immersion) without repeated credential prompts, while sharing your profile preferences across both tools.

How is my career data and audio practice kept private?

RYUN is built on a private-first architecture. Your resumes, compensation details, job notes, and voice recordings are stored securely in dedicated, containerized storage. We do not sell your personal data, train public models on your private documents, or share your career history with recruiters.

Can I export my resumes to ATS-friendly PDF and DOCX formats?

Yes. Ravina formats your resumes specifically for applicant tracking systems, avoiding unreadable multi-column tables, graphics, or nested text boxes. You can export pixel-perfect PDFs or editable DOCX files ready for direct submission.

What makes Nativra different from traditional flashcard apps?

Rather than memorizing disconnected vocabulary lists, Nativra lets you import authentic subtitles (SRT, VTT), YouTube videos, and podcasts. It generates interactive video-synced Read Along lessons, extracts CEFR-graded expressions, and schedules SM-2 spaced repetition reviews.

Can RYUN be self-hosted on a private server?

Yes. RYUN includes a complete Docker Compose and Caddy configuration optimized for self-hosting on VPS providers like Hetzner. It includes automated Let's Encrypt SSL certificates, PostgreSQL with pgvector, and Redis.

Launch Ravina

Data & AI • Career Strategy Blueprint

Data Scientist & ML Engineer ATS Resume & Interview Strategy

Evidence-grounded strategy blueprint for landing senior and leadership Data Scientist & ML Engineer positions. Powered by Ravina's 13-point ATS algorithm.

How to optimize a Data Scientist & ML Engineer resume for ATS:To pass modern applicant tracking systems as a Data Scientist & ML Engineer, align hard technical keywords (PyTorch / TensorFlow, LLM Fine-Tuning, Feature Engineering, SQL / BigQuery) directly with the job description, format all experience bullets with quantifiable outcome metrics (percentages, revenue, time saved), and maintain single-column reverse chronological formatting.

Essential ATS Keyword Matrix

Modern ATS algorithms search for these core competencies in Data Scientist & ML Engineer profiles:

PyTorch / TensorFlowLLM Fine-TuningFeature EngineeringSQL / BigQueryCausal InferenceMLOps Pipelines

High-Scoring Metric Bullet Template

Verified ATS Example:

"Engineered real-time recommendation model with PyTorch and Ray, improving click-through rate by 24% and generating €1.4M in incremental annual revenue."

STAR Behavioral Interview Simulation

High-Frequency Question:

"Describe an ML project where the model performed exceptionally in offline validation but degraded in live production."

Ravina Coaching Strategy: Explain data drift detection, feature store synchronization, canary deployment rollback thresholds, and shadow-mode telemetry.

Compensation Benchmark & Equity Range

Market Median Total Comp:

€85,000 – €150,000 + Equity

Model In Ravina