Premium AI Resources
Accelerate your local AI journey with battle-tested resources from my experience building and deploying AI models with 77K+ datasets.
The 77K Dataset Collection
Complete training dataset with preprocessing scripts and best practices from my personal experience building a 77K dataset.
- 77,000+ curated data points
- Preprocessing Python scripts
- Data cleaning notebooks
- Quality validation tools
- Fine-tuning guide included
- Lifetime updates
- 30-day money-back guarantee
Local AI Mastery Course
Go from zero to running production AI models locally in 30 days with step-by-step video tutorials.
- 20+ video tutorials
- Installation scripts
- Model optimization guides
- Hardware recommendations
- Private Discord community
- Weekly Q&A calls
AI Automation Toolkit
Ready-to-use Python scripts and bash automation for local AI workflows.
- 50+ Python scripts
- Batch processing tools
- Model switching automation
- Performance monitoring
- API integration examples
Prompt Engineering Pack
500+ tested prompts optimized for local models like Llama, Mistral, and CodeLlama.
- 500+ tested prompts
- Category organized
- Model-specific variations
- Chaining templates
- Monthly updates
Why Trust These Resources?
Frequently Asked Questions
Is there a refund policy?
Yes! All products come with a 30-day money-back guarantee. If you're not satisfied, just email for a full refund.
How do I access the products?
After purchase through Gumroad, you'll receive instant access via email with download links and access instructions.
Do I get updates?
Yes! All products include lifetime updates. As we improve and expand content, you'll get access automatically.
Written by Pattanaik Ramswarup
AI Engineer & Dataset Architect | Creator of the 77,000 Training Dataset
I've personally trained over 50 AI models from scratch and spent 2,000+ hours optimizing local AI deployments. My 77K dataset project revolutionized how businesses approach AI training. Every guide on this site is based on real hands-on experience, not theory. I test everything on my own hardware before writing about it.