Content Policy & Editorial Standards
Welcome to Local AI Master's Content Policy. This document explains our editorial standards, testing methodology, and commitment to providing authentic, human-created educational content. Unlike many AI tutorial sites that rely on AI-generated content and theoretical knowledge, every piece of content on this site is written by a real person based on actual hardware testing and real-world experience.
100% Human-Crafted & Personally Tested
Every piece of content on Local AI Master is:
- ✓Written by Pattanaik Ramswarup, a real person with 10+ years in AI/ML
- ✓Personally tested on actual hardware (my 192GB RAM, dual RTX 4090 rig)
- ✓Based on real experience from training 50+ models and building a 77K dataset
- ✓Updated monthly with new findings and community feedback
- ✓Verified with screenshots and performance metrics from actual runs
My Testing Lab
I personally test every tutorial, benchmark, and recommendation on:
- • Primary Rig: 192GB DDR5, Dual RTX 4090
- • Mac Studio M2 Ultra (192GB unified)
- • Budget Build: 32GB DDR4, RTX 3060 12GB
- • Laptop: MacBook Pro M3 Max 48GB
- • Linux Server: Ubuntu 22.04, 256GB ECC
- • Windows 11 Workstation
Real Data, Real Results
Every performance claim is backed by:
- • Actual benchmark results with timestamps
- • Screenshots from my testing sessions
- • Git commit history showing iterative improvements
- • Error logs and troubleshooting steps I personally encountered
- • Cost analysis from my actual AWS/electricity bills
The 77K Dataset Story
My 77,000 example dataset wasn't built overnight. It took:
- • 6 months of iterative development
- • $12,000 in compute costs
- • 500+ hours of manual curation
- • 50+ model training iterations
- • Collaboration with 3 Fortune 500 companies
This real-world experience informs every piece of content I write.
AI Disclosure Standards
Transparency is non-negotiable. Here's how we use AI tools and where we don't:
Where We DON'T Use AI
- • Tutorial Writing: Every tutorial is written 100% by me, from my actual testing experience
- • Model Reviews: All 155+ model reviews reflect my personal hands-on testing
- • Technical Analysis: Performance claims based on my benchmark results, not AI speculation
- • Code Examples: Every code snippet tested on my machines - I run the code before publishing
- • Troubleshooting Guides: Based on errors I actually encountered and solved
Where We MAY Use AI (With Disclosure)
- • Grammar Checking: AI tools help catch typos, but content is mine
- • Image Generation: Some OG images created with AI (always disclosed)
- • Code Formatting: AI may suggest better formatting, but logic is mine
Promise: If AI assists in creating any content, it's clearly disclosed inline. No hidden AI-generated content ever.
Editorial Independence
Local AI Master maintains strict editorial independence. Our reviews, recommendations, and comparisons are based solely on technical merit and testing results - never influenced by:
- • Sponsorships: We don't accept sponsored content or paid model placements
- • Affiliate Pressure: Affiliate links exist, but they NEVER influence our recommendations
- • Vendor Relationships: Model creators don't get preferential treatment or advance reviews
- • User Pressure: Popular opinion doesn't override testing data
Example: When Llama 3.1 8B underperformed in my coding tests (despite community hype), I reported the actual results. When a niche model like DeepSeek Coder V2 exceeded expectations, I highlighted it - even though it has zero affiliate potential.
Fact-Checking Process
Every technical claim undergoes rigorous verification:
Our 5-Step Fact-Checking
- 1. Primary Testing: I personally run every tutorial, benchmark, and installation guide on my hardware
- 2. Cross-Reference: Compare my results against official model docs and community reports
- 3. Edge Case Testing: Test with different hardware configurations (192GB workstation, 32GB budget build, Mac Studio)
- 4. Time-Based Verification: Re-test after 30 days to catch version-specific issues
- 5. Community Validation: Monitor feedback from readers who followed the tutorial
Commitment: If I can't personally verify a claim, I won't publish it. If a claim requires specialized hardware I don't own, I'll explicitly note "untested" or "community-reported."
Research Methodology
Our content creation follows a systematic research process:
For Model Reviews
- • Download and install model locally
- • Run standardized benchmark suite (my 77K dataset)
- • Test 10+ real-world use cases
- • Measure inference speed, RAM usage, quality
- • Compare against 3-5 similar models
- • Document all errors and solutions
- • Write review from testing notes
For Tutorials
- • Complete tutorial on clean system
- • Document every command with screenshots
- • Test on 2+ different OS (Windows/Linux/Mac)
- • Identify common errors (I encounter them too!)
- • Write troubleshooting section from real fixes
- • Have beta reader follow tutorial
- • Update with their feedback
Timeline: Model reviews take 8-12 hours of testing. Major tutorials require 15-20 hours from research to publication.
Transparency About Limitations
We're honest about what we don't know and what we can't test:
Our Testing Limitations
- Hardware Constraints: I can test up to dual RTX 4090 (48GB VRAM total). Claims about H100 or A100 performance are cited from official sources, not personal testing.
- Language Limitations: Native English speaker. Non-English model testing relies on automated metrics and community validation.
- Specialized Domains: Medical, legal, financial AI advice beyond my expertise is marked as "community perspective" or cited from domain experts.
- Enterprise Features: Can't personally test enterprise deployment, clustering, or cloud-scale infrastructure. These sections cite official docs and case studies.
- New Models: Can't test every model immediately upon release. "Recently Released" tag indicates testing in progress.
Transparency Markers: Look for labels like "[Untested]", "[Community Report]", "[Cited from Official Docs]" when content isn't from firsthand testing.
Sources and Citations
We cite authoritative sources to support claims:
- • Official Model Documentation: Direct links to model cards, research papers, and official repos
- • Academic Research: ArXiv papers, conference proceedings (NeurIPS, ICLR, CVPR)
- • Vendor Documentation: NVIDIA, AMD, Intel official technical docs
- • Community Benchmarks: HuggingFace leaderboards, MLPerf results (with timestamps)
- • Industry Reports: Gartner, Forrester, Stanford AI Index (for market trends)
Citation Standard: Performance claims without personal verification must include source link. Our benchmark results include timestamps and hardware specs for reproducibility.
Corrections and Updates Policy
We fix errors quickly and transparently:
Correction Process
- Minor Typos/Grammar: Fixed immediately without notice (doesn't affect technical accuracy)
- Technical Errors: Corrected within 24 hours with "Updated: [date]" notice at top of page
- Major Inaccuracies: Entire section rewritten with "[Correction: Original article stated X, testing revealed Y]" inline notice
- Breaking Changes: Model updates that break tutorials get prominent warning banner + updated instructions
Community Reporting: Found an error? Email support@localaimaster.com with "Content Correction" in subject. I personally review and respond within 24 hours. Contributors who report errors get credited (with permission).
Ethical AI Guidance
We promote responsible AI use and highlight ethical considerations:
Our Ethical Commitments
- Privacy First: Tutorials emphasize local deployment for data sovereignty. Cloud alternatives disclosed with privacy trade-offs.
- Bias Awareness: Model reviews include known biases (when documented). Recommend diverse testing datasets.
- Environmental Impact: Power consumption data included in hardware guides. Recommend efficient models when appropriate.
- Legal Compliance: Licensing clearly explained. No guidance on circumventing model licenses or usage restrictions.
- Harm Prevention: No tutorials for generating deepfakes, impersonation, or deceptive AI content.
Stance on Controversial Use Cases: We provide technical education, not judgment. However, we won't create content specifically for surveillance, manipulation, or illegal activities. If a model has known misuse potential, we include responsible use warnings.
Community Feedback Integration
Your feedback shapes our content. Here's how we incorporate community input:
Reader Contributions
- • Error Reports: Fixed within 24 hours
- • Alternative Solutions: Added to "Community Solutions" section
- • Hardware Variations: Incorporated into compatibility matrix
- • Use Case Ideas: Inspire new tutorials
- • Benchmark Results: Community benchmarks included (with credit)
Recent Community Updates
- • Added WSL2 installation guide (requested by 50+ readers)
- • Expanded 8GB RAM model recommendations (top request)
- • Created Raspberry Pi AI tutorial (community idea)
- • Added troubleshooting for Apple Silicon (Mac user feedback)
Recognition: Major contributions get credited in the article. Top contributors featured in annual blog post thanking the community.
Conflict of Interest Disclosure
Full transparency on potential conflicts of interest:
Financial Relationships
- Affiliate Links: Some hardware and cloud service links are affiliate links (disclosed inline). I only recommend products I personally use and test. Earn small commission at no cost to you.
- Ad Revenue: Site displays Google AdSense ads. Advertisers have zero influence on editorial content or model rankings.
- No Sponsorships: Local AI Master does not accept sponsored posts, paid reviews, or vendor partnerships that compromise editorial independence.
- No Consultinginfluence: While I consult independently, client work never influences site recommendations or model comparisons.
Promise: If any financial relationship ever influences content, it will be prominently disclosed at the top of the article. Your trust is more valuable than any commission.
Content Update Schedule
- Weekly: Test new model releases, update compatibility charts, fix reported errors
- Monthly: Re-run all major benchmarks, update performance data, audit top 50 pages for accuracy
- Quarterly: Major content audits, add new case studies, refresh outdated tutorials, survey community for content needs
- As Needed: Critical updates within 24 hours of major breaking changes (framework updates, model deprecation, security issues)
Last Major Audit: October 28, 2025 - Reviewed all 155+ model pages, updated 47 tutorials, added 12 new hardware guides
Contact Me Directly
Found an error? Have a question? I personally read and respond to every email:
Average response time: Under 24 hours