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 the Local AI Master team — practitioners with 10+ years in AI/ML
- ✓Measured on rented hardware when we measure at all — we do not own a GPU lab, so we rent the specific card on a cloud provider and say so, or we cite the vendor's published specifications
- ✓Based on real experience from training 50+ models and building a 77K dataset
- ✓Updated monthly with new findings and community feedback
- ✓Sourced or shown — every performance number is either attributed to a named source or derived with the arithmetic printed next to it
What hardware we actually have
We do not own a GPU lab, and you should be suspicious of any site that says it owns one of everything. What we have is:
- • An Apple Silicon Mac (M3 Pro, 18GB unified) — the one machine we can benchmark any time, and the source of the only first-party numbers on this site, published in our Apple Silicon buying guide
- • Rented cloud GPUs on AWS, GCP and RunPod — when we need a specific NVIDIA card, we rent that exact card by the hour rather than guess from a different one
That is the whole list. It means most of the per-card figures on this site are not ours: they are vendor-published specifications (we cross-check against the TechPowerUp GPU Database), or community-reported results we link to, or arithmetic we show our working for. We label which is which on the page, and we would rather tell you a number came from NVIDIA's spec sheet than pretend we measured it.
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: Our model pages are built from the model card, the vendor's published figures and independent benchmark results, each attributed on the page — we do not claim to have personally run every model listed
- • Technical Analysis: Performance claims are attributed to a named source, or derived with the arithmetic shown — never estimated and presented as measured
- • Code Examples: Commands and code are run before publishing, on the Mac or on a rented instance of the relevant hardware
- • 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: Check behaviour across the memory tiers readers actually have, using rented instances and Apple Silicon (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: A model page is not published until its claims are sourced and its commands have been run; a major tutorial takes longer than that because the failure modes have to be reproduced before they can be written up.
Transparency About Limitations
We're honest about what we don't know and what we can't test:
Our Testing Limitations
- Hardware Constraints: We own no NVIDIA hardware. Anything measured on an NVIDIA card was measured on a rented cloud instance and labelled as such; everything else is 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: August 23, 2026 — removed unsourced performance claims and first-hand testing language across the hardware and troubleshooting clusters, and rewrote this page to describe the hardware we actually have
Contact Me Directly
Found an error? Have a question? I personally read and respond to every email:
Average response time: Under 24 hours
Written by the Local AI Master Team
The team behind Local AI Master
We build Local AI Master around practical, testable local AI workflows: model selection, hardware planning, RAG systems, agents, and MLOps. The goal is to turn scattered tutorials into a structured learning path you can follow on your own hardware.