Editorial Policy & Transparency

Our Process

1. Source Collection: We gather information from multiple independent sources — official announcements, academic papers (arXiv), GitHub repositories, standardized leaderboards (MLPerch, LiveCodeBench, SWE-bench), and community discussions.
2. Draft Generation: AI (Allam 7B / Hermes Agent) generates draft articles from collected sources.
3. 7-Stage Review: Each draft passes through 7 specialized subagents (Benchmark Analyst, Source & Fact-Check, Editorial Standards, Financial Analyst, Technical Depth, Community & Trust, SEO Specialist).
4. Human Review: Our editorial team reviews the final article for tone, accuracy, and completeness.
5. Publication: Articles are published only after passing all quality gates.

AI Usage Disclosure

We use AI tools (Allam 7B by SDAIA, Hermes Agent) to assist with research, drafting, and verification. AI does not write articles autonomously — every claim is verified by human reviewers. We clearly distinguish between verified data, analysis, and speculation in every article.

Verification Standards

• Benchmark Claims: Must have at least 2 independent sources confirming the numbers. Self-reported numbers alone are marked as "UNVERIFIED."
• Financial Claims: Must link to SEC filings, official announcements, or audited reports.
• Technical Claims: Must be verified against model cards, research papers, or official documentation.
• Community Claims: Community-reported information is labeled as "community reports" and not treated as fact.

Confidence Levels

Correction Policy

Errors are corrected within 24 hours. Corrections are published prominently at the top of the article with a link to the corrected version.

Our Standards

• We do not accept payment for favorable reviews.
• We do not receive early access to model evaluations in exchange for coverage.
• Any sponsorship relationships are disclosed at the top of relevant articles.
• AI company partnerships are disclosed (e.g., Allam 7B by SDAIA).
• We maintain editorial independence regardless of our technology choices.

What We Value

• Accuracy over speed: We'd rather publish an article a day late with perfect data than a day early with hype.
• Transparency: We show our sources, our methodology, and our limitations.
• Community perspective: We include community feedback and real-world usage reports, not just press releases.
• Nuance: We avoid absolute claims. If a model is good on one benchmark, we say so — not that it "beats everything."

What We Don't Do

• We don't rewrite press releases.
• We don't use unsupported superlatives ("best ever," "game-changing").
• We don't publish articles without primary sources for major claims.
• We don't hide our AI usage — it's disclosed openly.