Editorial methodology and corrections

What “evidence-first” means here

Evidence-first does not mean every page is a scientific study. It means the strength and type of a claim should match the material supporting it. A product instruction can often be checked against primary documentation. A market opportunity requires evidence about a problem and buyer behavior. A future scenario is a structured exploration, not a factual report or prediction.

We keep those categories visible because readers should not have to infer whether a paragraph is documentation, analysis, or fiction. When a claim travels further from direct evidence, the assumptions and disconfirming signals should become more explicit.

Source hierarchy

Sources are selected for the claim being made. The default order is:

1. Primary sources: official documentation, specifications, release notes, public filings, regulatory text, first-party data, and direct product information.
2. Reproducible observations: a described test, captured output, or workflow that another reader can reasonably repeat.
3. Independent reporting and research: used for context, triangulation, and claims not available from a primary source.
4. Market signals: job posts, reviews, product listings, trend data, and recurring public complaints. These can reveal a hypothesis but rarely prove demand on their own.

Search snippets, unattributed summaries, and repeated claims without an identifiable origin are not treated as strong evidence. Time-sensitive claims should include a verification date. If a source changes or disappears, the claim must be rechecked rather than assumed valid.

Shared production sequence

Each publication has its own thresholds, but the common sequence is intentionally simple:

1. Collect candidates. Gather current documentation or external signals as untrusted input. The presence of a signal does not grant publication.
2. Extract claims. Separate checkable statements, interpretation, recommendations, unknowns, and—where relevant—fictional scenario elements.
3. Verify and challenge. Check source support, freshness, unsupported numbers, internal contradictions, duplicate topics, and template artifacts. A candidate can end with no publication when it does not meet the threshold.
4. Build both language versions. Korean and English may use natural phrasing, but names, dates, numbers, warnings, source counts, and recommendation strength should not drift.
5. Publish as one release unit. The paired artifact is preserved in version control, delivered to the hosting platform, and checked at the public URL. A repository commit alone is not treated as proof that readers can see the page.
6. Reconcile and correct. Delivery failures, public mismatches, and later corrections remain visible in operating records rather than being silently rewritten as if they never occurred.

Labels readers should expect

Standards by publication

AI Tools Hub

Tool pages prioritize current primary documentation, actionable steps, boundaries, and failure conditions. Product names, model identifiers, pricing, limits, and version-sensitive instructions require special attention because they change frequently. “Hands-on” is used only when execution evidence exists; otherwise the narrower “documentation-verified” label applies.

SaaS Ideas

Idea pages are hypotheses for further validation. They should identify the observed problem, intended buyer, existing alternatives, supporting signals, implementation boundary, unknowns, and a practical first test. A high automated score helps compare candidates; it does not establish demand. Ideas that lack adequate evidence are withheld, and weaker ideas should not be promoted as mature opportunities merely to maintain a publishing schedule.

Possible Futures

Scenario pages separate current observed signals from assumptions and constructed future events. They should contain a causal mechanism, a human consequence, trade-offs, counterarguments, and disconfirming signals. Similar premises are compared across the broader collection so that repeated stories do not create the illusion of independent likelihood. Real people and organizations require additional care when a fictional branch could imply misconduct, harm, or other reputational risk.

Automation and human review

Automated systems assist with research collection, drafting, source checks, duplicate detection, bilingual comparison, formatting, and public-output verification. Publication is gated: a run may finish successfully with no new page when quality criteria are not met.

Human review is selective, not universal. We do not describe every page as human-reviewed. Manual attention is directed to samples, corrections, sensitive topics, public-facing methodology, and material flagged by automated checks. Because automated authoring and evaluation can share blind spots, readers should use the cited evidence and limitations rather than relying on the mere existence of a score or quality gate.

Corrections and updates

Factual corrections should identify what changed and preserve the page's stable URL where possible. Material corrections are distinguished from routine spelling or formatting edits. Time-sensitive pages may show a last-verified date even when the publication date is older.

To report an error, email [email protected] with the page URL, the disputed statement, and a supporting primary source when available. We review specific, evidence-backed reports first. If a public page does not match its intended release artifact, the mismatch is treated as an operational defect as well as an editorial one.

Advertising and independence

Advertising helps support free access. Ads do not determine candidate selection, quality scores, conclusions, or whether a page is published. Sponsored or affiliate material, if introduced, must be clearly labeled and kept distinct from independent editorial conclusions. Pages whose primary purpose is policy, contact, or navigation are not intended to carry advertising.

Known limits

No automated pipeline can guarantee completeness, freshness, or freedom from bias. Primary sources can be incomplete. Market signals can overrepresent vocal users. Translation can change emphasis. Scenario selection can overproduce familiar themes. The system therefore favors visible limitations, fail-closed publication gates, corrections, and periodic sampling over claims of certainty.