EDITORIAL POLICY
How NakuNet researches, evaluates and recommends technology.
Our aim is to help readers make better software and technology decisions through clear research, practical comparisons and recommendations that explain both strengths and trade-offs.
Last updated: August 2026
EDITORIAL INDEPENDENCE
Recommendations are based on fit.
NakuNet may earn revenue through affiliate relationships, advertising or other commercial partnerships. Those relationships do not guarantee favourable coverage, higher rankings or inclusion in our recommendations.
A product can be criticised, ranked below a competitor or excluded entirely when our research indicates that another option is more suitable for the user, workflow or use case being evaluated.
HOW WE EVALUATE
We start with the decision the reader is actually trying to make.
There is no single scoring formula that makes sense for every type of software. The criteria depend on the product and the problem it is designed to solve, but our evaluations generally consider the following areas.
Fit
Who is the product actually designed for, and what problem does it solve particularly well?
Capability
Can the product perform the important tasks expected of it, and how useful are those capabilities in real workflows?
Usability
How difficult is the product to learn, configure and use consistently?
Cost
We consider more than the advertised entry price, including plan limits, upgrades and costs that can appear as usage grows.
Reliability
We consider consistency, stability, control and whether the product performs dependably enough for its intended use.
Alternatives
A recommendation is stronger when credible competing products have also been considered.
RESEARCH & TESTING
Evidence should match the claims we make.
NakuNet research may use product documentation, pricing information, technical specifications, demonstrations, reputable third-party sources, user feedback and direct product use where appropriate and available.
We do not describe a product as personally tested simply because we have researched it. When an article relies primarily on documentation, demonstrations or other external evidence, the conclusions should reflect that distinction.
Where direct testing is conducted, we focus on the functions relevant to the decision being examined rather than attempting to test every possible feature.
HOW WE CHOOSE
A comparison does not always need one universal winner.
Two good products may be appropriate for different users. Our comparisons aim to identify those differences rather than forcing every article into a single winner-and-loser conclusion.
Recommendations may therefore be expressed as “best for” particular situations—for example, a better option for beginners, teams, advanced users, smaller budgets or a particular workflow.
When we do identify an overall preference, the reasoning should be visible in the article so readers can decide whether the same priorities apply to them.
ACCURACY & UPDATES
Software changes. Our conclusions can change with it.
Software pricing, features, limits and availability can change after publication. We aim to review and update important content when meaningful changes become known, but readers should confirm current pricing and terms with the provider before making a purchase.
An older recommendation may also change when a product improves, deteriorates, changes its pricing or is overtaken by a stronger alternative.
CORRECTIONS
When something is wrong, we correct it.
If we discover a material factual error, misleading statement or outdated conclusion that significantly affects an article, we aim to correct the content rather than leave information online simply because it has already been published.
Minor editorial changes such as spelling, formatting or clarity improvements may be made without a separate correction notice. Significant changes may be noted where doing so helps readers understand what changed.
AI-ASSISTED WORK
AI can assist the process. It does not replace editorial judgement.
AI tools may be used to help organise research, analyse information, develop drafts or support production workflows. AI-generated output should not be treated as an authoritative source by itself. Important factual claims and conclusions should be supported by appropriate evidence and review.
COMMERCIAL RELATIONSHIPS
How NakuNet can earn revenue.
Some links on NakuNet may be affiliate links. If a reader follows one of those links and makes a purchase, NakuNet may receive a commission at no additional cost to the reader.
Affiliate availability or commission size should not determine which product receives the strongest recommendation.
CONTACT
See something we should review?
We welcome factual corrections, product updates and evidence that could materially improve our coverage.