How we decide whether AI actually works.
DiscoverAINow is built around reproducible tasks, transparent prompts and explicit failure modes—not logo collections or paid rankings.
Our core rule
We separate discovery from evaluation. APIs, public feeds, GitHub, Hugging Face and community sources can surface candidates. They do not determine our recommendation.
No fabricated resultsIf a benchmark has not been run, we label it as a methodology, experiment plan or unverified discovery item. We do not invent scores to make a comparison look complete.
What we test
Task completionDid the output actually solve the requested job?
CorrectnessAre claims, code and logic accurate?
CoverageDid it identify the important cases, not just obvious ones?
Manual cleanupHow much human correction is required before use?
Hallucination riskDid it invent requirements, APIs, facts or capabilities?
RepeatabilityDoes the workflow work again under similar conditions?
How comparisons work
- Define a real task and expected outcome before choosing a winner.
- Use the same source material and equivalent instructions across tools.
- Record exact prompts, settings and date/model information where available.
- Score against a published rubric rather than subjective impressions alone.
- Document important misses and human corrections.
- Update the page when a product changes enough to invalidate the test.
Commercial independence
Affiliate links and sponsorships may fund the site. They do not buy benchmark scores or editorial conclusions. Sponsored placements must be visibly labeled.