Calculation & Content Methodology

Formula and source selection

Established definitions and primary technical sources are preferred. CSS behavior is checked against W3C specifications, accessibility thresholds against W3C WCAG material, framework behavior against official framework documentation, and physical conversion factors against measurement authorities such as NIST.

When a value changes over time, the page should identify its effective date and source. Fixed definitions are distinguished from estimates, conventions, and configurable project assumptions.

Calculation implementation

Pure arithmetic and transformation logic is kept separate from presentation where practical. Interactive state runs in focused browser components, while formulas, examples, tables, guidance, and limitations remain available as server-rendered page content.

• Handle empty, zero, decimal, boundary, negative, invalid, and unusually large values as relevant.
• Reject impossible contextual values such as a zero font-size divisor.
• Keep internal precision until display formatting is applied.
• State the displayed precision and remove unnecessary trailing zeros.
• Reserve stable space for changing results to reduce layout movement.

Static and owner verification

Code changes receive static TypeScript and targeted calculation checks without treating those checks as proof of every browser behavior. The site owner reviews the rendered pages on mobile and desktop, checks keyboard operation and console output, and runs production-oriented audits before release.

A visible review date records when the content and assumptions were deliberately assessed. It should not imply continuous monitoring or professional certification.

Browser image-processing methodology

The HEIC converter, batch watermarker, image resizer, compressor, cropper, and format converter decode supported files with browser image APIs, draw the requested pixels to a canvas, and encode a new JPEG, PNG, or WebP result. Batch work runs sequentially so several full-resolution images are not decoded at once, and completed items remain available when another file fails.

The AI image upscaler runs a pinned Swin2SR 2× model locally on overlapping RGB tiles. It discards the contextual tile borders before joining the results and resizes the original alpha channel separately. The model estimates detail; it may change textures or introduce artifacts and cannot verify missing information.

Generated files are new encodings and intentionally omit EXIF and other embedded metadata. This protects against carrying location and camera details into the result, but it also removes orientation, resolution, authorship, and similar metadata. Canvas decoding and encoding can also normalize or change embedded color-profile information, so color-critical output should be reviewed in its destination workflow.

The AI image scanner runs a quantized classifier in a dedicated browser worker and reports cautious likelihood bands instead of verified authorship. Its output reflects patterns learned from a limited training dataset and can fail on edited, compressed, unfamiliar, or human-made images, so an inconclusive range and visible limitations are part of the method.

• Validate file type, file size, decoded dimensions, animation, and batch limits before processing.
• Treat compression target sizes as best-effort because browser encoders can produce different results.
• Keep JPEG transparency handling explicit by filling transparent pixels with the selected background color.
• Load ZIP creation code only when a visitor chooses to download a completed batch as a ZIP file.

Limitations and corrections

AyeCalc aims for accurate results but does not guarantee that every page is error-free or suitable for every purpose. Standards, frameworks, browser behavior, and legal requirements can change.

When a material error is confirmed, the preferred response is to correct the calculation and its explanation together, update the review date truthfully, and avoid preserving a misleading result for search traffic.