PixelDock

Small details.
Better results.

Worked examples, common mistakes and clear limits for the tools you use.

01

How to fit an image under a file-size limit

Pixels and kilobytes measure different things. A reliable export needs both.

Read the guide
02

JPG, PNG or WebP: which should you choose?

Choose the format for the image and the destination—not just the smallest number.

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03

Resize images without stretching them

A square image and a landscape image cannot fill the same box without a trade-off.

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04

What local image processing actually means

Your file is processed on your device. That is different from a claim that a website makes no network requests.

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05

A practical workflow for batch image exports

Use consistent settings, then inspect representative results before publishing.

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06

MB or MiB? Convert storage without a hidden 4.86% gap

A clear method for choosing decimal and binary units before comparing file sizes.

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07

Dates, durations and working days answer different questions

Avoid daylight-saving surprises and end-of-month mistakes in everyday date calculations.

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08

Convert CSV and JSON without breaking quotes or spreadsheets

Understand headers, multiline fields, string types and safe spreadsheet exports.

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09

Check text contrast with the actual foreground and background

Use luminance and transparent-color compositing instead of judging a swatch by eye.

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10

Hashing, Base64 and random identifiers are different tools

Choose the right operation without treating encoding or a UUID as a security boundary.

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11

Choose Grid or Flexbox before tuning the small details

Build a layout that survives narrow screens, then copy the rules you actually need.

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12

Merge and organize PDFs locally without losing track of pages

Control the exact page sequence, retain originals and inspect the final document.

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13

How to extract text from an image or scanned PDF

Choose OCR for pixels that contain text. Choose the right language and verify the words against the original.

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14

Semantic search or keyword matching: which tool fits?

Word overlap and embedding similarity answer different questions. Neither is a fact checker.

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15

Train a small text classifier with your own examples

A local Naive Bayes classifier can learn your labels. Its usefulness depends on the examples you supply.

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16

What local AI processing does and does not mean

Local inference keeps inputs out of a processing API. The website still downloads code and public model files.

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