For Those Afraid of CSS and Standards

If you’ve been feeling a bit timid about the whole CSS thing, A List Apart’s Ben Henick offers 12 Lessons for Those Afraid of CSS and Standards. It’s a great primer for those diving into their first Web Standards project and a nice review for seasoned CSS/Standards code wranglers.

Here’s a quick rundown of the 12 lessons:

  1. Everything you know is wrong—sort of
  2. It’s not going to look exactly the same everywhere unless you’re willing to face some grief—and possibly not even then
  3. You will be forced to choose between the ideal and the practicable
  4. Perfection is not when there’s nothing to add, but when there’s nothing to take away
  5. Some sites are steaming heaps of edge cases
  6. Longer lead times are inevitable
  7. Coherent and sensible source order is the best of Good Things
  8. Descendant selectors are the beginning and end of genuinely powerful CSS rules
  9. In the real world, stylesheet hacks will get your project across the finish line
  10. Working around rendering bugs is like playing Whack-a-Mole
  11. When you’re drowning in CSS layout problems, make sure of the width and height of the water, float without putting up a struggle, and get clear of the problems
  12. Background images will make the difference between the plain and the tastefully embellished

3 Responses to “For Those Afraid of CSS and Standards”

  1. Does anyone know what an “edge case” is, exactly?

  2. Steve: Not entirely sure, but it doesn’t sound like you’d want a “steaming heap” of one anytime soon. ;-)

  3. Hey, Steve. Thanks for linking to my article.

    And since there seems to be some confusion - in the context of the article, an edge case is a tiny slice of the spec that only turns up once or twice on the site. In the scenario presented, such edge cases often require long rules, with four-plus attributes set. The functional result is a stylesheet that not merely violates, but actually rapes the KISS Principle… and is consequently difficult to write.

    In statistics and economics they call it an outlier - a datum or sample that throws off the mean for the population and makes it worthless.

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