I have a Spec Driven Project with 674 documents incl 181 specs, 429 tasks, and 40 project docs. About 1/3 of these are an acceptance sub-project to show the root project meets fit-for-purpose requirements. I used Fable to convert these to and from AsciiDoc, Markdown, and HTML. From this, I tried to draw some conclusions about which format is better than the others for AI specifications. TL;DR: Markdown for AI working documents, AsciiDoc for curated human-reviewed specs, HTML only as a publishing target. Objective Metrics Corpus Tokens (Approx) Markdown: 594k, AsciiDoc: 598K, HTML: 661k Worst Single-File Ratio AsciiDoc: 1.023x, HTML: 1.26x (due to tables and code blocks) Markup share of file content Markdown 0.9%, AsciiDoc 1.3%, HTML 9.9% - a 10x overhead for HTML Read/write Assessment In terms of readability, both Markdown and AsciiDoc are good. HTML has far more tags, which add noise. One of the most common tags is <code> some code </c...
Thank you for the puzzle!
ReplyDeleteUnfortunately, I couldn't clearly understand the source of such behaviour. I suppose the the reason is in a way how String Constant Pool selects constants on a base of hashCode() execution. But I'm not sure.
So will wait for your solution :)
P.S. could you, please, allow comments and RSS subscription on your GitHub blog?
I read that internally String Constant Pool uses Hashtable for storing strings. So looks like equal hashCode() values isn't enough for getting puzzle effect. Also, at the first sight, external synchs on "bb" and "cC" really work on String instance wrappers not on internally char[] arrays...
DeleteSo the puzzle is really puzzle :)