While these specific challenges are trivial now because of increased memory and CPU, user expectations have increased too, requiring more advanced features whic

While these specific challenges are trivial now because of increased memory and CPU, user expectations have increased too, requiring more advanced features which are not trivial to implement across different platforms. Users now expect good auto-suggestion when doing text entry on mobile platforms and laugh when it fails ( http://damnyouautocorrect.com/ http://damnyouautocorrect.com/ ). I just launched an iPad app to improve communication for speech-disabled users and nearly everyone I talked to wanted word-completion and next-word suggestion, even when offline. While it seems trivial for Google or a database backed server to provide real-time intelligent suggestions (e.g. suggest 5 words that follow: Harry), implementing such a feature on iPad took me over a month even though I knew exactly what I wanted to make and had all the necessary data beforehand. I had a list of 1 million words and phrases with frequency of usage (16mb of text data) and wanted to suggest 3-7 words in real-time as the user typed each letter. And implementing this on iPad required quite a bit of engineering and optimization.