joshhansen.tech

  • Concept: “Dinner” app—meet new people by feeding them dinner

    Ad-hoc dinner groups. Ratings let you distinguish leeches.

  • Concept: Improved Reference System 1

    World Wide Web current failings:

    • No recognized way of referencing passages of documents
    • Fragment identifiers cannot be used to efficiently reference down to the word or character level.
    • Linked documents have a tendency to disappear, thus rendering references less useful
    • No recognized way of referring to a particular version of a document, whose integrity is guaranteed

    Solution:

    • Introduce sub-fragment references syntax for href:
      <a href=”http://example.com/document#fragment[495,w24,513-859,1021,1249-1250]”>
    • List of character/character range references
    • This depends on a knowing the document’s character set
    • List of word/word range references
    • This depends on a tokenization algorithm
    • Introduce document content hash syntax for href:
      <a href=”http://example.com/document@a9e1bb2429″>…</a>
      <a href=”object://a9e1bb2429″>…</a>
    • Extend HTTP to allow request of specific document versions ?
      This is actually probably handled fine at the application layer
    • Define standard for embedding referenced documents into an HTML document, e.g.
    <html>
<head>
 <object hash="a9e1bb2429">
  <!DOCTYPE html>
  <html>
  ...
  </html>
 </object>
 <object hash="ff4d042c90">
 ...
 </object>
</head>
<body>...</body>
</html>

    <object> is like an <iframe>

    The hash attribute has no effect? Just there to help developers? But it violates DRY because it can be computed.

  • Concept: Language Thing

    • Second language learning assistant by means of machine translation
    • See $HOME/Projects/Glossy
    • Uploading/archiving/tagging of speech samples — a dialect/pronunciation database
    • See $HOME/HowITalk
    • Word frequency map (geographical map) based on twitter data
    • See $HOME/Projects/WordFreqMining

  • Concept: Reccomenu

    User rates dishes purchased at restaurants. Make recommendations for other dishes (as function of adventuresomeness) using collaborative filtering with other users’ ratings.

    Strong emphasis on privacy / minimizing data collection. Simplicity.

    Users could be rated by how daring they are, clustered by their typical tastes, etc.

  • Concept: Calendar database / exchange

    • Allow uploading and searching of calendars, such as could be subscribed to in Google Calendar, etc.
      • Use case: subscribe to BYU events calendar instead of having to load a web page to view it
    • Automatic inference of calendar events from event websites?
    • Analytics on events