anchor.diff:  <div class="alert alert-danger">
   <p><B>WARNING:</B> BayesDB is rapidly-evolving, <B>alpha-quality</B> research software. Some key ideas behind its design and implementation have yet to be published, documentation is currently sparse, our release engineering is rough. We are sure there are critical bugs everywhere, including in the inference backend.</p></div>
   <p>
  -We are also surprised and pleased by the interest in the project, and are working to improve the situation. 
  -</p>
  -<p>
  -If you are a potential alpha tester, collaborator or (especially) a contributor, we'd love to hear from you. Please get in touch with us via bayesdb <at> mit.edu.
  +We are also humbled and excited by the interest in the project, and are working to improve the situation. If you have feature requests or ideas for applications, please let us know.
  +</p>
  +<p>
  +Also, if you think you might be interested in alpha testing, collaborating, or contributing, please write us! We'd love to hear from you at bayesdb [AT] mit.edu.
   </p>
   <h2>What kinds of data and problems is it good for?</h2>
   <p>Versions of BayesDB (and CrossCat) have been tried on datasets including surveys, performance metrics for hospitals, branches of nonprofits, econometric tables, feature vectors extracted from text/images, voting records, and gene expression microarrays. 
  
positive.code: In BQL, much like in traditional DBMSs, we see schema design (both choosing how to organize raw data into a table and choosing its types) as an avenue for users to articulate their prior knowledge. Right now the mechanisms for doing so are relatively limited, but we anticipate that this will change. 
  </p>
  <p>
  If it survives, BQL will likely evolve towards becoming a general-purpose probabilistic programming language, perhaps even one like <link>Venture</link> or Church. From this perspective, the current CrossCat backend corresponds to a simple kind of hierarchical Bayesian probabilistic program synthesis, where the class of probabilistic programs it is learning is extremely simple.
  <p>
  	</div>
  </div>
  
negative.code: Our models are memory efficient, our inference algorithms scale linearly in runtime, we have yet to do serious performance engineering, there is a lot of unexploited parallelism, and we haven't yet incorporated subsampling or more advanced inference techniques. Thus we do not think there are intrinsic barriers to greater scale, even remaining with the CrossCat backend.
  </p>
  <p>
  A commercial variant of CrossCat, built by the startups Prior Knowledge and Navia Systems (ultimately acquired by Salesforce), addressed many of these issues. If you asked some of us to bet, we would say that a billion rows and ~200 columns should be possible without algorithmic or statistical breakthroughs, but rather careful engineering.
  </p>
  
  <p>That said:</p>
  