Early in the morning, before #AstroHackWeek 2016 day three, I had a long phone call with Andy Casey (Cambridge) about *The Cannon*, *RAVE*, and *Gaia DR1*. He is trying to produce a catalog of detailed abundances from *RAVE* matched to *Gaia T-GAS* before the DR1 deadline, which is in two weeks! There are many hard parts to this project. One is to make a model of the red giants and a model of the main sequence, and somehow understand that these two models are physically consistent. Another is to get a training set of detailed abundances for main-sequence stars. We also talked about DR1 zero-day projects. I am still stumped as to what, exactly, I am going to do!

I also got a great email this morning from Megan Bedell (Chicago), demonstrating that the convection explanation is reasonable for the radial-velocity scatter she sees in her *HARPS* data. As my loyal reader may recall, we spent the last few months demonstrating that there is no evidence (and damn, did we search) that the *HARPS* pipeline is leaving radial-velocity information on the table. If it isn't, then the radial-velocity scatter must come from intrinsic stellar noise (or something much worse). What Bedell has shown is that the quantitative amplitude and granularity of stellar surface convection is sufficient to lead to meter-per-second jitter. Duh! Now we have to figure out how to deal with *that*. I have ideas, related to *The Cannon*.

The afternoon and evening of #AstroHackWeek was at GitHub headquarters in San Francisco, which is a (dangerously) fun place to hack. Jonathan Whitmore (Silicon Valley Data Science) gave a great presentation about all the crazy things you can do with a Jupyter notebook, which blew me away, and Phil Marshall (SLAC) gave a presentation to the company about how GitHub is integrated into astrophysics research these days (oh, and what features we would like).

From my (limited) perspective, the most important thing about day three was that Adrian Price-Whelan (Princeton) and I had the final realization that the importance sampling we wanted to do for the radial-velocity problem will not work. Sad, but true: We can't get a complex enough linear model that maps cleanly enough onto the Kepler problem. So we decided to switch gears today to Phil Marshall's favorite: Simple Monte Carlo. What we will do is sample the prior extremely densely, and then rejection sample to the posterior using the likelihood function. This is usually impossible! We will make it possible in this case by capitalizing on the linearity of two of our parameters: These two parameters we can analytically marginalize out at every sample in the four-dimensional space of non-linear parameters. That's our job for tomorrow.

Fail fast. That's what we are trying to do.