> I remember when using download statistics was enough.

No download statistics are currently available for Julia packages. That's essentially the issue that the Pkg.jl telemetry is trying to address.

Julia packages are github repos, where all we get are the traffic stats for the last 2 weeks for clones. It doesn't even provide the number of downloads of released software (the tarballs), or even basic stats that you could get from webserver access logs.

You could instead possibly: - move the project and ecosystem completely off GitHub - sell the project to a FAANG - go with the MathWorks model and just straight up sell closed source proprietary code (they have 5000+ employees and growing, you have ~30, maybe 40 if you count the JuliaLab that essentially work for JC as well)

As much as people in the Julia community have dunked on Matlab in the past, at least MathWorks has their business model worked out, people understand the tradeoffs being made, and the dark pattern being used is just closed source software instead of exploiting PII.

How are anonymous UUIDs PII and how is JC exploiting them?

They have no special access nor do I see a first order effect that benefits them. Just that the open source Julia ecosystem will benefit and that will feed back into JC's market.

Very impressive work, here's the arxiv link: http://arxiv.org/abs/1508.04874

Thanks! Even just the abstract is a much better overview than the vague article.

If you're interested in model generation time, have a look at JuMP (https://github.com/JuliaOpt/JuMP.jl). More discussions on the speed of modeling languages at http://arxiv.org/abs/1312.1431 and http://arxiv.org/abs/1508.01982

The only PhD defense I've seen with standing room only. Congrats!

Why was the PhD defense standing-room only?

There was an off-by-one error in calculating the required number of chairs.

Caused by Julia using 1-origin indexing. (I kid, I kid)

Some light reading for my next long flight.

I really would like to be able to see a proof of how Mathematica calculates a limit, for example. Doesn't need to be human friendly, just verifiable.

This is an impressive job. I'm curious why the arxiv paper uses Julia's benchmark suite but doesn't make any other mention of Julia in the discussion, given that it's quite relevant to this work.

The exchange would be based on the counterparty protocol, which allows anyone with a BTC address to issue and back assets in a decentralized manner. We'll have to see how this decentralization leaks into what it means to be a stock exchange.

To follow up on this, a lot of the overhead from reverse-mode AD can be avoided by flattening out the expression graphs and compiling specialized functions (at runtime). A number of the JuliaDiff packages support this approach. This is not a new idea, but Julia's hooks to LLVM make this surprisingly easy to do.

Should the IPLANG "compiler" try to detect multiple solutions?

I suppose it could, and would be fairly easy to do!