13 ms·

Julia Computing Raises $4.6M in Seed Funding

279 points by sandGorgon 9y ago

krupan 9y ago
What is the business model? How are they making money?
- rattray 9y ago
  https://juliacomputing.com/products/ https://juliacomputing.com/products/ - JuliaPro is the go-to product for anyone who wants to use Julia – professionals, engineers, software developers and data scientists. Beginners and experts can build better software quicker and dramatically improve performance. - JuliaRun is for scalable deployment of Julia in production for real time analytics and large-scale parallel simulations in the public cloud or a private cluster. - JuliaBox lets data scientists, quants and strats run Julia in Jupyter notebooks right in the browser. JuliaBox is the number one choice for universities teaching Julia since students can get started with Julia in seconds with no installation required. - JuliaFin is a suite of Julia packages that simplify the workflow for quantitative finance, reducing the gap between the quant and the trader. JuliaFin provides the fastest path from market data to market transactions by leveraging the full power of Julia for implementing new trading strategies where time is of the essence. EDIT: Oh, and consulting/training: https://juliacomputing.com/services/ https://juliacomputing.com/services/
  - SolarNet 9y ago
    Answering the actual question: JuliaPro is basically VisualStudio Express vs. Pro (e.g. there's a free version and then an expensive version with better integrations, better enterprise level tools, etc). JuliaRun is basically a cloud service (e.g. charge for hosting) designed for julia code. JuliaBox seems to be a cloud IDE / google docs / notebook style product. Probably with a similar model to github eventually (e.g. pay for teams / commercial use, etc). JuliaFin seems to be a way to charge banks a lot more money for the same stuff.
    - simonbyrne 9y ago
      - JuliaPro is free, but the paid version comes with support and integration with Excel and JuliaRun (possibly more features in future). - JuliaRun is for easily scalable deployment of code, either for cloud or local servers. - JuliaBox is as you say, available here https://juliabox.com/ https://juliabox.com/ - JuliaFin is completely separate, and currently consists of Bloomberg integration and the Miletus contract specification and pricing package. (I work for Julia Computing)
      - jordigh 9y ago
        Hey, I'd be interested in knowing if you're just selling plain ol' non-free software. Can I buy the PRO version and give it to my friends, can I study it, can I modify it? It's a little sad when we are trying to figure out how to make money for free software and the answer ends up being "sell (some) software non-freely". Okay, so it's impossible to make money without selling non-free software? There can only be one Red Hat in the world? I don't think it's any consolation that you are also producing free software; pretty much every major software vendor nowadays does that while keeping all the interesting secret sauce secret.
        thebooktocome 9y ago
        Having explored this option somewhat recently, I assure you: it is just plain, old, non-free software.
        simonbyrne 9y ago
        > Can I buy the PRO version and give it to my friends, can I study it, can I modify it? Mostly: JuliaPro is essentially a nice convenient installer of a huge collection of open source work (though I can't say for sure, as I'm not really involved in that part). A few small components (e.g. the Excel plugin) are not open, and we are moving to MKL for some stuff, so that will change slightly though. Open source business models are hard (as you're no doubt aware). You may disagree, but I think Julia Computing has so far done a good job of balancing the open source vs business aspect, which is part of the reason I originally joined.
  - jordigh 9y ago
    So, are they just selling plain ol' non-free software? The "PRO" features seem to be ripping out the GPL libraries so they can link to MKL with a support contract. I have hope this means that the paid version is also free software. https://juliacomputing.com/products/juliapro.html https://juliacomputing.com/products/juliapro.html
    - coldtea 9y ago
      >So, are they just selling plain ol' non-free software? Or, as we used to call it, just "software". They're selling software.
      - jordigh 9y ago
        Right, but selling non-free software isn't interesting for those of us who really want to see more businesses thrive around free software. If it's really impossible to make money without selling non-free software, then Julia isn't very interesting from an economic perspective. If Julia hasn't figured out how to make money without restricting customers via legal or technical means, trying to lock them in, or putting software in their machines that controls their machines without the full knowledge and consent of the users (i.e. source code and permission), then I'm not really interested in what they're doing economically.
  - wott 9y ago
    > JuliaBox is the number one choice for universities teaching Julia since students can get started with Julia in seconds with no installation required. I don't understand. Julia is not stable at all yet. Who would want to teach it now? What would be the point?
    - apesti 9y ago
      I recently used Julia in my numerical analysis course and it worked well for the most part. We couldn't use it for some of the larger projects because it wasn't as optimized as MATLAB for certain matrix operations. However, it was great most of the time and better than using MATLAB on the university computers.
      - elcritch 9y ago
        Implementing a missing matrix operator might make a great class project for a numerical methods course. ;)
- baybal2 9y ago
  By taking investor money and living on it. Have yet to hear about them making anymuch significant money except for seemingly staged "success stories"
- olympus 9y ago
  They sell an enterprise version of Julia for $1500 per year. The main things you get are support, MS Excel integration, and a non-GPL license. They also offer consulting, training, and a few other flavors of Julia (large scale server based gets its own edition). I'm sure the main draw will be companies that don't want to be stuck with a GPL license in their product.
  - Buttons840 9y ago
    Isn't Julia licensed under the MIT license? They advertise being MIT licensed on their website, and their Github repository is also under the MIT license.
    - sharpy 9y ago
      The core language is. The libraries are another matter. From the website: The core of the Julia implementation is licensed under the MIT license. Various libraries used by the Julia environment include their own licenses such as the GPL, LGPL, and BSD (therefore the environment, which consists of the language, user interfaces, and libraries, is under the GPL).
  - wodenokoto 9y ago
    So if I send a patch to Julia, do I have to give them the rights to GPL and a non-GPL version?
    - Buttons840 9y ago
      They advertise that Julia is MIT licensed on their website, so I think the parent comment is wrong.
      - olympus 9y ago
        They state "non-GPL" version on their website: https://juliacomputing.com/products/juliapro.html https://juliacomputing.com/products/juliapro.html
      - 9y ago
smaili 9y ago
For those thinking "What's Julia?", article has a nice tldr -- Julia is the fastest modern high performance open source computing language for data, analytics, algorithmic trading, machine learning and artificial intelligence.
dkarapetyan 9y ago
"Speed of C++ and Java"? What does that even mean?
- mr_overalls 9y ago
  Its performance rivals that of traditionally "fast" languages for a wide variety of benchmarks. https://julialang.org/benchmarks/ https://julialang.org/benchmarks/ https://www.ibm.com/developerworks/community/blogs/jfp/entry/A_Comparison_Of_C_Julia_Python_Numba_Cython_Scipy_and_BLAS_on_LU_Factorization?lang=en https://www.ibm.com/developerworks/community/blogs/jfp/entry... https://modelingguru.nasa.gov/docs/DOC-2625 https://modelingguru.nasa.gov/docs/DOC-2625
nickpeterson 9y ago
I'm not sure I buy the notion of, "Solves the two language problem." There is always going to be some library or environment consideration that makes a unified language for everything impractical (unless your entire business runs on an AS400).
- andlrc 9y ago
  > unless your entire business runs on an AS400 Even there you got support for C, C++, Java amung others as well as the whole IBM family (RPG/ILE, CL, REXX, ...) With somewhat decent bindings in RPG.
- rattray 9y ago
  They have a pretty good reputation for FFI to most complements, IIRC. eg; https://docs.julialang.org/en/stable/manual/calling-c-and-fortran-code/ https://docs.julialang.org/en/stable/manual/calling-c-and-fo... https://github.com/JuliaPy/PyCall.jl https://github.com/JuliaPy/PyCall.jl
- ffriend 9y ago
  The two language problem is more specific to scientific computing where literally every popular library is written in more then one language - fast C/C++/Fortran and convenient high-level R/Matlab/Python. NumPy, SciPy, Caffe, Theano, Tensorflow to name a few. I once had to rewrite Matlab + C code (for face tracking) to pure Julia - not only the resulting code was almost twice smaller, it also ran ~20% faster.
  - dnautics 9y ago
    I wrote a library in c to replace a Julia one I wrote (and make it more portable) and found that the c version was slower! (Couldn't inline functions as easily in c and was also being more parsimonious about memory usage in c)
mr_overalls 9y ago
This seems a little premature when considering that Julia is only up to v0.6, and the language is still making breaking changes. https://github.com/JuliaLang/julia/blob/release-0.6/NEWS.md https://github.com/JuliaLang/julia/blob/release-0.6/NEWS.md
sriram_sun 9y ago
I've heard good reviews of the language here. So I've no doubt about its technical capabilities. However, funding might suppress the ability to make breaking changes.. which I believe are pretty good until 1.0 or so. On the business side, will we end up with (hopefully) a better, cheaper Matlab?
- hatmatrix 9y ago
  $1500 for JuliaPro - maybe not?
  - sriram_sun 9y ago
    My guess is that we'll probably start seeing pricing split into various modules a la Matlab - Julia Financial, FEM, Semiconductor etc. Also Julia Enterprise etc. Maybe Mathworks will acquire them and call it MATLAB 2020!
zitterbewegung 9y ago
Sounds like a good idea for the language and a good startup idea. I know of two businesses that support python and since Data Science is a growing field we can always need competition in environments and languages.
drenvuk 9y ago
So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. That's some very slick but sketchy marketing tactics. I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use.
- deleted 9y ago
  [deleted]
- omginternets 9y ago
  What exactly is sketchy, here?
- ihnorton 9y ago
  > So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. Here's a performance testimonial with links to real code and benchmarks against Fortran (not Python), from Steven G. Johnson, one of the authors of FFTW: https://discourse.julialang.org/t/julia-vs-fortran-complaint/4366/7 https://discourse.julialang.org/t/julia-vs-fortran-complaint... I implemented an erfinv function Julia (https://github.com/JuliaLang/julia/pull/29874 https://github.com/JuliaLang/julia/pull/29874), and it was about 3x faster than Matlab or SciPy's erfinv function, both of which are taken from standard Fortran libraries. [...] I have found similar gains (compared to Fortran code called in SciPy) for other special functions, e.g. polygamma functions (https://github.com/JuliaLang/julia/pull/71255 https://github.com/JuliaLang/julia/pull/71255) and exponential integrals (https://github.com/JuliaMath/SpecialFunctions.jl/issues/19 https://github.com/JuliaMath/SpecialFunctions.jl/issues/19). The reason Julia can beat the Fortran code is that metaprogramming makes it easy to apply performance optimizations that are awkward in Fortran. We have metaprogramming macros (@evalpoly) that can easily inline polynomial evaluations, whereas the Fortran code makes function calls that loop over look-up tables of polynomial coefficients. Even greater speedups are possible for evaluating polynomials of complex arguments, where there is a fancy recurrence from Knuth that is almost impossible to use effectively without code generation. In principle, the Fortran authors could have done the same inlining and gotten similar performance, but the code would have been much more painful to write by hand. (They could even have written a program to generate Fortran code, but that is even more painful.) > I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use. The Julia core is MIT licensed. The language distribution is currently GPL licensed due to linked dependencies, but there has been a no-GPL build flag available for several releases. GPL (and other) dependencies are continually being removed, and there are only one or two (GPL) libraries left in the currently nightly build.
gruglife 9y ago
As someone that uses Python and R for data analysis, is it worth learning this language?
- flavio81 9y ago
  Yes, it is a nice language with clean syntax and high performance. Syntax is similar to python so it will be easy to learn for you.
- _1 9y ago
  I didn't find it worth the time. When doing HPC work, I really care about performance, and couldn't get anywhere near the speeds as C + MPI. If it's not HPC, I don't see a benefit over Python.
- gaius 9y ago
  No. Not a knock on the language (I've never used it) but look at the R and Python ecosystems - it will be incredibly hard to replicate them. Then you have all the proprietary tools - MATLAB, Mathematica, Q etc - they aren't going anywhere. Then there's all the legacy FORTRAN code... If it's still around in 10 years then it might be worth looking at (but still probably not).
  - ihnorton 9y ago
    > it will be incredibly hard to replicate them This is true, sort of. It was also true when people said the same about Perl (some people still say this about Perl!). Then Rails happened to Ruby, just as the web was going through another phase of explosive growth. Likewise, Node or Rust + GitHub effect. The point is not to compare the growth trajectories or projected peaks, but to emphasize the levering effect of an enjoyable language combined with modern collaborative development and infrastructure. A language doesn't need "everyone" to build a viable ecosystem, just a critical mass. > If it's still around in 10 years then it might be worth looking at (but still probably not). Knock on wood and all, but from past observation, I think Julia is at or very close to this critical mass already. I would compare Julia now to the SciPy ecosystem around 2009-2010. That's just when IPython was getting popular, early versions of Pandas were released, and people were starting to trust Cython. (I was a heavy Python user then, and frequently suggested it to Matlab users who had exactly your question even in to 2012-2013). There's already a surprising breadth of packages, they just need time to mature. Sometimes too many, actually -- I recently found three for a relatively obscure geophysics format I used to work with. That lever is big enough that people often don't mind rolling their own.
indescions_2017 9y ago
I know it's also gained a following at MIT's Broad Lab amongst genomics folks. And that seems to be Julia's sweet spot: easy parallelism for scientists who can't spare the time futzing with HPC internals. Congrats to the Julia language team and to the great scientific discoveries that will be enabled with this investment! http://julia.mit.edu/ http://julia.mit.edu/
- Q6T46nT668w6i3m 9y ago
  Interesting. I'm a member of a Broad Institute lab that recently evaluated Julia. We reached the conclusion that Julia, while promising, lacked the flexibility and performance of our existing tools. I suppose this is a problem that investment could address!
  - mac01021 9y ago
    What are the existing tools that it doesn't match up to?
    - Q6T46nT668w6i3m 9y ago
      Python’s scientific ecosystem (i.e. Keras, matplotlib, NumPy, pandas, scikit-image, scikit-learn, SciPy, and TensorFlow, et al.)
      - thebooktocome 9y ago
        * matplotlib is wrapped by PyPlot.jl. * TensorFlow is wrapped by TensorFlow.jl, improves on Python API (https://github.com/malmaud/TensorFlow.jl/blob/master/docs/src/why_julia.md https://github.com/malmaud/TensorFlow.jl/blob/master/docs/sr...) * Pandas is wrapped by Pandas.jl, interfaces with Julia's DataFrame, generally excellent. * Images.jl is a 60%-to-70% replacement for scikit-image. * NumPy and SciPy are mostly covered by StatsBase and Base, can't think of anything off-hand that's missing. Julia's still immature in the machine learning domain, but a lot of new work is being done. One interesting somewhat-new package is Madeleine Udell's LowRankModels.jl, which has a particularly Julian approach to low rank fitting.
skybrian 9y ago
I was a bit surprised by the idea of the FAA using Julia for an "Aircraft Collision Avoidance System", since it apparently doesn't have static type checking and deployment seems like a weak spot. Digging a bit deeper, it appears they use it to deliver specifications and example code to vendors? "[T]ransferring the specifications to industry using this legacy system required three different types of documentation: first, the specifications were written both in variable-based pseudocode and in English descriptive pseudocode. But this approach left gaps in interpretation, leading to possible confusion or disagreement. So programmers also created state charts to fill these gaps and eliminate the potential for misinterpretation." https://juliacomputing.com/case-studies/lincoln-labs.html https://juliacomputing.com/case-studies/lincoln-labs.html
- KenoFischer 9y ago
  Yes, it's quite cool. The specification is a literate Julia program and the specification document is built from it. Simultaneously, they run large scale simulations to make sure that the specification behaves as the theory says it should. There was some discussion with them about deployment, and they funded us for a proof of concept Julia-to-C compiler, but the continuation of that discussion is probably still a number of years off. It's a very long term project.
  - skybrian 9y ago
    Having read a little about TLA+ recently, I'm curious about how specifications are written in Julia. (For example, is there model-checking involved?)
    - simonbyrne 9y ago
      There was a talk at JuliaCon 2015: https://www.youtube.com/watch?v=19zm1Fn0S9M https://www.youtube.com/watch?v=19zm1Fn0S9M Slides here: http://juliacon.org/2015/images/juliacon2015_moss_v3.pdf http://juliacon.org/2015/images/juliacon2015_moss_v3.pdf
drej 9y ago
Julia frustrates me. I was in a mathematical modelling sphere, so I learned the language basics years ago and immediately fell in love (multiple dispatch, optional types, broadcasting, ...). But I couldn't get approvals at work to push it, because it was immature. Then a month later, all my code broke. I rewrote it using new APIs. It broke again. I know they reserved the right to make breaking changes up until 1.0, but they are hindering adoption because of all this. Sorry, guys, but I'll have to stick to Python, Go and (sigh) Fortran. That being said, I applaud getting good funding for a project that's actually more than beyond MVP, it has happy users/customers and it serves a purpose. That's not all that common these days. Good luck, guys.
- smt88 9y ago
  My girlfriend does incredibly complex modeling (she's a computational geneticist). From my limited understanding, it sounds like you may have some of the same challenges that she does. Do you think Python and Go are more suited to that domain than R or Julia?
  - flavio81 9y ago
    R is very, very slow compared to all the other programming platforms (including Ruby, a nice language but with some of the slowest implementations out there). R syntax is very unlike most programming languages so jumping to other languages can feel strange. Python with Numpy, SciPy and Pandas, is the current widespread alternative to R and a good starting point. Recommended!!
    - nnm 9y ago
      For all the cases I worked on, R is not slow at all. At home, I wrote a R package to do deep learning (include most common layers, but only conv layer was implemented in C++) from scratch, and I also wrote one in python (using numpy + numba). My R version is same fast as python on the MNIST dataset (without conv layers, hence all code is in plain R or python).
      - 9y ago
flavio81 9y ago
I wish them all the best. Julia seems to a good idea -- a high performance language with easy syntax (easy for Python users to jump to Julia), very good features for threading/multiprogramming, good type system... ...here it seem like any other modern language, until you see that Julia has something that many other languages lack: true macros (true metaprogramming.) A big feature. And multiple dispatch on all functions! (a very nice feature that puts it above many other languages in use.) You can even program Julia in s-expressions if you feel like it. (Some argue that Julia should be considered a Lisp dialect.) Compared to the other languages with Python-like, C-like or Algol-like syntax, Julia stands out from them as a more powerful alternative. (If you need more power and flexibility than Julia with good processing speed, i think only Common Lisp will clearly provide it.) A very recommendable language, especially now with this initiative for giving more "enterprise-like" support, and worth looking in depth, if you are also considering moving to Go and Rust.
- zbobet2012 9y ago
  Julia is a great language, but it is still focused on the numerical computing world and is not "1.0" (language and API's are not locked down). I wouldn't really compare it to Go or Rust at this point. Go is focused on building services at its core (e.g. external event driven workflows). Aka microservices. Rust is focused on being a "traditional" systems programming language. Things like web browsers (duh), office suites, games, etc. Julia is focused on taking the best of R, Python/Numpy, and Matlab. It is a _great_ language. But as far as I can see it's really focused on a different problem space.
  - flavio81 9y ago
    If you take a look at the features (esp. macros and multiple dispatch) you can see that is readily extensible for lots of applications. Thus the reason it is also sold as a "general purpose" language. Additonally, the very good support for threading and multiprocessing means it has a good future with services, async IO, and with event-loop web servers (a la node.js and friends). Finally, the focus on fast speed for numerical computation is particularly suited to games (as is the support for threading).
edshiro 9y ago
That's an impressive amount of money to raise in Seed funding. Here in the UK this is more like Series A money: a Seed round would probably be around $700K (no authoritative source - purely from knowledge of a few companies raising this kind of money). I do wonder though: have Julia been able to raise this much money thanks to awesome traction or the reputation of the team?
- CalChris 9y ago
  That's A money. They were founded in 2013. They have 11-50 employees. They already have products (read: MVP) and services. That's not proving a hypothesis seed money. That's scaling an enterprise A money. That said, Crunchbase [1] says seed round. But that's A money both by size and apparent intent. We are self-funded and have been profitable from day one. [2] [1] https://www.crunchbase.com/organization/julia-computing#/entity https://www.crunchbase.com/organization/julia-computing#/ent... [2] https://venturebeat.com/2015/05/18/why-the-creators-of-the-julia-programming-language-just-launched-a-startup/ https://venturebeat.com/2015/05/18/why-the-creators-of-the-j...
cyber1 9y ago
Julia seems very attractive with clear syntax and really fast! I wish you best! I think Julia should be very nice in server-side development.
rllin 9y ago
Any suggestions for learning Julia (detailed projects to follow) for fluent pythonistas?
- ninjin 9y ago
  I would recommend David P. Sanders talks at JuliaCon 2015 and 2016. https://www.youtube.com/watch?v=gQ1y5NUD_RI&index=3&list=PLP8iPy9hna6Sdx4soiGrSefrmOPdUWixM https://www.youtube.com/watch?v=gQ1y5NUD_RI&index=3&list=PLP... http://juliacon.org/2016/workshops.html#invitation http://juliacon.org/2016/workshops.html#invitation Personally, I enjoyed reading the manual when I learned it. But I realise that it isn't for everyone.
- simonbyrne 9y ago
  The basic syntax itself will look very familiar to someone coming from python. The immediate obvious differences: - 1-based indexing instead of 0-based - end keyword vs significant whitespace blocks (and no colon to start a block) - function instead of def (or defined inline f(x) = ...) - x -> ... instead of lambda x : ... - start:stop/start:step:stop instead range(start,stop)/range(start,stop,step) - strings are joined with * instead of + The actual internals, and features such as types and multiple dispatch can make it a very different to use, but hopefully that should be enough to get you started.
  - kem 9y ago
    FWIW, I think now technically indexing can either be 1- or 0-based.
pixelmonkey 9y ago
Anyway else think it's interesting that $4.6m could be labeled "seed funding"? Seed round 2016 average is around $1m per Crunchbase: https://techcrunch.com/2016/09/07/crunchbase-sees-rise-in-average-seed-round-in-2016/ https://techcrunch.com/2016/09/07/crunchbase-sees-rise-in-av... Cooley has seed round pre-moneys at $4-6m. (Thus, a $5m check is pretty much out of question at that stage.) https://www.cooleygo.com/trends/ https://www.cooleygo.com/trends/ So, this is much more like a Series A? I wonder if they were advised to call it seed funding to leave open the possibility of a "big" Series A, due to the level of interest. It does seem like SV VCs are making somewhat large bets on F/OSS-based companies, so maybe that is wise. I also realize a lot of this is semantics.
- ghc 9y ago
  General Catalyst and Founder Collective are both Boston funds, so SV rules don't apply here. For better or worse raising in Boston is very different. I did a bit of diligence on this deal as it was going around (for a different fund), and the dynamics of the company stage probably dictated the raise size. It's not uncommon for an institutional seed round to be $2-3MM on a valuation of $6-15MM in Boston right now. The size of rounds is partially a function of how frothy the OSS tools market has gotten. I'm looking at another hybrid enterprise/OSS deal that has a valuation north of $15MM. A rounds nowadays look for $!MM ARR so you can say the letters shifted as well :)
faitswulff 9y ago
I wonder if this would be a feasible funding strategy for Crystal (https://crystal-lang.org/ https://crystal-lang.org/) as well. From the scattered benchmarks I could find online†, Crystal seems to be just as fast, if not a bit faster. † https://github.com/kostya/benchmarks https://github.com/kostya/benchmarks, http://blog.seraum.com/crystal-lang-vs-nodejs-vs-golang-vs-julia-vs-rust-vs-luajit-vs-c-fibonacci-benchmark http://blog.seraum.com/crystal-lang-vs-nodejs-vs-golang-vs-j...
- throwaway7645 9y ago
  Well, they're both implemented in LLVM, but Julia uses a JIT and not straight to a native binary. I bet they have very similar performance. The use cases are different though. Julia is a replacement for MATLAB, R, Fortran, & scientific Python with speed and good macro support. Crystal is basically fast/native Ruby, so websites and business apps. You could technically use either for those applications, but the communities will be mostly scientific computing for Julia and business apps for Crystal.
  - cutler 9y ago
    Last time I looked Crystal's string handling was significantly slower than Ruby's.
    - throwaway7645 9y ago
      In a pre 1.0 language there will always be some things that are still waiting to be optimized. I saw some performance spec for one of their HTTP libraries awhile back and it was a lot faster than Ruby. It might have been faster than Go.
      - glwtta 9y ago
        It was faster than everything, and by a huge margin: https://github.com/costajob/app-servers#results https://github.com/costajob/app-servers#results It does seem that immature languages tend to win a lot of benchmarks, likely because they're still cutting a lot of corners. Still, though. I only wish they picked a more "Enterprise-friendly" name...
howfun 9y ago
Wasn't that a language with 1 based indexes? Is yes, then forget about wide adoption.
tempodox 9y ago
So, will Julia finally emit stand-alone binary executables, AOT-compiled?

279 points by sandGorgon 9y ago

krupan 9y ago

What is the business model? How are they making money?

rattray 9y ago
https://juliacomputing.com/products/ https://juliacomputing.com/products/ - JuliaPro is the go-to product for anyone who wants to use Julia – professionals, engineers, software developers and data scientists. Beginners and experts can build better software quicker and dramatically improve performance. - JuliaRun is for scalable deployment of Julia in production for real time analytics and large-scale parallel simulations in the public cloud or a private cluster. - JuliaBox lets data scientists, quants and strats run Julia in Jupyter notebooks right in the browser. JuliaBox is the number one choice for universities teaching Julia since students can get started with Julia in seconds with no installation required. - JuliaFin is a suite of Julia packages that simplify the workflow for quantitative finance, reducing the gap between the quant and the trader. JuliaFin provides the fastest path from market data to market transactions by leveraging the full power of Julia for implementing new trading strategies where time is of the essence. EDIT: Oh, and consulting/training: https://juliacomputing.com/services/ https://juliacomputing.com/services/
- SolarNet 9y ago
  Answering the actual question: JuliaPro is basically VisualStudio Express vs. Pro (e.g. there's a free version and then an expensive version with better integrations, better enterprise level tools, etc). JuliaRun is basically a cloud service (e.g. charge for hosting) designed for julia code. JuliaBox seems to be a cloud IDE / google docs / notebook style product. Probably with a similar model to github eventually (e.g. pay for teams / commercial use, etc). JuliaFin seems to be a way to charge banks a lot more money for the same stuff.
  - simonbyrne 9y ago
    - JuliaPro is free, but the paid version comes with support and integration with Excel and JuliaRun (possibly more features in future). - JuliaRun is for easily scalable deployment of code, either for cloud or local servers. - JuliaBox is as you say, available here https://juliabox.com/ https://juliabox.com/ - JuliaFin is completely separate, and currently consists of Bloomberg integration and the Miletus contract specification and pricing package. (I work for Julia Computing)
    - jordigh 9y ago
      Hey, I'd be interested in knowing if you're just selling plain ol' non-free software. Can I buy the PRO version and give it to my friends, can I study it, can I modify it? It's a little sad when we are trying to figure out how to make money for free software and the answer ends up being "sell (some) software non-freely". Okay, so it's impossible to make money without selling non-free software? There can only be one Red Hat in the world? I don't think it's any consolation that you are also producing free software; pretty much every major software vendor nowadays does that while keeping all the interesting secret sauce secret.
      - thebooktocome 9y ago
        Having explored this option somewhat recently, I assure you: it is just plain, old, non-free software.
      - simonbyrne 9y ago
        > Can I buy the PRO version and give it to my friends, can I study it, can I modify it? Mostly: JuliaPro is essentially a nice convenient installer of a huge collection of open source work (though I can't say for sure, as I'm not really involved in that part). A few small components (e.g. the Excel plugin) are not open, and we are moving to MKL for some stuff, so that will change slightly though. Open source business models are hard (as you're no doubt aware). You may disagree, but I think Julia Computing has so far done a good job of balancing the open source vs business aspect, which is part of the reason I originally joined.
- jordigh 9y ago
  So, are they just selling plain ol' non-free software? The "PRO" features seem to be ripping out the GPL libraries so they can link to MKL with a support contract. I have hope this means that the paid version is also free software. https://juliacomputing.com/products/juliapro.html https://juliacomputing.com/products/juliapro.html
  - coldtea 9y ago
    >So, are they just selling plain ol' non-free software? Or, as we used to call it, just "software". They're selling software.
    - jordigh 9y ago
      Right, but selling non-free software isn't interesting for those of us who really want to see more businesses thrive around free software. If it's really impossible to make money without selling non-free software, then Julia isn't very interesting from an economic perspective. If Julia hasn't figured out how to make money without restricting customers via legal or technical means, trying to lock them in, or putting software in their machines that controls their machines without the full knowledge and consent of the users (i.e. source code and permission), then I'm not really interested in what they're doing economically.
- wott 9y ago
  > JuliaBox is the number one choice for universities teaching Julia since students can get started with Julia in seconds with no installation required. I don't understand. Julia is not stable at all yet. Who would want to teach it now? What would be the point?
  - apesti 9y ago
    I recently used Julia in my numerical analysis course and it worked well for the most part. We couldn't use it for some of the larger projects because it wasn't as optimized as MATLAB for certain matrix operations. However, it was great most of the time and better than using MATLAB on the university computers.
    - elcritch 9y ago
      Implementing a missing matrix operator might make a great class project for a numerical methods course. ;)
baybal2 9y ago
By taking investor money and living on it. Have yet to hear about them making anymuch significant money except for seemingly staged "success stories"
olympus 9y ago
They sell an enterprise version of Julia for $1500 per year. The main things you get are support, MS Excel integration, and a non-GPL license. They also offer consulting, training, and a few other flavors of Julia (large scale server based gets its own edition). I'm sure the main draw will be companies that don't want to be stuck with a GPL license in their product.
- Buttons840 9y ago
  Isn't Julia licensed under the MIT license? They advertise being MIT licensed on their website, and their Github repository is also under the MIT license.
  - sharpy 9y ago
    The core language is. The libraries are another matter. From the website: The core of the Julia implementation is licensed under the MIT license. Various libraries used by the Julia environment include their own licenses such as the GPL, LGPL, and BSD (therefore the environment, which consists of the language, user interfaces, and libraries, is under the GPL).
- wodenokoto 9y ago
  So if I send a patch to Julia, do I have to give them the rights to GPL and a non-GPL version?
  - Buttons840 9y ago
    They advertise that Julia is MIT licensed on their website, so I think the parent comment is wrong.
    - olympus 9y ago
      They state "non-GPL" version on their website: https://juliacomputing.com/products/juliapro.html https://juliacomputing.com/products/juliapro.html
    - 9y ago

smaili 9y ago

For those thinking "What's Julia?", article has a nice tldr -- Julia is the fastest modern high performance open source computing language for data, analytics, algorithmic trading, machine learning and artificial intelligence.

dkarapetyan 9y ago

"Speed of C++ and Java"? What does that even mean?

mr_overalls 9y ago
Its performance rivals that of traditionally "fast" languages for a wide variety of benchmarks. https://julialang.org/benchmarks/ https://julialang.org/benchmarks/ https://www.ibm.com/developerworks/community/blogs/jfp/entry/A_Comparison_Of_C_Julia_Python_Numba_Cython_Scipy_and_BLAS_on_LU_Factorization?lang=en https://www.ibm.com/developerworks/community/blogs/jfp/entry... https://modelingguru.nasa.gov/docs/DOC-2625 https://modelingguru.nasa.gov/docs/DOC-2625

nickpeterson 9y ago

I'm not sure I buy the notion of, "Solves the two language problem." There is always going to be some library or environment consideration that makes a unified language for everything impractical (unless your entire business runs on an AS400).

andlrc 9y ago
> unless your entire business runs on an AS400 Even there you got support for C, C++, Java amung others as well as the whole IBM family (RPG/ILE, CL, REXX, ...) With somewhat decent bindings in RPG.
rattray 9y ago
They have a pretty good reputation for FFI to most complements, IIRC. eg; https://docs.julialang.org/en/stable/manual/calling-c-and-fortran-code/ https://docs.julialang.org/en/stable/manual/calling-c-and-fo... https://github.com/JuliaPy/PyCall.jl https://github.com/JuliaPy/PyCall.jl
ffriend 9y ago
The two language problem is more specific to scientific computing where literally every popular library is written in more then one language - fast C/C++/Fortran and convenient high-level R/Matlab/Python. NumPy, SciPy, Caffe, Theano, Tensorflow to name a few. I once had to rewrite Matlab + C code (for face tracking) to pure Julia - not only the resulting code was almost twice smaller, it also ran ~20% faster.
- dnautics 9y ago
  I wrote a library in c to replace a Julia one I wrote (and make it more portable) and found that the c version was slower! (Couldn't inline functions as easily in c and was also being more parsimonious about memory usage in c)

mr_overalls 9y ago

This seems a little premature when considering that Julia is only up to v0.6, and the language is still making breaking changes. https://github.com/JuliaLang/julia/blob/release-0.6/NEWS.md https://github.com/JuliaLang/julia/blob/release-0.6/NEWS.md

sriram_sun 9y ago

I've heard good reviews of the language here. So I've no doubt about its technical capabilities. However, funding might suppress the ability to make breaking changes.. which I believe are pretty good until 1.0 or so. On the business side, will we end up with (hopefully) a better, cheaper Matlab?

hatmatrix 9y ago
$1500 for JuliaPro - maybe not?
- sriram_sun 9y ago
  My guess is that we'll probably start seeing pricing split into various modules a la Matlab - Julia Financial, FEM, Semiconductor etc. Also Julia Enterprise etc. Maybe Mathworks will acquire them and call it MATLAB 2020!

zitterbewegung 9y ago

Sounds like a good idea for the language and a good startup idea. I know of two businesses that support python and since Data Science is a growing field we can always need competition in environments and languages.

drenvuk 9y ago

So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. That's some very slick but sketchy marketing tactics. I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use.

deleted 9y ago
[deleted]
omginternets 9y ago
What exactly is sketchy, here?
ihnorton 9y ago
> So this is why every once in a while we see some article about Julia being as easy to use as Python while having nearly the performance of C. Here's a performance testimonial with links to real code and benchmarks against Fortran (not Python), from Steven G. Johnson, one of the authors of FFTW: https://discourse.julialang.org/t/julia-vs-fortran-complaint/4366/7 https://discourse.julialang.org/t/julia-vs-fortran-complaint... I implemented an erfinv function Julia (https://github.com/JuliaLang/julia/pull/29874 https://github.com/JuliaLang/julia/pull/29874), and it was about 3x faster than Matlab or SciPy's erfinv function, both of which are taken from standard Fortran libraries. [...] I have found similar gains (compared to Fortran code called in SciPy) for other special functions, e.g. polygamma functions (https://github.com/JuliaLang/julia/pull/71255 https://github.com/JuliaLang/julia/pull/71255) and exponential integrals (https://github.com/JuliaMath/SpecialFunctions.jl/issues/19 https://github.com/JuliaMath/SpecialFunctions.jl/issues/19). The reason Julia can beat the Fortran code is that metaprogramming makes it easy to apply performance optimizations that are awkward in Fortran. We have metaprogramming macros (@evalpoly) that can easily inline polynomial evaluations, whereas the Fortran code makes function calls that loop over look-up tables of polynomial coefficients. Even greater speedups are possible for evaluating polynomials of complex arguments, where there is a fancy recurrence from Knuth that is almost impossible to use effectively without code generation. In principle, the Fortran authors could have done the same inlining and gotten similar performance, but the code would have been much more painful to write by hand. (They could even have written a program to generate Fortran code, but that is even more painful.) > I'd rather deal with Cython apache license than deal with this GPL stuff for commercial use. The Julia core is MIT licensed. The language distribution is currently GPL licensed due to linked dependencies, but there has been a no-GPL build flag available for several releases. GPL (and other) dependencies are continually being removed, and there are only one or two (GPL) libraries left in the currently nightly build.

gruglife 9y ago

As someone that uses Python and R for data analysis, is it worth learning this language?

flavio81 9y ago
Yes, it is a nice language with clean syntax and high performance. Syntax is similar to python so it will be easy to learn for you.
_1 9y ago
I didn't find it worth the time. When doing HPC work, I really care about performance, and couldn't get anywhere near the speeds as C + MPI. If it's not HPC, I don't see a benefit over Python.
gaius 9y ago
No. Not a knock on the language (I've never used it) but look at the R and Python ecosystems - it will be incredibly hard to replicate them. Then you have all the proprietary tools - MATLAB, Mathematica, Q etc - they aren't going anywhere. Then there's all the legacy FORTRAN code... If it's still around in 10 years then it might be worth looking at (but still probably not).
- ihnorton 9y ago
  > it will be incredibly hard to replicate them This is true, sort of. It was also true when people said the same about Perl (some people still say this about Perl!). Then Rails happened to Ruby, just as the web was going through another phase of explosive growth. Likewise, Node or Rust + GitHub effect. The point is not to compare the growth trajectories or projected peaks, but to emphasize the levering effect of an enjoyable language combined with modern collaborative development and infrastructure. A language doesn't need "everyone" to build a viable ecosystem, just a critical mass. > If it's still around in 10 years then it might be worth looking at (but still probably not). Knock on wood and all, but from past observation, I think Julia is at or very close to this critical mass already. I would compare Julia now to the SciPy ecosystem around 2009-2010. That's just when IPython was getting popular, early versions of Pandas were released, and people were starting to trust Cython. (I was a heavy Python user then, and frequently suggested it to Matlab users who had exactly your question even in to 2012-2013). There's already a surprising breadth of packages, they just need time to mature. Sometimes too many, actually -- I recently found three for a relatively obscure geophysics format I used to work with. That lever is big enough that people often don't mind rolling their own.

indescions_2017 9y ago

I know it's also gained a following at MIT's Broad Lab amongst genomics folks. And that seems to be Julia's sweet spot: easy parallelism for scientists who can't spare the time futzing with HPC internals. Congrats to the Julia language team and to the great scientific discoveries that will be enabled with this investment! http://julia.mit.edu/ http://julia.mit.edu/

Q6T46nT668w6i3m 9y ago
Interesting. I'm a member of a Broad Institute lab that recently evaluated Julia. We reached the conclusion that Julia, while promising, lacked the flexibility and performance of our existing tools. I suppose this is a problem that investment could address!
- mac01021 9y ago
  What are the existing tools that it doesn't match up to?
  - Q6T46nT668w6i3m 9y ago
    Python’s scientific ecosystem (i.e. Keras, matplotlib, NumPy, pandas, scikit-image, scikit-learn, SciPy, and TensorFlow, et al.)
    - thebooktocome 9y ago
      * matplotlib is wrapped by PyPlot.jl. * TensorFlow is wrapped by TensorFlow.jl, improves on Python API (https://github.com/malmaud/TensorFlow.jl/blob/master/docs/src/why_julia.md https://github.com/malmaud/TensorFlow.jl/blob/master/docs/sr...) * Pandas is wrapped by Pandas.jl, interfaces with Julia's DataFrame, generally excellent. * Images.jl is a 60%-to-70% replacement for scikit-image. * NumPy and SciPy are mostly covered by StatsBase and Base, can't think of anything off-hand that's missing. Julia's still immature in the machine learning domain, but a lot of new work is being done. One interesting somewhat-new package is Madeleine Udell's LowRankModels.jl, which has a particularly Julian approach to low rank fitting.

skybrian 9y ago

I was a bit surprised by the idea of the FAA using Julia for an "Aircraft Collision Avoidance System", since it apparently doesn't have static type checking and deployment seems like a weak spot. Digging a bit deeper, it appears they use it to deliver specifications and example code to vendors? "[T]ransferring the specifications to industry using this legacy system required three different types of documentation: first, the specifications were written both in variable-based pseudocode and in English descriptive pseudocode. But this approach left gaps in interpretation, leading to possible confusion or disagreement. So programmers also created state charts to fill these gaps and eliminate the potential for misinterpretation." https://juliacomputing.com/case-studies/lincoln-labs.html https://juliacomputing.com/case-studies/lincoln-labs.html

KenoFischer 9y ago
Yes, it's quite cool. The specification is a literate Julia program and the specification document is built from it. Simultaneously, they run large scale simulations to make sure that the specification behaves as the theory says it should. There was some discussion with them about deployment, and they funded us for a proof of concept Julia-to-C compiler, but the continuation of that discussion is probably still a number of years off. It's a very long term project.
- skybrian 9y ago
  Having read a little about TLA+ recently, I'm curious about how specifications are written in Julia. (For example, is there model-checking involved?)
  - simonbyrne 9y ago
    There was a talk at JuliaCon 2015: https://www.youtube.com/watch?v=19zm1Fn0S9M https://www.youtube.com/watch?v=19zm1Fn0S9M Slides here: http://juliacon.org/2015/images/juliacon2015_moss_v3.pdf http://juliacon.org/2015/images/juliacon2015_moss_v3.pdf

drej 9y ago

Julia frustrates me. I was in a mathematical modelling sphere, so I learned the language basics years ago and immediately fell in love (multiple dispatch, optional types, broadcasting, ...). But I couldn't get approvals at work to push it, because it was immature. Then a month later, all my code broke. I rewrote it using new APIs. It broke again. I know they reserved the right to make breaking changes up until 1.0, but they are hindering adoption because of all this. Sorry, guys, but I'll have to stick to Python, Go and (sigh) Fortran. That being said, I applaud getting good funding for a project that's actually more than beyond MVP, it has happy users/customers and it serves a purpose. That's not all that common these days. Good luck, guys.

smt88 9y ago
My girlfriend does incredibly complex modeling (she's a computational geneticist). From my limited understanding, it sounds like you may have some of the same challenges that she does. Do you think Python and Go are more suited to that domain than R or Julia?
- flavio81 9y ago
  R is very, very slow compared to all the other programming platforms (including Ruby, a nice language but with some of the slowest implementations out there). R syntax is very unlike most programming languages so jumping to other languages can feel strange. Python with Numpy, SciPy and Pandas, is the current widespread alternative to R and a good starting point. Recommended!!
  - nnm 9y ago
    For all the cases I worked on, R is not slow at all. At home, I wrote a R package to do deep learning (include most common layers, but only conv layer was implemented in C++) from scratch, and I also wrote one in python (using numpy + numba). My R version is same fast as python on the MNIST dataset (without conv layers, hence all code is in plain R or python).
    - 9y ago

flavio81 9y ago

I wish them all the best. Julia seems to a good idea -- a high performance language with easy syntax (easy for Python users to jump to Julia), very good features for threading/multiprogramming, good type system... ...here it seem like any other modern language, until you see that Julia has something that many other languages lack: true macros (true metaprogramming.) A big feature. And multiple dispatch on all functions! (a very nice feature that puts it above many other languages in use.) You can even program Julia in s-expressions if you feel like it. (Some argue that Julia should be considered a Lisp dialect.) Compared to the other languages with Python-like, C-like or Algol-like syntax, Julia stands out from them as a more powerful alternative. (If you need more power and flexibility than Julia with good processing speed, i think only Common Lisp will clearly provide it.) A very recommendable language, especially now with this initiative for giving more "enterprise-like" support, and worth looking in depth, if you are also considering moving to Go and Rust.

zbobet2012 9y ago
Julia is a great language, but it is still focused on the numerical computing world and is not "1.0" (language and API's are not locked down). I wouldn't really compare it to Go or Rust at this point. Go is focused on building services at its core (e.g. external event driven workflows). Aka microservices. Rust is focused on being a "traditional" systems programming language. Things like web browsers (duh), office suites, games, etc. Julia is focused on taking the best of R, Python/Numpy, and Matlab. It is a _great_ language. But as far as I can see it's really focused on a different problem space.
- flavio81 9y ago
  If you take a look at the features (esp. macros and multiple dispatch) you can see that is readily extensible for lots of applications. Thus the reason it is also sold as a "general purpose" language. Additonally, the very good support for threading and multiprocessing means it has a good future with services, async IO, and with event-loop web servers (a la node.js and friends). Finally, the focus on fast speed for numerical computation is particularly suited to games (as is the support for threading).

edshiro 9y ago

That's an impressive amount of money to raise in Seed funding. Here in the UK this is more like Series A money: a Seed round would probably be around $700K (no authoritative source - purely from knowledge of a few companies raising this kind of money). I do wonder though: have Julia been able to raise this much money thanks to awesome traction or the reputation of the team?

CalChris 9y ago
That's A money. They were founded in 2013. They have 11-50 employees. They already have products (read: MVP) and services. That's not proving a hypothesis seed money. That's scaling an enterprise A money. That said, Crunchbase [1] says seed round. But that's A money both by size and apparent intent. We are self-funded and have been profitable from day one. [2] [1] https://www.crunchbase.com/organization/julia-computing#/entity https://www.crunchbase.com/organization/julia-computing#/ent... [2] https://venturebeat.com/2015/05/18/why-the-creators-of-the-julia-programming-language-just-launched-a-startup/ https://venturebeat.com/2015/05/18/why-the-creators-of-the-j...

cyber1 9y ago

Julia seems very attractive with clear syntax and really fast! I wish you best! I think Julia should be very nice in server-side development.

rllin 9y ago

Any suggestions for learning Julia (detailed projects to follow) for fluent pythonistas?

ninjin 9y ago
I would recommend David P. Sanders talks at JuliaCon 2015 and 2016. https://www.youtube.com/watch?v=gQ1y5NUD_RI&index=3&list=PLP8iPy9hna6Sdx4soiGrSefrmOPdUWixM https://www.youtube.com/watch?v=gQ1y5NUD_RI&index=3&list=PLP... http://juliacon.org/2016/workshops.html#invitation http://juliacon.org/2016/workshops.html#invitation Personally, I enjoyed reading the manual when I learned it. But I realise that it isn't for everyone.
simonbyrne 9y ago
The basic syntax itself will look very familiar to someone coming from python. The immediate obvious differences: - 1-based indexing instead of 0-based - end keyword vs significant whitespace blocks (and no colon to start a block) - function instead of def (or defined inline f(x) = ...) - x -> ... instead of lambda x : ... - start:stop/start:step:stop instead range(start,stop)/range(start,stop,step) - strings are joined with * instead of + The actual internals, and features such as types and multiple dispatch can make it a very different to use, but hopefully that should be enough to get you started.
- kem 9y ago
  FWIW, I think now technically indexing can either be 1- or 0-based.

pixelmonkey 9y ago

Anyway else think it's interesting that $4.6m could be labeled "seed funding"? Seed round 2016 average is around $1m per Crunchbase: https://techcrunch.com/2016/09/07/crunchbase-sees-rise-in-average-seed-round-in-2016/ https://techcrunch.com/2016/09/07/crunchbase-sees-rise-in-av... Cooley has seed round pre-moneys at $4-6m. (Thus, a $5m check is pretty much out of question at that stage.) https://www.cooleygo.com/trends/ https://www.cooleygo.com/trends/ So, this is much more like a Series A? I wonder if they were advised to call it seed funding to leave open the possibility of a "big" Series A, due to the level of interest. It does seem like SV VCs are making somewhat large bets on F/OSS-based companies, so maybe that is wise. I also realize a lot of this is semantics.

ghc 9y ago
General Catalyst and Founder Collective are both Boston funds, so SV rules don't apply here. For better or worse raising in Boston is very different. I did a bit of diligence on this deal as it was going around (for a different fund), and the dynamics of the company stage probably dictated the raise size. It's not uncommon for an institutional seed round to be $2-3MM on a valuation of $6-15MM in Boston right now. The size of rounds is partially a function of how frothy the OSS tools market has gotten. I'm looking at another hybrid enterprise/OSS deal that has a valuation north of $15MM. A rounds nowadays look for $!MM ARR so you can say the letters shifted as well :)

faitswulff 9y ago

I wonder if this would be a feasible funding strategy for Crystal (https://crystal-lang.org/ https://crystal-lang.org/) as well. From the scattered benchmarks I could find online†, Crystal seems to be just as fast, if not a bit faster. † https://github.com/kostya/benchmarks https://github.com/kostya/benchmarks, http://blog.seraum.com/crystal-lang-vs-nodejs-vs-golang-vs-julia-vs-rust-vs-luajit-vs-c-fibonacci-benchmark http://blog.seraum.com/crystal-lang-vs-nodejs-vs-golang-vs-j...

throwaway7645 9y ago
Well, they're both implemented in LLVM, but Julia uses a JIT and not straight to a native binary. I bet they have very similar performance. The use cases are different though. Julia is a replacement for MATLAB, R, Fortran, & scientific Python with speed and good macro support. Crystal is basically fast/native Ruby, so websites and business apps. You could technically use either for those applications, but the communities will be mostly scientific computing for Julia and business apps for Crystal.
- cutler 9y ago
  Last time I looked Crystal's string handling was significantly slower than Ruby's.
  - throwaway7645 9y ago
    In a pre 1.0 language there will always be some things that are still waiting to be optimized. I saw some performance spec for one of their HTTP libraries awhile back and it was a lot faster than Ruby. It might have been faster than Go.
    - glwtta 9y ago
      It was faster than everything, and by a huge margin: https://github.com/costajob/app-servers#results https://github.com/costajob/app-servers#results It does seem that immature languages tend to win a lot of benchmarks, likely because they're still cutting a lot of corners. Still, though. I only wish they picked a more "Enterprise-friendly" name...

howfun 9y ago

Wasn't that a language with 1 based indexes? Is yes, then forget about wide adoption.

tempodox 9y ago

So, will Julia finally emit stand-alone binary executables, AOT-compiled?