To teach this more conveniently to his students, he wrote the original version of MATrixLABoratory to allow interactive exploration of the library functions without having to compile FORTRAN code. The original version was about 2000 lines of code in FORTRAN.

Engineering students loved it so much that he decided to make a company around this product. His buddy expanded and rewrote the interpreter in C, for a PC, and the rest is history:

"In 1983 Jack Little suggested the creation of a commercial product based on MATLAB. I said I thought that was a good idea, but I didn't join him initially. The IBM PC had been introduced only two years earlier and was barely powerful enough to run something like MATLAB, but Little anticipated its evolution. He left his job, bought a Compaq PC clone at Sears, moved into the hills behind Stanford, and, with my encouragement, spent a year and a half creating a new and extended version of MATLAB written in C. A friend, Steve Bangert, joined the project and worked on the new MATLAB in his spare time."

User guide for the original version of MATLAB: https://blogs.mathworks.com/cleve/2018/02/05/the-historic-ma...

The source code of the very early (1982?) FORTRAN version of MATLAB: https://github.com/johnsonjh/matlab

The origins of the first PC version: https://blogs.mathworks.com/cleve/2018/03/09/matlab-history-...

On a couple of occasions Cleve mentioned that he had fairly significant troubles sleeping and I was impressed by how well he performed with so little good sleep. Turns out, I had undiagnosed sleep apnea myself that only got worse over the years (until I had three surgeries to significantly reduce it). During my bad apnea days there were times where I pulled myself together and rallied by remembering Cleve. I'll never be able to repay Cleve for what he did indirectly for me, much less what was direct and deliberate, but I do try to help others and will remain inspired by him until my brain no longer processes.

While I personally try to avoid contemporary matlab at any cost, the open source ecosystem is great and matlab would be my go-to tool if they would not exist.

Cleve's papers were an inspiration. I soon published my own matrix package called matfunc. That work was heavily influenced by Cleve Moler and by algorithms in Golub and Van Loan. Even my more recent Python contributions, like the super accurate math.fsum(), math.hypot(), and math.sumprod() functions, have their roots in that fertile time in the Matlab ecosystem. In particular, it newsgroups and lists of papers taught me Cleve's never ending quest to create clean front-ends for numerically sophisticated code.

Thank you Cleve. Your legacy will live forever.

Great mathematician and a wonderful human being. I never stopped learning when I was around him. thttps://blogs.mathworks.com/cleve/2013/11/25/the-ardent-tita...

> I first met Cleve Moler when I was a graduate student and he visited Stanford, where his loud and friendly voice reverberated around Serra House. Moler is the antithesis of a European, and as a transatlantic soul, I love both Europeans and their antitheses. A room with Moler in it is a no-nonsense zone. He has no interest in showing you how your problem is connected with the theory of pseudodifferential operators. He just wants to get things done computationally, and nobody has done it better. Moler is about the same age as Knuth, and while Knuth was writing his great books on the analysis of discrete algorithms, Moler was creating the modern era of numerical software. He was an author of both of the foundational software packages of the 1970s, EISPACK and LINPACK, and he also published two influential software-based numerical analysis textbooks. And then, in around 1977 in the Computer Science department at the University of New Mexico, he invented Matlab, which changed the world.

I never liked using Matlab (the little I used it), but after reading this I understood better what its innovation was: “All the right algorithms would be invoked in all the right places, without the user needing to know the details.”

A footnote I found interesting:

> For me `eig(A)` epitomizes the successful contribution of numerical analysis to our technological world. Physicists, chemists, engineers, and mathematicians know that computing eigenvalues of matrices is a solved problem. Simply invoke `eig(A)`, or its equivalent in whatever language you are using, and you tap into the work of generations of numerical analysts. The algorithm involved, the QR algorithm, is completely reliable, utterly nonobvious, and amazingly fast. On my laptop, for a 1000 × 1000 matrix A, `eig(A)` computes all 1000 eigenvalues in half a second.

[1]: https://sites.math.rutgers.edu/~zeilberg/akherim/NickApology...