Presentation
I do research and development in applied mathematics for digital art matters, mostly photography, typically involving some physics and computer programming. With the maths models I built, I design end-user software and applications. For the applications I built, I design websites, write documentations and shoot educational videos. For the (scattered) documentations I wrote, I designed an (unified) AI-driven search engine.
I’m one of the very few open-source developers who write down the maths before opening their
IDE
to code anything silly. I have lost too much time because someone (sometimes me) tried hack something quickly and validated it on a non-significant sample where “it worked”. From cursory reading of such code, I can nowadays typically predict in which cases the hacks will fail, but without being able to propose a fix, because you can’t fix something that doesn’t state its goal in maths terms (especially since it gets buried into contextual technicalities as soon as computers are involved). Theory and abstraction win over inductivism and should be used from the inception of the project if you want to make good use of your time.
Engineering is the art of designing solutions supported by scientific knowledge and optimized by numerical simulation.
Designing is deciding, deciding is choosing priorities, and no amount of science will ever tell what you should rather choose: it is a discussion between you and your values. What science tells engineers is how to achieve a goal, not which goal to pick. What engineers sometimes forget is they are only fooling themselves when they hide behind numbers to make their choices look more objective. What non-engineers don’t know is design is not just bending technology to deliver something ASAP: technology is only one mean to an end, not the only one, not always the best one. What both — engineers or not — often overlook is it’s often better, and usually simpler, to fix workflows rather than to fix tools, which involves understanding the needs (past the requests), educating, communicating and changing habits but little to no design. Nevertheless, since those are typically not the forte of the guys enjoying science, technology and technics, everyone stays in their silo of expertise, looks for solutions under the lamp post , and when you have a hammer, everything looks like a nail .
Design stems from a definite goal, clearly understood, expressed in technical terms, and objectively measurable. From there, means are unrolled and tuned until we reach optimality of the solution under the given set of constraints. Hacks are thoughtless happy accidents guaranteed to backfire because they start and end with “doing”. You can’t improve on hacks, at best you can add alternative hacks. Since open-source is made of saturday-afternoon pet projects, that’s all you will get for your money though.
This is where I publish those maths, to serve as addendum to my code, and other research or reflections on design and software.
Trained in applied physics, then metrology and finally mechanical engineering, I’m only interested in scientific computation and numerical simulation of physical processes. I had to step on software engineer’s feet because it seems they can’t be entrusted neither with end-user applications nor with vector algebra. In the process, I have grown an acquired taste for end-user interfaces design.
I only use computers to solve real-world problems that require math processing, don’t mistake me for a technophile. My super-power is I hate programming, which makes me favour lean and simple solutions requiring the least amount of code, libs, deps, tech stacks and therefore the least amount of maintenance.
In a typical day, I write C, OpenCL and Python. I had to become proficient in PHP, Javascript, LaTeX and CSS for my websites and documentations. Since I moved all my websites to Hugo and had to build a custom template from scratch, I started writing a bit of Go as well.
I am very concerned by the inflationary spiral that is software and IT : there is no concept of “enough” software, we always need another layer and it’s often to work around the problems created by the previous layer. It also turns out that the next layer will often be more CPU-hungry than the previous.
That only leads to more energy consumption, device obsolescence, and yet fails to make workers more efficient (or people happier): they have to battle against more ever-changing tools and fix more post-upgrade messes. “One-size-fits-all” ACME frameworks are to blame here, along with the don’t reinvent the wheel dogma : people will install sprawling toolkits and bloated plugins, only to use a few features that could be replaced by 5-15 lines of custom code, just to avoid writing code themselves (or paying someone to do it). I find that people overestimate the burden that is maintaining their own simple code and underestimate the burden of debugging their whole third-party software stack through possibly-incompatible upgrades.
