Fighting with computers

  Ok, so you have your NAS and you have built it with some redundancy (RAID). But what if the whole thing dies (the system motherboard)? Well, at least you will need a spare unit. Nonetheless, having all your data on a single device that can die anytime does not seem like a good idea, so some sort of backup plan is needed. One possible idea is to have a second NAS unit that will be kept in sync with the main one, so that you can switch it back to be the main one in case of a failure of the main unit. Not cheap but a workable solution. However, certain risks like a robbery or a fire need to be covered, so keeping the two NAS at the same location is not a good idea.  So one possible solution is to have the backup system remote and to connect it to the office system VPN, so backups can take place. This opens up an interesting networking problem if the VPN server is not run by the NAS box, as the VPN addressing space is going to be different than the office network addressing spac...

My first recommendation is not to use that feature, but if it is a bit too late for me, as I did use it in the past. And old code needs some care from time to time. STL files represent triangular 3D meshes, but do not support color in any shape or form. However, some non-standard way of adding color has been used by some companies in the past, and somehow, here we are, using color on STL files.  If you do that, you may wonder what the actual colors are in your STL files, as this feature is quite sketchy for several reasons. So I asked DeepSeek to create me a single-page web app that could help me answer this question locally (without sending your 3D mesh to a web server). Not that the AI was completely willing initially, as it delivered only a shell with fake info, but a bit more persuasion produced a very usable result I am sharing with you here . I decided to write this post instead of placing the AI blog entry (which I created and reviewed, though).  I followed the AI advic...

 I was using Visual Studio Code with Gemini Code Assist to create a variation of an existing Python script. After a few minutes of back and forth, I got the desired code running. But when I try to run the same script again on the terminal, nothing happens. WTF? Thankfully, I asked Gemini to rebuild the file for me, and a few moments later, I got it back into my VSC editor. However, a minute later, the issue repeats itself.  What good is it to have an IA to write code for you if the file content with the code disappears seconds or minutes later? And it's not that the file disappears, it is just that suddenly the file becomes 0 bytes long. When asked about it, Gemini hinted that, as I was working on a Dropbox folder, it might be a known synchronization issue of Dropbox. Knowing the possible cause of the problem helps, but being a paying customer of Dropbox makes me wonder if I want to keep on supporting them. This is not the first weird issue I have had with them, but other prob...

 Many vector graphic algorithms have a cost proportional to the number of segments of the shapes involved. Thus, many different polygon simplification algorithms have been developed over the years. The main idea is to represent the original shape, but reduce the total number of edges of the shape's contour.  A favourite of mine is the Douglas-Peucker algorithm. It uses an error parameter that allows the user to tune the acceptable error introduced by the simplification. That works well in many scenarios, but as you may guess, it does not work well for some of my needs. Free form 2D nesting packs groups of parts inside bins, in my case, those bins are rectangles. The rules are that any two parts cannot overlap. Nesting libraries will determine where to place each part inside a bin and what rotation to apply to the part. The number of parts and the number of edges per part have a big influence on the time required to perform the nesting operation. So, being able to simplify the...

 While it was nice to be able to get a faster 2D nesting code, its efficiency was far from perfect. So, looking at the nesting results, you could see how it could have been done much better in certain cases.  And what we usually do when we overcome a hurdle is to look for the next one. In my case, I realized one of the heuristics of the code was to place small parts inside the unused space of the bounding box of the larger parts. That is ok, but what could be better is to use all the free space of a bin once the first placement of large parts is over.  This time, I leaned on another Chinese AI tool, called GLM-4.5 from the  z.AI company.  I explained the above idea in the chat, and I waited for the magic to happen. I used Roo Code within Visual Studio Code, accessing GLM-4.5 API. A few minutes later, I had a newer version of the library. I was pleasantly surprised when I saw a significant improvement in its packing ability, proving that the new heuristic was a ...

As I mentioned in my previous entry, I migrated a library from Java to C++ to make it easier to use from Python. I tested its performance on both Linux and OSX, and it was on par with the Java version. Sample file S266.txt would take around 45 seconds on my computer using the Java version, and the C++ version needed thirty-something seconds on a M1 MacBook Pro running OSX, a bit less than that on an i9-13900 server running Linux. From that, I could not claim there was a huge difference in performance compared to the original Java runtime. But I wanted to make a fair comparison, and I wanted to face the trouble of building the library for Windows. It was not a challenge, but it took a while. Let me summarize all the steps I needed to cover: Install Microsoft's Visual Studio (Community Edition) Install Microsoft's vcpkg (C++ package manager) Install CMake. Install dependencies (tbb, gtest, boost, pybind11) Clone the project repository . Build the project. It took me like an hour...

Today, we embarked on an ambitious software engineering journey: migrating a sophisticated 2D bin packing program from its original Java implementation into a high-performance C++ library, complete with Python bindings for ease of use. The goal was to retain 100% of the original complex packing logic while unlocking the raw speed of C++. The heart of this migration involved replacing Java's powerful java.awt.geom.Area class. We chose the world-class Boost.Geometry library as its C++ counterpart, meticulously re-implementing every geometric operation, from simple transformations to complex polygon intersections and subtractions. This formed the foundation upon which we rebuilt the original program's advanced packing heuristics. We successfully ported a multi-stage packing strategy, including bounding-box placement, a "drop" simulation for gravity-fed placement, and iterative compression algorithms. However, real-world testing with complex, high-vertex-count polygons r...