I never do stock picks.

Largely because I don’t spend a lot of time researching stocks. But as I have just reached a milestone in my work project utilizing NVidia Jetson Nano, and just ordered a personal Jetson nano to build a home workstation, I decided to check out the stock. The P/E is at over 50 now, after a lot of stock gains this year (no doubt resulting from the market anticipating what I’ve learned practically).

Such a high P/E suggests that the stock is over-valued, but I see incredible long term profitability for NVidia, as their GPUs overtake CPUs as the dominant tool for massively parallel computing power, whether it is cloud based or within local desktops and laptops. And with the growth of distributed home voice recognition systems (such as Alexa and OK Google), all of which require massively parallel cloud computing with machine learning, we can only expect more demand for NVidia products, and as those home assistant systems are upgraded to offer more and more capability, long term demand is assured.

A Novel Process

I invented a novel process improvement at work and the initial test results are promising, and warrant further investigation. This could end up being a big win for the company.

What makes a novel process improvement? Well I’m also working on a machine, and, although I won’t release any details, let’s say that it reduces the amount of rework required for a part of a manufacturing process. That makes the machine a process improvement, but it is not a novel process improvement, because we could pay other engineers to supply a machine that does the same thing. My machine may contain parts that are novel, but it’s not a novel process improvement.

In the case of my novel process improvement, there is no one else that can be paid to get you the same thing. Only Licap has it.

Busy, busy, busy…

I’m working mainly on a few exciting work projects these days. Mainly designing a new machine, also working with machine vendors on a new pilot line, and supporting maintenance and upgrades of existing machines, not to mention some forays into IT support.

Parts fab is at the forefront of my concerns at the moment: Do I print?, have printed?, have milled?, machine myself with or without CNC scribing? Or just all of the above? All of the above sounds right.

Nvidia Jetson Nano

I got a hold of a Jetson nano dev kit for a project for work. It’s got a lot of similarities to the raspberry pi, as far as initial setup. One loads the OS image on a memory card and hooks up monitor, keyboard, mouse, and ethernet, and the Jetson Nano becomes a single board computer, except it has a Tegra X1 SoC with a quad core CPU and 128 core GPU.

The Nano standard OS from NVIDIA is Ubuntu, which is debian based and has a familiar feel for me, which will be handy as I begin python scripting with the TensorFlow library.

All in all my first impression of the device is pleased and optimistic, and with the price being what it is for all the computing and graphics power I get, I wonder if I might be looking at NVIDIA products for my single board computing needs in the future. One drawback: the Jetson Nano dev kit lacks a builtin wifi radio, but then, so did the early raspberry Pi’s.

Cost Savings Placeholders in Process Development

It’s easy to get caught up in “get it working” fever and be so focused as to have an attitude of, if it works: don’t touch it. But this attitude can just be a way to reduce mental effort while working on multiple higher priority issues. This is highly reasonable, but, it can also be expensive, as rapidly developed solutions are usually optimized for meeting the success criteria with minimized guessed risks rather than for least cost. So, one must keep track of all of the unoptimized processes that accumulate in development as well as their associated cost and all of the guessed risks and devise plans to test for and eliminate the guessed risks and optimize the process for least cost and highest success rate.

Cheap Aliexpress Nanos.

I got some arduino nano replicas on aliexpress a couple of months ago for an incredibly low price. Not surprisingly, there was a reason for the discount. The pin assignments as they’d be programmed in the IDE did not match the pins as they were silkscreened on the board. The analog pins were silkscreened in reverse order to their actual hardwired positions.

Thus, among the plethora of reasons that a device might end up super cheap on ali-x are manufacturing defects. This problem happenned to just be with the silkscreen, but the silkscreen is a very important time saver for circuit assembly.

Nevertheless, the board I used still helped me blast together a device to run an experiment and log data. The physical chemistry involved has been more problematic than anything else for that experiment, but a change in approach will eliminate all such problems.

Openscad vs. Solidworks

I’m familiar with two 3d modeling programs; openscad (pronounced open-skad by the truely enlightened, and open-ess-cad by the technically correct) and solidworks. In openscad, one constructs 3d models programmatically, by typing in structured text functions to define shapes and extrusions and using loops for repeated features. This makes for fast and easy 3d modeling of simple structures. The same can be accomplished in solidworks, but the shapes are defined by cuts and extrusions of sketches which are graphically drawn on defined planes. This can also be quite fast.

Overall, If all one needs is a 3d model or stl file, I would recommend openscad in cases where all of the model is easily referenced to the origin, and I would recommend Solidworks for all cases in which the features are more easily defined based upon each other’s surfaces, especially when those may need to be adjusted later and the changes need to be carried through to all connected bodies.

If one needs to create drawings from the models, Solidworks is the clear choice, with easy dimensioning, automatic section cutting, and external views, it is a huge help for mechanical drawing generation.