Great point! ToF sensors can struggle in highly reflective environments due to light reflections/refracting. Testing and adjusting sensitivity might help, but in such cases, exploring alternative sensors designed for reflective conditions could be a better solution. Thanks for sharing!

You're welcome!

You're welcome

Thank you for your reply! I think what you’re advocating makes a lot of sense, and you’re absolutely right-high optimization isn’t necessary for many use cases. A high-standard SBC (Single Board Computer) is more than sufficient for a wide range of applications. Optimization becomes critical mainly when you’re trying to speed up simulations, train AI models, or run compute-intensive tasks on edge solutions. As we move further into a future where AI plays an increasingly significant role, the cost and efficiency of running these algorithms on drones and other systems will depend heavily on squeezing every ounce of compute power from the hardware. This is where HPC concepts truly shine. In addition, focusing on high optimization often leads to better products in the long run or helps uncover new insights that are hard to grasp when relying solely on abstractions. I firmly believe in a yin-and-yang approach. Use abstractions and speed up development as much as possible, but also allocate some resources to R&D and peeling back those abstractions. This can lead to deeper understanding, new innovations, and benefits that pay off in the long term. Lastly, from a job market perspective, most top AI companies are prioritizing HPC and distributed computing skills in some form at almost every level. This demand is only going to grow. My hunch is that companies and individuals who invest in these skills and focus on high optimization for relevant applications will have a significant competitive edge in the long run. Just my two cents! 😊

As far as I know, there’s no exact, one-size-fits-all formula for determining chord length. It’s not just picking a random number off a chart. Instead, think about your end goal first: what are your performance requirements-aircraft or rotorcraft, payload, and flight speed? From there, use the lift equation 22:25 to estimate how much lift you need and remember the mean chord formula (mean chord = wing area / wingspan). You can also lean on empirical data, handbook guidelines, and historical examples from aerodynamic literature. Additionally, browsing through databases like Airfoil Tools (airfoiltools.com/airfoil/details?airfoil=n0012-il) can give you insights into how a given airfoil behaves at certain chord lengths. Don’t forget about simulation tools and iterative testing either. In short, there’s no single formula. It’s a combination of theory, working backward from your desired outcome, learning from proven airfoil characteristics, running simulations, and fine-tuning through experience. Hope this helps!

Glad it was helpful!

Glad it was helpful!

You're welcome

Thank you!

Very good point. Will consider adding more videos on this.

I just use the debugger provided by the manufacturer - for e.g for the nordic I use their inbuilt segger/nrf SDK / vs code software for debugging and for NXP very similar I use their version. Nothing special. It comes with their development kits.

Any specific topic of interest ?