Tech-Driven Transformation: From Ideation to Implementation
Going from an idea to a real-world practical solution is the challenge of hard tech. While there are many great ideas, it's that zero-to-one phase that really defines whether or not the venture will get off the ground. Fortunately, we live in a day and age where so many tools and resources are available at low cost or free, which lowers the barrier to entry for many hard tech applications.
In robotics, AI, and Industry 4.0, I'm really excited by the tools and resources available. We leverage off-the-shelf embedded systems, virtual environments, and simulations to realize our dreams. These advancements bridge the gap between theoretical ideas and practical, scalable innovations. This is the pragmatic approach to innovation development. By taking a scalable, well-thought-through approach and leveraging platforms and readily available resources to address real-world challenges, we can align stakeholders better and set expectations, making our ventures more successful.
Sculpting Chaos: 3D Reconstruction with Gaussian Splatting
3D reconstruction from point clouds has been around for quite some time in various forms and levels of quality, but I'm personally really excited for the next generation of technologies that will enable more accurate and quick reconstruction of chaotic, unstructured environments. These reconstructions will be used for training intelligence systems, AI agents, and autonomous robots. One of the challenges we often face in robotics and AI is the lack of easily accessible test environments that accurately reflect the real world. Using techniques like Gaussian Splatting, we would be able to quickly reconstruct random environments that we come across, bringing them into our simulations to develop new test cases and scenarios for training and testing our intelligence systems. This approach eliminates the need for using expensive hardware and risking breakages, so from a safety, security, cost, and efficiency standpoint, this is a win-win situation.
Imagine being able to arrive at a client site, quickly scan the facility, and bring it into your AI training simulation. You could then return with a pre-trained robot without ever having to pre-deploy or muck about on the client site with hardware before the actual application and value get off the ground. Additionally, we could integrate these environments into our continuous integration and continuous deployment processes for more test scenarios, thereby creating more robust products.
For more details regarding Gaussian Splatting, read here.
Juicing Up the Edge: Power Optimization with NVIDIA Jetson
I'm a big fan of the NVIDIA Jetson lineup, which has significantly enabled power-efficient GPU compute at the edge, especially for robotics and mobile systems. It's examples like these that demonstrate just how fortunate we are to be building during this current time. We now have incredible resources available, where high-performance compute at the edge is a commodity that's off the shelf, not something that we have to build, debug, maintain, and support ourselves. Not only do we get access to robust embedded hardware, but Nvidia has also greatly enhanced the tooling surrounding the hardware. Now, we have real-time monitoring and management systems that make our practical applications much more realistic and operationally ready, helping us bridge the gap from prototype to production.
Pocket-Sized Powerhouse: Hello Raspberry Pi 5
While not the most powerful embedded system, Raspberry Pis are often the de facto choice for many startups and proof-of-concepts that need to get hardware systems off the ground. I'm super excited by the latest release of the Raspberry Pi 5, which pushes the boundaries of cost-effective embedded hardware yet another step further.
The small form factor, simplicity, and accessibility of these devices cannot be understated, as they truly set full-stack hardware and software teams up for success, especially when they may not have expertise in embedded devices. This allows them to focus purely on the application, their customers, and their value proposition in order to get their proof-of-concepts off the ground and deliver real-world value, without getting stuck in the weeds of embedded development.
Salami and Cheese: Inspirations from the Assembly Line
This video may seem random, but I enjoy watching and understanding how other systems are designed and run. It often inspires me to see automation and industrial systems in action and think about the design choices made for robustness, maintenance, support, and assembly—things we often don't consider in the early stages of hard tech development when we're just trying to get proofs-of-concept off the ground. By looking at these production systems and machinery, we can become better at designing practical solutions that deliver real-world value.