Vik | Practical AI & Tech

I have zero hardware background. Today a Shenzhen hardware founder told me that doesn't matter anymore. More and more people like me are starting physical AI companies. Here's why: 1. ChatGPT 6 Astra designs the board. Describe what you want and it draws the circuit. You still get an engineer to check it. 2. Shenzhen prints it in 2 days. A custom circuit board at your door in 48 hours. Flexible boards like the one inside these glasses can be back in 12 hours. 3. One supplier does everything. Some take you from a sketch all the way to mass production. 4. Building is no longer the bottleneck. Customers are. Robot and humanoid companies here don't need more partners. They need people who'll actually buy. 10 years ago, you needed a team and a million dollars to make hardware. Today, you need a laptop and a flight to Shenzhen. What coding did for software, AI plus Shenzhen is doing for hardware. What part have you been struggling to source? Drop it below and I'll go find it while I'm here.

🚀 MixAI: I’m Choosing a Mix of Local and Cloud AI — Here’s Why. (1/2) Almost a year of hands-on experiments. Hardware ranging from $500 to $15,000. Cloud APIs, AI agents, and real-world tasks for businesses and homes. From now on, I’ll be sharing more about this combined approach to AI — one I believe has a very promising future. Things are changing almost every day. But AI technology has already reached a point where we can achieve reliable results and realistically plan how to use it over the next year or two. And I’ve come to a simple conclusion: we don’t need to choose between local and cloud AI. We need to learn how to get the best out of both. This goes far beyond simply connecting different models or orchestrating them. It’s about planning the entire system: which tasks should run locally, which should go to external models, how to distribute computing resources, which subscriptions make sense, and what hardware is actually worth buying. We also need to plan our physical infrastructure — computers, servers, GPUs, memory, power consumption, and future expansion. I believe local AI will become an essential part of our lives. It’s already making its way into smartphones, personal computers, and even experimental operating systems. ⚖️ Four Things That Matter Most 1. Real value. The number one priority is getting useful, practical results. 2. Cost. How much are we actually paying to complete a task — not just for hardware, subscriptions, or tokens? 3. Security. This deserves its own category, outside any ranking. Personal data, privacy, and control over our infrastructure require special attention. 4. Ease of use. How easily, quickly, and reliably can we get the job done? These are the criteria I use to compare technologies and build my own setup. ☁️ Subscriptions and APIs: Two Worlds Starting to Merge This is an interesting topic that often gets overlooked. Cloud AI can be accessed through subscriptions with usage limits or through APIs where you pay for what you actually use. Each approach has its own economics, limitations, and advantages. And they need to be evaluated separately. But the line between them is starting to blur. A recent example is OpenAI. Eligible ChatGPT Pro subscribers can receive $100 to $500 per month in API credits, depending on their subscription tier. These credits require separate activation, and API billing remains separate. That’s a pretty interesting example of two pricing models coming together. So I’m not just comparing models. I’m also exploring the economics of subscriptions and APIs, their limitations, and how to combine them efficiently. ➡️ Coming up in Part 2/2: The three areas I’m actively researching, my hardware experiments, AI agents working together, and how I plan to make all this information accessible to you. 🚀

These kinds of shenanigans is how I got hearing damage. I had a 1U server in my room for years and now I have tinnitus and hearing difficulties. Don't be stupid, protect your ears.