This article reinforces what I have been saying for years: microservices are a big mistake, especially for developers who don’t understand distributed systems, high availability and observability. To be successful, they must be properly designed and implemented, unlike most of the copy-and-paste, we-don’t-need-no-stinkin-design development that is seen today.
From the article:
We engineers have an affliction. Itâ€™s called â€œwanting to use the latest tech because it sounds cool, even though itâ€™s technically more difficult.â€ Got that from the doctorâ€™s office, itâ€™s 100% legit. The diagnosis was written on my prescription for an over-the-counter monolith handbook. From 2004. Seriously though, we do this all the time. Every time something cool happens, we flock to it like moths to a campfire. And more often than not, we get burned.
Interesting post on how ‘magical experiences’ fueled by AI and machine learning will change how products are designed and used.
There is growing momentum demonstrated by technical progress and ecosystem development. One of the leading startups that are working on helping engineers take advantage of TinyML by automating data collection, training, testing, and deployment, isÂ Edge Impulse. Starting with embedded or IoT devices, Edge Impulse is offering developers the tools and guidance to collect data straight from edge devices, build a model that can detect â€œbehaviorâ€, discern right from wrong, noise from signal, so they can actually make sense of what happens in the real world, across billions of devices, in every place, and everything. By deploying the Edge Impulse model as part of everyoneâ€™s firmware, you create the biggest neural network on earth. Effectively, Edge Impulse gives brains to your previously passive devices so you can build better a product with neural personality.
Another interesting company isÂ Syntiant, whoâ€™s building a new processor for deep learning, dramatically different from traditional computing methods. By focusing on memory access and parallel processing, their Neural Decision Processors operate at efficiency levels that are orders of magnitude higher than any other technology. The company claims its processors can make devices approximately 200x more efficient by providing 20x the throughput over current low-power MCU solutions, and subsequently, enabling larger networks at significantly lower power. The result? Voice interfaces that allow a far richer and more reliable user experience, otherwise known as â€œWowâ€ and â€œHow did it do that?â€