JAVASCRIPT ON EMBEDDED DEVICES

Case Study: Svelte

(1) - Svelte is used for POS systems in Brazil. Roughly 200,000 POS devices are currently in the cities of Brazil. The device runs a very outdated WebKit on extremely memory-constraint hardware.

@sveltejs @SvelteSociety
(2) - They tried different frameworks. None of them could be used because when you press a button, it responded half a second later. Of course, users would be more likely to press it twice and may end up overcharging the customer. But when they tried Svelte, it worked smoothly.
(3) - This POS is built by Stone - A Brazilian company that specializes in integrated solutions for all types of entrepreneurs. This reveals the amazing power and potential of @sveltejs for low memory devices.

@stonepagamentos @sveltejs @SvelteSociety
(4) - Mustlab.ru, an app development agency in Russia, did the same for its smart TV apps and is expanding its use of Svelte to home automation.

@SvelteSociety @ColombiaSvelte @sociedadsvelte @sveltejsblr
(5) - According to Richard Harris, the next frontier the world is looking at is the embedded web: things like IoT, in-car entertainment, and wearables. All this is the prime hunting ground for Svelte.

@MasterSvelte
You even have mobile and application frameworks powered by @sveltejs , Native, and Sapper, in the suite of tools you can use to build powerful apps with small footprints.

So, are you ready to give Svelte a try?

#svelte #sveltejs #javascript #100DaysOfCode #iot #react

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More from @mrsaeeddev

4 Feb
1/ 🧠 🤖 📈 How to approach a Machine Learning problem?

🧵 A thread

👇

TIPS:

◉ Start with the simplest models

◉ Identify the patterns from data

◉ Let the data speak

◉ Don't force data to cry
2/ ◉ Spend most of your time in EDA, preprocessing, and feature engineering

◉ Choose the tech stack with which you are more familiar

◉ Try to involve people with domain expertise.

◉ Visualize your data. It helps a lot in understanding the patterns.
3/ ◉ Use statistical inference to understand the underlying distributions

◉ Initially solve what is possible

◉ Do market research

◉ See what other data scientists are doing in the same domain

◉ Try to relate the results of your work with the business value
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