if you’re getting into embedded systems,
learn these protocols in this order:
1. i2c
two wires. many devices.
perfect for sensors, displays, rtc modules, eeproms
master/slave architecture
simple to wire, easy to prototype
this is where most beginners should start.
2. spi
much faster than i2c
separate lines for data in, data out, clock, and chip select
ideal for displays, sd cards, high-speed sensors
more wires, much higher performance
when speed matters, spi wins.
3. uart
point-to-point communication
tx + rx
no clock line
used for debugging, gps, bluetooth modules, microcontroller communication
if you’ve ever opened a serial monitor,
you’ve already used uart.
learn them in this order:
i2c → understand device communication
spi → understand high-speed peripherals
uart → understand serial communication and debugging
these three protocols are everywhere.
master them once,
and you’ll find them in almost every embedded system you build.
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@levelsio even that's not the complete picture because the Chinese models are open weights with some providers running them on infra they have on us soil.
> be Kimi
> hear “Chinese open-source models are years behind” every week
> ship Kimi K3
> top the Frontend Code Arena
> outperform Opus 4.8 on every benchmark
> Fable 5 level coding but Sonnet price
> release the weights for everyone
this is the open source ChatGPT moment.
Google engineer explained how to fine-tune a tiny LLM from 46% to 90% accuracy on your phone in 21 minutes - better than $1500 on-device AI bootcamps.
pick Gemma 270M -> generate synthetic task data -> fine-tune with LoRA -> quantize to int4 -> deploy to Pixel and hit 2000 tokens per second.
That loop is how a 270M model beats a 70B one on your task, running fully offline in your pocket.
Gemma 270M + synthetic data + LoRA + int4 quantization + on-device runtime - that's the stack.
Watch and save it, then fine-tune your own tiny agent tonight.
For those interested in a written form of my talk at BSC to skim or search through, I have prepared a lengthy article with lots of content; diagrams, videos, tables, and Desmos graphs that mirrors the information of my talk, it's available on my website:
https://t.co/S0FGGrFYus
I've spent the last year working with Google's Gemini Embedding team. Here's a practical introduction to how embedding models work, from search to recommendation:
https://t.co/PTV6ntTtEN