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Every Dev Board Explained in 14 Minutes — Which One Is Best for You? — Transcript

by Mr. Byte · 2,257 words · 385 segments · language en · Watch on YouTube

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0:00Arduino Uno
0:02The Arduino Uno is the board that
0:03introduced millions of people to
0:05hardware. It runs an ATmega328P
0:08microcontroller clocked at just 16 MHz
0:11with 32 kilobits of flash memory and 2
0:13kilobits of SRAM. Those numbers are tiny
0:16by modern standards. But that is
0:18actually the point. Arduino is a
0:19microcontroller, not a computer. There
0:22is no operating system, no multitasking,
0:24no file system. Your code runs bare
0:27metal. That means it executes directly
0:29on the hardware, one instruction at a
0:32time without anything else getting in
0:34the way.
0:35You write a loop and it runs that loop
0:37every single time without interruption.
0:40That kind of predictability is actually
0:42a strength when you are controlling
0:43physical hardware. Where Arduino falls
0:46short is complexity.
0:48You cannot run machine learning, serve a
0:50webpage, or process video on it. But for
0:53reading sensors, driving motors,
0:55controlling LEDs, or building simple
0:58home automation, nothing is faster to
1:00set up. The Arduino IDE is free, the
1:03documentation is massive, and the
1:04community has answered almost every
1:06question you could have. It is the first
1:09board most people learn on, and for good
1:11reason. Simple, predictable, and backed
1:15by the largest hobbyist community in
1:17electronics.
1:19Raspberry Pi Pico
1:21The Pico is the Raspberry Pi
1:23Foundation's take on a microcontroller,
1:25but do not confuse it with the Raspberry
1:27Pi 4, which is a full computer. The Pico
1:29is a small, low-cost chip designed
1:32purely for hardware control. It runs the
1:34RP2040, a dual-core ARM Cortex-M0+
1:37clocked at 133 MHz with 264 kilobits of
1:41RAM and 2 megabits of onboard flash. The
1:44price is around $4. Compared to the
1:46Arduino Uno, the Pico is significantly
1:48faster, has more memory, and runs on a
1:50modern ARM architecture instead of the
1:53older AVR platform.
1:55It also supports MicroPython out of the
1:57box, which makes it much easier for
1:59beginners who already know Python. One
2:01standout feature is PIO, which stands
2:04for programmable IO.
2:06These are small state machines built
2:08into the chip that can generate precise
2:10hardware signals independently from the
2:12main CPU. That makes the Pico
2:15surprisingly capable for tasks like
2:17driving addressable LED strips or
2:19implementing custom communication
2:20protocols.
2:22The main trade-off compared to the ESP32
2:24is that the standard Pico has no
2:26built-in wireless. There is a Pico W
2:29variant that adds Wi-Fi, but it still
2:31lacks Bluetooth. For anyone choosing
2:33between an Arduino and something a bit
2:35more modern, the Pico is a strong option
2:38at a very similar price point.
2:40ESP32
2:42The ESP32 by Espressif is where things
2:45get really interesting. It has a
2:46dual-core Xtensa LX6 processor running
2:49at up to 240 MHz, 520 KB of SRAM,
2:53built-in Wi-Fi, Bluetooth 4.2, and BLE.
2:56All on a chip that costs around $2. That
2:59price alone makes it one of the best
3:00deals in the higher dev board market.
3:03Compare that to the Arduino Uno. The Uno
3:05has no wireless capability at all and
3:08runs about 15 times slower. The ESP32
3:11can run Arduino code, MicroPython, or
3:14Espressif's own ESP-IDF framework, so
3:16you have flexibility in how you build
3:18your project. It also has a built-in
3:20hall sensor, capacitive touch pins, two
3:22DACs, and multiple SPI, I2C, and UART
3:25interfaces. For the price, the feature
3:27list is almost hard to believe. The one
3:29trade-off is voltage.
3:31The ESP32 is a 3.3V device. So, if you
3:35are connecting it to 5V sensors or
3:37modules, you need level shifters to
3:39avoid damaging the board. But for any
3:41project that needs wireless connectivity
3:44like smart home devices, IoT sensors,
3:46weather stations, or remote data
3:48loggers, the ESP32 is the default
3:51choice. It is easily the most
3:53cost-effective connected microcontroller
3:55on the market right now, and the reason
3:57most IoT hobby projects start here.
4:00Teensy as why.
4:03The Teensy is an ARM-based
4:04microcontroller board built for speed
4:06and precision. The Teensy 4.1, which is
4:09its most popular version, runs an ARM
4:11Cortex M7 at 600 MHz with 1 MB of RAM
4:15and 8 MB of flash.
4:17That makes it one of the fastest
4:18microcontrollers you can buy on a
4:20development board today. Where the
4:22Teensy really stands out is audio and
4:24USB. It has a dedicated audio library
4:27that lets you build synthesizers,
4:29effects processors, audio analyzers, and
4:32multi-channel mixers with very low
4:33latency.
4:35It can also act as a native USB device.
4:37That means it can pretend to be a
4:38keyboard, a mouse, a MIDI controller, or
4:41a game controller without any extra
4:43hardware or drivers. You just plug it in
4:46and it works. Compared to an STM32, the
4:49Teensy is easier to get started with
4:51because it uses an Arduino-compatible
4:53toolchain. Compared to an ESP32, it is
4:56much faster but has no built-in
4:58wireless. The trade-off is price. A
5:00Teensy 4.1 costs significantly more than
5:03an ESP32 or Arduino. But for projects
5:06involving fast signal processing,
5:08real-time audio, or custom USB input
5:11devices, the Teensy has a loyal
5:13following and is genuinely hard to beat
5:15in its niche.
5:17STM32
5:19The STM32 family by ST Microelectronics
5:22is what separates hobbyists from
5:23professional embedded engineers. These
5:26chips are based on ARM Cortex M cores.
5:28The range is wide. At the low end, you
5:30have the M0+ for ultra-low power
5:32applications. At the high end, the M7
5:35core in the STM32H7 series runs at 480
5:38MHz with a double precision
5:40floating-point unit, hardware JPEG
5:42encoding, and up to 1 MB of RAM. Unlike
5:45the Arduino or ESP32, STM32 does not
5:49hold your hand. You configure clocks,
5:51DMA channels, interrupt priorities, and
5:53peripheral registers yourself, usually
5:55through STM32CubeIDE
5:57and the HAL or LL libraries. That
6:00complexity gives you something valuable
6:01in return.
6:03Deterministic real-time performance and
6:05industrial-grade reliability.
6:07Where the ESP32 might drop a Wi-Fi
6:10packet or have timer jitter, an STM32
6:13will execute with microsecond precision
6:15every time. That is why you find STM32
6:18chips inside drones, industrial
6:20controllers, medical devices, and
6:22consumer electronics.
6:24Not sitting on a desk as a hobby
6:26project, but inside actual shipping
6:28products used by real customers.
6:31The learning curve is steep and the
6:32documentation is dense.
6:34But if your goal is to build something
6:36production-ready, STM32 is the path most
6:39professional embedded engineers take.
6:42Raspberry Pi
6:45The Raspberry Pi is not a
6:46microcontroller. It is a full
6:48single-board computer, and that
6:50distinction changes everything. The Pi 4
6:52runs a quad-core ARM Cortex-A72 at 1.8
6:55GHz, supports up to 8 GB of LPDDR4 RAM,
6:58has USB 3.0 Gigabit Ethernet, dual micro
7:01HDMI outputs capable of driving 4K
7:03displays, and runs a full Linux
7:05operating system from a microSD card.
7:07Compare that to the ESP32 with 520 KB of
7:11RAM. The Pi 4 has over 15,000 times more
7:14memory. That gap explains the difference
7:17in capability. A Pi can run a web
7:19server, a database, Python scripts with
7:21full NumPy and OpenCV libraries, a web
7:24browser, and even a full desktop
7:26environment with a graphical interface.
7:29What it cannot do well is real-time
7:31hardware control. Linux is not a
7:33real-time operating system, so GPIO
7:35timing on a Pi is inconsistent and
7:37unreliable for precision tasks. You
7:40would not use a Pi to generate a precise
7:42PWM signal for a motor controller, but
7:44you would absolutely use one to process
7:46a camera feed, host a local API, or run
7:50a TensorFlow Lite model for image
7:51classification. Think of it less as a
7:53dev board and more as a tiny, hackable
7:56Linux computer that happens to have GPIO
7:58pins on the side.
8:00BeagleBone Black
8:02The BeagleBone Black is probably the
8:05most underrated board on this list. It
8:07runs a 1 GHz ARM Cortex-A8 with 512 MB
8:10of RAM and 4 GB of built-in eMMC
8:13storage. Like the Raspberry Pi, it runs
8:16full Linux, but the BeagleBone has
8:18something the Pi simply does not have:
8:20two onboard programmable real-time units
8:22or PRUs. These are 200 MHz 32-bit
8:25processors that run independently from
8:28the main CPU. They execute bare metal
8:30code with deterministic nanosecond-level
8:33timing. That means you get the best of
8:35both worlds on a single board: a full
8:37Linux environment for your application
8:39logic and networking, and
8:41microcontroller-grade precision for your
8:43hardware control layer. No second board
8:46needed. It also has 65 GPIO pins
8:49compared to the Pi's 40, more analog
8:51inputs, and multiple PWM outputs
8:54natively available. BeagleBone is widely
8:56used in CNC machines, robotics,
8:59industrial automation, and any
9:01application where Linux flexibility and
9:03real-time hardware precision must
9:05coexist on the same device. The
9:07community is smaller than the Pi's, and
9:09the documentation is denser. But for
9:11engineers who know exactly what they
9:13need, the BeagleBone is genuinely hard
9:15to replace.
9:17Nvidia Jetson Nano
9:19If the Raspberry Pi is a tiny Linux
9:21computer, the Jetson Nano is a tiny AI
9:23computer. It packs a quad-core ARM
9:25Cortex-A57 CPU alongside a 128-core
9:28Maxwell GPU with 4 GB of LPDDR4 RAM and
9:3216 GB of eMMC storage. The GPU is the
9:35entire point of this board. The
9:37Raspberry Pi 4 has no meaningful GPU
9:39compute capability for machine learning
9:42workloads. Its theoretical AI
9:44performance sits at roughly 0.1 tops.
9:47The Jetson Nano delivers 472 GFLOPs of
9:50FP16 compute. That is enough to run
9:53real-time object detection with YOLOv3,
9:55pose estimation, facial recognition, and
9:58semantic segmentation. All processed
10:00locally on the device with no cloud
10:02dependency whatsoever. It runs JetPack,
10:05which is Nvidia's Linux-based SDK, with
10:07full support for CUDA, TensorRT, and
10:10DeepStream.
10:11The trade-off versus a Raspberry Pi is
10:13cost and power consumption. The Jetson
10:15Nano is significantly more expensive and
10:18draws considerably more electricity.
10:21But, if your project involves a camera
10:24and a neural network running in
10:25real-time, there is no real comparison
10:27at this price point. The Jetson Nano
10:29sits in its own category for accessible
10:31edge AI development.
10:34Google Coral Dev Board
10:36The Google Coral is another option for
10:39running AI at the edge, but it takes a
10:41completely different approach from the
10:43Jetson Nano. Instead of using a GPU for
10:46neural network inference, the Coral uses
10:48Google's Edge TPU, which stands for
10:51Tensor Processing Unit. This is a
10:53purpose-built chip designed specifically
10:55to run TensorFlow Lite models with
10:56maximum efficiency and minimum power
10:59draw. The Coral Dev Board runs a
11:01quad-core ARM Cortex-A53
11:03at 1.5 GHz with 1 GB of RAM, plus the
11:07Edge TPU coprocessor, which delivers
11:10four tops of inference performance. That
11:12is actually higher raw throughput than
11:14the Jetson Nano on supported model
11:16types, while drawing significantly less
11:19power, usually around 2 W under full
11:21load, compared to the Jetson's 5 to 10
11:24W.
11:24The trade-off is flexibility. The Jetson
11:27Nano supports CUDA and can run a wide
11:29range of AI frameworks and model
11:32architectures.
11:33The Coral only runs TensorFlow Lite
11:35models, and those models must be
11:37compiled specifically for the Edge TPU
11:40before deployment.
11:41So, if your project fits within that
11:43ecosystem, the Coral is faster and far
11:45more power efficient per inference.
11:48If you need broader framework support,
11:50custom model architectures, or on-device
11:53training, the Jetson is still the better
11:55pick.
11:56Tessel 2
11:58The Tessel 2 takes a completely
12:00different approach from every other
12:01board on this list. It is built for
12:03JavaScript developers, not embedded
12:05engineers. It runs as MediaTek MT7620N
12:09processor at 580 MHz with 64 MB of RAM.
12:14Running a lightweight Linux environment
12:15with Node.js built in from the start.
12:18That means you write your firmware in
12:19JavaScript. You use NPM packages
12:22directly. You interact with sensors and
12:24motors via Jobs API.
12:27No C, no Python, no new development
12:30tools to learn. It also has built-in
12:32Wi-Fi, USB host support, and two
12:34dedicated module ports that accept
12:36plug-and-play hardware modules for
12:38things like GPS, Bluetooth, ambient
12:41light sensors, and servo motor control.
12:44Compared to the ESP32, the Tessel is far
12:47less power efficient and more expensive
12:49for what you get in raw hardware.
12:51Compared to a Raspberry Pi, it is more
12:53constrained but faster to prototype on
12:55if JavaScript is your primary language.
12:58The Tessel never captured widespread
13:00mainstream adoption.
13:02But, it represents a real and valid
13:04philosophy.
13:05The best dev board for any developer is
13:08the one that speaks their language.
13:11For full-stack web developers who want
13:13to build hardware without learning an
13:14entirely new toolchain, the Tessel
13:17lowers that barrier dramatically.
13:20Particle Photon
13:22The Particle Photon is less about raw
13:23specs and more about what surrounds the
13:25hardware. It runs an STM32F205
13:28ARM Cortex M3 at 120 MHz with 1 MB of
13:32flash and 128 KB of RAM. Those are solid
13:36microcontroller specs in the same
13:38ballpark as a mid-range STM32.
13:41It has built-in Wi-Fi through a Broadcom
13:43module and is programmed through
13:44Particle's cloud-based IDE. But, what
13:47makes it genuinely different from
13:48everything else on this list is the
13:50Particle device OS and cloud platform.
13:54Every Photon is automatically registered
13:56to your Particle account.
13:58It can receive over-the-air firmware
14:00updates, publish sensor data to the
14:02cloud, subscribe to remote commands, and
14:05be monitored across an entire fleet of
14:07devices.
14:08All out of the box with zero additional
14:10infrastructure setup. Compare that to an
14:13ESP32 where you would need to build all
14:15of that yourself.
14:17OTA update servers, fleet management
14:19dashboards, cloud messaging pipelines,
14:22device monitoring systems.
14:25For a hobbyist working on a single
14:26device, that overhead might not matter.
14:29But, for a startup shipping 500
14:31connected products to paying customers,
14:33Particle eliminates months of back-end
14:36engineering work.
14:38It bridges the gap between prototype and
14:40finished product better than any other
14:42board on this list. Anyway, I also made
14:45some other cool videos, too. Don't
14:47forget to watch them later. Okay.

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