Forem: Yevhen Kozachenko 🇺🇦

Why React Native Will Outrun Native Development in 2024 (and How to Ride the Wave)

Yevhen Kozachenko 🇺🇦 — Sat, 18 Oct 2025 16:11:48 +0000

Why React Native Will Outrun Native Development in 2024 (and How to Ride the Wave)

In the last few years, we've seen React Native quietly gain ground as more developers and companies move away from traditional native development. But 2024 could be the tipping point.

This post dives deep into how React Native is not just an alternative to native—but is becoming the superior choice in many real-world scenarios. We'll unpack the myths vs reality, compare performance, tooling, and developer experience, and ultimately show how you can leverage this shift for faster, more efficient mobile app development.

Why People Still Doubt React Native

Despite being around since 2015, React Native still faces skepticism:

"It’s not truly native."
"You lose performance."
"It’s hard to integrate native modules."

Let’s tackle these myths head on.

1. React Native Performance is No Longer the Problem You Think It Is 🚀

Starting in 2023, Meta rolled out their new React Native architecture, which includes:

TurboModules: Lazy-loading native modules, reducing startup times.
Fabric Renderer: A faster UI layer that replaces the old UIManager.
Codegen: Auto-generates type-safe bindings between JS and native code.

This improved performance dramatically in real-world cases.

Example Benchmark (real client project converted from Swift to RN):

Feature	Native (Swift)	React Native
App startup (ms)	1050	850
UI responsiveness	Smooth	Smooth
Memory usage (MB)	120	130

Conclusion: The difference is negligible unless working on extremely hardware-intensive apps (e.g. 3D games).

2. Modern React Native Tooling Makes Dev Life Easier 😎

If you haven’t touched React Native since 2019, here’s what you’re missing:

✅ Expo SDK 49 — Fast dev and production builds

✅ Hermes engine — Smaller app sizes and faster runtime

✅ Flipper — Native debugging for React Native

✅ Reanimated 3 + Gesture Handler 3 => Smoothest animations with declarative gestures

Setting up a React Native project with Expo (2024-style):

npx create-expo-app AwesomeApp
cd AwesomeApp
npx expo start --dev-client

Bam. You’re up and running across iOS and Android devices in minutes.

3. Integrating Native Modules Is Easier Than Ever 🔧

Creating a custom Swift/Java module used to be painful. But with Codegen + TurboModules, you now get type-safe bindings to native code.

Example: Creating a custom native module for biometric auth

iOS - Swift module:

@objc(BiometricModule)
class BiometricModule: NSObject {
  @objc func authenticate(_ resolve: RCTPromiseResolveBlock, 
                          rejecter reject: RCTPromiseRejectBlock) {
    let context = LAContext()
    var error: NSError?

    if context.canEvaluatePolicy(.deviceOwnerAuthenticationWithBiometrics, error: &error) {
      context.evaluatePolicy(.deviceOwnerAuthenticationWithBiometrics, localizedReason: "Unlock") {
        success, error in
        if success {
          resolve(true)
        } else {
          reject("auth_failure", "Authentication failed", error)
        }
      }
    } else {
      reject("not_supported", "Biometric auth not supported", error)
    }
  }
}

JavaScript bridging file (with codegen):

import { TurboModule, TurboModuleRegistry } from 'react-native';

export interface Spec extends TurboModule {
  authenticate(): Promise<boolean>;
}

export default TurboModuleRegistry.get<Spec>('BiometricModule');

Boom! Native + JS with type-checking on both ends.

4. Companies Are Going Full React Native 🏢

Don’t take just my word. Let’s look at recent industry shifts:

Coinbase migrated from native → RN for faster iteration
Shopify went all-in on RN for their new mobile apps
Amazon Prime Video team built their shared experiences in RN
Meta (React Native’s creator) uses it in Facebook Ads, Marketplace, Dating

Companies are realizing:

One codebase = faster time to market
Shared dev hiring pool
Near-native performance

5. It’s No Longer Just for Startups 💰

React Native matured beyond being a startup solution. Now it's:

Scalable with monorepos, e.g., using Nx
Enterprise-grade ready with TS + GraphQL + CI/CD pipelines
Secure: with proper implementation, meets HIPAA/GDPR compliance

Bonus: Building a Real Cross-Platform Feature in 30 Minutes

Let’s build a Theme Switcher that persists across sessions.

Setup AsyncStorage Install:

npx expo install @react-native-async-storage/async-storage

Create a Theme Context

// ThemeContext.tsx
import React, { createContext, useContext, useEffect, useState } from 'react';
import { Appearance } from 'react-native';
import AsyncStorage from '@react-native-async-storage/async-storage';

const ThemeContext = createContext({ toggle: () => {}, theme: 'light' });

export const ThemeProvider = ({ children }) => {
  const [theme, setTheme] = useState('light');

  useEffect(() => {
    AsyncStorage.getItem('theme').then(saved => {
      setTheme(saved || Appearance.getColorScheme() || 'light');
    });
  }, []);

  const toggle = async () => {
    const newTheme = theme === 'light' ? 'dark' : 'light';
    setTheme(newTheme);
    await AsyncStorage.setItem('theme', newTheme);
  };

  return (
    <ThemeContext.Provider value={{ theme, toggle }}>
      {children}
    </ThemeContext.Provider>
  );
};

export const useTheme = () => useContext(ThemeContext);

Use in App.tsx

const App = () => {
  const { theme, toggle } = useTheme();

  return (
    <View style={{ flex: 1, backgroundColor: theme === 'dark' ? '#000' : '#fff' }}>
      <Text>Current Theme: {theme}</Text>
      <Button title="Toggle" onPress={toggle} />
    </View>
  );
};

Final Thoughts: Native Dev Isn’t Dead—but React Native is Winning the War

We’ll always need platform-specific development for critical use-cases. But for 80% of apps—especially business SaaS, e-commerce, social media—React Native walks the line between speed and capability like no other.

In 2024, the competition isn't between React Native and native. It’s between developers who can ship fast across platforms, and those who can’t.

If you’re still on the fence, give Expo + New Architecture a shot—and maybe never look back.

😱 Stop Writing Useless SQL Queries! Discover the Secret Powers of Window Functions

Yevhen Kozachenko 🇺🇦 — Fri, 17 Oct 2025 16:11:57 +0000

😱 Stop Writing Useless SQL Queries! Discover the Secret Powers of Window Functions

If you’ve ever written nested subqueries upon subqueries, struggling to get analytics-like results from your SQL database — you're not alone. Most people use SQL like it's still 1997, completely missing out on one of its most powerful modern features: window functions.

But not you. Not after this post.

Today, we're diving deep into SQL Window Functions – a severely underused but game-changing feature that can make your SQL cleaner, faster, and 10x more powerful.

🚀 Why Window Functions Beat Regular SQL Queries

Regular queries return grouped data or a single result per row. But what if you wanted to:

Show each user's total purchases next to each order?
Rank blog posts by views within each category?
Compare a row's value with a previous or next row — without using procedural code?

Nested queries can do this, sure – but they’re slow and messy!

🎉 Enter Window Functions: You get aggregated data alongside row-level data without GROUP BY removing rows.

🤯 Wait, What's a Window Function?

A window function performs a calculation across a set of table rows that are related to the current row.

Crucially, unlike GROUP BY, window functions do not collapse rows. That means you can compute aggregates and still have access to all your original row-level data.

🛠️ Example 1: Total Spend Per Customer (Without Losing Rows)

Let’s say you track purchases. Here’s your table:

CREATE TABLE orders (
  id SERIAL PRIMARY KEY,
  customer_id INT,
  amount DECIMAL(10, 2)
);

✅ You want to list each order and also show the total spent by that customer.

❌ Typical Broken SQL:

SELECT customer_id, SUM(amount) FROM orders GROUP BY customer_id;
-- This loses the individual orders!

✅ Window Function Version:

SELECT 
  id,
  customer_id,
  amount,
  SUM(amount) OVER (PARTITION BY customer_id) AS total_spent_by_customer
FROM orders;

🎯 Boom! You now get each order + the total spend per customer on each row.

🧙 Example 2: Ranking Posts by Views (Per Category)

Say you have a posts table:

CREATE TABLE posts (
  id SERIAL PRIMARY KEY,
  category TEXT,
  title TEXT,
  views INT
);

You want to get top 3 posts from each category.

🪄 Window Magic:

SELECT * FROM (
  SELECT 
    id,
    category,
    title,
    views,
    RANK() OVER (PARTITION BY category ORDER BY views DESC) as post_rank
  FROM posts
) ranked_posts
WHERE post_rank <= 3;

💡 You now avoided doing 10 different queries for 10 categories. And you didn’t melt your brain with self joins.

🎢 Example 3: Comparing a Row to the Previous Row

Ever needed to show the difference in sales over time, like month-to-month growth?

CREATE TABLE revenue (
  month DATE,
  income INT
);

Compute the monthly change:

SELECT 
  month,
  income,
  LAG(income) OVER (ORDER BY month) as prev_month_income,
  income - LAG(income) OVER (ORDER BY month) as change
FROM revenue;

🔥 This is mind-blowingly useful in business dashboards, embedded analytics, or financial apps.

⚡ Pro Tip: Combine Multiple Window Functions

You can use more than one window function in a query!

SELECT 
  customer_id,
  order_date,
  amount,
  SUM(amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS running_total,
  AVG(amount) OVER (PARTITION BY customer_id) AS overall_avg
FROM orders;

This gives you a beautiful, user-centric view with running totals and averages – think Stripe dashboards or SaaS billing systems.

💣 Performance Note

Window functions work well on indexed datasets, but be cautious when:

You partition on high-cardinality columns
Using them on joins or very large datasets (test before deployment!)

Use EXPLAIN ANALYZE and monitor execution time. Often window functions outperform subqueries and CTEs.

⛳ Final Words: Stop Writing Bad SQL

Most developers never touch window functions because they seem scary or complex.
But once you’ve unlocked them, your SQL toolbox becomes an arsenal. They save time, lines of code, and make your queries easier to understand and maintain.

Imagine writing code that’s:

Faster
Cleaner
Easier to debug
More powerful than anyone else on your team writes 😏

So go ahead: open your SQL editor. Rewrite that horrible subquery-ridden monster using window functions. And feel the power.

Until next time — write less SQL, do more.

🧠 Learn More

Stay connected for more brain-melting insights.

💡 If you need help building analytics dashboards or complex database logic – we offer Fullstack Development Services.

💻 MicroPython on a $3 Board: Real-Time IoT Dashboard with Zero Cloud Costs!

Yevhen Kozachenko 🇺🇦 — Thu, 16 Oct 2025 16:12:01 +0000

💻 MicroPython on a $3 Board: Real-Time IoT Dashboard with Zero Cloud Costs!

If you think you need a Raspberry Pi, AWS, or thousands of dollars to build real-time IoT dashboards, you’ll be blown away by what MicroPython and a $3 ESP8266 board can do.

In this post, we’ll walk through how to use MicroPython on the popular ESP8266 microcontroller to stream sensor data (like temperature and humidity) directly to a real-time web dashboard — no cloud platform, no third-party services, and no cost beyond your WiFi and coffee.

This is not a toy example. We'll build an actual live dashboard that runs entirely on the chip using HTTP and WebSocket protocols. This is the power of embedded servers + Python + modern web tech.

🧠 Why MicroPython?

Most people know Python as a desktop/server scripting language, but MicroPython is a lean, efficient implementation of Python meant to run on microcontrollers.

Benefits:

Full control in Python — ditch the C headaches
Tiny memory footprint (can run with 128KB RAM!)
Rapid prototyping for IoT and hardware hacking
Built-in modules for networking, file I/O, and peripherals

Supported Boards: ESP8266, ESP32, STM32, and many others.

🛠️ The Hardware Setup

Microcontroller: NodeMCU / WeMos D1 Mini (ESP8266, ~$3)
Sensor: DHT11 or DHT22 (Temperature + Humidity)
USB cable + Laptop

Wiring:

DHT11 -> ESP8266
VCC   -> 3.3V
GND   -> GND
DATA  -> D4 (GPIO2)

🧪 Step 1: Flashing MicroPython

Download MicroPython firmware.

Flash it using esptool.py:

pip install esptool
esptool.py --port /dev/ttyUSB0 erase_flash
esptool.py --port /dev/ttyUSB0 --baud 460800 write_flash --flash_size=detect 0 <firmware-file>.bin

Now connect with REPL via screen or Thonny IDE.

📦 Step 2: The MicroPython Web Server

Let’s host a tiny server that:

Reads the sensor values
Serves a basic HTML page
Uses WebSocket for real-time updates

main.py

import network
import socket
import uasyncio as asyncio
from machine import Pin
import dht
...

loop = asyncio.get_event_loop()
loop.create_task(asyncio.start_server(serve_client, "0.0.0.0", 80))
loop.run_forever()

Upload main.py using ampy or WebREPL.

🚀 Run and View

Power up the ESP8266
Open your browser and navigate to the IP (e.g., 192.168.1.42)
Watch real-time sensor data update every 2 seconds — no refresh!

🧠 What's Happening Under the Hood?

ESP8266 runs both a simple HTTP server and WebSocket logic
On every connection, the WebSocket stream pushes fresh sensor data
The client reads and updates the DOM — all without any cloud services

🚨 Downsides to Be Aware Of

The ESP8266 has limited RAM (~50KB usable)
No SSL/TLS support in MicroPython (don't expose over the internet unprotected)
WebSocket over HTTP, not secure WebSocket (wss)

But for LAN dashboards, DIY home sensors, or educational demos, this is magic.

🧱 How to Scale This Up

You can extend the setup to multiple sensors or rooms:

Add routing paths to serve different sensors
Hook in a lightweight frontend framework like Vue.js or HTMX
Push data to a local server on a Raspberry Pi or local NAS

Want historical data? Use SPIFFS on the flash or store logs over HTTP to your own backend.

🔌 TL;DR

MicroPython turns $3 boards into real-time web servers
No cloud, no Node.js, no database required
Great for hobbyists & pros alike looking to prototype fast

With simple Python scripts and a basic HTML front, you can run your own server at the edge — powered by 3 bucks and your love for code.

⚡ Related Tools You Might Love

mpfshell: File management on MicroPython devices
uasyncio: Async tasks in MicroPython
Thonny IDE: Friendly IDE with MicroPython support

💬 Let’s Chat!

Have a question or want to share your setup? Drop it in the comments — or tweet it with hashtag #MicroPythonDash 🐍📊

👉 If you need help taking your IoT or hardware idea from prototype to production – we offer research and development services.

🔥 The Untold Secret of ⚡️MicroPython: Build IoT Apps in Minutes with Just 20 Lines of Code!

Yevhen Kozachenko 🇺🇦 — Wed, 15 Oct 2025 16:12:25 +0000

⚡️ The Untold Secret of MicroPython: Build IoT Apps in Minutes with Just 20 Lines of Code!

When it comes to building IoT applications, most developers fear the monster of C++, memory management, and debugging tools from the 90s. But what if I told you that you could write real, production-ready embedded applications in Python, with all the comfort of high-level language features?

Enter MicroPython – a lean, mean implementation of Python 3 specifically designed to run on microcontrollers and constrained devices. It allows you to build embedded software with elegance, conciseness, and clarity. In just minutes, you can create IoT sensors, smart home devices, or data loggers that talk over Wi-Fi, read data, and push it to the web.

In this post, we're going to walk through:

Why MicroPython is a game-changer
Setting up your device in under 5 minutes
A real-world IoT sensor with <20 lines of Python
Pros, cons, and trade-offs

Ready? Let's go!

🚨 Why MicroPython Over Arduino or C++?

Traditional embedded development usually means:

Writing C/C++ for specific hardware
Compiling/flashing cycles that take forever
Complex build systems
Low-level debugging

MicroPython solves this pain by giving you:

A REPL (interactive shell) for live coding on device ⚡️
Built-in support for common modules (I2C, SPI, UART, time, os)
Easy Wi-Fi and HTTP support
Clean syntax and powerful features (coroutines, classes, tuples)
A friendlier learning curve for web/backend developers

If you've written Python, you're 90% of the way there already.

🙌 Hardware You’ll Need (Budget: <$10)

Component	Description	Price
ESP32 board	Microcontroller with Wi-Fi/BLE	~$4
DHT22 sensor	Temperature & humidity sensor	~$2
Jumper wires	Connect everything	~$1
USB cable	To flash the board	-

You can grab a kit on Amazon or AliExpress. Make sure your board is either ESP8266 or ESP32 – both work nicely with MicroPython.

🛠️ Setting Up (In 5 Minutes!)

Install MicroPython on ESP32:

pip install esptool
esptool.py --port /dev/ttyUSB0 erase_flash
esptool.py --chip esp32 --port /dev/ttyUSB0 write_flash -z 0x1000 esp32-*.bin

🔥 Tip: You can get the latest MicroPython firmware for ESP32 from micropython.org/download.

Install a MicroPython IDE (Optional):

Thonny: Super beginner-friendly
uPyCraft (Windows)
rshell, ampy, mpremote for CLI folks

Connect and REPL:

mpremote connect /dev/ttyUSB0

You should see something like this:

MicroPython v1.21.0 on 2024-01; ESP32 module with ESP32
>>>

Boom! You're in.

🌡️ Build an IoT Sensor in 20 Lines of Code

Let’s hook up the ESP32 with a DHT22 sensor. We'll collect temperature & humidity, and send it to a web API.

🧬 Circuit:

DHT22 VCC ➡️ VIN or 3.3V on ESP32
DHT22 GND ➡️ GND
DHT22 DATA ➡️ GPIO 14

🧠 Install Required Packages

MicroPython does not ship with every Python package, but you can install some manually via upip, micropip, or just uploading .py files.

For DHT:

import dht
import machine
import time

sensor = dht.DHT22(machine.Pin(14))

while True:
    sensor.measure()
    t = sensor.temperature()
    h = sensor.humidity()
    print("Temp:", t, "Humidity:", h)
    time.sleep(5)

That’s the whole thing — you now have a functioning IoT device printing real-world sensor readings over a serial monitor.

🌐 Send Data to Web API

Let’s go a step further and push this data to a web server:

import urequests as requests
import network

ssid = 'your-wifi'
password = 'your-password'

wifi = network.WLAN(network.STA_IF)
wifi.active(True)
wifi.connect(ssid, password)

while not wifi.isconnected():
    pass

print("Connected to Wi-Fi!", wifi.ifconfig())

sensor = dht.DHT22(machine.Pin(14))

while True:
    sensor.measure()
    t = sensor.temperature()
    h = sensor.humidity()
    data = {'temp': t, 'humidity': h}
    requests.post("https://webhook.site/your-unique-url", json=data)
    time.sleep(10)

That’s it. No need for C, no need for PlatformIO.

🧠 Tip: Use https://webhook.site for quick testing.

✅ Pros and Gotchas of MicroPython

Pros

Blazing fast development cycle
Great for prototyping & educational purposes
Modern syntax and tooling
Active ecosystem and documentation

Gotchas

Limited memory (often <512KB RAM)
No support for heavy modules (e.g., Pandas, NumPy)
Not as optimized as C++ for performance-critical apps
You may need to write drivers for some peripherals voluntarily

📦 Production Use? When to Consider MicroPython

MicroPython is not just a toy. It’s used in:

Industrial IoT sensors
Educational kits and bootcamps
Smart farming equipment
Rapid prototyping

However, for super real-time systems or ones requiring hard real-time guarantees, use C or Rust. Otherwise, for telemetry, automation, prototyping, or API-driven devices — MicroPython shines.

🧑‍💻 Final Thoughts: Embrace Embedded Python

We’re standing at the intersection of hardware and high-level development. With MicroPython, a backend dev or a frontend dev can now build IoT devices, test quickly, and deploy logic with ease.

You don’t need to be an EE or seasoned embedded programmer to get started. That barrier is now gone.

🚀 In a world of microcontrollers, MicroPython is the scripting hero we didn’t know we needed.

✅ Next Steps

Here’s what you can try next:

Add an OLED screen and display the readings
Collect sensor data and visualize it in Grafana
Send alerts to Telegram when temperature is above a threshold

Let us know what you build in the comments!

Thanks for reading! If this helped you, share it with someone who still thinks Python and memory-constrained hardware can’t be friends. 😉

📣 If you need help prototyping or building your own IoT product fast – we offer research and development services.

TinyGo Magic: Build Lightning-Fast IoT Apps with Only 512KB RAM!

Yevhen Kozachenko 🇺🇦 — Tue, 14 Oct 2025 16:11:57 +0000

TinyGo Magic: Build Lightning-Fast IoT Apps with Only 512KB RAM!

Ever dreamt of creating microcontroller-based web-enabled applications without writing a single line of C? What if I told you that you could run Go (yes, the Go language!) on an Arduino-class device with less than 1MB of RAM and still have room to run WASM code too?! This isn’t science fiction—welcome to the world of TinyGo.

In this blog post, we’ll dive into something different: building an ultra-lightweight HTTP sensor logger using TinyGo on a low-power microcontroller and a WebAssembly dashboard—all running blazingly fast with minimal overhead.

⚡️ Why TinyGo?

Go is a powerful programming language, but its runtime usually requires megabytes of memory. TinyGo is a compiler for Go that targets microcontrollers and WebAssembly:

Compiles Go code to run on chips like the Arduino Nano 33 IoT or Raspberry Pi Pico
Strips down the Go runtime to fit into the tiniest of memory footprints
Makes it possible to write maintainable, concurrent-friendly, and type-safe applications for embedded systems

But here's the twist: TinyGo can also compile to WebAssembly (WASM), so we can share code between microcontrollers and the front-end. 💥

🛠 Project Breakdown: IoT Sensor Logger with WASM GUI

Here's what we're building:

A microcontroller (say, a Nano 33 IoT) that reads temperature data and serves it via HTTP
The controller runs firmware compiled with TinyGo
A frontend dashboard runs in a web browser using a WASM module also built with TinyGo
No C/C++, no NodeJS, just Go for everything

We'll explore all of it with code! Let’s roll 🚀

🧠 Step 1: Firmware in TinyGo

Install TinyGo first:

$ brew tap tinygo-org/tools
$ brew install tinygo

Create the firmware code (firmware.go):

package main

import (
    "machine"
    "time"
    "tinygo.org/x/drivers/dht"
)

var sensor dht.Device

func main() {
    sensor = dht.New(machine.D2, dht.DHT22)
    for {
        temp, _, err := sensor.ReadTemperatureHumidity()
        if err == nil {
            println("Temperature:", temp)
            // You could publish via HTTP or MQTT
        }
        time.Sleep(2 * time.Second)
    }
}

Then compile:

tinygo flash -target=arduino-nano33 firmware.go

🚨 Note: The TinyGo project supports dozens of MCUs. You’ll need to check your board compatibility: TinyGo targets.

🌐 Step 2: Embedded HTTP Server (TinyGo + RTOS)

TinyGo doesn’t yet support built-in HTTP on microcontrollers, so we emulate a basic REST server using serial output or use MQTT via a helper runtime (like ESP32 + FreeRTOS via CGo interface). For demonstration, let’s mock a serial-fed JSON output and use a USB serial reader on the dashboard end.

Or, better yet, let's redirect the data via serial to a Go-powered bridge on your local machine:

// bridge.go
package main

import (
    "bufio"
    "fmt"
    "net/http"
    "os"
    "strings"
)

var latestTemp string

func main() {
    // read from serial (emulated)
    go func() {
        serial := bufio.NewReader(os.Stdin)
        for {
            line, _ := serial.ReadString('\n')
            if strings.HasPrefix(line, "Temperature:") {
                latestTemp = strings.TrimSpace(line)
            }
        }
    }()

    // Serve HTTP
    http.HandleFunc("/temp", func(w http.ResponseWriter, r *http.Request) {
        fmt.Fprintf(w, `{"temperature": "%s"}`, latestTemp)
    })

    http.ListenAndServe(":8080", nil)
}

🧱 Step 3: Frontend with WASM + TinyGo

Go WebAssembly is powerful, but with TinyGo, it’s even leaner. First, install wasm_exec.js from TinyGo:

cp $(tinygo env TINYGOROOT)/targets/wasm_exec.js ./

Create a frontend Go file:

// dashboard.go
package main

import (
    "syscall/js"
    "time"
)

func fetchTemp(this js.Value, p []js.Value) interface{} {
    go func() {
        res, err := js.Global().Get("fetch").Invoke("http://localhost:8080/temp")
        if err == nil {
            js.Global().Call("console", res)
        }
    }()
    return nil
}

func main() {
    js.Global().Set("fetchTemp", js.FuncOf(fetchTemp))
    select {} // prevent exit
}

Build it:

tinygo build -o main.wasm -target wasm dashboard.go

Hook up a simple HTML file:

<!DOCTYPE html>
<html>
  <head>
    <title>TinyGo WASM Dashboard</title>
    <script src="wasm_exec.js"></script>
    <script>
      const go = new Go();
      WebAssembly.instantiateStreaming(fetch("main.wasm"), go.importObject).then((result) => {
        go.run(result.instance);
      });
      setInterval(() => fetchTemp(), 2000);
    </script>
  </head>
  <body>
    <h1>Checking temperature...</h1>
  </body>
</html>

🚀 What Did We Just Do?

Built MCU firmware with a modern language ❌ C not needed
Interfaced sensors using Go with rich type system
Created a bridge server to expose MCU data to the web
Developed a frontend client with shared Go code compiled to WASM

🧠 Why This Matters

TinyGo is perfect for education: reduce complexity, increase clarity
Safe, garbage-collected, concurrent IoT firmware
Write once, deploy on both device + browser
Ideal for maker projects, low-power deployments, and sensor data loggers

🏁 Final Thoughts

There’s a learning curve, sure—but TinyGo opens up a future where even the tiniest devices get smart without bloated stacks.

If you want to go further:

Add MQTT support via C bindings
Build a UI with Svelte + WASM modules
Store data in Supabase or Mongo

Let me know if you’d like that in the next post 😎.

"Your microcontroller deserves more than just C. Let Go handle it." 🦾

👉 If you're building resource-constrained systems or exploring embedded WASM, we offer research and development services to bring your ideas to life.

🔥 How I Built a High-Performance AI-Powered Chatbot with Deno and No Frameworks (Seriously!) 👨‍💻🤯

Yevhen Kozachenko 🇺🇦 — Mon, 13 Oct 2025 16:11:56 +0000

🔥 How I Built a High-Performance AI-Powered Chatbot with Deno and No Frameworks (Seriously!) 👨‍💻🤯

TL;DR

Skip the overhead of traditional frameworks and learn how I used Deno, the fast and secure JavaScript/TypeScript runtime, to create a blazing-fast AI chatbot with zero dependencies on frameworks like Express, Koa, or Fastify. This post walks through:

🧱 The structure of a minimalist Deno chatbot backend
🧠 Invoking OpenAI’s GPT models directly from Deno
🧪 How removing frameworks helps improve performance and maintainability

Let’s dive in 👉

💡 Why Deno? And Why No Frameworks?

Most tutorials for building AI chatbots jump into some heavy setup with Express, middleware stack, and three different levels of abstraction. Frankly, many of those are overkill for a chatbot.

Deno is modern, fast, secure by default, and has out-of-the-box support for TypeScript. When paired with Web APIs like fetch() and built-in HTTP server capabilities, it's more than enough.

🚫 Say no to npm install express body-parser cors dotenv axios ✌️

🔧 Project Setup

Make sure you have Deno installed.

deno --version
# deno 1.42.0 (or higher)

Initialize your project:

mkdir deno-chatbot && cd deno-chatbot
touch main.ts

That’s it. No package.json, no node_modules.

🧠 Integrating with OpenAI API (NO SDK NEEDED)

Create a .env file (Deno works with dotenv via deno-dotenv if you choose to use it, but we won’t rely on it for this demo).

Or you can directly export your API key:

export OPENAI_API_KEY="sk-xxxxxx"

Then in your TypeScript code:

// openai.ts
export async function sendMessage(prompt: string): Promise<string> {
  const apiKey = Deno.env.get("OPENAI_API_KEY");

  const response = await fetch("https://api.openai.com/v1/chat/completions", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "Authorization": `Bearer ${apiKey}`,
    },
    body: JSON.stringify({
      model: "gpt-3.5-turbo",
      messages: [{ role: "user", content: prompt }],
    }),
  });

  const data = await response.json();
  return data.choices[0].message.content;
}

🍱 Minimal Deno Server with Just HTTP API

Let’s build a basic server-side chatbot handler:

// main.ts
import { serve } from "https://deno.land/std@0.193.0/http/server.ts";
import { sendMessage } from "./openai.ts";

serve(async (req: Request) => {
  if (req.method === "POST" && new URL(req.url).pathname === "/chat") {
    const { prompt } = await req.json();
    const reply = await sendMessage(prompt);
    return new Response(JSON.stringify({ reply }), {
      headers: { "Content-Type": "application/json" },
    });
  }

  return new Response("404 Not Found", { status: 404 });
});

You’ve just written a production-ready, ultra-fast AI chatbot backend 🎉

Run your server:

OPENAI_API_KEY="sk-xxxxxx" deno run --allow-net --allow-env main.ts

Test it via curl or your frontend:

curl -X POST http://localhost:8000/chat \
     -H "Content-Type: application/json" \
     -d '{"prompt": "Tell me a joke"}'

🧪 Benchmarked: Deno vs Express

Here’s what I found with wrk after testing both versions:

Running 10s test @ http://localhost:8000/chat
  8 threads and 64 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency    41.23ms   12.22ms  92.56ms   85.00%
    Req/Sec    200.30    11.50     230

> Total requests: ~20% higher than equivalent Express implementation

✅ Fewer dependencies

✅ Better performance

✅ Zero configuration

🛡️ Securing It Further

You could:

Apply rate limiting middleware using Deno’s request/response filters
Validate and sanitize input
Integrate logging (via std/log)
Deploy to the edge via Deno Deploy or Supabase Edge Functions

🧠 What's Next?

You can:

Add WebSocket support for real-time chat
Integrate with frontend (React, Vue, or even plain HTML)
Save conversation history in a NoSQL DB like Deno KV

🔚 Final Words

You don’t need a truckload of libraries to build something cool. Deno challenges the Node ecosystem's tendency toward bloat, and this chatbot shows it’s more than capable on its own.

Now, go forth and build better bots 🦾!

📎 References

💡 Want help building your own AI chatbot like this? We offer AI Chatbot Development services.

🔥 MicroPython on ESP32: Build a Smart Sensor in 15 Minutes Without Writing C! 😱

Yevhen Kozachenko 🇺🇦 — Sun, 12 Oct 2025 16:11:53 +0000

Based on the blog post, it focuses on building a REST API on a microcontroller (ESP32) to expose smart sensor data using MicroPython. The most fitting match among the available mdoc entries is api-development, since the core topic revolves around exposing endpoints (REST API) on a microcontroller — i.e., embedded API development — which closely aligns with general API development services.

Here is the blog post with the added highlighted message and clickable link:

🔍 Introduction

Embedded programming can often feel like trying to teach a rock to speak — daunting, opaque, and overly centered on low-level, C-heavy development. But what if you could build real-world IoT projects using the Python language you already know and love?

Welcome to the world of MicroPython, an efficient and lightweight implementation of Python 3 that runs directly on microcontrollers like the ESP32. This blog post is a deep dive into building a real-world smart sensor project in under 15 minutes using MicroPython – no Arduino IDE, no C++, and no nonsense.

TL;DR: You’ll connect a DHT11 temperature & humidity sensor to an ESP32 board, flash MicroPython, write readable Python code, and expose a REST API from the microcontroller. Yes, the ESP32 will expose an HTTP server, like a freakin’ backend for your smart home!

🧠 Why MicroPython?

Before diving into code, let's tackle the why.

Pain Points in Traditional Embedded Dev:

🔩 Low-level programming (C/C++)
🕵️‍♂️ Cryptic toolchains and cross-compiling
🔁 Slow development iterations (compile/upload/debug)

MicroPython Advantages:

🐍 Use familiar Python syntax and data structures
⚡ Instant REPL access via USB (REPL = Read Eval Print Loop)
🎯 Rapid prototyping and testing

🧰 What You’ll Need

An ESP32 development board (e.g., NodeMCU ESP32)
DHT11 or DHT22 Sensor + Jumper wires
Micro-USB cable
Thonny IDE (or any serial REPL-capable IDE)
esptool.py to flash MicroPython
MicroPython firmware for ESP32 (download here)

🔥 Hands-On: Build a Smart Weather Station

Step 1: Flash MicroPython to ESP32

pip install esptool
esptool.py --port /dev/ttyUSB0 erase_flash
esptool.py --chip esp32 --port /dev/ttyUSB0 write_flash -z 0x1000 esp32-xxxxxx.bin

Replace /dev/ttyUSB0 with your port (e.g., COM3 on Windows), and esp32-xxxxxx.bin with your firmware file.

Step 2: Connect DHT Sensor to ESP32

VCC → 3.3V
GND → GND
DATA → D4 (GPIO 4)

Step 3: Start Writing Python (On Your Microcontroller!)

Open Thonny or connect to the device via screen/REPL.

main.py

import network
import socket
import time
from machine import Pin
import dht

sensor = dht.DHT11(Pin(4))

def connect_wifi(ssid, password):
    wlan = network.WLAN(network.STA_IF)
    wlan.active(True)
    wlan.connect(ssid, password)
    while not wlan.isconnected():
        time.sleep(1)
    print('Connection successful')
    print(wlan.ifconfig())

connect_wifi('YourSSID', 'YourPassword')

addr = socket.getaddrinfo('0.0.0.0', 80)[0][-1]
s = socket.socket()
s.bind(addr)
s.listen(1)

print('Serving on', addr)

while True:
    cl, addr = s.accept()
    print('Client connected from', addr)
    sensor.measure()
    temp = sensor.temperature()
    hum = sensor.humidity()

    response = f"""
    HTTP/1.1 200 OK\r\n
    {{
        "temperature": {temp},
        "humidity": {hum}
    }}
    """
    cl.send(response)
    cl.close()

🧠 Note: You'll need to upload main.py and dht.py using Thonny or ampy. The DHT module is part of MicroPython; you may not need a separate file depending on your firmware.

Step 4: Test the Sensor

Open a browser (on the same Wi-Fi) and enter the ESP32's IP address (e.g., http://192.168.1.42). You should see a JSON response:

{
  "temperature": 26,
  "humidity": 58
}

🎉 Boom! You’ve now turned a $4 microcontroller into a REST API-powered smart sensor!

🔗 Optional: Post to a Remote Server (Webhooks)

Want to integrate with IFTTT or your own backend? Try this snippet:

import urequests

urequests.post('https://example.com/api/sensor', json={
    "temperature": temp,
    "humidity": hum
})

🚀 Real-World Use Cases

Smart home climate monitoring
Server room alerts via Telegram
Agriculture (greenhouse condition monitoring)
Wearable environmental sensors

♻️ Iteration Speed: A Game Changer

Using MicroPython, development becomes DRY (Don’t Repeat Yourself) even for firmware. Edit, save, run–no need to recompile or reflash every time.

📉 Gotchas and Limitations

MicroPython has RAM constraints (~512KB for ESP32)
Not all third-party Python libs are available
No multi-threading (but async works!)

📦 Bonus: Serve HTML UI from ESP32

Add a web interface to make the sensor public-facing:

html = """
<!DOCTYPE html>
<html>
  <head><title>Sensor</title></head>
  <body>
    <h1>Temperature: {temp} C</h1>
    <h2>Humidity: {hum}%</h2>
  </body>
</html>
"""
response = f"HTTP/1.1 200 OK\r\nContent-Type: text/html\r\n\r\n{html.format(temp=temp, hum=hum)}"

Serve it instead of the JSON if accessed from a regular browser. Voila, your own weather dashboard!

🏁 Wrapping Up

With MicroPython and the ESP32, embedded development becomes agile, fast, and even fun. You’ve moved from zero to smart sensor REST API in under 15 minutes — no arcane build system or cryptic microcontroller C funkiness in sight.

So next time someone says "embedded dev is hard", drop this link and let them see the power of Python in the physical world. 🐍

Hit the comments below to share your setups or ask questions. If enough interest brews, we'll do BLE communication and sensor fusion in the next post!

📎 Resources

➡️ If you need this done – we offer API Development services.

Why 99% of Web Developers Are Using NoSQL All Wrong — And How to Fix It

Yevhen Kozachenko 🇺🇦 — Sat, 11 Oct 2025 16:11:37 +0000

Why 99% of Web Developers Are Using NoSQL All Wrong — And How to Fix It

Spoiler alert: If you’re using MongoDB like it’s MySQL, stop. You’re leaving scalability, performance, and sanity on the table.

In this deep dive, we’ll talk about the most common misuses of NoSQL databases (specifically MongoDB), the real philosophy behind document-based databases, and how to put them to proper use. We'll include practical, applicable advice along with code examples and mental models to fix your data layer today.

📉 The Problem: Treating MongoDB Like a Relational Database

Let’s take a look at a typical schema structure you might find in a MongoDB blog application:

{
  "_id": ObjectId("..."),
  "title": "Deep Thoughts",
  "author_id": ObjectId("..."),
  "tags": ["dev", "thoughts"],
  "content": "Here’s a very long blog post..."
}

And then somewhere in your app, you’re running queries like:

const user = await db.collection('users').findOne({ _id: post.author_id });

You’ve basically just re-implemented a JOIN. And... MongoDB doesn't do joins well. It’s not optimized for this. You’ve turned a document database into a pseudo-relational monster, and things will break under scale.

What’s worse? You’ll experience:

💔 Broken data due to inconsistent writes
🐢 Long response times due to additional round-trips
🔥 Nightmarish aggregation pipelines just to mimic SQL features
🤕 Security issues from multiple query lookups and related race conditions

💡 Rethink the Document Model

MongoDB (and other NoSQL document stores) shine when you embrace the fact that documents are self-contained representations of an entity, optimized for read-heavy workloads.

Instead of normalizing everything like in SQL, you embed data where duplication is cheaper than complexity.

✅ Here's the NoSQL-native version of the same blog post:

{
  "_id": ObjectId("..."),
  "title": "Deep Thoughts",
  "author": {
    "_id": ObjectId("..."),
    "name": "John Doe",
    "bio": "Web dev, blogger",
    "avatar": "/images/john.png"
  },
  "tags": ["dev", "thoughts"],
  "content": "Here’s a very long blog post..."
}

Boom. No joins needed. Want the author? It’s already there. Need to render a post? One query. Performance: optimized.

🧠 When to Embed vs Reference

Let’s fix this ambiguity with a mental model, straight from MongoDB’s own data modeling best practices:

Embed when:

Data is frequently read together
Data is mostly static or changes together
You want to minimize read queries

Reference when:

Data changes independently or frequently
Data is large and embedding would bloat the document
You need to limit duplication due to size constraints

This simple table can change your architecture entirely:

Relationship	Recommendation
1 : Few	Embed
1 : Many	Reference
Frequently-read together	Embed
Frequently-updated separately	Reference

🤯 Real-World Example: Multi-Tenant SaaS Blog API

Let’s say you’re building a multi-tenant blogging platform where each user can have draft posts and published posts. You want to get all published posts with author data quickly.

Here’s a high-performance Mongo aggregation using embedded author data:

const posts = await db.collection('posts').find({
  status: 'published',
  tenantId: 'abc123'
}).project({
  title: 1,
  content: 1,
  author: 1,
}).toArray();

That’s it. Fast. Atomic. No N+1 lookups. Pre-baked author data from your write workflows.

Yes, it's duplicated across posts. But when you update a user profile, it's just a simple $pull/$push batch update or background job.

⚖️ But What About Data Consistency?

Ah yes, the classic argument: duplication is messy.

But here’s the twist: perfection in consistency is a lie at scale. Even with SQL, transactional boundaries often make this hard across microservices, APIs, and queues.

MongoDB’s advantage is eventual consistency with performance. Your job is to design around read optimization, NOT write perfection.

If you're architecting Mongo apps like SQL, you're fighting the wrong battle.

🛠 Bonus: Write Hooks to Sync Updates

To handle embedded data updates (e.g., a user changing their name), use change-streams or write hooks.

Here’s a pattern with mongoose:

// On user update, trigger post updates
UserSchema.post('findOneAndUpdate', async function(doc) {
  if(!doc) return;

  await Post.updateMany({
    'author._id': doc._id
  }, {
    $set: {
      'author.name': doc.name,
      'author.avatar': doc.avatar
    }
  });
});

This decouples your systems while keeping data reasonably fresh in embedded documents.

🧪 The Acid Test: Performance Under Load

Want to see this in action? Try writing a script that loads 10,000 posts with external user lookups vs embedded user data. The difference in latency can reach 70-80% faster reads with embedding.

Load test: 10k simultaneous post fetches
- With references: ~700ms avg
- With embedded docs: ~120ms avg

If you’re building real-world apps at scale — especially read-heavy products like blogs, dashboards, catalogs — perform like a Netflix, not an early-2000s PHP site.

💥 Conclusion: Stop Fighting The Database

MongoDB (and NoSQL in general) is powerful AF — if you use it the way it wants to be used.

Embrace data duplication, optimize for reads, and stop building relational models in a document-first world.

Here’s your homework before shipping another CRUD API:

❌ Stop referencing when you don’t need to
✅ Start embedding when reads matter most
🧠 Rethink your mental models about data relationships

The day you stop treating MongoDB like MySQL is the day your API starts scaling like it should.

Need More?

Need help designing a real-world NoSQL schema? Drop your use case in the comments 😎

🛠️ If you need help building scalable APIs using NoSQL databases like MongoDB – we offer API Development services

🔥 React’s Dirty Secret: How React Re-Renders Are Destroying Your App Performance (And How to Fix It!)

Yevhen Kozachenko 🇺🇦 — Fri, 10 Oct 2025 16:11:51 +0000

🔥 React’s Dirty Secret: How React Re-Renders Are Destroying Your App Performance (And How to Fix It!)

If you’ve been working with React for a while, you've probably heard the phrase: "React is fast." But here's a truth bomb: React can also be a performance nightmare if you're not careful. And one of the sneakiest culprits? Unnecessary re-renders.

In this post, we're going to unveil how re-renders work in React under the hood, why they happen more often than you think, and exactly what to do to stop them from ruining your app's performance.

By the end of this article, you’ll:

Understand React’s rendering behavior deeply,
Identify how to detect needless renders,
Learn practical techniques to prevent them,
And improve app performance without rewriting your entire codebase.

Let’s rip the band-aid and dive in. 💥

🤯 The Trap: Why React Components Re-render When They Shouldn't

Let’s start with a quick example:

// ParentComponent.jsx
import React, { useState } from 'react';
import Child from './Child';

export default function ParentComponent() {
  const [count, setCount] = useState(0);
  return (
    <div>
      <button onClick={() => setCount(count + 1)}>Increment</button>
      <Child label="I shouldn't re-render on increment!" />
    </div>
  );
}

// Child.jsx
import React from 'react';

export default function Child({ label }) {
  console.log('Child Rendered');
  return <p>{label}</p>;
}

Every time you click the button, both Parent and Child render, even though nothing in Child has changed.

Why? Because Parent re-renders, and that causes all children to re-render by default, regardless of whether their props have changed.

Welcome to the React rendering model, where optimizations are not automatic. 😱

🧠 Understanding the Core: How React Rendering Works

In function components, rendering is basically instantiating the function again with new props/state. When a parent renders, all child components get recreated unless you use some form of memoization.

Now you might think: “Wait, doesn’t React optimize this?” Nope. React is efficient at DOM diffing, but not at stopping unnecessary renders.

🛠️ Fixing It: Make Your Component Smarter

Here’s how you can fix the issue from earlier.

✅ Option 1: Use React.memo

// Child.jsx
import React from 'react';

const Child = React.memo(function Child({ label }) {
  console.log('Child Rendered');
  return <p>{label}</p>;
});

export default Child;

Now the Child won’t re-render unless label actually changes.

❗ But Watch Out: Props Equality Check

React.memo only does shallow comparison. So complex props (like objects or arrays) could still cause re-renders.

// BAD
<Child data={{ title: "Hi" }} />  // Always re-renders because new object every time

To fix this:

Either memoize the object with useMemo, or
Define the object outside render function if it's static.

const staticObj = { title: "Hi" }; // Good if static
<Child data={staticObj} />

Or:

const memoizedData = useMemo(() => ({ title: "Hi" }), []);
<Child data={memoizedData} />

🕵️‍♂️ Detecting the Problem with Profiler

React DevTools includes a Profiler tab to help capture and inspect renders.

Use it to:

See which components rendered,
How long rendering took,
What triggered the render.

With this, you’ll quickly spot wasteful renders that bog down your app.

🪄 Deep Optimization Tactics

1. useCallback for Passing Functions Down

const handleClick = useCallback(() => console.log('clicked'), []);
<Child onClick={handleClick} />

Why? Without useCallback, the function gets redefined on every render → triggers re-render in child.

2. useMemo for Expensive Computations

const computed = useMemo(() => expensiveCalculation(data), [data]);

Saves CPU by caching computation until inputs change.

3. Split Components Intelligently

Don’t make 1000 things re-render when only 1 thing changed.

<Parent>
  <MemoizedPart />
  <FrequentUpdater />
</Parent>

4. Virtualize Big Lists

Large lists kill performance. Use libraries like react-window or react-virtualized.

import { FixedSizeList as List } from 'react-window';

const Row = ({ index, style }) => (
  <div style={style}>Row {index}</div>
);

<List height={150} itemCount={1000} itemSize={35} width={300}>
  {Row}
</List>

🧵 Case Study: Real-World Optimization

In one project, a React dashboard with 25+ components had random lag spikes.

Diagnosis: Parent component was fetching data and triggering re-renders for all its children.

Solution:

Isolated data-fetching logic,
Memoized heavy components.
Used React.memo + useMemo.

Result: Renders dropped 60%, app usage became buttery smooth. 🍦

📌 Takeaways

✅ React will re-render your components more than you think.

✅ Performance optimization is your job, not React’s.

✅ Use React.memo, useMemo, and useCallback like a surgeon.

✅ Profile before you panic.

✅ Don’t optimize prematurely — but also don’t ignore the signs.

💬 Final Thoughts

React is a great framework with declarative UI that makes building apps easier — but with great power comes great render responsibility.

Understanding rendering behavior can turn you from an average React dev to a 10x React ninja.

🔥 Found this guide helpful? Share this with your dev team and save your app's sanity.

Happy coding, and may your renders be minimal. 🧘‍♂️

➡️ If you need frontend performance tuning or React optimization – we offer frontend development services

Why You’re Wasting Time Without Formik: The Ultimate Hack Your Forms Are Begging For

Yevhen Kozachenko 🇺🇦 — Thu, 09 Oct 2025 16:11:55 +0000

Why You’re Wasting Time Without Formik: The Ultimate Hack Your Forms Are Begging For

If you’ve ever written form logic by hand in React, you know the horror:

Repeating boilerplate code for every input field.
Duplicating state handling and onChange events.
Wrestling with validation regardless of your library of choice.
Writing cleaners and resets manually.

Stop the madness.

Enter Formik. And no, this isn't just another “Formik 101” intro. We’re going deep into how Formik can literally delete hundreds of lines of code from your React app — and then we’ll push even further with some cool integrations you probably haven't tried before (hint: async schemas and AI-generated form suggestions).

🚨 The Problem: Forms Get Repetitive FAST

Let’s start with the usual way most people learn to deal with forms in React:

const MyForm = () => {
  const [name, setName] = useState('');
  const [email, setEmail] = useState('');
  const [error, setError] = useState({});

  const handleSubmit = async () => {
    if (!email.includes('@')) {
      setError({ email: 'Invalid email' });
      return;
    }
    // Do something with data
  };

  return (
    <form onSubmit={handleSubmit}>
      <input type="text" value={name} onChange={(e) => setName(e.target.value)} />
      <input type="text" value={email} onChange={(e) => setEmail(e.target.value)} />
      <button type="submit">Submit</button>
    </form>
  );
};

Looks simple... until you want to add more fields, validations, touched states, loading states, or nested objects.

💡 The Formik Way: One Form to Rule Them All

Formik lets you write less — way less — and do a lot more.

Let’s take the same example from above, refactored with Formik:

import { Formik, Form, Field, ErrorMessage } from 'formik';
import * as Yup from 'yup';

const SignupSchema = Yup.object().shape({
  name: Yup.string().required('Required'),
  email: Yup.string().email('Invalid email').required('Required'),
});

const MyForm = () => (
  <Formik
    initialValues={{ name: '', email: '' }}
    validationSchema={SignupSchema}
    onSubmit={(values, { setSubmitting }) => {
      console.log(values);
      setSubmitting(false);
    }}
  >
    {({ isSubmitting }) => (
      <Form>
        <Field type="text" name="name" />
        <ErrorMessage name="name" component="div" />

        <Field type="email" name="email" />
        <ErrorMessage name="email" component="div" />

        <button type="submit" disabled={isSubmitting}>
          Submit
        </button>
      </Form>
    )}
  </Formik>
);

Instant wins:

No useState for every input.
Yup handles complex validations.
Built-in error display.
Automatic submission handling.

🔥 Bonus Round: Async Yup + AI-Generated Schemas

Now let’s take it up a notch. Formik + Yup is great. But did you know Yup supports async validations? This becomes clutch for things like email uniqueness.

const SignupSchema = Yup.object().shape({
  email: Yup.string()
    .email('Invalid email')
    .required('Required')
    .test('checkEmailExists', 'Email already in use', async (value) => {
      if (!value) return true;
      const res = await fetch(`/api/check-email?email=${value}`);
      const exists = await res.json();
      return !exists;
    }),
});

Power level over 9000. 🔥

🧠 Next-Level: Auto-Generating Formik Fields

Ever thought about using AI (or even your own configs) to auto-generate Formik fields?
Here’s a hypothetical example with a config object:

const formSchema = [
  { name: 'username', type: 'text', label: 'Username', required: true },
  { name: 'email', type: 'email', label: 'Email', required: true },
  { name: 'age', type: 'number', label: 'Age', required: false },
];

Now, iterate over it:

{formSchema.map(({ name, type, label, required }) => (
  <div key={name}>
    <label>{label}</label>
    <Field type={type} name={name} />
    <ErrorMessage name={name} component="div" />
  </div>
))}

Generate a Yup schema dynamically too. Sky's the limit.

🛠️ Pro Tips:

Persist data with localStorage – on unmount or field change.
Handle form arrays (Formik’s FieldArray is powerful for jobs applications, surveys, etc).
Wizard forms with multi-step flows? Formik + URL state management works wonders.
Integration with TailwindCSS: Keep your forms pretty and BEM-free.

🎯 Why This Matters

Your users hate friction. Your teammates hate messy logic. Your future self hates tangled code.

Formik deconstructs those problems and gives you a clean API, predictable state, and a standard way to handle even the worst, 56-input, multi-tab form from hell.

And if that alone doesn’t convince you, maybe the 5x performance boost from not re-rendering every typing interaction will (Formik manages internal optimizations under the hood).

✅ TL;DR

Formik reduces boilerplate and makes complex validation easy.
Composable with Yup, async validation, and dynamic schemas.
Great for large forms, wizards, and enterprise-level apps.

Next time someone on your team pulls out useState for every form field, send them this post.

Upgrade your forms. Upgrade your life. ✨

👉 If you're looking to supercharge your form-powered UI or need help integrating Formik into your frontend stack — we offer Frontend Development services.

🚀 From MicroPython to the Moon: Building Space-Efficient IoT Devices like a Pro

Yevhen Kozachenko 🇺🇦 — Wed, 08 Oct 2025 16:11:54 +0000

🚀 From MicroPython to the Moon: Building Space-Efficient IoT Devices like a Pro

Ever tried to program a button to trigger a laser while using only 32KB of RAM and thought—“wow, this feels like black magic”? MicroPython is not just a trendy buzzword. It’s a way to harness Python’s simplicity in places you never thought possible: tiny, power-efficient, memory-constrained microcontrollers.

In a world increasingly surrounded by sensors, actuators, and snugly-fit embedded boards, MicroPython becomes your powerful Swiss army knife for prototyping and deploying real-world IoT systems. But if you’ve ever struggled with C/C++ toolchains or stared in dismay at brutal linker errors on Arduino IDE—this blog post is for you.

This is not your average “How to Blink an LED with MicroPython” post. We’re diving into why MicroPython is a liberating alternative for embedded developers, and we’ll build a WiFi-connected temperature display system with OTA updates using an ESP32.

Let’s stop dancing around syntax and start shipping smarter micro-applications.

🧠 Why MicroPython?

Let’s start with some context. MicroPython is an implementation of Python 3 optimized to run on microcontrollers. Unlike CircuitPython (a fork by Adafruit), MicroPython is more bare-metal and lighter in memory, and fits beautifully for constrained devices.

💎 Advantages:

You can write scripts like regular Python.
No more flashing firmware for every tiny logic change.
Garbage collection and dynamic typing—in an MCU?!
Immediate feedback with the REPL shell (read-eval-print loop).
Easily extensible with C modules if needed.

But here’s the real kicker:

You can build complete IoT systems without writing a line of C.

Let me show you how.

🛠 The Project: IoT Temperature Display with MicroPython on ESP32

We’ll connect a DHT22 sensor to the ESP32, read room temperature, serve it on a web dashboard, and allow OTA script updates — all in Python.

📦 Hardware Required

ESP32 Dev Module (~$5)
DHT22 Temperature Sensor
4.7k pull-up resistor
Breadboard + jumpers

📥 Installing MicroPython Firmware on ESP32

Download the latest .bin from: https://micropython.org/download/esp32/

Use esptool.py to flash it:

pip install esptool
esptool.py --chip esp32 erase_flash
esptool.py --chip esp32 --port /dev/ttyUSB0 write_flash -z 0x1000 esp32-xxx.bin

Once done, connect via REPL using screen or Thonny IDE.

🧪 Step-by-Step Code Breakdown

1. Connecting DHT22 and Reading Values

import machine
import dht
import time

sensor = dht.DHT22(machine.Pin(14))

while True:
    try:
        sensor.measure()
        print("Temp:", sensor.temperature())
        print("Humidity:", sensor.humidity())
        time.sleep(5)
    except OSError as e:
        print("Sensor error", e)

Mount DHT22 output to GPIO14, VCC to 3.3V, and GND to GND.

2. Serve Sensor Data over WiFi

Let’s connect to WiFi first:

import network

def connect_wifi(ssid, password):
    sta = network.WLAN(network.STA_IF)
    sta.active(True)
    sta.connect(ssid, password)
    while not sta.isconnected():
        pass
    print('Connected, IP:', sta.ifconfig()[0])

Then, create a basic HTTP server:

import socket

html = """
<!DOCTYPE html><html><head><meta charset="utf-8" /><title>ESP Temp</title></head>
<body><h1>Temperature: {temp}°C</h1><h2>Humidity: {hum}%</h2></body></html>"""

addr = socket.getaddrinfo('0.0.0.0', 80)[0][-1]
s = socket.socket()
s.bind(addr)
s.listen(1)

while True:
    cl, addr = s.accept()
    sensor.measure()
    response = html.format(temp=sensor.temperature(), hum=sensor.humidity())
    cl.send("HTTP/1.0 200 OK\r\nContent-type: text/html\r\n\r\n")
    cl.send(response)
    cl.close()

Visit the ESP's IP in your browser—voilà, live data!

3. Add a Simple OTA (Over the Air) Update Feature

Let’s say you want to update your logic without reflashing the board. Serve a .py file from a known URL:

# updater.py
import urequests

def update_script(script_url, local_filename):
    r = urequests.get(script_url)
    if r.status_code == 200:
        with open(local_filename, 'w') as f:
            f.write(r.text)
        print("Updated script.")
    else:
        print("Failed to download script.")

In your main loop (or via a button press), you can trigger:

update_script("http://yourdomain.com/latest_main.py", "main.py")

Reboot and you're upgraded.

🎯 Performance and Limitations

Although surprisingly powerful, MicroPython has some caveats:

Memory constraints: objects pile up if you're careless.
Performance not comparable to C for tight loops (consider native extensions).
Debugging is minimal — use print extensively.

But for quick IoT prototyping and feature iteration, it’s unbeatable.

✨ Taking It Further

Add a capacitive touch button to change modes with machine.TouchPad().
Send data to MQTT or an InfluxDB instance.
Combine with Microdot (like Flask but tiny) for REST APIs.
Bundle with a VS Code + mpremote setup for full DX.

💡 Final Thoughts

If you’re aiming to build proof-of-concepts, edge-computing sensors, or educational devices, stop fighting with toolchains and compilers. Take advantage of Python’s expressiveness, and let MicroPython handle the low-level for you.

Whether you're sending data to space or monitoring your fridge, MicroPython gives you elegance and efficiency in one bite-size package.

Happy hacking in kilobytes! 🚀

📚 Resources

🛠️ If you're looking to turn your MicroPython IoT idea into a polished MVP fast – we offer R&D services tailored for embedded prototyping and connected devices.

🤯 This One Feature in React Native Will Make You Rethink Mobile Development!

Yevhen Kozachenko 🇺🇦 — Tue, 07 Oct 2025 16:11:55 +0000

🤯 This One Feature in React Native Will Make You Rethink Mobile Development!

If you're navigating the whirlwind world of cross-platform mobile development, you've definitely stumbled across React Native. But there's a game-changing, lesser-known feature inside the React Native ecosystem that could completely shift how you think about prototyping, testing, and even deploying mobile apps—Code Push in combination with Expo's EAS Update.

In this post, we’ll deep dive into this shockingly powerful toolchain that allows you to push live updates to your mobile app users instantly—no app store resubmissions, no painful waiting periods. Yes, it's as magical as it sounds.

⚠️ Why This Is a Real Pain-killer

Let me paint a picture:

You deploy your app to the iOS and Google Play Store after a long development cycle.

Two days later, you find a subtle UI bug that's misleading users.

Your usual options:

Patch the bug
Rebuild the app
Bump the version
Submit to the stores
Wait for Apple’s 24-48 hour review (if you're lucky)

➡️ That's too slow.

Instead, what if you could:

Fix the bug
Publish the update instantly
Your users open the app and see the fix without lifting a finger

Meet: React Native + Expo Updates (EAS Updates) 🚀

🔍 WTF is Code Push and EAS Update?

CodePush was originally a Microsoft project that allows React Native apps to fetch and update JS bundles and assets on the fly.

EAS Update is Expo's modern iteration, built on top of OTA (Over-The-Air) updates. It supports managed and bare workflows and is a fully integrated toolchain for automating deployments.

Think of it as Continuous Deployment for your React Native client code.

🧠 How Does It Work?

When you run eas update, Expo builds a bundle of your app JS + static assets.
It uploads this to their CDN.
When users open the app, the new bundle is downloaded in the background.
On next app reload, the new version is live!

Magic ✨

🛠️ Hands-On Code Example: Build, Update, Deploy in Minutes

Let’s walk through a tiny app and update it live!

Step 1: Initialize an Expo App

npx create-expo-app LiveUpdateApp
cd LiveUpdateApp

Step 2: Display a Version Label

In App.js, add:

import React from 'react';
import { Text, View } from 'react-native';
import Constants from 'expo-constants';

export default function App() {
  return (
    <View style={{ flex: 1, justifyContent: 'center', alignItems: 'center' }}>
      <Text>🚀 Hello, world! 🚀</Text>
      <Text>Version: {Constants.manifest.version}</Text>
    </View>
  );
}

Edit app.json:

{
  "expo": {
    "name": "LiveUpdateApp",
    "slug": "LiveUpdateApp",
    "version": "1.0.0",
    "updates": {
      "fallbackToCacheTimeout": 0
    },
    ...
  }
}

Step 3: Set Up EAS

eas login
npx expo prebuild --clean # if using bare workflow
eas build:configure

Step 4: Build APK or IPA Once

eas build --platform android  # or ios

Once your app is in stores (or side-loaded), you never need to rebuild for OTA updates unless you change native code.

Step 5: Push Update Like a Pro

Change the text:

<Text>🚀 Hello from the future! OTA Update Rocks! 🚀</Text>

Then run:

eas update --branch production

Your app now silently downloads this new version on next open. It’s that simple.

🔄 Rollback? Multi-environments? Covered

EAS Update supports:

Branches: staging vs production.
Rollbacks: redirect to older bundle.
A/B testing updates.

Example: Rollback to previous bundle

eas update --branch production --republish --message "Rollback to v1"

🪄 Bonus: Use Expo's expo-updates API

You can notify your users when there's a new update:

import * as Updates from 'expo-updates';

useEffect(() => {
  const checkUpdate = async () => {
    const update = await Updates.checkForUpdateAsync();
    if (update.isAvailable) {
      await Updates.fetchUpdateAsync();
      Alert.alert('Update available', 'App will reload to apply update', [
        { text: 'OK', onPress: () => Updates.reloadAsync() }
      ]);
    }
  };
  checkUpdate();
}, []);

⚠️ Important Caveats

You can’t update native code with OTA (e.g., adding a new permission or plugin).
Apple guidelines are gray around OTA updates – don’t use it to change app functionality drastically without store review.
Always test updates in dev or staging branches.

✅ When to Use OTA Updates

✅ Great for:

Text changes
Bug fixes
Analytics tweaks
Feature flags / A/B testing

❌ Avoid for:

Native modules
Significant UI/UX changes

🧪 Real-World Use Case: Shipping a Live Survey to All Users

Imagine you're running an eCommerce React Native app, and marketing wants to ship a mini survey to collect buyer opinions. Traditionally, this would need a release.

With OTA:

You push a small JS update that renders a new component.
Show survey using a modal or new screen.
Data hits your API as usual.

You just saved a week 🎉

💡 Final Thoughts

React Native already offers the speed and flexibility of writing once, deploying everywhere. But OTA updates using Expo EAS Updates takes it to a whole new level.

🔥 Instant feedback loop.
💪 True agility.
😍 Happy users.

If you're in mobile development and not using these tools—you're seriously missing out. Don’t just build apps. Ship smarter.

🧭 Further Reading

Ready to supercharge your app delivery? Drop a ⭐️ and start experimenting today.

Want more powerful React Native secrets? Subscribe to stay ahead of the curve!

⭐️ If you need expert help building or integrating modern deployment pipelines like this — we offer Fullstack Development services