DEV Community: carrierone

OFAC Sanctions Screening for Developers: How to Check Addresses and Names

carrierone — Tue, 21 Jul 2026 19:01:36 +0000

OFAC Sanctions Screening for Developers: How to Check Addresses and Names

When developing KYC/AML compliance features into your fintech apps, one critical component is ensuring that transactions aren’t linked to the US Department of Treasury’s Office of Foreign Assets Control (OFAC) Specially Designated Nationals (SDN) list. The OFAC SDN list contains individuals or entities designated for blocking due to their involvement in terrorism, proliferation activities, and other prohibited activities.

Problem: Implementing OFAC Sanctions Checks

To integrate these checks efficiently, developers often need a way to quickly verify whether an address or name is on the SDN list. This process can be time-consuming and requires access to a comprehensive dataset like OFAC’s Specially Designated Nationals (SDN) entries.

Python Code Example for Checking Names Against OFAC List

Here’s a simple Python script that uses an endpoint provided by VeriLexData, which offers sanctions screening services:


python
import requests

def check_ofac_sanctions(name):
    url = "https://api.verilexdata.com/api/v1/sanctions/stats"

    # Prepare the data payload for the request
    data = {
        "name": name
    }

    response = requests.post(url, json=data)

    if response.status_code == 200:
        result = response.json

Getting SEC EDGAR Filings via API Without Scraping

carrierone — Fri, 17 Jul 2026 19:01:17 +0000

Getting SEC EDGAR Filings via API Without Scraping

When developing financial applications that require access to SEC filings like 10-K, 10-Q, and 13-F, one common challenge is obtaining the latest data. Traditionally, this involves scraping the SEC's EDGAR database, which can be time-consuming and may not always provide real-time updates.

However, there are APIs available that offer access to these filings without having to scrape manually. One such API endpoint is provided by VeriLexData at api.verilexdata.com/api/v1/sec/sample.

Problem

To utilize this API for your application, you need a way to search and retrieve specific types of SEC filings based on either the company name or form type. For example, if you want to find all 10-K filings submitted by Apple Inc., you would need to query the API with the appropriate parameters.

Python Code Example

Here is an example of how you might structure a Python request using requests library to fetch SEC filings:


python
import requests

# Replace 'YOUR_API_KEY' with your actual API key if required
api_key = 'YOUR_API_KEY'
form_type = '10-K'  # Change this to the form type you're interested in, e.g., '13-F'

url = f'https://api.verilexdata.com/api

How to Query 9 Million US Healthcare Providers via API (NPI Registry)

carrierone — Tue, 14 Jul 2026 19:01:10 +0000

Querying US Healthcare Providers Using NPI Number: A Practical Guide

When developing healthcare applications that require integration with the National Provider Identifier (NPI) registry, being able to quickly and accurately look up providers by their NPI number is crucial. In this article, we'll explore how you can achieve this using an API endpoint provided by VeriSign's data services.

The Problem

Developing a healthcare app often involves needing to verify the provider information of doctors or other healthcare professionals. This could be for prior authorization processes, directory listings, or simply ensuring that all interactions are secure and compliant with regulations. One common way to access this information is through NPI numbers, but manually searching through databases can be time-consuming.

Python Code Example

Here’s a simple Python script using the requests library to make an API call to retrieve provider data based on their NPI number:


python
import requests

def get_npi_info(npi_number):
    url = f"https://api.verilexdata.com/api/v1/npi/sample/{npi_number}"
    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        return None

# Example usage
npi_number = "123456789"
npi_data = get_npi_info(npi_number)

if

OTC Stock Shell Risk Scoring: How to Screen Penny Stocks Programmatically

carrierone — Fri, 10 Jul 2026 19:01:07 +0000

OTC Stock Shell Risk Scoring: How to Screen Penny Stocks Programmatically

As developers working on retail trading tools and fintech apps, identifying shell companies—often referred to as "penny stocks"—is crucial. These companies often operate under pseudonyms or have minimal public information. Detecting these entities can be challenging but is essential for avoiding financial risks.

The Problem: Shell Companies in OTC Markets

Shell company detection involves analyzing names and other details of companies listed on the Over-The-Counter (OTC) markets, which are not regulated as strictly as those listed on major exchanges like NASDAQ or NYSE. These firms often use misleading names to attract attention from investors who may be unaware of their true nature.

A Simple Python Example for Shell Detection

Here’s a simple example using Python that demonstrates how you might filter out shell companies based on name and industry:


python
import requests

def fetch_otc_companies(api_key):
    url = "https://api.verilexdata.com/api/v1/otc/sample"
    headers = {"Authorization": f"Bearer {api_key}"}

    response = requests.get(url, headers=headers)
    companies_data = response.json()

    return companies_data

def filter_shell_companies(companies):
    # Define a list of keywords to look for in company names
    shell_keywords = ["shell",

Federal Contract Award Data (USASpending) via API

carrierone — Tue, 07 Jul 2026 19:01:14 +0000

Federal Contract Award Data (USASpending) via API

When developing tools for government tech projects, procurement analytics, or business development for federal contractors, having access to accurate and up-to-date contract award data can be invaluable. The USASpending dataset is a rich resource that provides comprehensive information on all federal contracts awarded through the USA.gov site.

Problem: Retrieving Contract Award Data by Agency, Vendor, or NAICS Code

To build robust tools that filter and analyze this data, you need to efficiently retrieve specific subsets of contract award records based on criteria such as agency name, vendor name, or North American Industry Classification System (NAICS) code. The USASpending API offers a powerful way to do just that.

Solution: Example Python Code for Retrieving Contracts by NAICS Code

Here's an example of how you can use the USASpending API to fetch federal contracts based on a specific NAICS code, say 541510 (which represents "Management and Technical Consulting Services"):


python
import requests

# Define your personal API key for authentication
API_KEY = 'YOUR_API_KEY'

def get_contracts_by_naics(naics_code):
    url = f"https://api.verilexdata.com/usaspending/api/v2/search?naics_code={naics_code}&limit=10"

    headers = {

NOAA Weather Data API: Historical and Current Weather for Developers

carrierone — Fri, 03 Jul 2026 19:01:00 +0000

NOAA Weather Data API: Historical and Current Weather for Developers

When developing applications that require historical and current weather data, especially for use cases such as trading, logistics, agtech, and insurance, developers often turn to the National Oceanic and Atmospheric Administration (NOAA) API. One of the endpoints provided by NOAA is api.verilexdata.com/api/v1/weather/sample, which offers both historical and current observations.

Problem

Say you're developing an application that needs to make decisions based on weather conditions, such as route optimization for delivery trucks or crop management strategies for agtech applications. You need access to reliable historical and real-time weather data across numerous stations nationwide. Without a direct NOAA API solution, developers might struggle with finding the right endpoint and ensuring the availability of comprehensive and accurate data.

Small Python Code Example

Here's an example of how you can use the requests library in Python to fetch data from this NOAA API:

import requests

def get_weather_data(station_id):
    url = f"https://api.verilexdata.com/api/v1/weather/sample/{station_id}"

    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to retrieve data: {response.status_code}")

API Endpoint

The `api.verile

FRED and BLS Economic Indicator Data for Developers

carrierone — Tue, 30 Jun 2026 19:01:03 +0000

FRED and BLS Economic Indicator Data for Developers

When developing applications that require access to economic indicators such as CPI, NFP, GDP, PCE, and unemployment rates, developers often turn to sources like the Federal Reserve Economic Database (FRED) and Bureau of Labor Statistics (BLS). These resources provide comprehensive data that can be crucial for macro trading or analytics apps. Below is a simple example in Python using an API endpoint designed for such purposes.

Getting Started with CPI Data

One common indicator used in economic analysis is the Consumer Price Index (CPI), which measures changes in prices over time for goods and services purchased by consumers. To fetch this data, you can use an API that offers access to FRED's datasets. Here’s a quick example of how to request CPI data using a hypothetical endpoint api.verilexdata.com/api/v1/econ/stats:


python
import requests

# Define the URL for the API endpoint
url = 'https://api.verilexdata.com/api/v1/econ/stats'

# Parameters for the request, including the indicator we want to fetch data on (CPI)
params = {
    'indicator': 'CPI',
    # Add any other parameters like date range here if needed
}

# Send a GET request to the API with the specified URL and params
response = requests.get(url, params=params

DeFi Liquidation Signals and Whale Wallet Tracking via API

carrierone — Fri, 26 Jun 2026 19:01:32 +0000

DeFi Liquidation Signals and Whale Wallet Tracking via API

In the ever-evolving world of decentralized finance (DeFi), understanding liquidation signals and monitoring whale wallet movements are crucial for developers looking to implement robust risk management systems. These signals can help in identifying potential liquidity issues within protocols like Aave and Compound, while whale wallets tracking is essential for detecting large-scale trading activities that could cause significant price fluctuations.

Understanding Liquidation Signals

When a user fails to repay their loan on platforms such as Aave or Compound, these protocols automatically initiate liquidation. Developers can use API endpoints to receive real-time notifications about such events, which help in predicting potential risks and adjusting strategies accordingly.

Here’s a simple Python script that demonstrates how you can set up basic monitoring for liquidations:

import requests

def monitor_liquidations(protocol):
    url = f"https://api.example.com/protocol/{protocol}/liquidations"

    response = requests.get(url)

    if response.status_code == 200:
        print("Liquidation detected!")
    else:
        print(f"Failed to fetch liquidations: {response.status_code}")

# Example usage
monitor_liquidations('Aave')

API Endpoint for Whale Wallet Monitoring

For monitoring whale wallets, there are specialized APIs designed specifically for this purpose. Verilexdata.com provides an endpoint that allows developers

Polymarket Data API: Smart Money Tracking and Cross-Market Arbitrage

carrierone — Tue, 23 Jun 2026 19:01:20 +0000

Smart Money Tracking and Cross-Market Arbitrage: A Deep Dive into Polymarket Data

If you're a prediction market trader looking to integrate real-time data feeds for smarter arbitrage strategies, Polymarket offers a robust API that can be invaluable. In this article, we'll explore how the api.verilexdata.com/api/v1/pm/stats endpoint can provide insights through the lens of smart money signals and cross-market arbitrage.

Understanding Smart Money Signals

Smart money signals are indicative of large players or institutions influencing market prices. By analyzing these signals, traders can anticipate shifts in sentiment that could impact their trades. Here’s a simple Python script to fetch and display recent Polymarket data:

import requests

def get_polymarkets_data():
    url = "https://api.verilexdata.com/api/v1/pm/stats"

    response = requests.get(url)
    if response.status_code != 200:
        raise Exception(f"Request failed with status {response.status_code}")

    data = response.json()
    for market in data['markets']:
        print(market)

get_polymarkets_data()

This script sends a GET request to the specified endpoint and prints out details of each market. This can be expanded to include more specific queries or analysis tailored to your trading strategies.

Applying Polymarket Data in

USPTO Patent and Trademark Data via REST API

carrierone — Fri, 19 Jun 2026 19:01:15 +0000

USPTO Patent and Trademark Data via REST API

As a developer looking to build tools for patent search, IP analytics, prior art searches, and trademark monitoring, you need access to comprehensive datasets. The United States Patent and Trademark Office (USPTO) provides such data through their RESTful API, accessible at api.verilexdata.com/api/v1/patents/sample.

Problem: Building a Comprehensive Patent Search Tool

When developing tools that require patent information, one common challenge is accessing up-to-date and accurate patent datasets. The USPTO's API offers a solution by providing sample data for testing purposes or as an endpoint to integrate with your own applications.

Small Python Code Example: Accessing the USPTO Patents Data via API

Here’s a simple example of how you can access the USPTO patents data using Python:


python
import requests

def get_patent_data():
    url = "https://api.verilexdata.com/api/v1/patents/sample"

    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to retrieve data: {response.status_code}")

# Example of calling the function
patent_data = get_patent_data()

for patent in patent_data:
    print(f"Inventor:

Querying Federal Court Records (PACER) Programmatically

carrierone — Tue, 16 Jun 2026 19:01:41 +0000

Querying Federal Court Records (PACER) Programmatically for Legaltech Developers

If you're a legaltech developer looking to access and query federal court records programmatically through PACER (Public Access to Court Electronic Records), here’s how you can do it using Python. PACER provides a structured data API that allows developers to fetch case information, filings, and documents in an organized manner.

Problem

Fetching data from the Federal Register or other public databases often requires navigating multiple endpoints with varying structures and authentication methods, which can be cumbersome and error-prone. With PACER’s API, you get standardized endpoints for fetching case details and documents, reducing the complexity of your codebase.

Example Python Code:

Here is a simple example using requests library to fetch basic case information from PACER:


python
import requests

def get_case_info(api_key, court_id):
    url = f"https://api.pacerpc.net/v1/cases/{court_id}"
    headers = {
        "Authorization": api_key,
        "Content-Type": "application/json"
    }

    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Failed to retrieve case information: {response.text}")

# Replace 'your_api_key' with your actual PACER API key
api_key

OFAC Sanctions Screening for Developers: How to Check Addresses and Names

carrierone — Fri, 12 Jun 2026 19:01:34 +0000

OFAC Sanctions Screening for Developers: How to Check Addresses and Names

When developing KYC/AML compliance systems, one of the critical steps is ensuring that your users or transactions are not associated with individuals listed on the U.S. Department of the Treasury’s Office of Foreign Assets Control (OFAC) Specially Designated Nationals (SDN) list. The SDN list contains entities and individuals designated under various sanctions programs. These include terrorist organizations, narcotics traffickers, weapons proliferators, and others.

Problem

Checking these entries can be time-consuming and error-prone due to the sheer number of names and wallet addresses against the OFAC SDN list, which currently has over 18,706 entries. Manually checking each address or name would not only be impractical but also inefficient for large-scale applications.

Solution: Python Code Example

Here's a simple example in Python to demonstrate how you can query the API endpoint provided by api.verilexdata.com to check if an address or name is on the OFAC SDN list. This code uses the requests library to make HTTP requests and parse JSON responses.


python
import requests

def check_sanction(address_or_name):
    url = f"https://api.verilexdata.com/api/v1/sanctions/stats?query={address_or_name}"
    response = requests.get