Loading…
Topic
Twilio Lookup
5 posts about Twilio Lookup. Every summary links to the original.
- 5 posts
- 1 company
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
Newest first
Posts
Avoid Sending SMS to Landlines in PHP with Twilio's Lookup v2 API
SMS has a 98% open rate and works without internet, but sending to landlines wastes money on failed deliveries. This tutorial demonstrates filtering landlines in PHP using Twilio’s Lookup v2 API, which provides line type intelligence via the “line_type_intelligence” field. The core class extends PHP’s FilterIterator and overrides the accept() method to fetch each number’s line type, returning true only for “mobile” types and catching TwilioException on failure. The application uses Composer dependencies including monolog, twilio/sdk, and phpdotenv, loads credentials from a .env file, iterates over an ArrayIterator of phone numbers, logs each with Monolog to stdout, and sends an SMS only to mobile numbers. This approach avoids unnecessary charges and is straightforward using Twilio’s PHP helper library and SPL iterators.
Avoid Sending SMS to Landlines with the Lookup v2 API and Go
Sending SMS to landlines causes failed deliveries and unnecessary charges, a problem that Twilio’s Lookup v2 API solves by identifying phone number types. The API’s line_type_intelligence field returns a classification such as “mobile” or “landline”, enabling applications to filter out non-mobile numbers before sending an SMS. This Go tutorial loads Twilio credentials from a .env file and uses Go 1.23’s iter package to create a custom iterator over a phone number list. Within the iterator, each number is validated by calling FetchPhoneNumber with the line_type_intelligence parameter, and if the type is not “mobile” or the call fails, the number is skipped. Only mobile numbers are then passed to the sendSMS function, which sends the message using the Twilio Go helper library’s CreateMessage, and the terminal finally logs each success or identifies a skipped landline.
How to Confirm Phone Number Ownership with Lookup Identity Match in Python
The tutorial shows how to use Twilio Lookup Identity Match in Python to confirm that signup information is associated with a phone number and reduce signup fraud. It builds a Flask application that accepts an E.164-formatted number, first name, and last name, then calls the Lookup API with fields="identity_match" and the supplied identity attributes. The response exposes statuses including exact_match, high_partial_match, no_match, and no_data_available; the example accepts a result only when the number is valid and both names are exact or high partial matches. The implementation covers project setup with Flask, twilio, and python-dotenv, environment-based credentials, an intl-tel-input form, and frontend handling for success and failure messages. It also notes that Identity Match can use combinations of name, address, national ID, or date of birth, and points to other Lookup packages such as Line Type Intelligence and SIM swap detection.
How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify in Python
This Python tutorial builds a signup flow that screens phone numbers for fake accounts while aiming to keep legitimate onboarding smooth. It combines Twilio Lookup’s Line Type Intelligence, optional Line Status, and Identity Match checks before using Twilio Verify to send an OTP and confirm possession of the number. The provided Flask example collects a phone number and first and last names, rejects landlines, nonfixed VoIP, toll-free numbers, and pagers, and accepts Identity Match only when both name fields are exact_match or high_partial_match. The post shows how to configure the project, run it locally, inspect outcomes such as LINE_TYPE_BLOCKED or identity-match failure, and consider pricing because the four API calls have different costs.
Top user authentication trends for 2026
The article outlines authentication trends expected to shape identity verification, fraud prevention, payments, and access in 2026, against a backdrop of deepfakes, synthetic identities, regulatory change, and increasingly online services. It describes identity orchestration that combines document checks, biometrics, device intelligence, and behavioral signals, while fraud systems assess behavior and may allow, challenge, delay, or block access. For AI agents, Know Your Agent links a bot to its enterprise and official communication channel, with document verification and Twilio Lookup presented as a combined onboarding approach. The recommended strategies add continuous anomaly monitoring, risk-based MFA, human-in-the-loop approvals, multichannel authorization, passkeys, digital IDs, and layered controls, while concluding that businesses and regulators should remain agile and use services such as Twilio Verify, Lookup API, and Verify Fraud Guard.
Related topics
All topics →Weekly digest
The best engineering reads, once a week.