Beeceptor

Reliable software is built by testing failure, not just success. Here is how to simulate a deterministic 400 → 400 → 200 API retry sequence with Beeceptor’s stateful virtual APIs to validate resilience in a production-like way. Link to tutorial: https://lnkd.in/gHwkiPUt

Beeceptor is the only real-time API virtualization platform out there, and our users ❤️ it everyday. "I really like how responsive it is every time a request comes through to my custom Beeceptor URL, and shows immediately in the web interface."

The Impossible Interview Question. The 60LPA Microservice Question This question has been asked to me last year and when i ask to my seniors and friends, 90% give the textbook answer. The top 1% give the architect answer. The Scenario: You are testing the Checkout service at a company like Airbnb. It depends on 15 downstream services (Payments, Fraud, Inventory, etc.). The Fraud service is currently a disaster in Staging, it’s unstable and causes 40% of your automation runs to fail. But the Checkout code itself is perfectly fine. The Constraints: 1. You can’t just Mock everything (that hides real integration bugs). 2. You can't wait for the Fraud team to fix their environment (you have a release in 2 days). The Question: How do you architect a testing strategy that guarantees stability for Checkout without losing the Integration value? Let's see how you guys will answer this one, and we'll discuss this question's best possible answer in next post. Follow Sahil kapoor for more ! Start your interview prep: https://lnkd.in/gnka-4K8

We were delighted to welcome Beeceptor as our Utility Partner 🤝✨ The outing offered a wonderful opportunity to connect, share ideas, and set the stage for a strong and meaningful collaboration. We truly appreciate Beeceptor’s support and look forward to working together to drive innovation and create lasting impact 🚀 A big thank you to Beeceptor for backing Calcutta <Hacks/>!

We've added an 𝗔𝗜 𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗼𝗿 to the Beeceptor toolkit. How it works: 👉 Give a sample JSON, CSV, list of fields, or short requirements. 👉 The tool generates structured, realistic datasets. 👉 Use the output for API mocking or database seeding. You can try it here: https://lnkd.in/gF_YbEUq

Beeceptor supports path templates that work the same way developers define routes in Django, Spring Boot, Express, FastAPI, and Rails! Write something like `/orders/:orderId/items/:itemIndex` and it matches just as you expect. Beeceptor extracts each named parameter, URL-decodes it, and makes it available in responses through the following syntax: {{pathParam 'orderId'}} Here, 👉 the trailing slashes don’t cause mismatches 👉 dynamic segments resolve consistently, and, 👉 your mock rules stay clean Like many other features, this is available with the Free plan. Refer to the demo, and try today.

Beeceptor’s 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝗹𝗶𝘀𝘁𝘀 just got supercharged! You can now simulate real-world data structures like 𝘀𝘁𝗮𝗰𝗸𝘀, 𝗾𝘂𝗲𝘂𝗲𝘀, 𝘀𝗲𝘁𝘀, and 𝗮𝗿𝗿𝗮𝘆𝘀 right inside your mock servers — no backend required. The enhanced list now supports intuitive operations such as: 👉 size – Get the current number of items. 👉 shift – Remove and return the first item (perfect for queues). 👉 unshift – Add an item to the beginning of the list. 👉 contains – Check if a value already exists. 👉 push-unique – Add only if it doesn’t already exist (like a set). 👉 update – Modify existing items in place. These updates unlock powerful new use cases. You can now simulate evolving data between requests and test how your API reacts to stateful conditions — all within Beeceptor. Build your first queue, push some data, and Watch your mock server come alive, no backend required! Read more: https://lnkd.in/giT8km7d

The new 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗕𝗼𝗱𝘆 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴 operator enhances request matching for REST APIs that send structured JSON data in the request body. Beeceptor now parses the body and evaluates specific parameters, and lets you match incoming requests based on nested fields or items in an array. You use dot notation to reference any field within a JSON data. This is like XPath, but for JSON. For example, match requests using keys like `user.email`, `user.demographic.country`, or `user.addresses.0.city`. Here is a quick demo 👇