Requestly

Securing APIs requires a multi-layered approach. Relying on a single testing method leaves your endpoints vulnerable to attacks. Here are 10 essential methods for comprehensive API Security Testing: 1️⃣ Static Code Analysis (SAST): Scanning source code for vulnerabilities before the application is running. 2️⃣ Dynamic Testing (DAST): Simulating real-world attacks on the running API to find runtime flaws. 3️⃣ Manual Security Testing: Uncovering complex business-logic flaws that automated tools miss. 4️⃣ Automated Security Scanners: Catching low-hanging fruit (like OWASP Top 10) in your CI/CD pipeline. 5️⃣ Compliance & Privacy Testing: Ensuring PII is handled securely and complies with GDPR/HIPAA. 6️⃣ Functional & Boundary Testing: Pushing APIs to the limit with massive payloads or unexpected data. 7️⃣ Rate Limiting & Throttling: Testing how the system behaves under heavy traffic or DDoS attempts. 8️⃣ Security Header Validation: Verifying proper HTTP headers (CORS, CSP, HSTS) are in place. 9️⃣ API Gateway & Firewall Testing: Ensuring your WAF properly blocks malicious traffic. 🔟 Data Protection Testing: Checking encryption (in transit and at rest) and authorization (OAuth/JWT). 💡 Requestly API Client helps you take full control of your endpoints. You can easily craft custom requests, push edge-case payloads, and validate your token/auth flows—all from one streamlined workspace! #API #Security

AI isn’t just a buzzword anymore—it’s actively changing how we build, debug, and test software. Whether you are an engineer integrating LLMs into your app or a QA figuring out how to test unpredictable AI API responses, upskilling is no longer optional. But with so much noise out there, where should developers actually start? There are 5 incredible resources available at no cost: 1. Microsoft’s "Generative AI for Beginners" : A hands-on curriculum teaching you how to build GenAI applications using Python and TypeScript. 🔗 https://lnkd.in/dzBQYvuy 2. DeepLearning.AI : Short practical tutorials focused entirely on interacting with and testing LLMs via API calls. 🔗 https://lnkd.in/ejWCNAhT 3. DAIR.AI Prompt Engineering Guide: A comprehensive framework for crafting precise prompts and evaluating AI outputs for consistent, testable results. 🔗 https://lnkd.in/dNG7DqKr 4. Anthropic's Educational Courses: GitHub tutorials on constraining models, reducing hallucinations, and forcing strict JSON API responses. 🔗 https://lnkd.in/ehh-8fEM 5. Hugging Face: A introduction to practically implementing open-source AI models without relying solely on paid APIs. 🔗 https://lnkd.in/dVbuvuFV As you start building and testing new AI features, you will be making a massive amount of API calls. And debugging those requests is exactly where Requestly comes in to save you time. 💙

Did you know teams using a strict design-first #API approach ship features noticeably faster than those writing code first? Yet half the industry is still writing API docs manually. 🫠 Let's clear up the oldest debate in the API world: Swagger is the tooling, OpenAPI is the specification. Think of OpenAPI as the blueprint for your house—it tells everyone exactly where the plumbing goes so you don't accidentally flood the basement. Stop writing manual docs and let the spec do the heavy lifting. 💡 Pro tip: If you aren't using your #OpenAPI specs to automatically generate collections and client SDKs in your CI/CD pipeline, you're leaking engineering velocity. Design-first > Code-first. 🚀

The oldest trope in software development just might be, "Did you clear your cache?" But it seems the confusion between #cookies and browser #cache still trips up a lot of stakeholders on a daily basis. So here is the absolute simplest way to break it down for your team: ✅ Cookies are your digital ID badge. They are tiny text files that remember who you are and your preferences (like your login session or dark mode). ✅ Cache is the heavy lifter. It stores massive files locally—like images, videos, and scripts—so the site doesn't have to download them from scratch every time you visit. Why does this matter for leadership and product teams? Because it diagnoses the problem instantly. If a stakeholder says the new UI deployment looks broken, their cache is stubbornly holding onto the old version. If they are stuck in an endless login loop, their cookies are corrupted. It’s a 2-minute watch. 👇

💙 Excited to be the Mega Sponsor for Binary v2 - BINARY, Kalyani Government Engineering College! There’s nothing quite like the energy of a #hackathon — where raw ideas evolve into real innovation, and breakthrough solutions are born! Proud to back a community of builders who challenge boundaries and turn bold thinking into reality. Can’t wait to see the incredible projects you create! 🔥

🕔 Think about how much time your team spends manually tweaking test data, writing throwaway pre-request scripts, or debugging API failures just because a static email or UUID collided with an existing record. It sounds small, but these add up and drain momentum. We built Dynamic Variables into our API client to eliminate this exact bottleneck, and the use cases go much deeper than just injecting random strings. In this quick walkthrough, we look at how you can generate contextual, precise test data on the fly. Need a random email, but require it to match a specific user persona for your test? You can pass arguments right into the variable (e.g., {{$randomEmail 'John' 'Doe'}}) to get exactly what you need, instantly. When we build tools at Requestly, the goal is simple: help developers spend less time on friction and more time shipping. 👇 See it in action and try it out: https://lnkd.in/d_G3un43

Don't be like this. Just mock it. 🚀

We just added Dynamic Variables to the Requestly #API client. No more writing custom scripts just to generate random test data. Now you can use dynamic variables like {{$randomFirstName}}, {{$randomEmail}}, and {{$randomUUID}} to instantly generate realistic, production-like data anywhere within your request. Less friction. Faster testing! 📄 Docs: https://lnkd.in/d_G3un43

Most of the APIs we’ve spent years building are practically invisible to the AI agents now trying to use them. We’ve optimized for human intuition. Developers can interpret vague endpoints, skim messy docs, and fill in context gaps. AI agents can’t. If the context isn’t explicit, they fail. We need to shift our infrastructure from being "human-readable" to "machine-reasonable." Here is a quick breakdown of 3 operational shifts to make your backend "Agent-Ready": 1️⃣ Semantic discovery: Use OpenAPI 3.1 as a structured map so LLMs can reliably discover and understand your functions. 2️⃣ Actionable metadata: Treat endpoint descriptions as prompts for AI agents — clear, specific, outcome-oriented. 3️⃣ Self-debugging errors: Errors should contain enough structured detail for an agent to diagnose the issue and correct its own request. As you test how agents navigate your APIs, tools like Requestly can dramatically speed up debugging and response validation. We’re entering API Economy 2.0. If your APIs aren’t machine-reasonable, you’re already behind. 👇 Watch the full breakdown in the video.

Celebrating a massive month of innovation! 🚀 Over the past months, we’ve partnered with some incredible hackathons across cutting-edge domains like AI Pipelines, Smart Cities, and Cloud-Native DevOps. Seeing developers leverage Requestly to intercept, mock, and bend the web to their will has been absolutely inspiring. A huge shoutout to our brilliant Requestly Track Winners in Part 1 of our February month roundup, and the masterminds behind these projects: 🏆 The Jesters (Mrudduni J Modha, Aditya Singh, Yajat Malik) — HackJNU 4.0 🏆 Team Stratify (Harsh Shukla, Aditi Sood, Shreya Taluja) — HackTU 7.0 🏆 Team Bitbots (Pushpendra Sharma, Archit Jain, Rachit Verma, Deepti Yadav) — HackTheThrone Massive shoutout to the organizers behind the scenes at the School of Engineering, JNU, Thapar Institute of Engineering & Technology, and Indian Institute of Information Technology Una—for fostering such vibrant communities where innovation thrives. Swipe through to see the amazing projects these teams built, and stay tuned for Part 2!