Radhakrishnan Perumal

  𝐏𝐨𝐩𝐮𝐥𝐚𝐫 𝐀𝐏𝐈 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐒𝐭𝐲𝐥𝐞𝐬 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰 1️⃣ gRPC 💡 What it is: A high-performance, open-source RPC framework by Google that uses HTTP/2 and Protocol Buffers, offering built-in features like authentication and load balancing. 🔎 Best for: Microservices, inter-service communication, and high-throughput, low-latency systems. 2️⃣ SOAP (Simple Object Access Protocol) 💡 What it is: A protocol that uses XML to exchange structured data over HTTP or SMTP, with strict standards and formal contracts (WSDL). 🔎 Best for: Enterprise systems that require high security, ACID transactions, and strong contractual interfaces. 3️⃣ GraphQL 💡 What it is: A query language and runtime that lets clients fetch exactly the data they need, no over- or under-fetching. 🔎 Best for: Complex data models and apps that demand flexible, efficient data retrieval. 4️⃣ Webhooks 💡 What they are: Event-driven HTTP callbacks that notify external...

  Informed Consent in Human Research: Ethics, Understanding, Deception, and Modern Approaches Introduction Informed consent is one of the most important ethical requirements in research involving human participants. It protects the dignity, autonomy, rights, and welfare of individuals who agree to participate in research. Rather than being simply a form that a participant signs, informed consent should be understood as an ongoing communication process between the researcher and the participant . For informed consent to be ethically valid, participants must understand what the research involves, make their decision voluntarily, and have the capacity to make that decision. Modern research, however, introduces several challenges to this process. Complex scientific terminology, lengthy consent forms, deception, therapeutic misconception, digital technologies, and research involving individuals with diminished decision-making capacity can all make genuine informed consent difficult to a...

  Research Integrity: The Foundation Every Future Researcher Should Learn Before Publishing For every future PhD scholar, researcher, academician, reviewer, or research professional , learning research methodology is only one part of becoming a good researcher. The other part is understanding Research Ethics and Research Integrity . After completing the KU Leuven MOOC Research Ethics: A Guide to Responsible Research with Human Subjects , one lesson stood out clearly: Good research is not only about discovering something new. It is about discovering it honestly, responsibly, and transparently. Research ethics mainly focuses on protecting research participants, their rights, safety, privacy, and informed consent . Research integrity goes further. It asks whether the entire research process—from the original idea to data collection, analysis, authorship, publication, and peer review—has been conducted responsibly. The European Code of Conduct for Research Integrity highlights four fun...

  𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝘁𝗵𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗼𝗳 𝗔𝗜. Remember when we moved from monolithic applications to microservices? We didn't make that change just for the sake of architecture. We did it because one giant application became difficult to: ❌ Scale ❌ Maintain ❌ Deploy ❌ Extend ❌ Recover from failures 𝗡𝗼𝘄 𝗔𝗜 𝗶𝘀 𝗳𝗮𝗰𝗶𝗻𝗴 𝗮 𝘀𝗶𝗺𝗶𝗹𝗮𝗿 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. Instead of asking one powerful LLM to do everything, we can create multiple specialized agents. 🧠 𝗣𝗹𝗮𝗻𝗻𝗲𝗿 𝗔𝗴𝗲𝗻𝘁 → Breaks the problem into tasks 🔎 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 → Finds and analyzes information 💻 𝗖𝗼𝗱𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 → Writes or modifies code 📊 𝗗𝗮𝘁𝗮 𝗔𝗴𝗲𝗻𝘁 → Works with databases and analytics ✍️ 𝗪𝗿𝗶𝘁𝗲𝗿 𝗔𝗴𝗲𝗻𝘁 → Generates the final response 🔍 𝗥𝗲𝘃𝗶𝗲𝘄𝗲𝗿 𝗔𝗴𝗲𝗻𝘁 → Validates the result And an 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 𝗔𝗴𝗲𝗻𝘁 coordinates the entire workflow. This creates something very similar to microserv...

  Using Claude Code Efficiently An Architect’s Guide to AI-Assisted Software Development AI coding tools are changing software development, but there is an important distinction: Using an AI coding tool is easy. Using it efficiently on a real production codebase is an engineering discipline. Claude Code is more than a place to type: Create an API for me. It is an agentic coding environment capable of reading a codebase, modifying files, executing commands, exploring dependencies, and integrating with development tools. That creates enormous productivity potential—but it also creates a new architectural challenge: How do we give an AI enough freedom to accelerate development without giving it so much freedom that it creates unnecessary code, architectural drift, security problems, or expensive rework? After working with AI-assisted development workflows, I have found that the most effective approach looks less like: Prompt → Code and more like: Understand ↓ Plan ↓ Constrain ...