Jonathan Handoyo

From being a Testing Engineer → Backend Development ⭐! (Sharing my colleague story) So, my friend who started his Software Engineering career as a Tester at Adobe, made a switch to development internally after dedicated more than 5 years!💪 For them, the decision was a difficult one but not an impossible one. Their transition was not at all easy and had its challenges to cross. So, here are 3 points that helped them greatly, hope they help you too → 1️⃣ If you dream of moving into Development, UPSKILL Rigorously. 👍 You need to have a solid understanding of not just DSA but System Design as well. Like, they dedicated significant time to learning new programming languages & tech to keep themselves updated. ✅ To become the best in topics, they revised it thoroughly and made sure to engage into projects. 2️⃣ The Testing Experience helped them a lot. It’s the background only that made them a better Backend Developer. 🙌 Their testing experience helped them design keeping the tests in mind which improved the Code quality. 3️⃣ Lastly, NETWORKING was the net worth. 💪 They made sure to extensively connect with like-minded people on LinkedIn & other platforms, which helped them get a lot of great opportunities. Now, I know switching careers, especially within such different domains of tech, is not easy, and has its own set of issues. However, if you really dream of moving into Development, and need the right support & guidance, I’d suggest taking the help of MAANG experts at Bosscoder Academy. Check them here: https://bit.ly/4b0gHmI Enrol in a course that’s specially designed for 7+ years of experienced professionals and get: ✅ Structured curriculum heavily focused on DSA & System Design. ✅ Live Weekend Classes, with Limited Batch Size with only experienced Working Professionals. ✅ Personal Mentorship from Industry Veterans working with MAANG. ✅ 24/7 Doubt Support & Placement Assistance for Senior Roles. #adobe #collab #development

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Open API and OpenAPI: What's the Difference? - https://lnkd.in/eNzpfs2y Thanks Erik Wilde , for nicely clarifying the confusion between Open API and OpenAPI

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Guide: Enable SSO in a self hosted supabase instance! https://lnkd.in/gVVtT9xZ

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In part 4 of the series "Building Services at Airbnb" lets see how a schema based testing infrastructure was built into the standard service platform. But why schema based testing infra ? 🔹 Breaking API changes - The testing was leaky, there were many cases where breaking api changes were detected well into production & had to be rolled back. 🔹 Lack of usable API mock data - Each of the microservices used to mock its boundary services, this led to a lot of effort duplication also the setup to spin up these mocks were unnecessarily heavy weight 🔹 Lack of assurance of the mock's semantic correctness - So the mocks mentioned in the above point could always get out of sync with the actual implementation. 🔹 Lack of proper validation - Service owners are finding their own heavy lifting to set up validations against their API endpoints, which can be different from the set of validations used by the API consumers. 🔹 Lack of real time metrics for API test quality - For API tests, there is no metric equivalent to code coverage, hence its difficult to know if the API tests were enough. 🔹 Lack of lightweight and scalable tests - The above points show us that tests were getting slow, untrustworthy, complex, tightly coupled & flaky. there was a need for tests to be lightweight, fast & scalable. And what were the measures taken to tackle above ? 🔹 Static API Schema Validation - This was tool built to detect API breaking changes, it was built into the CI pipeline so that such changes could be detected as early as possible. This tool also performed a schema validation which ensured correctness of the API schema 🔹 Schema-based API Data Factory - API Data Factory defines language-agnostic, schema-validated request and response fixtures as yaml files. Each API Data Factory fixture file is associated with a single API endpoint. It provides help with mocking service dependencies & also mock data which is always in sync with the source endpoint. 🔹 API Mocking Framework - API Mocking Framework is a component embedded in the core service framework that provides simulated near real-world API interaction as a replacement of real services in tests. In addition to embedding mock clients, you can also interact with a live service application in “mock mode” to get fake API data in a reliable and cheap manner. 🔹 API Integration Testing Framework (AIT) - This is a framework that allows services to validate themselves with predefined test data in production environments without writing boilerplate, and that ensures semantic correctness of mock data. https://lnkd.in/gE5ewibE For more interesting engineering stories, follow me Abhilash G Raja #Microservices #Monolith #SoftwareArchitecture #Engineering #Airbnb

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$199 Android app? Remember the Rabbit R1, that sleek AI-powered gadget promising a revolution in how we interact with information? Well, buckle up for a surprise! As per Android Authority, it turns out the Rabbit R1 might be less "revolutionary gadget" and more "glorified Android app." Here's the tea: a tipster shared the R1's app file (APK) with Android Authority, and guess what? They were able to run it flawlessly on a regular smartphone! This raises some serious questions. Is the Rabbit R1 just a fancy shell for an app powered by their cloud AI (aka Large Action Model)? Early reviews for the R1 haven't been stellar either. Users are disappointed by its lack of basic functionalities like setting alarms or timers. Let's face it, we all carry smartphones capable of running powerful AI apps and even housing on-device AI models. So, what's the point of a dedicated AI gadget that lacks basic features? This might spell trouble for standalone AI devices like the R1. With Android and iOS rapidly upping their AI game, seamless AI integration on smartphones might make these dedicated gadgets obsolete. However, the future of AI isn't all doom and gloom for hardware. Here are my predictions on where AI gadgets might actually shine in the very near future: ✔ AI Glasses (Not the VR Kind): Ray-Ban style glasses with built-in AI that helps you understand your surroundings. They become an extension of your vision, providing information and assistance hands-free. When you are walking on a street and you spot a restaurant and the glasses tell you the rating of that restaurant and also what's the top rated dish here. Looking inside your refrigerator and it can suggest a recipe for you. More use cases to come. ✔ Earbuds: These are a natural fit for AI integration. Imagine hands-free access to information, assistance, realtime translation, personalized audio filter and much more all through your everyday earbuds. So, while the Rabbit R1 & Humane AI Pin might have stumbled, the future of AI in wearables isn't completely out of the picture. It's all about finding the right form factor and functionalities that seamlessly integrate with our existing habits and needs. #aidevices #genai #rabbitr1 #aigadgets Image Credit: Android Authority

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Oh this will be interesting to watch unfold.. https://lnkd.in/geg9gTgT The significance of a schedule change like this on approval windows, min-targets, expected end of support and more can't be understated. The arguments made for justifying it are fair though! #androidenterprise

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https://lnkd.in/g8bTYPyc - #Vue 3 has `ref(val)` and `reactive({})`. They're very similar but subtly different. Thanks https://lnkd.in/gA8rS7cU for showing the differences. Use `ref` for single values and `reactive` for objects.

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🚀 "Leveraging Value to Elevate Your Decision Maker Conversations" Thank You for having me and engaging in this lively talk Tom Pisello and April Morley - Value Storytelling! It was great to learn & share about how  #Value can be your key weapon to optimise both internal & external #GTM motions #4Eeffect #storytelling #valueselling #caseforchange

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How to learn #SystemDesign 👇 📌 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 𝐊𝐞𝐲 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 - Scalability: lnkd.in/gpge_z76 - Latency vs Throughput: lnkd.in/g_amhAtN - CAP Theorem: lnkd.in/g3hmVamx - ACID Transactions: lnkd.in/gMe2JqaF - Consistent Hashing: lnkd.in/gd3eAQKA - Rate Limiting: lnkd.in/gWsTDR3m - API Design: lnkd.in/ghYzrr8q - Strong vs Eventual Consistency: lnkd.in/gJ-uXQXZ - Synchronous vs. asynchronous communications: lnkd.in/g4EqcckR - REST vs RPC: lnkd.in/gN__zcAB - Batch Processing vs Stream Processing: lnkd.in/gaAnP_fT - Fault Tolerance: lnkd.in/dVJ6n3wA - Consensus Algorithms: lnkd.in/ggc3tFbr - Gossip Protocol: lnkd.in/gfPMtrJZ - Service Discovery: lnkd.in/gjnrYkyF - Disaster Recovery: lnkd.in/g8rnr3V3 - Distributed Tracing: lnkd.in/d6r5RdXG - Top 15 Tradeoffs: lnkd.in/gnM8QC-z 🛠️ 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐁𝐥𝐨𝐜𝐤𝐬 - Horizontal vs Vertical Scaling: lnkd.in/gAH2e9du - Databases: lnkd.in/gti8gjpz - Content Delivery Network (CDN): lnkd.in/gjJrEJeH - Domain Name System (DNS): lnkd.in/gkMcZW8V - Caching: lnkd.in/gC9piQbJ - Distributed Caching: lnkd.in/g7WKydNg - Load Balancing: lnkd.in/gQaa8sXK - SQL vs NoSQL: lnkd.in/g3WC_yxn - Database Indexes: lnkd.in/dGnZiNmM - HeartBeats: lnkd.in/gfb9-hpN - Circuit Breaker: lnkd.in/gCxyFzKm - Idempotency: lnkd.in/gPm6EtKJ - Database Scaling: lnkd.in/gAXpSyWQ - Data Replication: lnkd.in/gVAJxTpS - Data Redundancy: lnkd.in/gNN7TF7n - Database Sharding: lnkd.in/gRHb-67m - Failover: lnkd.in/dihZ-cEG - Proxy Server: lnkd.in/gi8KnKS6 - Message Queues: lnkd.in/gTzY6uk8 - WebSockets: lnkd.in/g76Gv2KQ - Bloom Filters: lnkd.in/dt4QbSUz - API Gateway: lnkd.in/gnsJGJaM - Distributed Locking: lnkd.in/gRxNJwWE - Checksum: lnkd.in/gCTa4DrS 🖇️ 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐚𝐥 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 - Client-Server Architecture: lnkd.in/dAARQYzq - Microservices Architecture: lnkd.in/gFXUrz_T - Serverless Architecture: lnkd.in/gQNAXKkb - Event-Driven Architecture: lnkd.in/dp8CPvey - Peer-to-Peer (P2P) Architecture: lnkd.in/di32HDu3

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The Gang of Four design patterns is a collection of 23 design patterns that offer solutions to common problems we encounter in software design. These patterns are broken down into 3 groups: 1. Creational 2. Structural 3. Behavioural In this series I'll be exploring the 3 groups and highlighting some patterns that I have found particularly useful. Part 1 covers Creational patterns and how they can help you. https://lnkd.in/dUfRCiap

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Great post! Totally agree with John Crickett. My entire career has been trying to turn less-than-ideal situations into mutually beneficial win-win outcomes by focusing on what I could control (which is really my own inner state, attittude, beliefs, and the actions that flow from them). See also: See also: https://lnkd.in/erRGZ66X

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Rocky Bhatia's post presents a brief overview of the progression of software architecture paradigms, highlighting the transition from monolithic architectures to more decoupled and modular approaches like microservices and micro frontends. The author outlines the practical implications of architectural evolution for software development, demonstrating how it meets the need for agility and scalability. Here are some additional insights: 1. Many legacy systems still operate on monolithic architecture, and transitioning them to more modern architectures can be costly and challenging. 2. A hybrid monolith (frontend and backend separation) is often seen as a stepping stone towards fully decoupled architectures, offering immediate benefits without a full-scale overhaul of the backend. 3. A microservice backend and monolithic frontend architecture enables teams to specialize in certain services or business domains, fostering expertise and accelerating development. 4. Micro frontend and microservice architecture promotes a culture of autonomy among teams, allowing them to choose the best tools and processes for their specific requirements. #softwareArchitecture #monolithic #hybridMonolith #microservices #microFrontend

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Output vs outcome is a choice that affects many layers of measurement. In all cases I can think of, output is easier to measure but is the least informative metric.

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🚀 𝙀𝙣𝙘𝙤𝙧𝙚.𝙩𝙨: 𝘼 𝙎𝙥𝙚𝙚𝙙 𝘿𝙚𝙢𝙤𝙣 𝙞𝙣 𝙩𝙝𝙚 𝘽𝙖𝙘𝙠𝙚𝙣𝙙 𝙒𝙤𝙧𝙡𝙙 🚀 Encore.ts is shaking up the backend development world, claiming to be 9x faster than Express.js! ⚡ Why is Encore.ts turning heads? • 𝗧𝘆𝗽𝗲𝗦𝗰𝗿𝗶𝗽𝘁 𝗙𝗶𝗿𝘀𝘁: Built from the ground up for TypeScript, ensuring type safety. • 𝗠𝘂𝗹𝘁𝗶-𝗧𝗵𝗿𝗲𝗮𝗱𝗲𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Leverages Rust for high-performance I/O operations, putting an event loop in your event loop. • 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴: Precomputes request and response schemas for speed, and handles request validation directly in Rust, mitigating many denial of service attacks. • 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Seamlessly integrates with popular cloud services and includes implementations for most common Pub/Sub technologies like AWS SQS+SNS, GCP Pub/Sub, and NSQ, with more planned (Kafka, NATS, Azure Service Bus, etc.). Check out the reference benchmarks here: https://lnkd.in/dYA5xpmi Encore.ts is a game-changer for developers seeking top-tier performance and seamless integration. #EncoreTS #TypeScript #BackendDevelopment #Performance #Nodejs What do you think about this new framework? Share your thoughts below!

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