before I'm dead, I must be the Superior in Dev

Lakas ng dating ni Argel. 🔥

@Bossanova2545 สยอง แค่ตัวเดียวก็กริ๊ดแตก นี่มาทั้งครอก คนช็อกตายก่อน

ขนลุก!!เพิ่งเคยกินหนูกินนม 😄😄

Software Engineers what's your plan B if AI replaces you?

@psmark1821 หมอนวด + เสริม

อยากรู้ว่าของทุกคนคืออะไรกันบ้าง???

@belza_09 ต้องมี skill อะไรบ้างหรอครับ ถึงสามารถทำงานในระดับนั้นได้

รีวิวชีวิตการทำงานที่ amazon prime video ในช่วง 4.5 ปีที่ผ่านมา จดไว้ก่อนตัวเองลืม 1. คนชอบเข้าใจผิดว่าทำงานที่นี่จะได้ subscription ฟรี คือไม่มีจ้า ต้องจ่ายเงินเอง 😅 2. software engineer 1 คน = ทำทุกอย่างใน SDLC + devops + QA + oncall ละบางโปรเจ็กต์ก็เป็น data sci/eng/PM ไปด้วย

@w_wkanaZhang 😘

@scopi_no @grok อ้าว grok ใจร้ายจัง

@scopi_no @grok ยาแก้ปวดมีไว้ทำอะไร

มันผิดที่การตั้งคำถามหรือยังไง

@pd_091 ที่ช้าเพราะเค้ารวบหมดแล้ว ไม่ต้องพยายามรีบสร้างผลงานสมัยหน้าก็นอนมาค่ะ ต่อให้ชาวบ้านไม่เลือกแต่กรรมการอยู่ข้างเราจะกลัวทำไม 🙂‍↕️

@porporlees มีโอกาสที่จะได้รธน ฉบับใหม่ (ที่เลวร้ายกว่าเดิม)

What I see in most Java resumes: - Java - Spring - Microservices Great, I see you know Java. But as a senior, can you build, run, scale, observe, and secure Java systems in production? A few tips and topics with accurate subskills that you need to mention if you need your resume to stand out: 1. Distributed Caching – mention Redis/Memcached 2. Monitoring & Observability – most popular ones are Splunk, Dynatrace, Grafana, ELK 3. Messaging – list one among Kafka, JMS or RabbitMQ 4. Testing – most popular frameworks/methods are TDD, Mockito, JUnit 5. CI/CD & Containers – devops skills like Jenkins, GitHub Actions, Docker, Kubernetes make your profile distinct 6. Frameworks – no prize for guessing: Spring Boot, Spring MVC, Apache Camel 7. Microservices Internals – try to mention one or two at least from Config Server, API Gateway, Service Discovery, Resilience4j 8. Multithreading & Concurrency – do not skip, mention a few of Executors, ForkJoin, CompletableFuture 9. Security – most used and popular ones are Spring Security, OAuth2, JWT 10. Persistence – you should have been using Hibernate, JPA or MyBatis so put one 11. API Development – basics of APIs: REST, Swagger, OpenAPI 12. Reactive Programming – your resume shines with words like WebFlux, Reactor or RxJava 13. Build Tools – mention Maven/Gradle 14. Code Quality – mentioning SonarQube, PMD, Checkstyle shows you care about quality 15. Cloud – shouldn't miss one from AWS, GCP, Azure 16. Java Versions – list a few Java 8 to 21 features you used 17. Design Principles – foundations of large codebase: SOLID, Design Patterns, Clean Architecture If you are targeting Senior, Staff, or Lead roles: - Focus on impact, not buzzwords - Show production ownership - Highlight architecture and reliability thinking Good luck

@Wirezd57 @BoardOfPeace Pol Pot already did his aim.

@BoardOfPeace He would NEVER have allowed this by the way.

The Board of Peace welcomes Cambodia as a founding member of our growing international organization.

If you want to level up as a backend engineer in 2026, consider reading these 7 books: 1) Clean Code 2) Head First Design Patterns 3) Designing Data-Intensive Applications 4) Fundamentals of Data Engineering 5) Database Internals 6) Building Microservices 7) Head First Software Architecture Which other book would you add to this list? ♻️ Repost to help others in your network

@timtamcottam Nice pit, Ahh

🧸🤘🏻

Gini kan?

Legs getting thicker while leg hair getting longer🙂‍↕️

@nicoarson_ Nice armpits and sweet.

morning 🪵

7 must-know runtime complexities for coding interviews: 1. 𝐎(1) - 𝐂𝐨𝐧𝐬𝐭𝐚𝐧𝐭 𝐭𝐢𝐦𝐞 - The runtime doesn't change regardless of the input size. - Example: Accessing an element in an array by its index. 2. 𝐎(𝐥𝐨𝐠 𝐧) - 𝐋𝐨𝐠𝐚𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐭𝐢𝐦𝐞 - The runtime grows slowly as the input size increases. Typically seen in algorithms that divide the problem in half with each step. - Example: Binary search in a sorted array. 3. 𝐎(𝐧) - 𝐋𝐢𝐧𝐞𝐚𝐫 𝐭𝐢𝐦𝐞 - The runtime grows linearly with the input size. - Example: Finding an element in an array by iterating through each element. 4. 𝐎(𝐧 𝐥𝐨𝐠 𝐧) - 𝐋𝐢𝐧𝐞𝐚𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐭𝐢𝐦𝐞 - The runtime grows slightly faster than linear time. It involves a logarithmic number of operations for each element in the input. - Example: Sorting an array using quick sort or merge sort. 5. 𝐎(𝐧^2) - 𝐐𝐮𝐚𝐝𝐫𝐚𝐭𝐢𝐜 𝐭𝐢𝐦𝐞 - The runtime grows proportionally to the square of the input size. - Example: Bubble sort algorithm which compares and potentially swaps every pair of elements. 6. 𝐎(2^𝐧) - 𝐄𝐱𝐩𝐨𝐧𝐞𝐧𝐭𝐢𝐚𝐥 𝐭𝐢𝐦𝐞 - The runtime doubles with each addition to the input. These algorithms become impractical for larger input sizes. - Example: Generating all subsets of a set. 7. 𝐎(𝐧!) - 𝐅𝐚𝐜𝐭𝐨𝐫𝐢𝐚𝐥 𝐭𝐢𝐦𝐞 - Runtime is proportional to the factorial of the input size. - Example: Generating all permutations of a set. ♻️ Repost to help others in your network.