This year, I plan to level up my skills in API development and API testing automation. As modern applications rely heavily on APIs, strengthening these skills will help me build scalable, secure, and reliable systems. I am focusing on improving my understanding of API design best practices, automated testing workflows, and real-world use cases using tools like Postman, as this will enhance my problem-solving ability and prepare me for industry-ready roles in backend and full-stack development.
This year, I’m committing to sharpening my ability to design agentic AI systems—not just models that respond to prompts, but systems that can understand goals, plan multi-step actions, use tools, collaborate with other agents, and adapt over time.
As AI moves from “assistive” to “autonomous,” the real impact comes from how we orchestrate models, integrate them with tools, data, and workflows, and ensure they remain reliable, ethical, and human-aligned. This skill directly supports how I work by helping me:
Build end-to-end AI solutions, not just isolated models
Collaborate better with product, data, and engineering teams by translating business goals into intelligent systems
Grow as a problem-solver who can apply AI to real-world, high-stakes domains (safety, operations, decision support)
I chose this skill because it sits at the intersection of AI engineering, system thinking, and human-centered design—and mastering it will future-proof my career as AI becomes a core part of how teams think, decide, and act.
The Skill: Orchestrating AI Agents and building Knowledge Graphs for RAG.
Why: As a Computer Science major currently building an AI-based Knowledge Graph Builder for Enterprise Intelligence, I’ve realized that LLMs are powerful but need structure to be reliable in a business setting.
I am committing to mastering the intersection of Graph Databases and API-driven AI workflows. I chose this because I want to move beyond simple “chatbots” and build intelligent systems that can truly “reason” over complex data. Mastering this ensures I’m not just coding features, but architecting the future of enterprise data interaction.
This year, I’m committing to improving my skills in automation, AWS deployment, and MLOps to build and scale AI agents more effectively. I chose these skills because they turn AI projects into real, production-ready solutions, and they’re highly in demand. My goal is to use them to create impactful, reliable systems that solve real-world problems.
This Year, I wanted to learn System Design and Low Level Design Concepts and also got some intrest on compititive programming. I am planning my time accordingly, regarding development. I have some goals like building a MCP server and using an LLM for an offline project.
That’s what I have planned for 2026, but there will be alot of unplanned dev stuff that we do.
This Year in 2026, I chose to focus on API Observability and Governance because as systems scale, APIs don’t fail due to missing features — they fail due to poor visibility, inconsistent contracts, and lack of standards. Understanding how APIs behave in real time is becoming just as important as building them.
This skill will help me design more reliable, secure, and debuggable systems, and make me better at owning APIs end-to-end — from design to production monitoring.
My learning plan includes API contract testing, schema validation, versioning strategies, monitoring metrics, and tools like Postman, OpenAPI, and AsyncAPI for event-driven systems.
In my organization, this can lead to better API quality, fewer production issues, faster debugging, and stronger collaboration between frontend, backend, and platform teams.
Postman has helped me immensely by acting as a single platform for API design, testing, documentation, and collaboration — making governance and observability practical instead of theoretical.
This year, the skill I’m committing to is building and enhancing generative AI applications using large language models, with a strong focus on LLM operations (LLMOps). Working with LLMs is moving from one-off prototypes to reliable products, so I want to deepen my ability to design, deploy, and maintain real-world GenAI systems end to end. I chose this because my work already revolves around AI projects and hackathons, and mastering LLMOps—things like evaluation, monitoring, prompt and model versioning, and scalable deployment—will help me ship more robust, production-ready applications rather than just demos. This skill will strengthen how I collaborate with teams, bridge the gap between experimentation and delivery, and position me to contribute as a go-to person for GenAI solutions in my community and future roles.
Skill: API Test Automation (Postman-centric, CI/CD-ready)
1. Why I selected this skill
API test automation sits at the intersection of backend quality, scalability, and delivery speed. Manual API testing does not scale, and superficial “happy-path” testing is functionally useless in real systems. I selected this skill because modern software reliability depends heavily on well-designed, automated API tests that run continuously—not occasionally and not manually.
2. How I plan to develop or improve this skill
I will develop this skill through deliberate, hands-on execution, not passive learning.
Structured learning
Study Postman scripting (JavaScript-based test scripts, pre-request scripts)
Learn API testing fundamentals: contract testing, negative testing, idempotency, and edge cases
Practice through projects
Build and test multiple public and self-hosted APIs
Create reusable Postman collections with environments, variables, and data files
Automation and integration
Run collections using Newman
Integrate API tests into a CI pipeline (GitHub Actions or equivalent)
Documentation and review
Document test strategy, assumptions, and coverage gaps for each project
No shortcuts, no “watched a tutorial so I know it” nonsense—only verifiable outputs.
3. Specific goals and milestones
These are concrete and measurable:
Month 1
Write robust Postman test scripts (assertions, schema validation, chaining requests)
Build one complete automated test suite for a REST API
Month 2
Execute tests via Newman with environment-specific configs
Introduce negative and boundary testing
Month 3
Integrate API tests into a CI workflow
Produce a documented API test strategy with coverage metrics
By year end
Maintain multiple real-world API automation projects
Be able to design an API test framework from scratch without templates
If I cannot demonstrate these outcomes, the skill is not learned—period.
4. Why this skill matters to me
Professionally:
API automation is a leverage skill. It multiplies impact, reduces regression risk, and aligns directly with backend, DevOps, and QA roles. Employers do not pay for theory—they pay for systems that catch failures before production.
Personally:
I value precision and repeatability. Automated testing enforces both. It replaces guesswork with evidence and replaces manual effort with systems thinking.
Bottom line:
This is not a vague “learning goal.” It is an execution plan with checkpoints, outputs, and accountability. Either the tests run and catch defects, or the skill does not exist.
This year, I’m starting with improving my API testing and understanding how APIs are designed and consumed in real applications.
I’m already building backend projects, and I realized that knowing how to properly test, document, and think about APIs from a consumer’s perspective is a skill I want to develop. Tools like Postman make that learning very practical, so this felt like the right place to start.
The very first time I interacted with backend languages and APIs was the Postman Notebook Challenge in July 2025. It opened my eyes to the excitement behind building / using APIs and how they interact with the frontend/UI of our everyday applications. I did a simple playlist curation using Spotify API. Fast forward to November, I was using Golang to interact with a web3 agent SDK (Teneo).
Now my main goal for 2026 is improving my knowledge on building/using APIs and SDKs as well as thoroughly understanding their use cases, pros and cons and finally pick the best language to use in developing my backend skills.
Gonna start with a free Golang course on youtube before looking at which paid courses would be best to use.
At the end of last year, I faced a situation where an important detail wasn’t communicated clearly and it caused unnecessary confusion. That experience made me realize how important clear technical writing really is. This year, I want to improve how I explain my work, especially in API documentation and code reviews, as I believe this is a base foundation for building reliable and understandable software.
My focus is on writing for people, not just for systems. I’ll treat every document or pull-request description like a short guide using clear bullet points, real examples and simple explanations of why something works, not just how. While AI tools can help, my goal is to build these skills myself and use AI only as support, not as a replacement for my own thinking.
This matters because clear documentation saves time and reduces back and forth. When an API is explained with a simple example, others can understand and use it immediately. Clear commit messages and specifications also help new contributors get comfortable with the codebase faster.
To build this habit, I plan to write at least one clear explanation or example each week for something I create. I’ll actively ask for feedback and refine my writing based on what others find confusing or unclear.
By the end of the year, I want my notes and comments to read like helpful guides instead of brief, unclear instructions. That will be my measure of success: clearer communication, better collaboration and smoother development overall.
This year, I’m committing to strengthening my API fundamentals and communication skills.
APIs are at the core of how modern systems interact, and I want to go beyond just “using” them to truly understand how they’re designed, tested, and documented. Alongside that, improving how I communicate technical ideas will help me collaborate better, ask the right questions, and contribute more effectively in team and open-source environments.
I chose these skills because they directly support long-term growth as a developer and make learning more impactful, not just faster.
This year, I am committing to strengthening my API design, solving real world problems, and automation skills.
As a full-stack developer, APIs are the backbone of everything I build, but I want to move beyond just “making them work.” My goal is to design scalable, well-documented, and testable APIs that improve collaboration between frontend, backend, and external teams.
I chose this skill because better APIs directly improve developer experience, system reliability, and team productivity. By mastering tools like Postman for automated testing, monitoring, and documentation, I can ship features faster, catch issues earlier, and contribute more effectively to real-world, production-grade systems.
This skill supports how I work today and prepares me for more impactful engineering roles in the future.
I’ve always focused heavily on sharpening my technical skills to keep up with new tools and innovations. This year, however, I’m intentionally shifting my focus to a soft skill, interpersonal and technical communication.
Previously, my work experience was mostly limited to a local, non-English-speaking environment. Recently, I stepped out of my comfort zone and started working in international settings. That experience made it clear how important it is to communicate technical ideas clearly across different cultures, backgrounds and levels of technical understanding.
Improving how I explain issues, ask questions and collaborate with people from different countries helps avoid misunderstandings and makes teamwork more effective, especially when working with APIs and distributed teams.
This year, I’m focusing on leveling up a skill I didn’t take seriously enough before: turning API work into real, production-ready systems — not just code that works locally.
I already build and test APIs, but I’ve realized the real growth happens when you start thinking beyond endpoints:
How does this scale?
How does a new developer understand this in 6 months?
How do I make this easy to test, monitor, and trust in production?
So in 2026, I’m deliberately improving how I:
Design APIs before writing code
Automate testing instead of doing it manually
Use tools like Postman not just to send requests, but to shape workflows, docs, and team collaboration
I chose this because I don’t just want to be someone who writes features — I want to be someone who builds systems people rely on.
i’ve to remain focus for the entire year and this time, I want to give open-source contribution a try (i have contributed in the past). i believe participating actively in open-source and in communities like Postman would open doors of opportunities and that is what i want to leverage on and build upon.
also, i’d monitor the new releases and features from postman and build when it is required.
This year, I’m intentionally sharpening system design and technical decision-making for scalable backend systems, alongside clear technical communication.
On the technical side, I want to go deeper into designing resilient, high-throughput services—thinking end-to-end about trade-offs around data modeling, consistency, fault tolerance, async processing, and observability. I’ve worked on real production systems, but I want to be faster and more deliberate in why a particular design is chosen, not just how it’s implemented. This directly improves the quality, reliability, and long-term maintainability of the systems I build.
In parallel, I’m strengthening how I communicate technical ideas—writing clear design docs, explaining complex concepts simply, and aligning with cross-functional teams early. Strong communication helps avoid rework, builds trust, and ensures that good engineering decisions translate into real business impact.
Together, these skills help me grow from an execution-focused engineer into someone who can own problems end-to-end, collaborate effectively, and contribute at a higher design and leadership level.
Happy New Year to all of you and the entire Postman team
New year, new skills, and new ways of building better things together.
My focus skill for 2026 is Applied AI with API automation.
Instead of just learning AI concepts, I’m focusing on how AI actually fits into real-world workflows using APIs. For me, that means understanding what AI can and can’t do, writing better prompts so outputs are reliable, and always verifying responses before trusting them.
I’m also working on connecting AI with APIs using tools like Postman, webhooks, and automations to remove repetitive work and turn ideas into something practical.
AI alone is powerful, but AI + APIs is where real productivity starts. When models can read data, call endpoints, validate results, and trigger workflows, they move from being demos to becoming real tools.
Going into 2026, I believe the most valuable professionals won’t just “know AI”, they’ll know how to design, test, and automate AI-driven API workflows responsibly.
Wishing everyone a year of learning, building, and shipping meaningful solutions
What’s one skill you’re committing to learning or improving this year, and what made you choose it?
