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, Introduction
People often ask about my tech stack, specifically what I use to build web applications, train machine learning models, and manage data science workflows. In short, I rely on a balanced mix of AI-powered and non-AI tools that enable me to work efficiently without compromising on quality. These tools support everything from planning and project management to development, testing, and deployment.
The best part? These are easy to adopt. Most come with quick-start guides, sensible defaults, and seamless integration with existing workflows, allowing you to incorporate them into your tech stack with minimal effort.
In this article, I’ll highlight seven essential tools that can elevate your workflow to a professional level. These tools will help you become a better teammate, more efficient coder, and more effective developer, from initial idea to production.
, 1. Git and GitHub: Version control made simple
git It is essential for almost all developers and technical professionals. It helps you track your code changes, debug, and visualize the progress of a project. You can also use it to version your models, datasets, and experiments. GitHub The most popular platform that allows you to host your projects and offers plenty of tools and management features to help you turn your ideas into production-ready projects in one place.
Why it’s great:
- Branching and Merger: Safely explore ideas on branches, then merge when ready
- History and recovery: Use
git log,git diff,git stashAnd reblog undo and restore - Pull Requests and Reviews: Discuss changes, check in and keep the main branch clean
- GitHub Actions: Automate testing, building, and deploying with simple YAML
- Issues and Projects: Track tasks, bugs and roadmap with your code
- Release and Package: Tag versions, publish artifacts, and manage changelogs
- Security & Compliance: Dependabot, code scanning, branch security, and essential reviews
I use Git almost every day. Even when I’m coding vibes, it’s still an important part of my workflow. When I accidentally make unwanted changes or edits to a previous commit, I use Git to fix it. Trust me, I often put in a lot of junk code and later realize I could have made simple edits.
, 2. Cursor: AI-powered code editor
cursor Is a modern editor built on AI. It looks like VS Code but adds a layer of intelligence that helps you write, fix, and refactor code faster. I believe it is an important tool for all your coding problems. It now comes with multi-agent support, which means you can ask it to run multiple agents simultaneously to solve problems simultaneously. I use it daily for coding, editing, autocompletion, and testing and bringing new changes to projects.
Why it’s great:
- Inline AI editing: Ask for changes directly to the file; Get the perfect, different-style patches
- Repo-level references: Reasons for multiple files, symbols, and project architecture
- Multi-Agent Support: Decompose problems and let coordinated agents handle subtasks
- Chat + Terminal Awareness: Reference logs, test outputs, and commands for targeted fixes
- Refactors that stick: Secure names, interface changes, test generation and migration assistance
- Deep Git integration: Forum savvy, craft commit messages, and open PRs without leaving the editor
- VS Code Ecosystem: Keep your theme, keybindings, and most extensions
A lot of AI CLI tools offer integration with cursors, allowing me to use tools like Droid, ask them to create things for me, and see the changes in the cursor IDE. It gives me control and helps make things faster.
, 3. Cloud Code: Understands your entire project
cloud code Designed for developers who work with large codebases. It can read your entire repository and reason across multiple files at once. I really like Cloud Code, and I don’t even pay for the API or cloud plan. I use it with the GLM coding scheme, which costs $3 per month, and it works better for me than any of the Cloud Sonnet models.
Why it’s great:
- whole-repo arguments: Understands symbols, cross-file dependencies, and architecture decisions
- Project-wide editing: Proposes targeted differences/patches instead of dumping walls of code
- Strong Scaffolding: Develops services, CLI, and boilerplate with sensible structure and documentation
- Testing & Debugging: Generates unit/integration tests, detects failures and suggests solutions
- Use of equipment: Executes commands, reads/writes files, runs linters, and inspects logs through connected servers
- Documents and Reviews: Summarizes modules, drafts readmes, and conducts thoughtful code reviews
Cloud Code is excellent for troubleshooting your problems or building new applications. I’ve used it to build a payments platform from the ground up, and it’s impressive in its capabilities. To get the most out of cloud code, I highly recommend using MCP Server, Cloud Skills, and Cloud Planning Markdown. Ask it to first plan, then execute.
, 4. Postman: Test Your API Easily
postman API is a toolkit for development. This makes it easier to access endpoints, observe and visualize responses, and debug faster. Even if you’re building a machine-learning app, you still need to validate your inferences and admin endpoints. Postman gives you a clear, visual view of how your API is performing.
Why it’s great:
- Collection and environment: Manage requests, switch configuration (dev/stage/prod) with variables
- Built-in Test: Write quick JavaScript assertions for status codes, payloads, and latency
- Monitor and Automation: Schedules runs and gets alerts when something breaks
- Fake Server: Prototype endpoint before backend is ready
- Collaboration: Archive and share documents with your team in one click
There are plenty of options, and you can even script your own testers, but Postman is known for its ease of use, rich feature set, and robust collaboration tools.
, 5. Excalidraw: Visualize your ideas
When you run out of words, draw a diagram. exalidra The system makes it easy to map out design, workflow, and architecture, perfect for project planning, blogging, presentations, or brainstorming as a complex problem grows.
Why it’s great:
- Sharp, hand-crafted feel: Communicate concepts without getting stuck on pixel-perfect details
- Sizes, connectors and labels: Ideal for flowcharts, ERDs, sequence diagrams and app maps
- Component Library: Reuse UI stencils, cloud icons, and your own saved blocks
- Real Time Collaboration: Brainstorm together, leave comments, and iterate live
- Easy export and embed: Drop diagram into deck, document or wiki (PNG/SVG/link)
, 6. Linear: Keep your projects on track
linear Brings speed and clarity in issue tracking. It’s fast, minimal, and built for engineering and product teams, great for planning content or shipping software without all the clutter. I mainly use Linear for my work, and I love it. You can assign tasks, provide preliminary plans, and move objects to different positions. As you progress, you can view a history of changes and conversations, providing a structured approach to content creation and project development.
Why it’s great:
- Lightning-fast UX and shortcuts: Shine through triage, updates, and searches.
- Issues, Projects and Cycles: Structure work from Backlog → Sprint → Done with clear status flow.
- Custom Workflows and Labels: Create conditions, priorities, SLAs and automation for your team.
- Deep Integration: Sync with GitHub/Bitbucket, link PRs, receive Slack updates, attach designs, and connect Notion docs.
- Real Time Collaboration: Comments, mentions and activity timelines keep context in one place.
- Roadmap and Insights: Track progress, velocity, and scope changes at a glance.
, 7. Docker Desktop: Run Anytime, Anywhere
Docker makes your environment consistent. Package your app and all its dependencies so that it runs identically on every machine, with no “works on my laptop” surprises. I use docker desktop For almost every project: local testing, rapid deployment, and secure sandboxes for MLOPS, data science, web development, and trying out new AI models without touching my actual files.
Why it’s great:
- Reproducible Environment: Ship code + dependencies together as images for predictive runs
- Isolation and Security: Containers sandbox processes and file access so experiments don’t leak into your system
- For multi-service apps write: Spin up APIs, DBs, caches and queues with a single Docker compose
- Fast Repetition: Layered builds, buildkits, and caching speed up dev loops
- GPU and ML Support: Run CUDA/ROCm-enabled containers for training/inference locally
- Multi-Arc and Portability: Build for x86/ARM and deploy the same image to any cloud or on-premises
- dev container: Standardize the toolchain for your team in VS Code or JetBrains with one configuration
, final thoughts
If you’re starting out or transitioning into a developer role, becoming proficient in these tools will help you become faster and more effective. You’ll be able to ship features faster, collaborate better, and advance your career with confidence.
All the tools I mentioned are part of my daily toolkit: Git, Docker, Cloud Code, Cursor, Xcalidraw, and Linear. I use them for content creation as well as building machine learning and AI applications.
I hope this article has provided you with a clear starting point and helped you choose the right tool for your coding journey.
abid ali awan ,@1Abidaliyawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a master’s degree in technology management and a bachelor’s degree in telecommunication engineering. Their vision is to create AI products using graph neural networks for students struggling with mental illness.
