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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? They are easy to adopt. Most workflows come with quick-start guides, sensible defaults, and seamless integration, allowing you to add them to 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, a faster coder, and a more efficient developer from initial idea to production.
# 1. Git and GitHub: Version control made easy
Gut A must for almost all developers and tech professionals. It helps you track your code changes, debug and view the progress of a project. You can even use it for your models, datasets and experiments. can use GitHub is the most popular platform that allows you to host your projects and provides a host of tools and management features to help you turn your ideas into production-ready projects in one place.
Why it’s great:
- Branching and Mergers: Safely explore ideas on branches, then merge when ready
- History and Recovery: use
git logfor , for , for , .git difffor , for , for , .git stashand Refrig To undo and restore - Pull requests and reviews: Discuss changes, check in, and keep a clean branch
- GitHub Action: Automate tests, builds and deployments with simple Yaml
- Problems and Projects: Track tasks, bugs and roadmaps alongside your code
- Releases and Packages: Tag versions, publish samples, and manage changelogs
- Security and Compliance: Dependencies, code scanning, branch protections, and required reviews
I use Git almost every day. Even when I’m webcoding, it’s an important part of my workflow. When I accidentally push unwanted changes or edit a previous commit, I use git to fix it. Trust me, I often end up pushing a lot of junk code and later realize I can make easier edits.
# 2. Cursor: AI-powered code editor
Cursor is an advanced editor built around AI. It’s similar to Vs Code but adds a layer of intelligence that helps you write, debug, and refactor code faster. I believe this is the main source of all your coding problems. It now comes with multi-agent support, meaning you can have multiple agents running simultaneously to solve problems simultaneously. I use it daily for coding, editing, automation, and testing and pushing new changes to projects.
Why it’s great:
- Inline AI Editing: Ask for correct changes to a file. Precisely, get different style screws
- Repo level context: This is due to multiple files, symbols and project architecture
- Multi-Agent Support: Decompose problems and let integrated agents handle subtasks
- Chat + Terminal Awareness: Commands for reference logs, test output, and target configuration fixes
- Refactors that stick: Support for protected names, interface changes, test generation, and migration
- Deep Git Integration: Stage hunks, craft commit messages, and PRS without leaving the editor
- Vs Code Ecosystem: Keep your themes, key bindings and most extensions
Many of AICLI’s tools provide integration with Cursor, allowing me to use tools like Droid, have them create things for me, and view changes in the Cursor IDE. It gives me control and helps me build things faster.
# 3. Cloud Code: Understands your entire project
Claude Code Designed for developers working with large codebases. It can read your entire collection and logic across multiple files at once. I really like CloudCode, and I don’t even pay for the API or the Cloud plan. I use it with the GLM coding plan, which costs $3 per month, and it works better for me than any of the Claude Sont models.
Why it’s great:
- The entire repo argument: Understands symbols, cross-file dependencies, and architecture decisions
- Project wide modifications: Instead of throwing up walls of code, the target suggests a patch/patch
- Strong Scaffolding: Revolves services, CLI and boilerplate with sensible structure and documentation
- Testing and Debugging: Develops unit/integration tests, tracks failures, and suggests improvements
- Using the tool: Executes commands, reads/writes files, runs linters, and inspects logins by connected servers.
- Documentation and reviews: Summarizes modules, drafts rewrites, and performs thoughtful code reviews
Cloud code is perfect for debugging your problems or building new applications. I’ve used it to build a payment 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 Expertise and Cloud Planning Markdown. Ask to plan it first, then execute it.
# 4. Postman: Test your APIs with ease
The postman is the go-to toolkit for API development. This makes it easy to target endpoints, inspect and visualize responses, and debug quickly. Even if you’re building a machine learning app, you still need to validate your predictions and admin endpoints. Postman gives you a clear, visual view of how your API is performing.
Why it’s great:
- Collections and Environment: Manage applications, change configuration (dev/stage/prod) with variables
- Built-in Tests: Write quick JavaScript assertions for status codes, payloads, and latency
- Monitor and Automation: Schedule runs and get alerts when something breaks
- Fun server: Prototype endpoints before the backend is developed
- Collaboration: Collate and share documents with your team in a click
There are many alternatives, and you can even script your own testers, but Postman stands out for its ease of use, rich feature set, and strong collaboration tools.
# 5. AccelDraw: Visualize your ideas
When words are shortened, outline them. Accelerate Perfect for project planning, blogs, presentations, or just thinking through a messy problem, makes it easy to map out system designs, workflows, and architectures.
Why it’s great:
- Sharp, hand-crafted feel: Communicate concepts without getting stuck on pixel-perfect details
- Shapes, Connectors and Labels: Ideal for flowcharts, ERDs, sequence diagrams, and app maps
- Component Libraries: Reuse UI stencils, cloud icons, and your own saved blocks
- Real-time support: Brainstorm together, leave comments, and iterate live
- Easy export and embedding: Drop diagrams into decks, documents, or wikis (PNG/SVG/links).
# 6. Linear: Keep your plans on track
Linear Brings speed and clarity to issuing tracking. It’s fast, minimal, and built for engineering and product teams, great for planning content or shipping software without clutter. I mainly use Linear for my job, and I love it. You can assign tasks, provide initial plans, and move items through different locations. As you progress, you can see a history of changes and conversations, which provides a structured approach to content creation and project development.
Why it’s great:
- Lightning fast UX and shortcuts: Fire through triage, updates, and searches.
- Issues, Projects and Cycles: Structure is done with a clear status flow from Backlog → Spirit →
- Custom workflows and labels: Taylor states, priorities, SLAs, and automations to your team.
- Deep Integration: Sync with GitHub/Bitbucket, link PRs, get Slack updates, attach designs, and link theory documentation.
- Real-time support: Comments, mentions and activity timelines keep context in one place.
- Roadmaps and Insights: Track progress, speed, and scope changes at a glance.
# 7. Docker Desktop: Run Anywhere Anytime
Docker makes your environment persistent. Package your app and all its dependencies so that it runs the same on every machine, no “doesn’t work on my laptop” exclamation. I use Docker Desktop For almost every project: local testing, quick deployment, and secure sandboxes for MLOPS, data science, web development, and trying new AI models without touching my original files.
Why it’s great:
- Reproductive environment: Shipcode + dependencies together as images for predictable runs
- Isolation and Safety: Containers sandbox processes and file access so experiments don’t leak into your system
- Write for multi-service apps: Deploy APIs, DBS, caches, and queues with a Docker
- Rapid Repetition: Building layers, buildkits, and caching speed up the dev loop
- GPU and ML support: Run a CUDA/ROCM-capable container for training/estimation locally
- Multi-Arch and Portability: Build for x86/arm and deploy a single image to any cloud or on-prem
- Dev Containers: Standardize toolchains for your team in VS Code or JetBrain with a build
# Final thoughts
If you’re starting out or transitioning into a developer role, becoming proficient with these tools will help you become faster and more efficient. 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, CloudCode, Cursor, Acceledra, and Linear. I use them for content creation as well as building machine learning and AI applications.
I hope this article has given you a clear starting point and helps you choose the right tools for your coding journey.
Abid Ali Owan For centuries.@1abidaliawan) 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. His vision is to create an AI product using graph neural networks for students with mental illness.