Essential Developer Tools 2025: The Complete Productivity Stack
Your productivity stack is probably incomplete.
Most engineering teams have their IDE dialed in. They've got their CI/CD pipeline humming. Observability? Check. Project management? Jira or Linear, pick your poison.
But there's a gap. Between writing code and tracking tasks, there's this dead zone where context goes to die. You're constantly asking: What does this service do? Who owns that legacy module? Where's the documentation for this API?
Let me walk through the complete stack for 2025, including the parts everyone forgets.
The Core: IDE and Code Editor
still dominates, and for good reason. The extension ecosystem is unmatched. But is eating VS Code's lunch for AI-native development. If you're still treating AI assistance as a chatbot on the side, you're behind.
Cursor's built-in context awareness—pulling in relevant files without you asking—is the UX leap we needed. You describe what you want, it shows you a diff, you accept or iterate. That tight loop is everything.
For terminal-first developers, Neovim with LSP configs and AI plugins (like copilot.vim or codeium.vim) gives you similar power with more control. The learning curve is real, though.
JetBrains IDEs (IntelliJ, PyCharm, GoLand) remain the gold standard for refactoring and deep language intelligence. If you're working in a statically-typed language and doing major refactors, nothing beats JetBrains' rename and extract operations.
The problem? Your IDE only knows about your code at this moment. It doesn't know why this function exists, who last touched it, or what feature it's part of.
AI Assistants: Beyond Autocomplete
GitHub Copilot was the starting gun. Now we have Cursor, Cody, Supermaven, and Codeium all competing on context window size and model quality.
Here's what matters: context retrieval. The best autocomplete is useless if it's suggesting patterns from the wrong part of your codebase. Most AI tools still struggle with this. They'll confidently generate code using a deprecated API or a pattern your team explicitly moved away from six months ago.
Claude (via API or Desktop) and GPT-4 are essential for the bigger questions. "Here's our auth flow, how should I handle token refresh?" That's a Claude conversation, not a Copilot inline suggestion.
The cutting edge right now is MCP (Model Context Protocol)—the standard that lets AI assistants tap into external context sources. Your AI can query your production logs, your internal docs, your database schemas. Cursor and Claude both support it.
This is where tools like Glue become relevant. MCP isn't magic on its own—you need structured context to feed it. Glue indexes your entire codebase, discovers what features exist, and makes that queryable. Your AI assistant can ask "show me all code related to payment processing" and actually get a meaningful answer, not just grep results.
Terminal and Command Line
iTerm2 or WezTerm for Mac. Windows Terminal for Windows. Alacritty if you want GPU-accelerated speed.
But the terminal itself matters less than what you run in it:
Zsh with Oh My Zsh or Starship prompt. Git aliases. Jump commands (z or autojump). These save hundreds of keystrokes daily.
tmux or Zellij for session management. I can't work without persistent sessions anymore. SSH into a server, start tmux, detach, close laptop, reconnect from anywhere, resume exactly where you left off.
ripgrep (rg) and fd replace grep and find. They're absurdly fast and have sane defaults. rg "TODO" --type rust just works.
jq for JSON wrangling. yq for YAML. fx for interactive JSON exploration. If you work with APIs, these are non-negotiable.
Git and Version Control
Git itself, obviously. But the interface matters.
Command line purists: lazygit is a TUI that makes complex git operations obvious. Staging partial hunks, interactive rebasing, cherry-picking—all with intuitive keybindings.
GUI people: Fork (Mac/Windows) or GitKraken. Seeing the visual graph prevents so many merge disasters.
GitHub CLI (gh) is underrated. gh pr create --fill creates a PR with your commit messages. gh pr checkout 123 checks out someone else's PR locally. It eliminates so much browser tab switching.
Documentation and Knowledge Management
This is where most stacks fall apart.
You've got Notion or Confluence for planning docs. Markdown files in your repo for technical docs (good). README files that are six months stale (bad).
The real problem: documentation lives in a different universe from code. You update the code, forget to update the docs, and six months later someone's debugging with outdated information.
What teams need is automatic documentation generation from code. Not just API docs—those are table stakes. Feature-level documentation that explains what your code actually does.
Glue does this by analyzing your codebase and generating human-readable descriptions of features, not just functions. It maps out what parts of the code work together to deliver user-facing functionality. When you refactor, it updates automatically.
The alternative is hoping someone remembers to update the wiki. Good luck with that.
Project Management and Task Tracking
Linear has won the "good UX for engineers" war. Keyboard shortcuts everywhere, fast as hell, Git integration that actually works.
Jira isn't going anywhere for larger orgs. It's bloated, but the customization depth is real.
Height and Plane are newer alternatives worth watching.
Here's the trap: these tools track what to build, not where in the code to build it. You get a ticket: "Fix the search timeout issue." Cool. Which of your 47 microservices handles search? What's the actual function that times out?
You grep the codebase. Ask in Slack. Maybe someone remembers. Maybe not.
Code intelligence tools close this gap. Glue maps features to code, so you can go from "fix payment retry logic" directly to the relevant files and functions. It shows you churn rates (how often that code changes) and complexity scores. High churn + high complexity = here be dragons.
Code Review and Collaboration
GitHub or GitLab for hosting and PRs. Bitbucket still exists for Atlassian shops.
Graphite improves the stacked diffs workflow if your team works that way. Much better than manually managing a chain of dependent PRs.
Review Board or Gerrit for teams that need more structured review processes.
The real productivity gain in code review comes from automation:
Danger for enforcing PR conventions. "You modified the API but didn't update the OpenAPI spec." Auto-comment.
CodeRabbit or Bito for AI-powered review. They catch bugs and suggest improvements. Hit rate is maybe 30%, but that 30% is often stuff humans miss.
Prettier and ESLint (or equivalent for your language) as pre-commit hooks. Never argue about formatting again.
Observability and Debugging
DataDog, New Relic, or Honeycomb for production monitoring.
Sentry for error tracking. It's good. Pay for it.
OpenTelemetry for instrumentation if you're building your own stack or want vendor flexibility.
But observability has the same context problem as everything else. Your logs say "payment service error in process_refund." Which version of the code is deployed? Who last changed that function? What's the complexity score? Is this a known trouble spot?
Glue adds that development-time context to your production data. It shows ownership (who knows this code), churn history (how often it breaks), and relationships (what else depends on this). When you're debugging at 2 AM, that context is gold.
Communication
Slack or Discord for team chat. Zoom or Google Meet for video.
Loom for async video updates. "Here's why I built it this way" as a 3-minute recording beats a 500-word doc.
The productivity win here is reducing interruptions. Set up good notification boundaries. Use threads. Create specific channels for specific topics so people can opt out of noise.
The Missing Layer: Code Intelligence
Here's what's missing from most stacks: a system that understands your codebase as a whole.
Your IDE understands syntax and symbols. Your AI understands patterns. Your PM tool understands tasks. But nothing connects them.
You need a tool that answers:
What features exist in this codebase?
Where is the code for X feature?
What's the health of this module? (complexity, churn, test coverage)
Who owns this? Who should I ask?
What's the gap between what PM thinks exists and what actually exists?
This is code intelligence. It's the layer between your IDE and your project management tool.
Glue builds this by indexing your entire codebase, using AI to discover features (not just functions—actual user-facing capabilities), and maintaining a live map of code health and ownership. It integrates with MCP, so your AI assistants can query it directly.
"Show me all untested, high-complexity code in the auth service" becomes a one-line query instead of a day of archaeology.
What to Actually Do
Start with your IDE and AI assistant. Get those right first. Cursor if you want the future. VS Code + Copilot if you want stable and proven.
Fix your terminal workflow. Muscle memory here pays dividends forever.
Automate formatting and linting. Make the robots argue about semicolons.
Then—and this is the part most teams skip—add code intelligence. Something that maintains a living map of what your codebase actually does and where the problems are.
The best productivity stack isn't about having more tools. It's about having the right tools that actually talk to each other. Close the gaps. Connect the layers.