6 Tools That Made My Life Easier as a Software Engineer

Git is a distributed version control system. In simple words, it records snapshots of your project over time so you can go back to any earlier version, work on changes in parallel (branches) and combine work from many people.

\"Distributed\" means every developer has the full history on their own machine. You do not need a server to look at history, create branches or commit.

The problem it actually solved for me

Everyone uses Git to save and share code. What changed my work was learning that Git is also a recovery tool and a debugging tool. For a long time I treated Git like a upload button. I was careful about rebasing, scared of reset and when a bug appeared I read diffs by hand to guess which change caused it.

Once I understood a few internals and three or four commands, that fear went away.

How Git works (just enough internals)

You only need three ideas to stop being scared of Git:

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• A commit is a snapshot of your project, plus a pointer to its parent commit.
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• A branch is just a movable label that points to one commit.
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• HEAD is a pointer to what you currently have checked out (usually a branch).
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\n   A ◄── B ◄── C ◄── D        main  ──► D\n               ▲\n               └──── E ◄── F   feature ──► F   ◄── HEAD\n

When you \"delete\" a branch, you delete the label, not the commits. The commits stay in the repository until Git's garbage collection removes unreachable objects later. That single fact is why so many \"I lost everything\" moments are recoverable.

Feature 1: git reflog for recovering \"lost\" work

What it does: the reflog is a local log of where HEAD and your branches pointed over time. Every checkout, commit, reset and rebase adds an entry.

If you run a bad reset, finish a rebase you regret or delete a branch, the old commits are usually still listed there.

\ngit reflog\n# a1b2c3d HEAD@{0}: reset: moving to HEAD~3\n# 9f8e7d6 HEAD@{1}: commit: add payment retry logic\n# ...\n\n# Safest recovery: create a new branch at the old position\ngit branch recovered-work HEAD@{1}\n

I prefer creating a new branch over git reset --hard HEAD@{1}, because reset --hard also throws away any uncommitted changes in your working directory.

Limits you should know:

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• The reflog is local only. It is not pushed. A teammate's reflog cannot save your work and yours cannot save theirs.
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• Entries expire. By default, Git keeps reflog entries for 90 days and 30 days for entries that are no longer reachable (controlled by gc.reflogExpire and gc.reflogExpireUnreachable).
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• Changes that were never committed (and never stashed) are not in the reflog. Git cannot recover what it never saw.
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Official docs: git-reflog

Feature 2: git bisect for finding the commit that broke something

What it does: git bisect runs a binary search over your commit history to find the first commit where a bug appears.

Why it matters: if a bug exists now but did not exist in the last release and there are 1,000 commits in between, bisect needs around 10 test steps to find the exact commit, because each step cuts the search space in half (2^10 = 1,024).

\n   good                                             bad\n    │                                                │\n    v1.4 ─── ... ─── ... ─── ... ─── ... ─── ... ─── HEAD\n                            ▲\n                     Step 1: test the middle\n                            │\n           bug here? ── yes ─► search the left half\n                     └─ no ──► search the right half\n     ... repeat until one commit remains ...\n

Manual bisect:

\ngit bisect start\ngit bisect bad              # the current commit has the bug\ngit bisect good v1.4.0      # this tag was known to be fine\n\n# Git checks out a commit in the middle.\n# Test it, then tell Git the result:\ngit bisect good             # or: git bisect bad\n\n# Repeat until Git prints \"<sha> is the first bad commit\"\ngit bisect reset            # return to where you started\n

Automated bisect is where it really shines. If you can write a script or test that fails when the bug exists, Git will do the whole search for you:

\ngit bisect start HEAD v1.4.0\ngit bisect run npm test -- tests/checkout.test.js\n

The rule for git bisect run: exit code 0 means the commit is good, 125 means \"skip this commit\" (for example, it does not build) and other codes from 1 to 127 mean bad.

Edge cases:

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• If some commits in the range do not compile, use git bisect skip (or exit 125 in your script).
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• Bisect works best when commits are small and each one builds. Huge \"WIP\" commits make the final answer less useful, because the bad commit might contain 40 changed files.
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• A flaky test gives flaky bisect results. Make the check deterministic first.
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Official docs: git-bisect

Feature 3: interactive rebase and fixup commits for clean pull requests

When a reviewer asks for a change in a pull request, I used to add commits like \"fix review comment\" and \"fix again\". Reviewers then had to read a messy history.

Now I use fixup commits:

\n# Make the fix, then attach it to the commit it belongs to\ngit commit --fixup=<sha-of-original-commit>\n\n# Later, squash all fixups into their targets automatically\ngit rebase -i --autosquash origin/main\n

After rewriting history on a branch that is already pushed, use this instead of a plain force push:

git push --force-with-lease\n

--force-with-lease refuses to overwrite the remote branch if someone else pushed to it since you last fetched. A plain --force would silently delete their work.

Rule I follow: rewrite history only on your own branches. Never rebase a shared branch like main that others have already pulled.

Feature 4: git worktree for handling urgent fixes without stashing

When a production bug comes in while I am halfway through a feature, I do not stash and switch anymore. I open a second working directory attached to the same repository:

\ngit worktree add ../hotfix-checkout origin/main\ncd ../hotfix-checkout\n# fix, commit, push, then clean up:\ngit worktree remove ../hotfix-checkout\n

Both directories share the same Git history, but each has its own checked-out branch. My half-finished feature stays exactly as it was. Note that one branch cannot be checked out in two worktrees at the same time by default.

Official docs: git-worktree

Common Git mistakes

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• Committing secrets. Removing an API key in a later commit does not remove it from history. If a secret was pushed, treat it as leaked: rotate it first, then clean history if needed.
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• Using git add . blindly. It is easy to commit build output, .env files or huge logs. Set up .gitignore early and review git status before committing.
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• Giant commits. They make review, revert and bisect harder.
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• Force pushing to shared branches. Use branch protection rules on your Git host to block it.
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When Git is not the right tool

Git is designed for text files. Large binary files (videos, big datasets, design files) bloat the repository because every version is stored in full. Use Git LFS or a separate storage system for those.

For a deeper free reference, the Pro Git book is still one of the clearest resources.

Visual Studio Code (VS Code) is a free code editor from Microsoft that runs on Windows, macOS and Linux. On its own it is lightweight and extensions add support for languages, debuggers, linters and tools.

It is not the same product as Visual Studio, which is Microsoft's full IDE mainly used for .NET and C++ on Windows.

The problem it solved: too many windows, too much console.log

My old workflow was an editor, a separate terminal, a separate database client and a lot of print statements for debugging. The two things that changed that were the built-in debugger and remote development.

Feature 1: the debugger (stop using print statements for everything)

A debugger lets you pause a running program at a line (a breakpoint), inspect variable values and step through code one line at a time.

For a Node.js API, a basic .vscode/launch.json looks like this:

{\n  \"version\": \"0.2.0\",\n  \"configurations\": [\n    {\n      \"type\": \"node\",\n      \"request\": \"launch\",\n      \"name\": \"Debug API\",\n      \"program\": \"${workspaceFolder}/src/server.js\",\n      \"env\": { \"NODE_ENV\": \"development\" }\n    }\n  ]\n}\n

The features that saved me the most time:

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• Conditional breakpoints. Right-click a breakpoint and add a condition like order.total > 10000. The program pauses only for the case you care about, instead of on every request.
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• Logpoints. A logpoint prints a message when a line runs, without pausing and without editing the code. Useful when you are debugging something timing-sensitive where pausing changes the behavior.
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• Call stack view. When an error comes from deep inside a library, the call stack shows which of your functions led there.
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Debugger support depends on the language extension (Python, Go, Java, Dart/Flutter and others each have their own). Official docs: Debugging in VS Code

Feature 2: Remote Development (SSH, Dev Containers, WSL)

Remote Development lets VS Code run its UI on your machine while the code, terminal and extensions run somewhere else: a remote server over SSH, a Docker container or WSL on Windows.

\n┌──────────────────┐           ┌──────────────────────────────┐\n│  Your laptop     │           │  Remote machine / container  │\n│                  │  SSH or   │                              │\n│  VS Code UI      │◄─────────►│  VS Code Server              │\n│  (editor, keys,  │  Docker   │  - your source code          │\n│   themes)        │           │  - terminal, compilers       │\n│                  │           │  - language extensions       │\n└──────────────────┘           └──────────────────────────────┘\n

The one I use most is Dev Containers. You add a .devcontainer/devcontainer.json file to the repository and anyone who opens the project gets the same runtime versions, tools and extensions inside a container.

{\n  \"name\": \"orders-service\",\n  \"image\": \"mcr.microsoft.com/devcontainers/typescript-node:22\",\n  \"forwardPorts\": [3000],\n  \"postCreateCommand\": \"npm ci\",\n  \"customizations\": {\n    \"vscode\": {\n      \"extensions\": [\"dbaeumer.vscode-eslint\", \"esbenp.prettier-vscode\"]\n    }\n  }\n}\n

This is especially helpful for onboarding. A new teammate does not need a 3-page setup document. Official docs: Developing inside a Container

Small features that add up

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• Command Palette (Ctrl+Shift+P or Cmd+Shift+P on macOS): run almost any command by typing its name.
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• Quick Open (Ctrl+P / Cmd+P): jump to any file by typing part of its name.
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• Multi-cursor editing (Alt+Click on Windows/Linux, Option+Click on macOS): edit several lines at once.
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• Workspace extension recommendations: add a .vscode/extensions.json file so the team gets prompted to install the same linters and formatters.
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Common VS Code mistakes

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• Installing too many extensions. Each one can slow startup. Run Developer: Show Running Extensions from the Command Palette to see which ones are expensive.
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• Trusting every extension. Extensions run code on your machine with your user permissions. Prefer well-known, verified publishers, especially on a work laptop.
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• Keeping personal settings in the repo. Commit shared config (formatter, linter rules), not your theme or font size.
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VS Code vs JetBrains IDEs

This question comes up often, so here is a fair comparison:

Neither is universally better. For large Java or Kotlin codebases, many developers prefer IntelliJ IDEA for its refactoring tools. For polyglot work (a bit of TypeScript, Python, YAML, Dockerfiles and shell scripts in one day), VS Code is hard to beat.

Docker is a tool for packaging an application with everything it needs to run (runtime, libraries, system packages, config) into a standard unit called a container.

A few terms explained simply:

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• Image: a read-only template, like a recipe plus all the ingredients, frozen. Built from a Dockerfile.
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• Container: a running instance of an image. You can run many containers from one image.
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• Dockerfile: a text file with step-by-step instructions to build an image.
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• Registry: a place to store and share images, such as Docker Hub or your cloud provider's registry.
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The problem it solved

Before Docker, running a project locally often meant installing a specific database version, a specific language runtime and a message broker directly on my laptop. Two projects needing different PostgreSQL versions meant pain. And a bug that only happened in production was often caused by a small difference between my machine and the server.

Containers make the environment part of the code.

How containers differ from virtual machines

This is one of the most asked beginner interview questions and the diagram makes it clear:

\n      Virtual Machines                      Containers\n┌──────────┬──────────┐            ┌──────────┬──────────┐\n│  App A   │  App B   │            │  App A   │  App B   │\n├──────────┼──────────┤            ├──────────┼──────────┤\n│ Libs     │ Libs     │            │ Libs     │ Libs     │\n├──────────┼──────────┤            ├──────────┴──────────┤\n│ Guest OS │ Guest OS │            │  Container runtime  │\n├──────────┴──────────┤            ├─────────────────────┤\n│     Hypervisor      │            │   Host OS kernel    │\n├─────────────────────┤            ├─────────────────────┤\n│      Hardware       │            │      Hardware       │\n└─────────────────────┘            └─────────────────────┘\n

A virtual machine runs a full guest operating system on virtual hardware. A container shares the host's operating system kernel and isolates processes using Linux kernel features (namespaces for isolation and cgroups for resource limits). That is why containers usually start in seconds and use less memory than VMs.

The trade-off: containers provide weaker isolation than VMs because they share a kernel. Also, Linux containers need a Linux kernel. On macOS and Windows, Docker Desktop runs a lightweight Linux VM behind the scenes.

How image layers and caching work

Each instruction in a Dockerfile creates a layer. Docker caches layers and if an instruction and its inputs have not changed, it reuses the cached layer. Once one layer changes, every layer after it is rebuilt.

That is why instruction order matters:

\n  Slow order                          Fast order\n  ───────────                         ──────────\n  COPY . .            ◄─ any code     COPY package*.json ./\n  RUN npm ci             change       RUN npm ci          ◄─ cached unless\n                         reinstalls                          dependencies change\n                         everything   COPY . .            ◄─ only this layer\n                                                             rebuilds on code edits\n

A production-style Dockerfile (multi-stage build)

A multi-stage build uses one stage to build the app and a second, smaller stage to run it. Build tools and dev dependencies never reach the final image.

\n# ---- Build stage ----\nFROM node:22-alpine AS build\nWORKDIR /app\nCOPY package*.json ./\nRUN npm ci\nCOPY . .\nRUN npm run build\n\n# ---- Runtime stage ----\nFROM node:22-alpine\nWORKDIR /app\nENV NODE_ENV=production\nCOPY package*.json ./\nRUN npm ci --omit=dev\nCOPY --from=build /app/dist ./dist\nUSER node\nEXPOSE 3000\nCMD [\"node\", \"dist/server.js\"]\n

What each choice does:

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• npm ci installs exact versions from the lockfile, which makes builds repeatable.
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• --omit=dev keeps test and build tools out of the runtime image.
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• USER node runs the app as a non-root user (the official Node images include a node user). If an attacker breaks into the app, they do not get root inside the container.
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• Also add a .dockerignore file with entries like node_modules, .git and .env, so they are not sent to the build or baked into the image.
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Official docs: Multi-stage builds

Docker Compose: running the whole stack with one command

Docker Compose lets you define several containers (API, database, cache) in one YAML file and start them together with docker compose up.

An illustrative setup for an orders API with PostgreSQL:

\nservices:\n  api:\n    build: .\n    ports:\n      - \"3000:3000\"\n    environment:\n      DATABASE_URL: postgres://app:app@db:5432/app\n    depends_on:\n      db:\n        condition: service_healthy\n\n  db:\n    image: postgres:16\n    environment:\n      POSTGRES_USER: app\n      POSTGRES_PASSWORD: app\n      POSTGRES_DB: app\n    healthcheck:\n      test: [\"CMD-SHELL\", \"pg_isready -U app -d app\"]\n      interval: 5s\n      timeout: 3s\n      retries: 10\n    volumes:\n      - pgdata:/var/lib/postgresql/data\n\nvolumes:\n  pgdata:\n

Two details that trip people up:

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1. depends_on alone only controls start order, not readiness. Your API can start while PostgreSQL is still initializing and crash with a connection error. Adding a healthcheck and condition: service_healthy makes Compose wait until the database actually accepts connections. Your app should still retry connections, because databases can restart at any time in real environments.
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2. Inside the Compose network, services reach each other by service name. The API connects to db:5432, not localhost:5432. Inside a container, localhost means the container itself.
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Also note: the top-level version: key you see in older Compose files is obsolete in the current Compose specification and can be removed. Official docs: Docker Compose

Common Docker mistakes (and why they hurt)

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• Putting secrets in ENV or ARG. Values set this way can be visible in the image metadata and history. Use runtime environment variables from your platform's secret manager or build secrets for build-time credentials.
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• Using the latest tag in production. latest is just a tag name, not \"the newest stable version\". Pin versions so the same deploy produces the same result.
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• Storing data inside the container. When a container is removed, data written inside it is gone. Use volumes for databases.
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• Running as root. It is the default in many images. Switch to a non-root user.
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• Giant images. Slow to push, slow to pull, more to scan for vulnerabilities. Multi-stage builds and smaller base images help.
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Licensing note for teams

Docker Engine is open source. Docker Desktop, the app most people use on macOS and Windows, is free for personal use, education and small businesses, but requires a paid subscription for larger companies (the current terms define this by employee count and annual revenue). Check the official license terms before rolling it out at work. Some teams use alternatives like Podman for this reason.

When Docker is not worth it

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• A small script or CLI tool that only you run.
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• Native desktop or mobile apps (a Flutter iOS build still needs macOS and Xcode).
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• When file-heavy workloads on macOS or Windows feel slow because of bind mounts through the VM. It can be tuned, but it is a real cost.
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Postman is an API client: a tool for sending HTTP requests to an API and inspecting the responses. It also lets you save requests, group them, add automated checks and share them with a team.

An API (Application Programming Interface) here means a set of URLs your backend exposes, like POST /orders or GET /users/42, that other programs call.

The problem it solved

As a backend developer, I often finished an endpoint before the frontend existed. Testing it meant writing long curl commands, copying tokens by hand and repeating the same 5-step flow (log in, create cart, add item, create order, fetch order) every time I changed something.

Postman turned that flow into a saved, repeatable sequence.

Core concepts

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• Collection: a folder of saved requests, for example \"Orders API\".
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• Environment: a set of variables such as baseUrl and token, with different values for local, staging and production.
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• Variables: placeholders like {{baseUrl}}/orders that get replaced at send time.
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• Scripts: small JavaScript snippets that run before a request (pre-request) or after the response (tests).
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Chaining requests with test scripts

An illustrative e-commerce flow: create an order, save its ID, then fetch it.

In the Tests / post-response script of POST {{baseUrl}}/orders:

\npm.test(\"order is created\", function () {\n  pm.response.to.have.status(201);\n});\n\nconst body = pm.response.json();\npm.environment.set(\"orderId\", body.id);\n

The next request can then use GET {{baseUrl}}/orders/{{orderId}}.

For payment APIs, you often need an idempotency key: a unique value sent in a header so that if a client retries the same request, the server does not charge the customer twice. Postman's built-in dynamic variable {{$guid}} generates a new unique ID per request, which is handy for testing that behavior. To test a retry, you would generate the key once in a pre-request script, store it in a variable and send the same request twice.

Running collections in CI with Newman

Newman is Postman's open-source command-line runner. It lets you run a collection in a CI pipeline, so your API checks run on every merge instead of only when someone remembers.

\nnpm install -g newman\nnewman run orders.postman_collection.json -e staging.postman_environment.json\n

Newman returns a non-zero exit code when tests fail, so the CI job fails too. Postman also offers its own Postman CLI. Official sources: Newman on GitHub and the Postman Learning Center.

Common Postman mistakes

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• Storing secrets in shared workspaces. Postman syncs collections and environments to the cloud workspace. It separates values that are shared with the team from values that stay on your machine. Check where your tokens are stored before sharing a workspace and follow your company's policy for production credentials.
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• Only testing the happy path. Add requests for 400, 401, 403, 404 and 409 responses. Many real bugs are in error handling.
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• Letting collections drift from the real API. If your API has an OpenAPI spec, import from it or keep both in sync.
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Postman vs curl vs file-based API clients

I still use curl every day for fast checks:

\ncurl -i -X POST http://localhost:3000/orders \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"productId\": \"p_123\", \"quantity\": 2}'\n

A useful trick that connects two tools in this list: in Chrome DevTools, right-click any network request and choose Copy → Copy as cURL. You can paste that into a terminal or import it into Postman, to reproduce exactly what the browser sent. More on DevTools next.

Chrome DevTools is a set of debugging tools built into the Google Chrome browser. Open it with F12, Ctrl+Shift+I (Windows/Linux) or Cmd+Option+I (macOS). It shows the page's HTML and CSS, JavaScript console, network traffic, storage and performance data.

Even as a mostly backend engineer, I use it constantly, because many \"backend bugs\" reported by users are actually cache, CORS, cookie or frontend timing issues and DevTools settles the question in minutes.

The Network panel: the one to learn first

The Network panel lists every request the page makes, with status codes, headers, payloads and timing.

Click a request and open the Timing tab to see where the time went:

\n Request timeline for GET /api/orders\n ───────────────────────────────────────────────────────────\n Queueing / Stalled           ▌                 waiting for a free connection\n DNS lookup                    ▌                resolving the domain\n Initial connection / SSL       ▌▌              TCP + TLS handshake\n Request sent                     ▏\n Waiting for server response        ██████████  ◄─ time the server took\n Content download                             ▌▌  downloading the body\n ───────────────────────────────────────────────────────────\n

This is incredibly useful in a \"frontend vs backend\" blame discussion. If most of the time is in waiting for server response, the server is slow. If it is in content download, the response might be too large. If it is stalled, the browser may be waiting for an available connection.

Network panel settings I use all the time:

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• Preserve log: keeps requests across page reloads and redirects. Essential for debugging login flows.
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• Disable cache: forces fresh downloads. It only applies while DevTools is open, which confuses people when a bug \"disappears\".
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• Throttling: simulates slower networks (for example, a Slow 4G preset) so you can see what users on weaker connections experience.
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• Request blocking: block a specific URL to see how the page behaves when a script or API fails.
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Debugging JavaScript in the Sources panel

Like VS Code, DevTools has breakpoints, but with some browser-specific ones:

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• XHR/fetch breakpoints: pause whenever a request URL contains a given string.
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• DOM change breakpoints: right-click an element in the Elements panel and choose Break on → subtree modifications to find which script is changing it.
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• Event listener breakpoints: pause on clicks, key presses and other events.
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Small Console helpers: $0 refers to the element currently selected in the Elements panel and copy(someObject) copies a value to your clipboard.

Performance, Lighthouse and Core Web Vitals

Core Web Vitals are Google's metrics for real-world page experience:

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• LCP (Largest Contentful Paint): how fast the main content appears. Good is 2.5 seconds or less.
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• INP (Interaction to Next Paint): how quickly the page responds to user input. Good is 200 milliseconds or less. INP replaced FID as a Core Web Vital in March 2024.
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• CLS (Cumulative Layout Shift): how much the layout jumps around. Good is 0.1 or less.
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The Performance panel records what the browser did (scripting, rendering, painting) so you can find long tasks that block the main thread. Lighthouse runs an automated audit for performance, accessibility, SEO and best practices. Official reference: Web Vitals on web.dev.

Finding memory leaks

In single-page apps, a common leak is detached DOM nodes: elements removed from the page but still referenced by JavaScript, so they are never freed. The Memory panel lets you take heap snapshots before and after an action and compare them to find what keeps growing.

Common DevTools mistakes

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• Profiling on a fast developer laptop only. Use CPU and network throttling to get closer to real user conditions.
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• Running Lighthouse with browser extensions active. Extensions can affect results. Use an Incognito window or a clean profile.
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• Treating Lighthouse scores as the truth. Lighthouse is lab data from one run. Real-user data (field data) can differ.
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Official docs: Chrome DevTools. Firefox DevTools and Safari Web Inspector offer similar features and testing in more than one browser is still a good habit.

An AI coding assistant is a tool built on a large language model (LLM) that suggests, explains or writes code based on the context you give it. Examples include GitHub Copilot, Claude Code and Cursor.

A large language model is a model trained on huge amounts of text and code that generates output by predicting what should come next based on patterns. That explains both its strength and its weakness: it is very good at producing code that looks right and it does not truly know whether it is right for your system.

Two styles of assistant

\n┌─────────────────────────────┐      ┌─────────────────────────────────┐\n│  Inline / chat assistant    │      │  Agentic assistant              │\n├─────────────────────────────┤      ├─────────────────────────────────┤\n│ Suggests the next lines     │      │ Reads multiple files            │\n│ Answers questions in chat   │      │ Edits code across the project   │\n│ You apply every change      │      │ Can run commands and tests      │\n│                             │      │ You review diffs and approve    │\n├─────────────────────────────┤      ├─────────────────────────────────┤\n│ Lower risk, smaller scope   │      │ More leverage, more to review   │\n└─────────────────────────────┘      └─────────────────────────────────┘\n

Many products now offer both styles.

Where it genuinely saves me time

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• Understanding unfamiliar code. Asking \"what does this module do and where is it called from?\" is a faster first pass than reading 15 files cold. I still verify the answer.
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• Boilerplate. DTOs, mappers, config files, repetitive test setup.
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• First drafts of tests. Especially edge cases I might skip: empty lists, null values, duplicate requests, time zone boundaries.
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• Regular expressions, SQL and shell one-liners, followed by testing them on real input.
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• Rubber-duck review. Asking it to point out possible race conditions or missing error handling in a function often surfaces something worth checking.
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What can go wrong

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• Plausible but wrong code. It can call functions that do not exist, use outdated library APIs or miss a business rule it was never told about.
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• Subtle security issues. For example, building SQL with string concatenation, weak input validation or logging sensitive data.
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• Data exposure. Pasting secrets, customer data or proprietary code into a tool your company has not approved can violate policy or contracts.
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• Agent risks. When an agent can run commands, untrusted content it reads (a web page, an issue comment, a file) might contain instructions that try to steer it. Keep permission prompts on for risky actions and review what it runs.
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• Skill erosion. If it writes code you do not understand, you will not be able to debug it at 2 a.m. during an incident.
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How I use it well

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1. Give context. Name the framework, versions, constraints and the existing pattern to follow.
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2. Ask for small, reviewable changes instead of a whole feature at once.
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3. Ask for tests alongside the code, then run them. Check that the tests would actually fail if the code were wrong.
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4. Read every diff like you would read a pull request from a new teammate.
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5. Follow your company's AI usage policy on what code and data can be shared.
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Tool knowledge comes up in software engineering interviews more than people expect, especially Git and Docker. Here are questions with explanations, not one-line answers.

Beginner

1. What is the difference between git merge and git rebase?

Both bring changes from one branch into another. merge creates a merge commit that joins two histories and keeps them as they happened. rebase replays your commits on top of another branch, producing a straight line of history with new commit IDs. Rebase gives cleaner history, but because it rewrites commits, you should not rebase branches that others have already pulled.

2. What is the difference between a Docker image and a container?

An image is a read-only template built from a Dockerfile. A container is a running instance of that image with its own writable layer. One image can run as many containers.

3. What is the difference between HTTP 401 and 403?

401 Unauthorized means the request is not authenticated (missing or invalid credentials). 403 Forbidden means the server knows who you are, but you are not allowed to do this action. You see these constantly while testing APIs in Postman.

Intermediate

4. A bug exists in production but not in the previous release. How do you find the cause?

Reproduce it, write a check (ideally an automated test) that fails when the bug is present and use git bisect run between the last good tag and the current commit. Then read the identified commit, fix the root cause and keep the test as a regression test.

5. Why might a Docker image be 1.5 GB and how would you reduce it?

Common causes are a large base image, build tools and dev dependencies left in the final image, missing .dockerignore and cache files from package managers. Fixes include multi-stage builds, smaller base images, --omit=dev style installs and combining cleanup into the same layer that creates the files.

6. Why does my API container fail to connect to the database on startup even with depends_on?

depends_on only controls start order. The database container may be running but not yet ready. Use a healthcheck with condition: service_healthy and add connection retries in the application.

Advanced

7. You accidentally ran git reset --hard and lost three commits. How do you recover?

Run git reflog, find the entry before the reset and create a branch at that position with git branch recovered <sha>. Mention that this works only for committed work, only on the same machine and only until reflog entries expire and garbage collection runs.

8. A secret was committed and pushed to a Docker image and a Git repo. What do you do?

Rotate the secret immediately, because anyone who pulled the repo or image may have it. Then remove it from Git history (with a history-rewriting tool) and rebuild images without it, using runtime secrets or build secrets instead of ENV/ARG. Finally, add secret scanning to CI to prevent a repeat.

9. A page feels slow. How do you decide if the problem is frontend or backend?

Open the Network panel and look at the timing breakdown of the key requests. Long \"waiting for server response\" points to the backend. If requests are fast but the page is still slow, record in the Performance panel and look for long JavaScript tasks, heavy rendering or large images affecting LCP.

Scenario-based

10. Your team keeps hitting \"works on my machine\" issues. What would you set up?

A Dockerfile used in both CI and production, a Compose file for local dependencies, a Dev Container config for consistent tooling, pinned versions everywhere and a lockfile-based install (npm ci or equivalent). Then document the one command needed to start the project.

11. A teammate wants to use an AI agent to refactor a payments module. What guardrails would you suggest?

Confirm the tool is approved for that code. Require good test coverage before the refactor, have the agent work in small steps on a branch, keep command approvals on, review every diff and run the full test suite plus targeted tests for money-related edge cases like retries, rounding and duplicate requests.

1. What tools should every software engineer know?

Every software engineer should know Git well, one code editor or IDE and its debugger and a way to test APIs (Postman or curl). If you work on web apps, add browser DevTools. For backend and cloud work, Docker is close to essential. These cover the daily needs of writing, running, testing, debugging and sharing code.

2. What is the best code editor for software engineers?

There is no single best editor. VS Code is free, lightweight and works for almost any language through extensions. JetBrains IDEs offer deeper refactoring and analysis for specific ecosystems like Java and Kotlin. Neovim suits developers who prefer a keyboard-driven, highly customized setup. Pick based on your main language and team.

3. Is Docker hard to learn for beginners?

The basics are not hard. You can learn images, containers, Dockerfiles and docker compose up in a weekend. The harder parts are networking, volumes, image optimization and security, which you learn gradually as you use Docker in real projects.

4. Is Postman free?

Postman has a free plan that covers most individual use, with paid plans for larger teams and advanced collaboration features. Plan limits change over time, so check Postman's pricing page for current details. Free alternatives include curl, HTTPie, Insomnia and Bruno.

5. Do frontend developers need Docker?

Not always. Many frontend projects run fine with just Node.js installed. Docker becomes useful when you need to run the backend, a database or other services locally or when your team uses Dev Containers for a consistent environment.

6. Are AI coding assistants safe to use for company code?

They can be, when your company has approved the specific tool and its data handling terms. The main risks are sharing sensitive data, accepting incorrect or insecure code without review and giving agents too many permissions. Treat AI output like code from a new teammate: review it and test it.

7. Which developer tool should a beginner learn first?

Learn Git first. Almost every job, open source project and team workflow depends on it. Then learn your editor's debugger, because it changes how you understand code. After that, add API testing, browser DevTools and Docker based on the kind of work you do.

8. Do these tools matter in software engineering interviews?

Yes, especially Git and Docker. Interviewers often ask about merge vs rebase, recovering lost commits, image vs container and how you would debug a slow page or a production bug. Practical tool knowledge shows that you have worked on real projects, not only solved algorithm problems.