DEV Community: Sam

20 AI Tools for Coding, Debugging, Testing, and Code Review in 2026

Sam — Tue, 18 Aug 2026 08:23:26 +0000

AI development tools have evolved far beyond simple autocomplete. Developers can now use AI to write features, understand unfamiliar codebases, debug problems, generate tests, review pull requests, detect vulnerabilities, document workflows, and even delegate complete engineering tasks.

But choosing the right tool isn't always straightforward. Some are excellent coding assistants, while others specialize in code review, testing, security, or developer communication.

Below are 20 of the most useful AI tools for developers in 2026, organized around what they actually do best.

GitHub Copilot

Best for: AI-assisted coding and everyday development

GitHub Copilot is one of the most widely adopted AI coding assistants and integrates directly into popular development environments and GitHub workflows.

It can generate code, explain existing code, suggest improvements, help with debugging, and assist with pull requests.

Best Use Case

Use GitHub Copilot when you want an AI pair programmer directly inside your existing IDE without changing your entire development workflow.

Pros

Excellent IDE integration
Strong code completion
Supports many programming languages
GitHub integration
Useful for code explanations
Increasingly powerful agentic capabilities

Cons

Heavy usage can consume plan limits
Generated code still requires review
Results can vary depending on context

Our Take

Best overall starting point for developers who are new to AI coding tools.

Cursor

Best for: AI-first development and large codebases

Cursor is an AI-powered code editor designed around the idea that AI should understand the entire project rather than simply complete individual lines.

It is particularly useful for multi-file changes, debugging and agent-based development.

Best Use Case

Use Cursor when you want an AI agent to understand your repository and make changes across multiple files.

Pros

Excellent codebase context
Strong agentic capabilities
Multiple AI models
Powerful multi-file editing
Good debugging workflow
MCP and agent integrations

Cons

Requires learning another editor
Agent usage can become expensive
AI can occasionally make broader changes than expected

Our Take

One of the strongest choices for developers who want AI to actively work on their projects.

Claude Code

Best for: Complex coding and debugging

Claude Code takes a more agent-oriented approach to software development. Instead of simply suggesting code, it can inspect repositories, modify files, execute commands and work through multi-step engineering tasks.

Best Use Case

Use Claude Code for complex debugging, large refactoring tasks and understanding unfamiliar codebases.

Pros

Strong reasoning
Excellent repository understanding
Powerful terminal workflow
Good debugging capabilities
Handles complex multi-file tasks

Cons

Less beginner-friendly
Requires careful instructions
Generated changes need testing
Heavy usage can consume significant resources

Our Take

If your problem is:

"I have a complicated bug and need the AI to investigate the entire codebase."

Claude Code is one of the strongest options.

OpenAI Codex

Best for: Autonomous software engineering

Codex is designed to handle complete engineering tasks rather than only generating snippets.

It can work on features, refactoring, migrations, testing and other multi-step development tasks.

Best Use Case

Use Codex when you want to delegate a clearly defined engineering task to an AI coding agent.

Pros

Strong coding agents
Multi-step task execution
Good for refactoring
Useful for testing
Can work on tasks in parallel

Cons

Requires human supervision
Complex tasks can consume significant resources
Generated changes still need testing

Our Take

Codex is particularly useful when AI moves from "help me code" to "complete this engineering task."

Windsurf

Best for: Agentic development inside an AI-first IDE

Windsurf combines an AI-powered editor with agentic development capabilities.

Best Use Case

Use Windsurf when you want a Cursor-style AI development environment with an emphasis on agentic workflows.

Pros

AI-native development
Strong context awareness
Agentic workflows
Multi-file editing
Good developer experience

Cons

Another editor to learn
AI changes need supervision
Heavy usage requires monitoring

Our Take

A strong alternative to Cursor for developers who want an AI-first coding environment.

Replit

Best for: Rapid prototyping

Replit combines coding, AI assistance, execution and deployment into one browser-based environment.

Best Use Case

Use Replit when you want to turn an idea into a working prototype quickly without setting up a complete local development environment.

Pros

Fast prototyping
Browser-based
AI-assisted development
Easy deployment
Beginner-friendly

Cons

Not ideal for every enterprise workflow
Complex applications may eventually need traditional development environments
AI-generated applications still require testing Our Take

One of the best choices for developers, founders and product teams who prioritize speed of experimentation.

CodeRabbit

Best for: AI-powered pull-request reviews

CodeRabbit focuses specifically on code review rather than trying to replace your entire development environment.

It analyzes pull requests and provides contextual feedback, summaries and suggestions.

Best Use Case

Use CodeRabbit when you want to automate the first layer of pull-request review before a human reviewer looks at the code.

Pros

Automated PR reviews
Inline comments
PR summaries
GitHub/GitLab workflow integration
Useful for AI-generated pull requests

Cons

Can occasionally produce unnecessary comments
Doesn't replace human architectural review
Review quality depends on available context

Our Take

Particularly useful for teams dealing with a growing number of AI-generated pull requests.

Qodo

Best for: AI code review and test generation

Qodo combines AI code review with automated testing workflows.

Best Use Case

Use Qodo when you want to generate tests and review code within the same quality workflow.

Pros

Strong test generation
AI code review
Repository context
Test improvement
Useful for engineering teams

Cons

More complex than simple coding assistants
May be excessive for small projects
AI-generated tests still require validation

Our Take

One of the better choices for teams that want to combine AI code review + AI testing.

Greptile

Best for: Full-codebase code review

Greptile differentiates itself by looking beyond the changed lines in a pull request and considering the broader codebase.

Best Use Case

Use Greptile when you're working with a large repository or monorepo where a change can affect code outside the immediate pull request.

Pros

Codebase-wide context
Cross-file analysis
Useful for large repositories
Good for AI-generated code
Repository-aware reviews

Cons

Can be unnecessary for small projects
Deep analysis can take longer
Should complement static-analysis tools

Our Take

Its biggest advantage is simple:

It tries to understand the codebase, not just the diff.

Clipy

Best for: AI screen recording, bug reproduction and developer documentation

Clipy is different from the coding assistants above.

Instead of generating code, Clipy helps developers capture and communicate what is happening inside software.

It supports AI agent-native screen recording, allowing agents such as Claude Code, Codex and Cursor to record their own work. Recordings can provide both human-readable video and structured Markdown context for AI agents.

Best Use Case

Use Clipy when you need to show a bug, demonstrate a feature, document a development workflow, or give an AI agent visual context about what happened on screen.

Pros

AI-native screen recording
Useful for bug reproduction
Developer-focused recording workflows
Code walkthroughs
Visual documentation
AI-readable recording reports
AI agents can record their own work
Useful for code-review explanations

Cons

Not a coding assistant
Some agent workflows require setup
Native desktop recording has specific requirements
Should complement, not replace, traditional debugging tools

Our Take

Clipy fills an interesting gap in the modern AI development stack:

Coding agents can write and modify code, but they don't always have a good way to communicate what actually happened visually.

For example:

Bug reported → Clipy recording → AI agent analyzes context → developer fixes issue → Clipy records the fix

That makes Clipy particularly relevant to AI-assisted development teams, even though it isn't a traditional coding tool.

Amazon Q Developer

Best for: AWS development

Amazon Q Developer is particularly useful for developers working heavily with AWS.

Best Use Case

Use it when your application depends heavily on AWS services, infrastructure and cloud development.

Pros

AWS-aware assistance
Code generation
Debugging
Security assistance
Cloud development support

Cons

Biggest advantage is within AWS
Less compelling for developers outside the AWS ecosystem

Our Take

A strong option for AWS-heavy development teams.

Gemini Code Assist

Best for: Google Cloud development

Gemini Code Assist integrates Google's Gemini models into developer workflows.

Best Use Case

Use it when your development environment is closely connected to Google Cloud or Google's developer ecosystem.

Pros

AI coding assistance
Code explanation
Google ecosystem integration
Large-context workflows

Cons

Some benefits are ecosystem-specific
Generated code requires validation

Our Take

A strong alternative to Copilot, particularly for Google Cloud developers.

JetBrains AI Assistant

Best for: JetBrains IDE users

JetBrains AI Assistant integrates AI directly into environments such as IntelliJ IDEA, PyCharm and other JetBrains products.

Best Use Case

Use it when your team already works inside JetBrains IDEs and wants AI without changing its development environment.

Pros

Excellent IDE integration
Code generation
Refactoring
Code explanation
Agentic workflows

Cons

Most valuable to JetBrains users
Some features depend on AI/model availability

Our Take

A natural choice for teams already invested in JetBrains.

Tabnine

Best for: Enterprise AI coding and privacy

Tabnine focuses heavily on enterprise AI coding and deployment flexibility.

Best Use Case

Use Tabnine when privacy, security and control over AI deployment are major requirements.

Pros

Enterprise focus
Privacy-oriented
Code completion
Multiple model options
Customization

Cons

Less agent-centric than some newer tools
Advanced enterprise workflows can require additional configuration

Our Take

A particularly interesting option for companies that don't want their AI coding workflow to depend entirely on public cloud services.

Sourcery

Best for: AI-powered refactoring

Sourcery focuses on improving existing code rather than simply generating new code.

Best Use Case

Use Sourcery when your code already works but you want to make it cleaner, simpler and easier to maintain.

Pros

Refactoring suggestions
Code-quality improvements
Easy developer workflow
Particularly useful for Python

Cons

More specialized
Not a complete AI coding environment

Our Take

A good specialist tool for developers who care about cleaner code rather than simply more code.

SonarQube

Best for: Code quality and static analysis

SonarQube is different from LLM-based coding tools. It provides deterministic analysis for bugs, vulnerabilities and code-quality issues.

Best Use Case

Use SonarQube as a quality gate before code reaches production.

Pros

Mature static analysis
Security checks
Quality gates
CI/CD integration
Broad language support

Cons

Not an AI coding assistant
Findings need developer triage
Doesn't replace human review

Our Take

Even with powerful AI coding agents, deterministic code analysis remains important.

DeepSource

Best for: Automated bug detection and code quality

DeepSource provides automated analysis for bugs, security problems and code-quality issues.

Best Use Case

Use DeepSource to automatically identify quality problems during development and CI/CD.

Pros

Automated analysis
CI/CD integration
Bug detection
Code-quality monitoring

Cons

Doesn't replace an AI coding assistant
Findings still require human judgment

Our Take

Best viewed as part of your automated quality layer.

Diffblue Cover

Best for: AI-generated Java unit tests

Diffblue Cover specializes in automatically generating unit tests for Java applications.

Best Use Case

Use Diffblue when you have a large Java codebase with insufficient unit-test coverage.

Pros

Automated Java test generation
Useful for legacy applications
Test coverage improvement
Highly specialized

Cons

Java-focused
Generated tests require validation
Narrower use case

Our Take

A specialized tool can sometimes outperform a general-purpose AI because it is designed around one specific problem.

Snyk

Best for: AI-assisted developer security

Snyk focuses on identifying security vulnerabilities across application dependencies, code and infrastructure.

Best Use Case

Use Snyk to scan AI-generated and human-written code for security vulnerabilities before deployment.

Pros

Security-focused
Dependency scanning
Code security
CI/CD integration
Developer-focused workflow

Cons

Not a general-purpose coding assistant
Security findings require triage

Our Take

As AI makes it easier to generate code quickly, automated security scanning becomes even more important.

Devin

Best for: Autonomous software engineering

Devin is designed to operate more like an AI software engineer than a traditional coding assistant.

Best Use Case

Use Devin when you have well-defined engineering tasks that can be delegated to an autonomous agent.

Pros

Autonomous task execution
Coding
Debugging
Testing
Multi-step development

Cons

Requires supervision
Complex tasks can require significant resources
Human review remains necessary

Our Take

Devin represents where AI coding is heading: from AI that helps developers write code to AI that performs entire engineering tasks.

Which AI Tool Should Developers Choose?

There isn't one winner because these tools solve different problems.

For everyday coding

GitHub Copilot

For AI-first development

Cursor

For complex debugging

Claude Code

For autonomous engineering

OpenAI Codex / Devin

For rapid prototyping

Replit

For AI code review

CodeRabbit

For code review + testing

Qodo

For large-codebase review

Greptile

For code quality

SonarQube

For security

Snyk

For Java testing

Diffblue Cover

For developer documentation and visual communication

Clipy

Final Thoughts

The future of AI-assisted software development isn't about finding one AI tool that does everything.

Instead, developers are increasingly building workflows where different tools handle different stages:

AI coding agent
↓
Automated testing
↓
AI code review
↓
Security scanning
↓
Human review
↓
Visual documentation / proof

Tools such as Cursor, Claude Code, Codex and Devin are making software development increasingly agentic. CodeRabbit, Qodo and Greptile are addressing the growing need to review AI-generated code. SonarQube and Snyk provide additional quality and security controls.

And Clipy addresses a different but increasingly important problem: communicating what happened inside the software.

That's particularly valuable as AI agents become more autonomous. Developers won't only need AI that can write code. They'll need tools that can help humans and AI agents understand, verify and communicate the work being done.

That's what makes the modern AI developer toolkit more than just a collection of coding assistants.

AI Agents Can Now Record Their Own Development Workflows

Sam — Thu, 06 Aug 2026 09:31:26 +0000

AI coding assistants like Claude Code, Cursor, and Codex have fundamentally changed how developers build software. They're great at generating code, fixing bugs, and accelerating development—but one problem still exists:

How do you document everything the AI did?

Most teams still rely on screenshots, meeting notes, or manual documentation after completing a task. Traditional screen recorders capture the session, but they don't preserve the context behind AI-assisted development.

As AI becomes part of our daily workflow, documenting how code was created is becoming just as important as the final implementation.

Why Traditional Screen Recorders Fall Short

A development session isn't just about what's visible on the screen.

Developers constantly:

Write prompts for AI assistants
Review generated code
Run terminal commands
Test applications
Debug issues
Iterate on solutions

A video recording alone doesn't make those workflows easy to review or reuse.

That's why many engineering teams are looking for tools that combine recordings with structured documentation.

The Rise of AI Agent-Native Recording

Instead of simply recording your screen, the next generation of developer tools focuses on capturing the entire AI-assisted workflow.

Imagine finishing a coding session and instantly having:

🎥 A shareable recording
📝 Structured Markdown documentation
🤝 Better collaboration with teammates
🔍 Searchable engineering knowledge

This approach makes debugging, onboarding, code reviews, and knowledge sharing much more efficient.

Why This Matters

As AI coding assistants become more autonomous, developers need better ways to preserve context rather than just outcomes.

Good documentation isn't just for humans anymore—it's also becoming valuable for AI-assisted workflows, future maintenance, and engineering collaboration.

I recently explored this idea in more detail and explained how AI agent-native recording can improve modern software development.

👉 Read the complete article here:
https://clipy.online/blogs/ai-agent-records-its-own-screen/

I'm curious to hear what other developers think.

Do you believe AI coding tools should automatically document development workflows, or is traditional screen recording still enough?

Stop Trusting AI Bug Fixes Blindly: Here's How to Verify They Actually Work

Sam — Wed, 05 Aug 2026 11:25:45 +0000

AI coding assistants like Claude Code, Cursor, GitHub Copilot, and Codex have dramatically reduced the time it takes to fix bugs. In many cases, they can identify the root cause, generate a patch, and even write tests within minutes.

But there is one question every developer should ask before merging AI-generated code:

Did the AI actually fix the problem—or did it just produce a plausible-looking patch?

Why "Looks Fixed" Isn't Good Enough

AI models are excellent at generating code that appears correct. However, software bugs often depend on context that isn't visible in a prompt:

User interactions
UI state
Browser or operating system behavior
Timing issues
Multi-step workflows
Visual regressions

Without this context, an AI agent can easily solve the wrong problem.

What an AI Needs to Verify a Fix

Instead of relying only on stack traces or screenshots, give your AI enough evidence to reproduce the issue.

A high-quality bug report should include:

Clear reproduction steps
Screen recording of the failure
Expected vs. actual behavior
Console or runtime errors (when applicable)
Relevant screenshots
Application state
Timestamps or key moments during the failure

The richer the context, the more likely the AI is to produce the correct fix. Recent research also suggests that well-localized, structured bug reports improve repair success for AI agents.

Verify the Fix Instead of Assuming It Works

Once the AI generates a patch:

Run the same reproduction steps.
Compare the new behavior with the original issue.
Check for UI regressions.
Test edge cases.
Confirm that the original bug no longer occurs.

A successful build does not necessarily mean the bug has been resolved.

Visual Proof Makes Reviews Faster

Many bugs are easier to validate visually than through logs alone.

A short recording showing:

the original issue,
the applied fix,
and the expected behavior,

gives reviewers confidence that the patch solves the right problem.

This is especially useful when collaborating asynchronously or reviewing AI-generated pull requests.

A Better Workflow

A practical AI debugging workflow looks like this:

Record the bug.
Capture the important user interactions.
Let the AI analyze the reproduction.
Generate a proposed fix.
Replay the same scenario.
Verify the result before merging.

This reduces guesswork and creates an auditable trail showing why the change was made.

Final Thoughts

AI coding agents are becoming an essential part of modern software development, but they still depend on the quality of the context we provide.

Instead of asking an AI to "fix the bug," give it enough information to understand the bug, reproduce it, and verify the outcome. The combination of structured bug reports, reproducible evidence, and human verification leads to more reliable fixes and fewer regressions.

Why Every Developer Should Record Bugs Instead of Taking Screenshots

Sam — Thu, 30 Jul 2026 07:15:31 +0000

Every developer has experienced it.

A bug gets reported with a screenshot and a short message:

"The page crashes here."

That's all.

No steps to reproduce. No browser version. No console logs. No indication of what happened before the issue appeared.

The developer spends the next 30 minutes asking follow-up questions instead of fixing the bug.

Now imagine receiving a 45-second screen recording instead.

You see every click, every form input, every browser interaction, the exact error message, and even the browser console. You understand the issue before writing a single line of code.

This simple change can dramatically improve debugging, collaboration, and productivity.

Why Screenshots Aren't Enough

Screenshots capture a single moment.

Software bugs are usually caused by a sequence of actions.

A screenshot cannot show:

Which button was clicked first
Mouse movements
Keyboard inputs
Loading behavior
Browser navigation
Animation issues
Timing problems
Console errors
Network requests
Audio issues

Developers are forced to guess what happened.

That guesswork costs time.

Every Missing Detail Creates Another Conversation

A typical bug report often looks like this.

QA: Login page is broken.

Developer:Which browser?

QA: Chrome.

Developer: Which version?

QA: Latest.

Developer: What were you doing before this happened?

QA: I clicked Login.

Developer: What happened after clicking?

QA: Nothing.

Developer: Can you reproduce it?

QA: Let me check...

This cycle continues for several messages.

Instead of fixing bugs, everyone spends time explaining them.

Screen Recordings Tell the Complete Story

A short recording provides everything developers need.

Within one minute, the viewer can understand:

How the issue started
Exact reproduction steps
Browser interactions
Mouse clicks
Scrolling behavior
Form inputs
Error messages
Console output
Unexpected UI behavior
Timing issues

There is no guessing.

Only context.

Why Visual Context Matters

Modern applications are interactive.

Many issues cannot be explained using static images.

Examples include:

Dropdown menus disappearing
Infinite loading
Broken animations
Incorrect redirects
Responsive layout issues
Drag-and-drop failures
Video playback bugs
Payment flow interruptions

These problems only become obvious when someone watches the workflow.

Visual context allows developers to reproduce issues much faster.

AI Coding Assistants Still Need Better Context

Many teams now use AI coding assistants such as Claude Code, Cursor, GitHub Copilot, and ChatGPT.

These tools can generate code quickly.

But AI still depends on the information you provide.

A screenshot usually lacks enough context for useful troubleshooting.

A screen recording, on the other hand, shows the entire workflow—from user actions to browser behavior and error messages—making it easier for developers to understand the issue and use AI tools more effectively during debugging.

Better input leads to better suggestions.

A Better Bug Reporting Workflow

Instead of sending multiple screenshots:

Reproduce the issue.
Record the screen while explaining the problem.
Show the browser console if relevant.
Include the Network tab for API failures.
Narrate the expected behavior.
Share the recording with the team.

That's it.

One recording often replaces dozens of chat messages.

Benefits for the Entire Team
Developers
Faster debugging
Fewer clarification requests
Easier issue reproduction
Better understanding of complex bugs
QA Engineers
More accurate bug reports
Less repetitive documentation
Faster validation
Product Managers
Better visibility into customer issues
Easier prioritization
Improved communication with engineering
Customer Support

Instead of collecting screenshots from users, support teams can request a short recording, making it much easier to understand and resolve reported problems.

Choosing the Right Recording Tool

The best bug-reporting tools should make recording and sharing effortless. Look for features such as:

Browser-based recording
No complicated setup
Quick shareable links
Audio narration
Browser tab or full-screen capture
High-quality video
Fast uploads

For teams that want a lightweight, browser-first workflow, Clipy provides a simple way to capture and share bug reports without installing heavy desktop software.

Small Change, Big Impact

Switching from screenshots to screen recordings doesn't require changing your development process.

It only changes how issues are communicated.

Teams that adopt visual bug reporting often notice:

Faster issue resolution
Better collaboration
Less back-and-forth communication
Clearer documentation
Improved onboarding for new developers
More efficient remote teamwork

Even a 30-second recording can save far more time than a long thread of messages.

Final Thoughts

Software development is becoming increasingly collaborative, distributed, and AI-assisted. As applications grow more interactive, screenshots alone often fail to capture the information developers need.

Recording bugs instead of taking screenshots gives your team richer context, reduces misunderstandings, and helps everyone—from QA to engineering—work more efficiently.

The next time you find a bug, skip the screenshot.

Record it instead.

Two ways to install Jenkins

Sam — Wed, 05 Jul 2023 08:13:06 +0000

Step to install jenkins on AWS
How can you easly setup jenkins on AWS ec2 instance(Ubuntu)

Step 1
Install java

Update your system
sudo apt update

Install java
sudo apt install openjdk-11-jre

Validate Installation
java –version

It should look something like this
openjdk 11.0.17 2022-10-18
OpenJDK Runtime Environment (build 11.0.17+8-post-Ubuntu-1ubuntu222.04)
OpenJDK 64-Bit Server VM (build 11.0.17+8-post-Ubuntu-1ubuntu222.04, mixed mode, sharing)

Step 2
Install Jenkins
Just copy these commands and paste them onto your terminal.
curl -fsSL https://pkg.jenkins.io/debian/jenkins.io.key | sudo tee \ /usr/share/keyrings/jenkins-keyring.asc > /dev/null echo deb [signed-by=/usr/share/keyrings/jenkins-keyring.asc] \ https://pkg.jenkins.io/debian binary/ | sudo tee \ /etc/apt/sources.list.d/jenkins.list > /dev/null

Add Repository Key
You have to add Jenkins repository into Ubuntu server, use below command for that.
wget -q -O - https://pkg.jenkins.io/debian-stable/jenkins.io.key | sudo apt-key add -Add Package Repository
Use below command to add package repository
sudo sh -c 'echo deb http://pkg.jenkins.io/debian-stable binary/ > /etc/apt/sources.list.d/jenkins.list'
Now, to update the server you have to use below command;
sudo apt update

Now, you can use below command to install Jenkins
sudo apt install jenkins -y
Once jenkins installation completed you have to start Jenkins service for that use below command
sudo systemctl start jenkins sudo systemctl status jenkins

Unlock jenkins
sudo cat /var/lib/jenkins/secrets/initialAdminPassword
And get the password.

Install jenkins using Docker
First of all stop all the running jenkins

Check the status of running jenkins

sudo systemctl status jenkins

Install docker first.

Command to install docker

sudo apt install docker.io Y

Command to install jenkins using docker

sudo docker pull jenkins/jenkins

sudo docker run -d -p 8080:8080 docker.io/jenkins/jenkins:latest

Now you can access jenkins with port 8080
Ip address of your instance:8080(http://54.178.81.5:8080/)

Now we have to unlock jenkins first

How to get the password

sudo docker exec -it <container_id> bash //come inside the container

cd /var/jenkins_home/secrets
cat initialAdminPassword

And get the password and copy paste into jenkins and install all the suggested password.