DEV Community: Matt Tanner

What is Code Refactoring? Tools, Tips, and Best Practices

Matt Tanner — Tue, 04 Nov 2025 17:56:45 +0000

I love a good refactor. Who doesn't? Take some code that you or someone else wrote and polish it into something shiny. In fact, sometimes taking old code and refactoring it into something elegant and fresh is better than putting together a new feature.

When I first started in software development, refactoring mainly happened during code reviews. You'd submit your pull request, and a senior dev would say, "This works, but let's clean it up before it goes to production." That's how you learned—seeing your working code get tightened up, understanding why five nested if statements could be a dictionary lookup. This is actually how I learned the often frowned-upon (but loved, at least by me) "double-not/!!" operator in JavaScript to turn a value into a hacky boolean. As a junior and intermediate developer, that feedback loop made you better.

Refactoring is one of those things every developer knows they should do more often, but it's also the first thing that gets pushed to "next sprint" when deadlines loom. Unfortunately, though, bad code doesn't just sit there; it compounds. What starts as a shortcut becomes technical debt.

Luckily, since I started writing code almost 15 years ago, the tooling has evolved dramatically. In the last 2 years, this whole subject seems to have flipped upside-down. IDEs got smarter with built-in refactoring. Linters caught code smells. Now, AI-powered tools can handle complex, multi-file refactoring that used to take days in a few minutes. There's quite a bit to cover on this subject, so let me walk you through what refactoring actually means, why it matters, and which tools handle different scenarios best.

Introduction to Code Refactoring

I likely view "refactoring" through a broader lens than some folks do. So that we are all on the same page, let's get the definition out of the way: refactoring is restructuring existing code without changing its external behavior. You're making the code cleaner, more maintainable, or more efficient—but from the outside, it does exactly the same thing it did before. This is the textbook definition, but there is a bit more to it.

In practice, refactoring means two different things depending on where you are in your career and what you're working on.

The first type is incremental refactoring—you write code that works, ship it, and then come back to make it more elegant and resilient. Maybe you extract a function, consolidate some duplication, or improve error handling. This is the refactoring that happens in code reviews, where someone says, "This works, but here's how we can make it better." It's usually contained to a module or a few related files.

The second type is modernization refactoring—rewriting legacy code to fit a new paradigm, language, or technology. This is what happens when you're migrating mainframe code exposed via SOAP to RESTful APIs (been there, done that too many times), converting a monolith to microservices, or updating a codebase from Python 2 to Python 3. This is system-wide work that touches dozens or hundreds of files, and it's where things get risky.

In both cases, the true goal is not adding features. You're not fixing bugs (well, sometimes you find them, but that's not the goal). You're improving the internal structure so the code is easier to work with tomorrow, next month, or when someone else inherits your codebase.

Martin Fowler wrote the book on this—literally, it's called "Refactoring"—and his definition still holds up: "a disciplined technique for restructuring an existing body of code, altering its internal structure without changing its external behavior." That discipline part matters because cowboy refactoring without tests is how you ship broken code while feeling productive.

Benefits of Code Refactoring

If you've ever been knee-deep in a project that works but is an absolute disaster in terms of code quality, then you know: Code that works isn't the same as good code. When you refactor to improve code, you get quite a few benefits, which include:

Maintainability: When you need to add a feature six months from now, you want organized code with clear responsibilities. Refactored code means less time figuring out what's happening and more time making changes.

Fewer bugs: Duplicate code means duplicate bugs. Consolidate logic; you have fewer places for things to go wrong.

Better performance: When you restructure, you spot inefficiencies—unnecessary loops, redundant API calls, poor data structures.

Easier onboarding: New developers can read refactored code and understand it faster.

Compounds over time: Good code stays good longer. Bad code gets worse faster. Small, consistent costs beat massive ones when everything breaks.

So, why isn't everyone refactoring all of the time?! The catch: Refactoring takes time upfront. That's why teams skip it. The actual business value is a little harder to convey compared to building new features. But developers and leaders in tech know that bad code quickly halts velocity. The good news is that we live in a time where AI tools can compress the timelines for these tasks significantly, making refactoring realistic even when you're under pressure and time constraints. All you need is the right tool, or even multiple tools.

Best Code Refactoring Tools

The ecosystem of refactoring tools has evolved dramatically in the past few years, especially with AI entering the picture. Let's break down what's out there.

Traditional IDE refactoring tools have been around forever—IntelliJ, VS Code, Eclipse—they all have built-in support for basic refactoring operations. Rename variable, extract method, move class. These work great for local, well-defined changes. The limitation? They're mechanical. They can't reason about your codebase as a system. You need to do everything still by hand, but with a little less effort than doing it completely manually.

Linting and static analysis tools like ESLint, Pylint, or SonarQube detect code smells (I hate this term, but for some reason the industry still uses it) and suggest fixes. They'll yell at you for complexity, duplication, or style violations. Useful for enforcing standards, but they won't actually do the refactoring for you—they just tell you what's wrong.

AI coding assistants like GitHub Copilot and Cursor can help with code suggestions and explanations. They're good at generating code and answering questions, but they're more useful as assistants, not agents, when it comes to large codebases and complex tasks. For the most part, you're still driving; they're just helping. Similar to Tesla's FSD tech, pretty good most of the time, but you still want your hands near the wheel.

Then there's Sourcegraph Amp, which sits in a different category than the others. It's not an assistant—it's an agentic coding tool. The difference? You can tell Amp, "Refactor the authentication error handling across all our API routes," and it will create a plan, make the changes, and run tests. Other platforms like Cursor and Windsurf can do this as well, but Amp is truly enterprise-ready and built to handle complex, multi-file refactoring tasks autonomously.

Amp runs on Claude Sonnet 4.5 as its main model, with up to 1 million tokens of context. That means it can see and reason about your entire codebase, not just the file you have open. And when it needs deeper analysis—like figuring out system-wide implications of a refactoring—it can consult "the Oracle" (currently GPT-5), which excels at complex reasoning tasks.

What makes Amp particularly effective for refactoring is its subagent system. When you have a large refactoring task, Amp can spawn multiple subagents to work in parallel—one handling API routes, another updating tests, another checking documentation. Each subagent has its own context window and reports back when done, keeping your main thread clean.

From the command line, refactoring with Amp looks like this:

amp -x "Analyze these files for code duplication, suggest refactoring opportunities, and estimate the impact of proposed changes"

Or for something more complex:

amp -x "Use Amp Oracle to create a comprehensive refactoring plan for this codebase, including architectural debt assessment, effort estimation, and risk analysis"

That's the kind of work that used to take hours of manual analysis. Now it happens in minutes, and you get a detailed plan before any code changes. Then you can let Amp spin off a bunch of sub-agents to implement everything.

Each of these tools has a place.

Code Smells and Legacy Code

As I mentioned before, I really can't stand this term, but I'm going to use it here anyway! Code smells are warning signs that your code needs refactoring. Not bugs—your code runs—but indicators that something's off (AKA something is stanky). The types of things you want to pick up and most tools focus on here are:

Duplicate code: Same logic in multiple places. Change one, forget another, introduce a bug.

Long methods: Functions doing too many things. Hard to test, understand, and modify.

Large classes: The "God object" that knows everything and does everything.

Long parameter lists: Functions taking eight arguments are trying to be too flexible.

Divergent change: Modifying the same class for unrelated reasons.

Shotgun surgery: Single change requires updates everywhere.

How different tools handle code smells

For simple, localized smells, IDE refactoring tools excel. IntelliJ will spot a long method and let you extract it in seconds—select the code, hit the refactoring shortcut, name the new method. Done. VS Code with the right extensions does similar work for JavaScript/TypeScript. These are perfect for the code review refactoring we talked about earlier.

SonarQube takes a different approach—it identifies the smells across your entire codebase and gives you metrics. Here's your complexity score, here's your duplication percentage, and here are the files that need attention. It won't fix anything, but it tells you where to look.

Legacy code—code without tests, code written by someone who left five years ago—is where these smells concentrate. At the senior level, legacy refactoring often becomes part of modernization efforts. You're migrating mainframe systems to REST APIs, converting procedural code to object-oriented patterns, and updating frameworks untouched for a decade. This is where the stakes get higher and the scope gets wider.

This is where AI agents like Amp change the equation. Before touching anything, you can get a comprehensive analysis:

Ask the Oracle to analyze this module and identify code smells, duplicate patterns, and suggest a refactoring approach that minimizes risk.

The Oracle provides a detailed analysis of dependencies, potential breaking points, and a phased approach. For duplicate notification logic spread across channels (email, SMS, push):

Analyze how sendEmailNotification and sendSMSNotification are used throughout the codebase. Then work with the oracle to figure out how we can refactor the duplication between them while keeping changes backwards compatible.

Amp searches your code, understands usage patterns, consults the Oracle for strategic planning, and then proposes a refactoring plan that extracts common logic without breaking functionality.

Automate Code Refactoring with AI Tools

Refactoring used to be entirely manual. You'd open a file, read the code, make changes, update tests, and hope you didn't break something three layers deep. For incremental refactoring, this was manageable. For system-wide refactoring—migrating patterns across dozens of files—it was brutal.

The shift to AI-assisted refactoring has changed the game in the past couple of years. Early attempts were messy—AI would suggest changes that looked right but broke subtle dependencies. Modern agents like Amp have gotten significantly better, with high success rates on complex refactoring tasks.

The key difference: AI agents can understand existing functionality across your entire codebase, encapsulate that understanding (either in context or through generated tests), then refactor en masse while maintaining behavioral consistency. You're no longer limited to one module at a time.

Where different tools excel

GitHub Copilot and Cursor are great for quick, interactive refactoring. You highlight a 50-line function, ask "extract the validation logic," and it suggests the extraction immediately. Fast feedback loop, perfect when you're already in the flow. But they work at the file level—trying to coordinate changes across 20 files means 20 separate interactions.

Traditional IDE refactoring (IntelliJ, PyCharm) handles mechanical transformations reliably. Rename a variable across your entire project? It'll find every reference, including string literals if you want. Move a class? It updates all imports. These are safe operations because the IDE understands the language's syntax tree.

AI agents like Amp handle complex, multi-file refactoring where you need reasoning about system behavior. Say you're updating API error handling across 20 route handlers. Manually, that's hours of reading context, making consistent changes, and updating tests. With Amp:

Use subagents to refactor our API error handling. Assign different route groups to different subagents so they can work in parallel.

Each subagent has its own context window and works independently, preventing the context contamination that happens when you're juggling multiple complex changes.

For pattern recognition, AI spots repeated patterns you might miss—logic implemented slightly differently across files:

amp -x "Analyze all .js and .ts files in the current directory for code duplication and suggest refactoring opportunities"

Dependency analysis before making changes prevents "change one thing, break five others":

I need to refactor our database query layer to use a connection pool. Use the oracle to analyze the current implementation and identify all the places that will need updates, considering both direct dependencies and indirect effects.

What AI still can't do well

Understanding the business logic context, not in the code. If there's a hack because of a client requirement from three years ago, AI won't know unless it's documented.

Making judgment calls about what "better" means for your situation. Is readability more important than performance? Depends on your team.

Knowing when not to refactor. Sometimes legacy code is scary but stable, and touching it is riskier than leaving it alone.

Code Optimization and Code Duplication

Code duplication is the most common refactoring target. But not all duplication is bad, and not all optimization is worth it.

When to refactor duplication

If you're changing the same logic in multiple places when requirements change, eliminate the duplication. DRY (Don't Repeat Yourself) matters when repeated code represents the same business rule.

When duplication is fine

Sometimes copied code serves different purposes that look similar now but will diverge later. Sometimes, abstraction overhead isn't worth it for three lines of code.

Different approaches to handling duplication

ESLint or Pylint will flag duplicate code blocks and yell at you. SonarQube gives you a duplication percentage and tells you which files are the worst offenders. Useful for knowing where to focus, but you still do the work manually.

With AI agents like Amp, you can identify and plan duplication removal strategically:

Analyze the duplication:

Find all instances where we're manually validating user input across our API endpoints. I want to understand the patterns before consolidating.

Consult the Oracle for a refactoring plan:

Based on the validation patterns you found, ask the oracle to design a refactoring approach that consolidates this logic while maintaining backwards compatibility.

Implement the plan:

Please implement the refactoring plan that the Oracle outlined. Proceed with each phase, and make sure the implementation at each phase works as expected.

Code optimization through refactoring

Refactoring often reveals performance issues. When you consolidate logic, you spot inefficient patterns—N+1 queries, unnecessary loops, data fetched but never used.

IntelliJ will warn you about unused variables and inefficient patterns. Static analysis tools catch obvious performance issues. But understanding system-wide performance implications requires deeper analysis, which can be done with Amp like this:

Analyze our database query patterns in the user service. Use the oracle to identify performance bottlenecks and suggest refactoring approaches that would improve query efficiency.

The key: optimization should be measured. Refactor for structure first, optimize when you have data showing it's necessary.

Code Readability and Best Practices

Readable code is code you can understand six months from now. Refactoring for readability is often more valuable than refactoring for performance.

What makes code readable

Clear naming. Functions and variables describe what they do, not how. getUserEmailAddress() beats doTheThing().

Appropriate abstraction levels. Functions do one thing at one level. Don't mix high-level business logic with low-level data manipulation.

Consistent patterns. Handle errors the same way across modules. Consistency reduces cognitive load.

Documentation where it matters. Good code is self-documenting most of the time, but complex decisions and edge cases need comments.

How different tools improve readability

Prettier and ESLint enforce consistent formatting automatically. Same indentation, same quote style, same spacing. Removes bikeshedding from code reviews.

IDEs like PyCharm suggest better variable names based on type and usage. They'll also warn when functions get too complex—if your cyclomatic complexity hits 15, you'll see a warning.

For larger readability refactoring, you can use Amp's AI agents to analyze and suggest structural improvements:

Review this module for readability issues. Suggest better function names, identify overly complex logic, and recommend how to break down large functions.

For controllers that have grown too large, a prompt may look like this:

This controller has grown too large and handles too many responsibilities. Use the oracle to propose a refactoring that separates concerns while maintaining the current API contract.

Best practices when refactoring:

Work in a branch. Always. Even with AI doing the work.

Start with tests. If you don't have tests, write them first. You can get Amp to do this with a prompt like this:

Generate comprehensive unit tests for this module before we refactor it. I want to ensure we don't break existing behavior.

Review AI-generated changes carefully. Treat it like a code review—check logic, verify tests pass, ensure changes align with what you intended.

Use analysis tools for planning, not just implementation. This is where Amp's Oracle can come in to help:

Before we make any changes, use the oracle to analyze the impact of refactoring this authentication layer. What are the risks? What dependencies exist? What should we test?

Conclusion

Refactoring isn't optional—it's maintenance. You can defer it, but technical debt compounds. The difference now is that tools have changed the economics of refactoring. What used to take days can happen in hours.

The right tool depends on the job:

Quick fixes while coding? Use your IDE or Copilot.
Identifying what needs refactoring? Use traditional tools like SonarQube, ESLint, or newer tools like Amp's Oracle.
Complex, multi-file refactoring? AI agents like Amp.
Mechanical transformations? Traditional IDE refactoring is still the safest bet.

But tools don't fix bad processes. If your team doesn't value refactoring, if you don't have tests, if you ship features without considering maintainability, better tools won't save you. They'll just help you create technical debt faster. Set aside time regularly to clean up code. Write tests. Review changes carefully—whether you wrote them or AI did. The goal isn't perfect code, it's code you can maintain and extend without pain.

Looking Beyond Cline? Here's The 7 Best Cline Alternatives to Check Out!

Matt Tanner — Tue, 28 Oct 2025 19:52:04 +0000

Cline has carved out a unique position in the AI coding space. It's open source, runs natively in Visual Studio Code, and gives developers complete control over their model choices and workflows. For teams that value transparency, flexibility, and the ability to bring their own API keys, Cline delivers exactly what it promises — agentic coding without vendor lock-in or proprietary constraints.

But that same flexibility comes with trade-offs. You need to manage your own model access, configure your environment, and handle token costs directly. For some developers, that's a feature. For others, it's friction they'd rather avoid. This post explores seven Cline alternatives that include some of the best AI coding assistants out there. First, let's look at what Cline does well, since there are plenty of use cases where it is still a great platform to use.

What Cline Does Well

Before looking at alternatives, it's worth recognizing where Cline actually performs really strongly. These reasons include:

True transparency — Open source architecture means you can see exactly how it works and modify it if needed.
Model flexibility — Connect OpenAI, Anthropic, or any compatible API without being locked to one provider.
Native VS Code integration — Works inside the editor you already use, preserving your extensions and workflows.
Agentic execution — Plans, executes, and validates multi-file changes with full visibility into each step.
Cost control — Pay only for the tokens you use through your own API keys.

For developers who want maximum control and are comfortable managing infrastructure, Cline is hard to beat. But if you need something that works out of the box, scales across teams, or handles model routing automatically, there are compelling alternatives worth considering.

1. Amp by Sourcegraph

Amp reimagines what AI coding assistance should look like when built for teams instead of individuals. Where Cline requires you to manage your own models and configurations, Amp handles that complexity automatically — routing tasks to the best available model and scaling compute resources as needed.

Best when: you need collaborative AI injected into a modern development workflow. Amp works across your entire team in ways that other platforms just can't compete with.

Key Features

Multi-agent orchestration that divides complex tasks across specialized AI agents
Shared chat threads that build institutional knowledge, your team can search and reference
Automatic model selection that picks the right LLM for each context without manual configuration
Universal integration across VS Code, Cursor, Windsurf, and CLI environments
Ad-supported free tier that makes powerful AI accessible without immediate subscription costs

Amp eliminates the setup burden Cline requires while adding collaboration features that isolated AI assistants can't provide. Built by the team behind Sourcegraph's Code Search and Cody (which was deprecated back in July), it's designed for engineering teams that need shared context and collective learning, not just individual productivity.

2. Cursor

If Cline brings agentic AI into VS Code, Cursor builds an entire IDE around it. Cursor started as a VS Code fork but evolved into something fundamentally different — an editor where AI isn't a plugin but the core architecture.

Best when: you want deep project understanding without managing configurations.

Key Features

Cross-file reasoning that maintains awareness of your entire codebase structure
Multi-model support where you can still bring your own API keys for cost control
Inline refactoring that handles complex changes across dependencies and imports
Agent mode that executes multi-step tasks with terminal integration
Built-in context management without manual indexing or setup

Cursor and Cline share similar goals but different philosophies. Cline preserves VS Code; Cursor reinvents it. If you're willing to switch editors, Cursor offers more sophisticated reasoning. If you want to stay in standard VS Code, Cline remains the better choice.

3. GitHub Copilot

GitHub Copilot represents the opposite approach from Cline. Where Cline emphasizes control and transparency, Copilot prioritizes simplicity and integration. There's no API key management, no model selection, no configuration — just an assistant that understands your repository and works across every major IDE.

Best when: you want AI that fits seamlessly into existing workflows.

Key Features

Deep GitHub integration with repo-aware context and pull request understanding
Multi-IDE support including VS Code, JetBrains, and Visual Studio
Copilot Workspace for structured task planning and execution
Enterprise controls for usage analytics, compliance, and team management
Straightforward pricing at $10/month for individuals or $39/month for enhanced capabilities

Copilot trades Cline's flexibility for convenience. You won't choose your models or optimize token costs, but you also won't spend time on setup or infrastructure management. For developers embedded in the GitHub ecosystem, it's the path of least resistance.

4. Windsurf

Windsurf shares Cline's agentic approach but packages it differently. Originally developed under the Codeium brand and later acquired by Cognition Labs, Windsurf is a standalone IDE built around autonomous agents rather than a VS Code extension.

Best when: you want aggressive automation without configuration overhead.

Key Features

Cascade agent system that coordinates multi-step workflows automatically
Zero-setup experience — no API keys or manual configuration required
Cross-file awareness similar to Cursor's project understanding
Works as a standalone editor or VS Code-compatible extension
Free for individual developers with enterprise features for teams

Windsurf and Cline both emphasize autonomy, but Windsurf removes the infrastructure burden. If you value Cline's agentic capabilities but don't want to manage models yourself, Windsurf delivers similar power with less friction.

5. Tabnine

Where Cline gives you control through model choice, Tabnine offers control through deployment. It's built for organizations that can't send code to external APIs — running entirely on-premises or in air-gapped environments while still delivering intelligent code assistance.

Best when: security and compliance outweigh flexibility.

Key Features

Local or on-premises deployment with zero external data transmission
Custom model training on private repositories without cloud dependencies
Enterprise-grade compliance, including SOC 2 and GDPR certification
Multi-IDE support across VS Code, JetBrains, and Visual Studio
Admin-level governance and policy enforcement

Tabnine doesn't offer the same agentic depth as Cline, but for regulated industries or security-conscious teams, it's the only option that keeps code completely within your environment. You sacrifice some intelligence for total data control.

6. Replit

Replit takes a completely different approach. Where Cline runs locally in VS Code, Replit operates entirely in the browser. There's no installation, no environment setup, and no local dependencies.

Best when: you need instant collaboration and zero-friction prototyping.

Key Features

Browser-based IDE requiring no local setup or configuration
Real-time multiplayer coding for instant collaboration
Built-in AI assistance for code generation and debugging
Instant deployment pipelines that take projects from idea to live in minutes
Language-agnostic runtime support across frameworks

Replit isn't built for large enterprise codebases, but for teaching, pair programming, or rapid prototyping, it removes every barrier Cline introduces. You trade local control for immediate accessibility.

7. Claude Code

Claude Code strips AI coding down to its essence — a command-line tool powered by Anthropic's Claude models. Where Cline works inside VS Code, Claude Code works inside your terminal, reading files, executing commands, and reasoning through problems without any GUI.

Best when: you live in the terminal and want minimal overhead.

Key Features

Direct command-line integration without IDE dependencies
File and shell command execution for autonomous problem-solving
Powered by Claude Sonnet and Opus for deep reasoning and context understanding
Git integration for reviewing AI-generated changes before committing
Perfect for SSH workflows, Vim, Emacs, or headless environments

Claude Code appeals to the same developers who appreciate Cline's transparency, but it goes further — removing even the IDE layer. If you work primarily in the terminal, it's the most direct path to AI-assisted development.

Choosing Your Path Forward

Cline remains one of the best options for developers who want open-source transparency and full control over their AI stack. But different workflows demand different tools:

Choose Amp for a free-tier that goes far, team collaboration, and high-caliber agentic coding flows.
Choose Cursor for deeper IDE-native AI integration with project-wide reasoning.
Choose Copilot for seamless GitHub integration without configuration overhead.
Choose Windsurf for autonomous agents with zero-setup requirements.
Choose Tabnine for on-premises deployment and strict data privacy.
Choose Replit for instant browser-based collaboration and prototyping.
Choose Claude Code for terminal-native workflows without IDE dependencies.

The Bottom Line

Cline proves that open-source, transparent AI coding tools can compete with proprietary alternatives. Its model flexibility and VS Code integration make it ideal for developers who want control without constraints.

But not every team needs that level of control. Some want a solution that is cost-effective, has automatic model routing, and a strong focus on team collaboration (Amp). Others need zero-configuration autonomy (Windsurf). And some require complete data sovereignty (Tabnine).

If Cline is your primary tool, you've already made a strong choice. But if you're looking for different trade-offs — whether that's collaboration, simplicity, or privacy — these alternatives fill gaps that Cline doesn't aim to address. The goal isn't replacement; it's finding the right tool for each part of your workflow.

Best AI Coding Tools of 2025: What Tools Should You Use?

Matt Tanner — Tue, 28 Oct 2025 19:00:27 +0000

As developers, we love to do everything faster. Developer productivity has been a major industry for quite some time, but AI coding tools have somewhat rewritten the playbook. We used to be happy with stuff like simple autocomplete (IntelliSense + C# coding in Visual Studio, ahhh, the good old days!). Now, we need something that can take our thoughts and spit out massive amounts of correct and well-written code. Well, most days, I feel like we are "almost" there, until my coding agent gets stuck in a loop and spits out 20 testing-harness.MD files while it tries to backpedal on its spiraling logic. Even though these tools may be imperfect at times, they still bring significant value. Developers using AI coding tools report productivity gains of around 25–30%, with some early adopters claiming dramatic shifts toward near-full AI-assisted code generation. While not universally verified as the "perfect" tech, the trajectory is clear: in 2025, the question isn’t whether to use AI tools, but which ones truly transform your workflow versus those that simply act as smarter autocomplete.

If you've begun using these platforms, you realize that most AI coding tools promise 10× productivity while delivering marginally better suggestions. The real difference in performance, as many of us have learned, lies in context understanding, agentic capabilities, and collaborative workflows. The tools that matter don’t just complete code (that is so 2022); instead, they aim to understand your architecture, coordinate multi-file changes, and even work autonomously on complete tasks. It's actually quite wild how much 2-3 years have changed this domain.

In this breakdown, we will cover what actually works, why these tools matter, and how to choose the right one for your workflow. Let's get started!

Why AI Coding Tools Matter in 2025

Ever pair-programmed with a really talented senior developer? You know how they spot issues before you do, catch small mistakes early, and help you reason through tricky logic. That’s what the best AI coding assistants are starting to feel like — always available, never impatient, and fast enough to keep up.

The real reason developers need to care in 2025 isn’t hype; it’s more about scale. Modern codebases are huge, toolchains are fragmented, and release cycles are faster than ever. You’re expected to move quickly and maintain quality across languages, frameworks, and services. That’s nearly impossible without help.

AI tools fill that gap. They automate boilerplate, generate tests, catch regressions, and summarize unfamiliar code so you can stay focused on solving real problems. These tools are now a critical piece of a developer toolkit, almost as essential as an IDE. Although many think that they do, they don’t replace developers. However, they have changed expectations for what developers should be able to do and produce, and good AI tools make it possible to keep up with the pace of modern software development.

And with the latest wave of agentic tools, things are shifting again. Instead of just completing code, these tools can plan and execute changes across multiple files, write docs, or even open pull requests. Think less autocomplete, more reliable junior teammate who never sleeps. In some cases, these tools are producing code that rivals that of really talented senior and staff-level developers in code-level assessments.

In short, developers don’t need AI tools because it’s trendy to use them; they need them because the job has outgrown human bandwidth. The teams using them aren’t necessarily replacing engineers with these tools, but they are using them to stay competitive in a rapidly changing landscape.

How AI Coding Tools Work

At a high level, AI coding tools translate your instructions into code by using large language models trained on millions of code examples. These platforms are trained on everything from open-source projects to documentation and Q&A sites. When you type a prompt like “add logging and error handling to this function,” the model predicts what code should come next based on context and past examples in tandem with understanding how it fits into your actual code. This is natural language code generation at work.

What makes modern tools powerful is their ability to utilize context. They can keep multiple files, dependencies, and even recent conversations in memory, understanding how your project fits together. That’s how tools like Cursor or Amp can refactor modules, update imports, and write matching tests without breaking your build. And context windows are growing, which means that these tools are generally getting better at making holistic decisions for changes they are applying to projects and files.

Many tools now run agentic workflows, meaning they plan and execute several steps on their own — like editing a file, running a test, reading the output, and then fixing an error before showing you the final result. Some even connect to external systems (like your repo in GitHub or your issue tracker of choice, like Sentry) to open pull requests or document changes automatically.

In short, these tools are doing more than just following in the footsteps of simply autocompleting code. They reason about what you’re building, act across your project, and learn from the feedback you give them.

From Autocomplete to Autonomous Agents

The evolution of AI coding tools can be seen in four broad generations:

Autocomplete (Gen 1) — Next-character prediction within a line, simple real-time code suggestions.
Context-Aware Generation (Gen 2) — Function-level completion (e.g., OG versions of GitHub Copilot).
Agentic Coding (Gen 3) — Multi-step planning and coordinated changes across files.
Autonomous Agents (Gen 4) — Emerging tools that can monitor systems, identify issues, and propose or implement fixes.

Most teams today sit somewhere between Gen 2 and Gen 3 adoption. Gen 4 remains experimental — promising, but still requiring human oversight. That said, I foresee teams continuing to use a mix of Gen 2, 3, and 4 tools for the foreseeable future. Developers will work in an IDE with their favorite Gen 2/3 tools, then let Gen 4 tools handle the nuances and smaller issues that don't need a massive amount of human firepower and logic (but do drain developer time).

The Major Players: AI Coding Tools That Actually Matter

Okay, so we know what these tools are and what they can do. However, in case you haven't noticed... There are A LOT of tools hitting the market all the time. To make things easier, I've put together some of my favorites (and industry favorites) that are good places to start!

Amp by Sourcegraph

Amp delivers true agentic development that scales from simple prompts to full-scale projects. Rather than suggesting code snippets, Amp coordinates multiple AI agents to plan, edit, and execute code autonomously across your repository.

When you assign Amp a complex task — like implementing a new authentication system — it can spawn parallel sub-agents for each layer: database schema, API endpoints, frontend components, and tests. These agents communicate with each other to maintain architectural consistency and follow your project’s conventions.

As you can read in the Amp Manual, its large context window and unconstrained token usage let it reason across multiple files at once without hitting arbitrary context limits. Each coding session is organized into “threads,” which can be shared with your team — turning every AI-assisted conversation into reusable institutional knowledge that’s searchable across your organization.

Amp runs across VS Code (Visual Studio Code), Cursor, Windsurf, and the CLI. It automatically selects the best underlying model for each subtask — for example, GPT-5 for reasoning or Claude Sonnet for general coding — so you don’t have to manage API keys manually.

Even better is that you can use Amp at a fraction of the cost of other tools with their new Ad-supported Free tier. So that means you can start with the free plan, which includes usage credits, or scale to a paid plan as your team’s projects grow. You can also follow new feature releases on the Amp News page for updates on models, extensions, and workflow improvements.

GitHub Copilot

GitHub Copilot has evolved far beyond its origins as a smart autocomplete. With the introduction of Copilot Agent Mode, it now works as a true assistant capable of understanding your project, planning tasks, and editing code across multiple files. You can ask Copilot to refactor a service, write unit tests, or even debug an issue, and it will execute step by step — asking for confirmation before committing changes.

Because it’s integrated directly into the GitHub ecosystem, Copilot has a level of contextual understanding few others can match. It knows your repository structure, recent pull requests, commit history, and even comments from teammates. This allows it to generate code that’s not just syntactically correct but consistent with your project’s conventions.

Copilot works seamlessly across VS Code, JetBrains IDEs, and Visual Studio. It can open pull requests automatically, analyze diffs, and run commands within your dev environment — all under your supervision. For developers already using GitHub Actions or Issues, Copilot fits naturally into your workflow without new tools or setup overhead.

Pricing is straightforward: individuals can use Copilot Pro for about $10/month, with business and enterprise plans offering role-based access and analytics. For most developers, it’s the easiest way to bring AI into daily development without changing editors or workflows.

Maybe it's the nostalgia of being the first tool like this that I used, but I still see Copilot as a reliable workhorse (that costs me about $10 a month!). I don't always use it, but if I need something simple, I'll sometimes crack open VS Code and let the agent give it a shot. It's not flashy, but deeply integrated, fast, and consistent. It’s perfect for developers who want AI assistance that feels like a natural extension of GitHub rather than a separate system.

Cursor

Cursor reimagines the development environment itself by building AI deeply into every layer of the editor. It’s based on VS Code, so it feels familiar from day one, but everything from file search to terminal commands has been redesigned around context-aware AI.

What sets Cursor apart is its project-wide reasoning. Instead of working line-by-line, Cursor keeps an index of your entire codebase in memory, understanding the relationships between modules, components, and dependencies. You can ask it to “migrate all components to use the new API pattern” or “convert this service to async/await,” and it will plan and apply coordinated edits across multiple files. It even updates imports and related tests automatically.

Developers can bring their own API keys for OpenAI, Anthropic, or other models, giving full control over model choice, latency, and cost. For teams, Cursor supports shared context, chat threads, and model presets to ensure consistency across environments.

Cursor’s Agent mode also integrates with your terminal, meaning you can run builds or tests as part of its reasoning loop. It will read outputs, identify errors, and automatically propose or apply fixes.

Pricing starts with a generous free tier, while Pro and Business plans add unlimited context, team collaboration, and enterprise-grade privacy options.

To summarize the above, Cursor is for developers who want AI at the center of their workflow, not bolted on as a VS Code extension. If you’re willing to switch code editors, it delivers deeper reasoning and context than anything else available today.

Windsurf

Windsurf takes a different approach to AI-assisted coding: it’s designed around autonomous agents that can plan and execute multi-step tasks. The heart of Windsurf is its Cascade agent — an AI system that doesn’t just respond to prompts but actively manages workflows.

You might ask Windsurf to “add a profile picture upload feature,” and Cascade will create a plan, modify backend routes, update frontend components, and even generate matching tests. It continuously evaluates its own work, refining until it reaches a complete, working solution. You stay in the loop throughout — reviewing diffs, running tests, or approving commits.

Windsurf emphasizes zero configuration. There’s no API key setup, no plugin management, and no complex onboarding. You install the editor, describe what you want, and start building. It runs locally but can connect to major cloud providers for collaboration and shared environments.

The tool’s focus is speed and creativity. It’s excellent for prototyping, MVP development, and early-stage product work where iteration speed matters more than strict architectural control. For production-grade refactors, the agent’s autonomy still benefits from human review.

Pricing includes a free starter plan and affordable Pro and Team tiers with unlimited Cascade runs and collaboration features.

Putting everything together, Windsurf feels less like an autocomplete tool and more like a junior developer on your team. It’s fast, bold, and sometimes imperfect — but it gets real work done while you focus on direction.

Cline

Cline brings agentic coding directly into VS Code without requiring you to change editors or subscriptions. It’s open source, fully transparent, and designed to give developers complete control over their AI stack.

Cline can plan, execute, and validate tasks inside your project, managing multi-file edits while coordinating with your terminal, version control, and build tools. For example, you can say “update the API to use the new auth flow and regenerate the client,” and Cline will modify the necessary files, run builds, and even clean up test cases.

The magic lies in its Bring Your Own Model setup — you can connect OpenAI, Anthropic, or any model endpoint you prefer. This keeps costs predictable and lets you fine-tune the trade-off between speed, accuracy, and privacy.

Because it runs natively in VS Code, you keep your extensions, themes, and shortcuts — but gain powerful automation on top. You can view and approve every step Cline takes, ensuring you stay in control even when it’s working autonomously.

Cline is the perfect bridge between traditional IDEs and fully agentic coding. It’s ideal for developers who love VS Code but want the autonomy of newer tools without losing flexibility or transparency.

Claude Code

Claude Code takes a minimalist but powerful approach — bringing Anthropic’s Claude models directly into your command line. It’s designed for developers who live in the terminal and want AI to enhance their natural workflow rather than replace it.

Claude Code can read and modify local files, run shell commands, and reason through complex debugging or automation tasks. For instance, you can ask it to “find and fix the SQL query causing latency in user login,” and it will analyze your logs, trace code paths, and suggest optimized changes.

Because it’s powered by Claude Sonnet and Opus, it excels at reasoning, summarizing, and refactoring — especially when dealing with larger contexts or documentation-heavy projects. It integrates directly with Git, enabling you to review diffs before committing AI-generated changes.

For developers using Vim, Emacs, or SSH-based workflows, Claude Code offers a way to add intelligent assistance without switching to a GUI editor.

When I first started using Claude Code in the terminal, I really liked it (although my wallet took a beating from a lot of prepaid token usage). It isn’t flashy — it’s a pragmatic, command-line companion that understands your environment. Perfect for backend engineers, sysadmins, and developers who prefer focus and simplicity over full-fledged IDE experiences.

Kiro (AWS Preview)

Kiro is Amazon’s experimental AI-first IDE built on Code OSS (the same open foundation as VS Code). It introduces a concept called spec-driven development, where Kiro helps you generate architectural specs and design documents from natural language — then implements them as code.

You might start by describing a feature like “build a REST API for user sessions with DynamoDB persistence,” and Kiro will produce a complete spec outlining routes, schema, and components. It then uses Claude or internal AWS models to generate and refine the implementation.

Because it’s built by AWS, Kiro integrates seamlessly with services like CloudFormation, Lambda, and CodeWhisperer. Its structure-first approach encourages maintainable, documented systems — a contrast to tools that prioritize speed over clarity.

Currently available through AWS’s preview program, Kiro is still evolving, but it’s a promising glimpse of what “enterprise-grade AI coding” could look like: structured, repeatable, and tightly coupled with cloud infrastructure.

Although I haven't played around with this platform much, it's easy to see that Kiro is best suited for AWS-heavy teams that value clear specifications and automated scaffolding over raw generation speed. It’s less about building fast — and more about building right (in the land of AWS).

Tabnine

Tabnine has quietly become the go-to AI coding assistant for organizations that prioritize security and privacy. Unlike cloud-based tools that send code to external servers, Tabnine can run entirely on-premises or within your private VPC, ensuring sensitive code never leaves your environment.

Its team-trained models adapt to your codebase over time, learning naming conventions, patterns, and architectural preferences. The more your team codes, the smarter Tabnine becomes — without sharing data externally. For regulated industries like healthcare, finance, and defense, this control is a major advantage.

Tabnine integrates with nearly every editor — VS Code, JetBrains, Vim, Sublime, and more. Teams can also connect custom-trained models for domain-specific projects, ensuring code suggestions align with internal frameworks or SDKs.

Pricing starts with a free plan for individuals, with Pro and Enterprise tiers adding features like self-hosted deployment and SOC 2 compliance.

Tabnine likely isn't super high on anyone's list of tools to try if you're coding at home or in a more progressive workplace embracing the latest wave of tools mentioned above. However, what I've noticed is that Tabnine is a common contender when an "enterprise-grade" choice, mainly due to its offline capabilities. It’s not about bleeding-edge autonomy — it’s about control, reliability, and data security for serious engineering organizations that can't run any of the other tools above.

Beyond the Editor: Emerging Autonomous Agents

There is also a camp of developers and startups betting on AI development outside the IDE. Autonomous agents are beginning to monitor production systems, analyze logs, and automatically propose code fixes. These capabilities remain experimental (for the most part) but show immense potential.

Traditional assistants still rely on developer prompts and supervision. Autonomous agents, however, identify issues independently, propose solutions, and even generate pull requests for human review. The most advanced prototypes behave like junior engineers, allowing agents to pick up tasks, iterate, and then submit solutions for review. These tools also have a place within the AI coding tool stack that most developers are building. There are two main ones that you should keep your eye on if this type of tool fits into your workflow.

Tembo

Tembo focuses on the unglamorous but critical side of development — maintenance, performance, and reliability. It acts as a background engineer that monitors production metrics and fixes problems automatically.

Tembo integrates with observability tools like Sentry, Datadog, and New Relic to detect performance regressions, query slowdowns, and recurring exceptions. When it finds issues, it analyzes recent commits, traces the root cause, and opens pull requests with fixes or optimizations — often before anyone notices the problem.

An example of where it can augment a developer’s workflow would be using its deep understanding of database performance, which makes it especially valuable for database-heavy applications. Tembo can identify inefficient queries, suggest indexes, and automatically tune schemas. Then, the developer can focus on their code and review and test the updated database logic once the agent is done.

Integration is simple: connect your GitHub repo and monitoring tools, and Tembo begins learning your system patterns. Over time, it improves its detection and fix accuracy based on your review feedback.

Although you can use Tembo to write new features, it's much better suited to taking over background tasks that consume developer time —the stuff that keeps your software healthy. For teams drowning in technical debt or constantly fighting production fires, it’s like having a tireless SRE or junior developer quietly improving stability behind the scenes.

Devin (Cognition Labs)

Devin by Cognition Labs is one of the first AI systems positioned as a fully autonomous software engineer. Rather than suggesting code or edits, Devin takes complete tasks — from tickets to feature requests — and implements them end-to-end.

You can assign Devin an issue like “implement password reset with email verification,” and it will plan the work, modify files, run builds, test changes, and submit a pull request. It operates in its own sandbox with a browser, terminal, and editor, simulating a real developer environment.

Devin integrates with GitHub, Linear, Jira, and Slack for collaboration. It provides progress updates, asks clarifying questions, and adapts to feedback. Benchmarks like SWE-Bench show it solving a significant percentage of real-world GitHub issues autonomously — though results vary depending on task complexity.

While Devin isn’t widely available yet (access is still limited via waitlist), it represents where AI coding is heading: persistent agents that don’t just assist you, but actively ship code.

Devin is experimental but can be a good addition for teams exploring large-scale automation or seeking a glimpse into what “AI engineers” might actually look like.

Building a Complete AI Coding Stack

As I've mentioned, no single AI coding tool can do everything. The best approach is layering:

Real-time assistants (Copilot, Cursor, Amp) for in-editor productivity.
Autonomous agents (Devin, Tembo) for background performance and code optimization, and error resolution.

Some people, including myself, even use multiple platforms that overlap. I know when I want to use Amp, and then, for maybe simpler stuff, I just want to use Co-pilot. There are different mixes that can help developers to balance speed, autonomy, and oversight.

Making the Choice: What Actually Matters

If you haven’t chosen one yet, you need to jump in somewhere! There are lots of tools to pick from, but when evaluating tools, focus on these key factors:

Integration Philosophy: Does it fit your existing environment? Can you adopt it incrementally?
Context Understanding: Does it reason across files and recognize your architecture?
Autonomy Level: Does it suggest, implement, or maintain?
Collaboration Features: Can your team share insights and build institutional AI knowledge?
Cost Structure: Understand both subscription and API token costs.
Security & Privacy: On-prem options (like Tabnine) matter for regulated industries.

For individuals, GitHub Copilot offers the best balance of reliability, cost, and ubiquity. For someone who wants the best setup for teams and individuals, Amp delivers multi-agent collaboration and deep context. For security-focused enterprises, Tabnine ensures data sovereignty. For ongoing system health, Tembo automates maintenance. And for rapid prototyping, Windsurf accelerates iteration.

The Future Isn’t One Tool

The industry is shifting from single-tool dependence to specialized AI coding stacks. Each phase of the software lifecycle — creation, refactoring, maintenance — benefits from a different AI companion.

Teams winning with AI aren't focused solely on writing code faster; they build and maintain systems more intelligently, continuously reducing technical debt and improving code quality.

Choose tools that amplify thinking, not just typing. Use AI to handle what machines do best, so developers can focus on what only humans can: architecture, creativity, and problem-solving.

Love Cursor but Need Options? 7 AI Tools That Excel Where Cursor Doesn’t

Matt Tanner — Mon, 27 Oct 2025 17:47:22 +0000

Cursor has quickly become the go-to environment for developers who want a coding experience built around AI, not bolted onto it. It’s fast, intuitive, and deeply aware of your project. With context windows that stretch across files, multi-model support, and a focus on natural-language refactoring, Cursor gets a lot right — and for many, it’s the gold standard of AI-native development.

But like any focused tool, Cursor isn’t perfect for every workflow. Some teams need stronger collaboration. Others care more about privacy, browser-based workflows, or cost transparency. This post looks at seven Cursor alternatives that complement its strengths — ideal for teams and developers who want different things from their AI stack.

Where Cursor Excels

Before diving into alternatives, it’s worth acknowledging what Cursor does better than almost anyone else:

Deep context understanding — Cursor doesn’t just autocomplete; it reads and interprets your project structure across multiple files.
Multi-model flexibility — You can connect your own API keys for OpenAI, Anthropic, or Gemini models.
Inline reasoning — Natural-language editing, instant refactors, and real-time problem solving.
Developer-first design — Built for speed and usability, not just novelty.

For developers who spend most of their day inside one codebase, Cursor remains hard to beat. But if you’re managing a larger team, working across multiple environments, or prioritizing specific needs like compliance or scalability, there are alternatives worth exploring.

1. Amp by Sourcegraph

Amp builds on many of the same ideas as Cursor — contextual understanding, fast refactoring, and multi-model intelligence — but scales them for teams. Developed by the team behind Cody and Code Search, Amp adds collaboration and shared memory to the mix.

Best when: you need multi-agent teamwork and collective learning.

Key Features

Distributed AI agents that divide complex tasks across files and repos
Persistent memory for institutional knowledge and reusable chat threads
Smart model routing that automatically picks the best LLM for each task
Works in any IDE or CLI, not just one environment
Ad-supported free tier that still makes sense for real use

In an industry-first, Amp’s revamped free tier uses light developer-focused ads to offset compute costs, letting users explore real multi-agent workflows without worrying about immediate token spend. This can work really well for folks who need a powerful tool but don't want to slam their personal or company-related budgets.

Amp is what you’d get if Cursor were rebuilt for entire engineering teams instead of individual developers.

2. GitHub Copilot

If you like Cursor’s intelligence but prefer staying inside your existing tools, GitHub Copilot is still the most natural companion. It’s embedded into VS Code, JetBrains, and Visual Studio, and now includes Copilot Workspace for structured planning.

Best when: you want AI help without changing editors.

Key Features

Deep GitHub integration with repo-aware context
Multiple model backends, including GPT-4 and Claude
Task planning and auto-refactor features in Copilot Workspace
Enterprise controls, analytics, and SSO integration
Straightforward pricing — $10/month (Pro) or $39/month (Pro+)

Copilot won’t match Cursor’s cross-file reasoning depth, but it’s unbeatable for seamless setup and familiarity.

3. Tabnine

Tabnine is the go-to for teams that prioritize data privacy and control. Instead of relying on cloud APIs, it runs locally or on-premises, keeping your code and prompts completely within your environment.

Best when: security and compliance matter more than model variety.

Key Features

Local or air-gapped deployment with zero data retention
Custom model training on private repos
Works with VS Code, JetBrains, and Visual Studio
Admin-level policy management and governance
Enterprise-ready SOC 2 and GDPR compliance

Tabnine doesn’t offer the same multi-agent reasoning as Cursor, but it’s the clear winner for private environments and regulated industries.

4. Windsurf

Windsurf is another IDE built from the ground up for AI — originally developed under the Codeium brand and acquired by Cognition Labs. Its Cascade agent system handles multi-step workflows like test generation or large refactors automatically.

Best when: you want an AI-native IDE with out-of-the-box automation.

Key Features

Cascade multi-agent coordination for autonomous coding
Instant setup — no API keys or manual configuration
Cross-file context understanding similar to Cursor
Works standalone or as a VS Code-compatible extension
Free for individuals; enterprise features add collaboration tools

Windsurf and Cursor share the same DNA. Windsurf leans more into automation, while Cursor emphasizes control and transparency.

5. Replit

If Cursor is about deep local context, Replit flips the model entirely. Its Ghostwriter AI sits inside a browser-based IDE that requires zero setup and supports instant collaboration.

Best when: you want to code anywhere, share easily, and deploy fast.

Key Features

Entire IDE in the browser — no installs needed
Real-time multiplayer collaboration
Built-in debugging and AI code generation
Instant deployment pipelines
Language-agnostic runtime support

Replit isn’t built for huge enterprise codebases, but for prototyping, teaching, and pair coding, it’s hard to beat.

6. Amazon Q Developer

Amazon Q Developer fits a completely different niche. It’s not a general IDE like Cursor — it’s a cloud-native assistant built for AWS developers working on infrastructure and services.

Best when: you live inside AWS and want cloud-aware automation.

Key Features

Tight AWS integration for S3, EC2, and Lambda workflows
Automated Java and .NET code modernization
Built-in AWS-specific security scanning
Agentic task execution for infrastructure management
Simple pricing structure

For cloud specialists, Q’s deep AWS awareness makes it a powerful alternative — but outside that ecosystem, it’s limited.

7. Qodo (formerly Codeium)

Qodo offers one of the simplest ways to use AI in your existing IDE without worrying about subscriptions or tokens. It’s free, multi-language, and available in VS Code, JetBrains, and Visual Studio.

Best when: you want free, unlimited AI help for everyday coding.

Key Features

Free forever — no credit system or caps
Multi-language support across frameworks
Fast, context-aware completions
Simple setup and quick onboarding
Frequent updates based on user feedback

Qodo lacks Cursor’s architectural depth, but it’s the easiest entry point for developers new to AI assistance.

Choosing the Right Fit

Cursor remains one of the best AI development environments on the market — but not every workflow needs a dedicated IDE.

Choose Amp for large teams and multi-agent collaboration.
Choose Copilot for stable integration into existing environments.
Choose Tabnine for strict data privacy.
Choose Windsurf for autonomous task execution.
Choose Replit for fast, shareable browser dev.
Choose Amazon Q Developer for AWS-centric projects.
Choose Qodo if you want something lightweight and free.

The Bottom Line

Cursor redefined what AI coding could look like. But no tool covers every use case — and that’s a good thing. The ecosystem is evolving fast, with tools like Amp pushing collaboration forward and Tabnine redefining privacy boundaries.

If Cursor is your main IDE, you’re already ahead. But if you’re looking to extend your workflow, these alternatives fill the gaps that Cursor doesn’t aim to solve — not to replace it, but to complement it.

Windsurf Not Cutting It? 7 AI Coding Tools That Raise the Bar

Matt Tanner — Mon, 27 Oct 2025 16:40:18 +0000

AI-assisted development has changed fast. Most of us have experimented with coding copilots or AI IDEs—some helpful, some not. One of the most visible tools from this first generation was Windsurf, originally built under the Codeium brand and later acquired by Cognition Labs. While it popularized the idea of “agentic” coding through its Cascade system, the platform’s credit-based model, pricing opacity, and inconsistent context handling often slow teams down more than they help.

This piece looks at seven alternatives that have pushed beyond those limits—tools that actually streamline development instead of adding friction.

What Makes a Great Windsurf Alternative

Real alternatives don’t just autocomplete code; they understand it. That means context-aware suggestions across multiple files, cross-language support, team collaboration, and the ability to work in your existing editor rather than a proprietary one. They should maintain a persistent project context, respect data privacy, and integrate naturally with Git, issue tracking, and CI/CD.

Alternatives should still deliver real-time suggestions, broad language support, automated code reviews, and intelligent bug detection, all while being transparent about pricing and data handling. The best ones scale from individual use to team-wide collaboration without introducing new constraints.

1. Amp by Sourcegraph

Amp eliminates the bottlenecks that limit many AI coding tools. Instead of credit systems that throttle complex tasks, Amp provides unlimited scalability. Built by the team behind Sourcegraph’s Code Search and Cody, it extends individual coding assistance into organizational collaboration.

Key Features

Distributed agent architecture that assigns specialized AI agents to different tasks simultaneously
Collective intelligence building that captures and shares problem-solving patterns across teams
Dynamic resource allocation that automatically scales compute power for complex problems
Model optimization and routing that selects the most capable models without manual setup
Universal IDE and CLI integration that works across any environment

Amp turns individual assistance into a team-wide development advantage, enabling true collaborative AI development.

2. GitHub Copilot

GitHub Copilot enhances existing IDEs instead of replacing them. It provides contextual code suggestions, refactoring help, and chat capabilities while preserving existing workflows. Powered by OpenAI and Anthropic models, it’s ideal for teams already in the GitHub and Microsoft ecosystem.

Key Features

Broad integration across VS Code, JetBrains, and Visual Studio
Repository-native understanding that leverages GitHub’s codebase knowledge
Multiple AI models for different task types and coding languages
Administrative tools for enterprise usage tracking and permissions
Language-agnostic code support with consistent quality

Copilot excels at improving productivity within familiar workflows but doesn’t yet handle multi-agent coordination or deep architectural context.

3. Cursor

Cursor reimagines the IDE itself. It’s a VS Code fork with built-in multi-model reasoning and deep codebase awareness. Cursor analyzes relationships between files, understands architectural dependencies, and lets developers use their own API keys for OpenAI, Anthropic, or Google models.

Key Features

Project-wide context understanding across files and dependencies
Advanced agent workflows for multi-file refactoring and feature implementation
Model flexibility through API key integration for cost control
Inline natural-language editing and multi-line autocompletion
Predictive import handling and intelligent autocomplete

Cursor delivers powerful AI integration for teams willing to commit to its editor. For those who prefer existing IDEs or need flexibility, the lock-in may be a limitation.

4. Tabnine

Tabnine focuses on privacy and enterprise security. It runs models locally or on-premises, allowing teams to train on private codebases without sending data to the cloud. This makes it a trusted choice for regulated industries or any organization that prioritizes compliance.

Key Features

Privacy-first architecture with on-premises and air-gapped deployment options
Custom model training on private repositories for tailored suggestions
Zero data retention policies and enterprise-grade compliance
Wide IDE compatibility, including VS Code, JetBrains, and Visual Studio
Team collaboration features for enforcing coding standards

Tabnine provides strong privacy and control, though its AI depth can be lighter compared to cloud-based options like Amp or Cursor.

5. Replit

Replit offers a browser-based development environment with integrated AI assistance. It removes the friction of local setup, making it ideal for fast prototyping, teaching, or pair programming. Replit provides real-time code generation, debugging, and chat support.

Key Features

Fully browser-based development—no setup required
Real-time collaboration for multiple developers
Integrated AI assistance and debugging
Multi-language runtime support with automatic environment configuration
Built-in deployment pipelines for rapid testing and iteration

Replit shines for quick experimentation and learning, but lacks the deep contextual intelligence needed for large, production-grade projects.

6. Amazon Q Developer

Amazon Q Developer targets AWS-native teams. It provides code generation, refactoring, and cloud-aware automation within the AWS ecosystem. Its tight integration makes it valuable for teams deeply embedded in Amazon’s infrastructure.

Key Features

Deep AWS integration for service-aware code suggestions
Automated code transformation for Java and .NET migrations
Built-in security scanning for AWS environments
Agent-based assistance for repetitive infrastructure tasks
Enterprise-level analytics and administrative integration

Amazon Q Developer is effective for AWS-heavy teams but introduces the same vendor dependence that many are trying to move away from.

7. Qodo (formerly Codeium)

Qodo—previously known as Codeium—provides free AI code assistance with no credit system or subscription gating. It supports a wide range of languages and integrates seamlessly with popular IDEs, making it a strong entry-level choice for individual developers.

Key Features

Unlimited free usage without subscription limits
Multi-language and multi-framework support
Integrations for VS Code, JetBrains, and Visual Studio
Context-aware code suggestions with real-time feedback
Minimal setup and fast onboarding

Qodo is a solid tool for individual developers or small teams looking for accessible AI coding assistance, though it lacks the advanced agent systems found in platforms like Amp or Cursor.

Choosing the Best Windsurf Alternative

The right choice depends on your priorities and team structure.

Teams requiring strict privacy and security should consider Tabnine’s local deployment options.
AWS-focused organizations will benefit from Amazon Q Developer’s cloud-native optimization.
Advanced teams that need deep project context should look at Cursor, balancing its power against editor lock-in.
For rapid prototyping and learning environments, Replit’s browser-based approach fits best.
GitHub-heavy teams will find Copilot the most seamless addition to their workflow.
Developers seeking free, unlimited assistance can rely on Qodo’s simple model.
For teams building complex software that demands multi-agent coordination and shared learning, Amp stands out as the most complete solution.

Where Windsurf Falls Short — And Where AI Coding Is Headed

AI-assisted development is evolving from individual productivity tools toward collaborative system design. The future belongs to platforms that understand entire codebases, coordinate across files and teams, and build institutional memory.

Windsurf’s credit-based pricing and single-user orientation make it difficult to scale. The next generation of tools—led by platforms like Amp and Cursor—focuses on team coordination, persistent project context, and scalable reasoning.

AI integration will soon move from suggestion to execution. Tools that understand repositories, manage pull requests, and integrate securely with CI/CD will define the next phase of development.

In short, individual productivity is table stakes at this point. The real advantage will come from AI that supports architectural thinking and team-scale collaboration.

Conclusion

Windsurf helped popularize AI-native development, but its model-based limits and individual focus make it less practical for modern, team-driven projects. The alternatives above represent the next step—AI platforms that scale with your workflow rather than constraining it.

One of my favorites, Amp, in particular solves a fundamental issue others don’t: it treats AI as a collaborative system, not a solo assistant. Complex software requires coordination, shared context, and architectural reasoning—exactly what Amp was built to do.

How to use subagents in AI coding with Amp

Matt Tanner — Mon, 27 Oct 2025 16:06:22 +0000

I recently watched a presentation by Sourcegraph’s Thorsten Ball, in which he mentioned something that I’ve heard is gaining traction in recent times when it comes to AI coding: subagents. If you've been using any type of AI coding tool in the last while, you've probably run into the dreaded context window problem. You know the drill - you're deep into a chat/session, the agent reads a bunch of files, runs some bash commands that produce tons of output, and suddenly you're hitting context limits and need to move to a new chat or your context gets compacted, losing some of its viability. As Thorsten puts it in his talk: "Some tokens are more important than others, and some tokens are just garbage, and they fill up."

That's where subagents come in. They fundamentally change your relationship with the context window by giving you a "multiplication of context windows." Instead of everything ending up in your main conversation, you get fresh context windows for each task. Of course, there are pros and cons to this and some nuances that potential users of subagents should be aware of. In this post, we will cover the basics and how to use subagents in AI coding workflows.

What exactly are subagents?

A subagent is essentially a mini-Amp that the main agent can create to handle specific tasks. When Amp determines that a task would benefit from independent execution, it can spawn one or more subagents to work on different pieces simultaneously.
Here's what makes this powerful: each subagent operates with its own context window. All the back-and-forth debugging, failed attempts, and iterative improvements happen in that subagent's isolated space. Your main conversation only sees the final results, keeping things clean and focused.

The main agent can also spawn multiple subagents simultaneously, enabling true parallel development workflows that would be impossible with a single-agent approach.

The context window garbage problem

Before diving into practical applications, it's worth understanding the core problem subagents solve. Every user message you send, every assistant response, every tool call, and every tool result ends up in your context window.

This becomes problematic when agents go off track. Maybe the agent reads too many files, runs bash commands that produce a lot of output, or gets stuck in a debugging loop. Soon, you'll see "context window limit reached" or copy important parts to a new conversation.

With subagents, all those "garbage tokens" that accumulate during these processes remain in the subagent's isolated context window. Only the final, useful result gets returned to your main conversation. You can have a subagent consume 50k tokens debugging something, but your main thread only sees the clean summary.

When should you use subagents?

Subagents work best when you can break a complex task into independent pieces. They're particularly valuable for:

Multi-file refactoring projects: When you need to apply the same changes across multiple files, each subagent can handle a subset without interfering with others.
Unrelated bug fixes: If you have several bugs in different modules, subagents can tackle each one independently, preventing context contamination between different problem domains.
Parallel feature development: Large features often have components that can be built simultaneously - database schemas, API endpoints, frontend components, tests, documentation.
Token-intensive tasks: Anything that involves lots of iteration, file reading, or analysis that would otherwise consume your main context window. However, subagents do have limitations. They can't communicate with each other, and you can't guide them mid-task. Once spawned, they work independently until completion. This makes them less suitable for exploratory development, where you need ongoing input and iteration.

Beyond basic subagents: The Oracle

Amp takes the subagent concept further with something called "the oracle" (all lowercase). The Oracle uses OpenAI's GPT-5 model, providing a different context window with a different quality because a different model fills it.

While GPT-5 is slower and more expensive than Claude Sonnet 4, it excels at complex reasoning tasks. The Oracle runs in its own isolated context window, performing deep analysis without consuming your main thread's tokens. You get GPT-5's reasoning power running in a separate context window.

This creates interesting possibilities for combining different models—using Claude's speed for implementation while leveraging GPT-5's reasoning power for complex analysis, all within the same workflow but in separate context windows.

Real examples from practice

Let me walk through some actual examples that demonstrate how subagents work in practice.

Code search with the search agent

One of the most common use cases is searching through your codebase. Instead of having the main agent read through dozens of files looking for something specific, you can delegate this to a search subagent:
Where do we check the authentication headers in the server? Use the search agent.

Here, the main agent spawns a search subagent with its own context window. This subagent uses grep, searches through multiple files, reads different portions of files, and might consume 30k+ tokens doing the investigation. But when it's done, only the final answer comes back to your main conversation—maybe just a few hundred tokens explaining where the authentication logic lives.

Bulk file operations across multiple files

Another powerful pattern is distributing file operations across multiple subagents. Thorsten demonstrated this by removing comments from his blog posts:

Use five subagents to remove comments from the files in posts/

The main agent automatically counted the number of files in the posts directory, then distributed them across five subagents. Each subagent worked independently to edit files without affecting the main context window. Even though it processed 24 files, including all the reading, editing, and potential error handling that entails, it used only 9% of the main context window.

As Thorsten notes: "If you've done anything with agents, you know that they sometimes fail to edit files or they fail to run Bash commands or something, and they run into errors, and it sucks up tokens, and it's frustrating. And you can actually shove this sometimes into subagents, and the subagents will do the dirty work."

Advanced subagent patterns

Based on these core patterns, you can apply subagents to more complex scenarios:

The distributed refactoring pattern
Use subagents to refactor our API error handling. Assign different route groups to different subagents so they can work in parallel.

The research and analysis pattern
Analyze our database performance issues. Use separate subagents to check the slow query logs, analyze index usage, and review the connection pooling configuration.

The validation and testing pattern
After these changes, use subagents to: run the full test suite, check for performance regressions, and validate API documentation is current.

Best practices for subagent success

Be specific about scope: Each subagent needs a well-defined, independent task. Vague instructions lead to poor results since you can't guide them during execution.
Ensure true independence: If tasks require constant coordination or shared intermediate results, you're better off using the main agent.
Plan for integration upfront: Think about how the pieces will fit together and mention any integration concerns in your initial prompt.
Use for token-heavy work: Subagents are perfect for tasks involving lots of iteration, testing, or analysis that would otherwise consume your main context window.

When to stick with the main agent

Don't use subagents for:

Exploratory development requiring ongoing guidance
Simple, single-file changes that don't benefit from parallelization
Tightly coupled changes that need constant coordination
Tasks where you need to provide input or direction during execution

How to encourage subagent usage within Amp

Amp can decide to use subagents automatically, but you can encourage their use by:

Explicitly mentioning "subagents" in your prompts
Breaking complex tasks into clearly parallel components
Using phrases like "use separate subagents to..."
Describing work that would benefit from parallel execution

Some example prompts that work well:

Use subagents to convert these 6 JavaScript files to TypeScript, one subagent per file.

Fix these three unrelated bugs using separate subagents so the debugging sessions don't interfere with each other.

Use subagents to: set up the CI/CD pipeline, configure monitoring and alerting, and update deployment documentation.

Looking ahead: What else can we build?

The multiplication of context windows opens up fascinating possibilities. What if we could merge information from one context window into another? Can we freeze them and reuse them? Should we be able to split them and reuse only parts of them?

Maybe we could have fixed context windows that are always cached and reused across the same codebase. There's still a lot of unexplored territory here - most of it won't work, but some of it might be magical.

Making the most of parallel development

Subagents represent a fundamental shift in how we can approach complex development tasks. Instead of working through everything sequentially and managing complex context in a single thread, we can coordinate teams of specialized agents working in parallel.

The key to success is recognizing when a task can be broken down into truly independent pieces and being explicit about what you want each subagent to accomplish. When used effectively, subagents can dramatically speed up development while keeping your main conversation focused and manageable.

Start experimenting with subagents on your next multi-component feature or refactoring project. You'll quickly discover how powerful it is to have multiple specialized agents working on your behalf while you coordinate the overall strategy.

Top Amazon Q Alternatives: Tools That Don’t Lock You In

Matt Tanner — Mon, 27 Oct 2025 15:42:49 +0000

If you’ve spent time with Amazon Q Developer, you’ve probably noticed one thing — it only really shines when your world revolves around AWS. And that’s fine if your infrastructure, stack, and team are all-in on Amazon’s ecosystem. But if you’re building across multiple clouds or want a coding assistant that adapts to your setup rather than the other way around, you’ll likely want more flexibility.

This post walks through several alternatives to Amazon Q Developer that give you the same (or better) capabilities without the ecosystem lock-in. Let’s start with a quick refresher on what Q actually does.

What Amazon Q Developer Is (and Isn’t)

Amazon Q Developer is AWS’s generative coding assistant designed to help developers build and modernize applications on AWS infrastructure. It offers real-time code suggestions, helps with Java and .NET upgrades, and integrates directly with AWS services. You can ask it about architecture choices, cost optimization, or security best practices, and it’ll respond in the context of your cloud environment.

In other words, it’s the AI layer on top of AWS development — an assistant that understands S3, EC2, IAM, and the rest of the alphabet soup that makes up a typical AWS workload.

Key features

Code suggestions and context-aware chat inside your IDE
Automated refactoring and legacy upgrades for common frameworks
AWS-aware recommendations for cost, security, and performance
Autonomous features that can modify or extend your code

Pricing

Amazon Q offers a free tier that includes 50 agentic requests per month and up to 1,000 transformed lines of code. The Pro tier is $19 per user per month and raises the limit to 4,000 transformed lines per user (pooled at the account level). Each additional line costs $0.003. Enterprise pricing adds governance and AWS integration options.

It’s a capable tool if you live in AWS. But that dependency is exactly where it starts to feel limiting.

Why You Might Want an Alternative

Amazon Q is built for one purpose: making AWS development faster. That focus means trade-offs:

It’s most useful when your code and infrastructure are already running on AWS.
AWS controls the underlying model and routing logic — you can’t swap providers or fine-tune behavior.
Costs can climb quickly when you layer Q on top of other AWS services.
It supports major IDEs, including VS Code, JetBrains, Visual Studio, and Eclipse, as well as deep integration with the AWS Management Console. Earlier versions emphasized Cloud9, but recent updates have brought near-parity across mainstream editors.
Its guidance is often AWS-specific, making it less relevant to developers working across different frameworks or environments.

If you want a tool that fits your workflow rather than your cloud vendor’s roadmap, there are several stronger, more flexible options.

Amp by Sourcegraph

Sourcegraph Amp takes a very different approach. Instead of limiting itself to one ecosystem, Amp is designed to work wherever your code lives. It’s built by the team behind Sourcegraph’s Code Search and Cody, and its goal is to make AI-assisted development collaborative and contextually aware.

Amp doesn’t rely on AWS integration at all. It can orchestrate multiple agents to handle larger tasks, share chat threads across teams, and automatically route requests to whichever model its backend deems most effective for that context (developers don’t manually select models). The result feels less like a plugin and more like a platform.

Its free tier is ad-supported, but the ads are developer-focused, making free use practical for small teams or individual engineers.

How it compares:

Where Amazon Q is deeply tied to AWS, Amp is cloud-agnostic. It scales with your workload, not your subscription tier, and the conversation-sharing feature gives teams a persistent memory that Q doesn’t offer.

GitHub Copilot

GitHub Copilot is still the most widely used coding assistant, and for good reason. It integrates naturally into existing developer workflows and doesn’t ask you to change how you work. Owned by Microsoft, it runs on the same foundation as OpenAI’s GPT models, but GitHub also integrates Anthropic models and its own routing logic depending on the task.

Copilot works in most popular IDEs, like VS Code, JetBrains, and Visual Studio, and now includes Copilot Workspace, a feature that lets you describe a task, see a plan, and apply the resulting code changes directly in your editor.

Pricing is straightforward: the Pro plan costs $10 per user per month, and Pro+ costs $39 per user per month with expanded usage quotas. A free tier is available for students and open-source maintainers.

How it compares:

Copilot is broader but less specialized. It won’t know your AWS architecture the way Q does, but it works anywhere. If your workflow already revolves around GitHub, it’s a natural fit.

Cursor

Cursor rethinks what an IDE can be when AI is built in from the start. It’s a fork of VS Code that adds deep context awareness and multi-model reasoning. Cursor indexes your entire codebase so that when you ask it to “add caching” or “refactor this function,” it understands the relationships between files and can make project-wide changes.

Developers can connect their own API keys for models from OpenAI, Anthropic, or Google, which means you aren’t locked into one provider.

Cursor’s pricing starts with a free plan, then scales to Pro ($20/month), Pro+ ($60/month), and Ultra ($200/month) tiers that raise context limits and request volumes.

How it compares:

Amazon Q integrates with your existing IDE; Cursor is your IDE. It’s the better option if you want an AI-native environment with full-project understanding and control over which model powers it.

Tabnine

Tabnine was one of the first coding assistants to emphasize privacy and enterprise control. It runs on models trained entirely on permissive open-source code and supports deployment in air-gapped or on-premises environments. For teams in regulated industries, that’s a major advantage.

Tabnine integrates with all major editors, including VS Code, JetBrains, and Visual Studio. It also supports organization-specific model training so your team’s private codebase can drive better completions.

The company offers a free individual plan and paid tiers for teams and enterprises that need custom deployment or governance.

How it compares:

Tabnine trades cloud integration for data ownership. It’s ideal for teams that prioritize privacy or operate under strict compliance requirements. Amazon Q, by contrast, requires AWS connectivity and stores context in the cloud.

Windsurf by Codeium

Windsurf is a full IDE originally developed under the Codeium brand. The company later rebranded around Windsurf, and in mid-2025, Cognition Labs — the team behind the autonomous engineer project Devin — acquired what remained of the Codeium business after an earlier partial acquisition by Google. The result combines Cognition’s reasoning systems with Windsurf’s AI-native editor.

Windsurf’s Cascade system chains multiple agents together to complete multi-step tasks like generating tests, fixing errors, or restructuring codebases. It runs as a standalone editor or a VS Code extension and doesn’t require configuration or API keys.

It’s free for individual developers, with enterprise plans available for teams that need collaboration and policy controls.

How it compares:

Windsurf focuses on end-to-end automation, while Amazon Q focuses on AWS-specific assistance. If you want an IDE that can operate independently of your cloud stack and handle larger workflows, Windsurf is worth testing.

Final Thoughts

Amazon Q Developer is a strong option for anyone deeply invested in AWS. Its context awareness and built-in security and performance insights make it uniquely valuable for that environment. But its narrow focus also limits how far you can take it outside AWS.

If your work spans multiple stacks or clouds, or you want to keep control over which models you use, tools like Sourcegraph Amp, GitHub Copilot, Cursor, Tabnine, and Windsurf give you more flexibility.

Each has its own trade-offs: Amp emphasizes collaboration, Copilot offers ubiquity, Cursor provides deep context, Tabnine focuses on privacy, and Windsurf aims for autonomy. But all of them share one important trait: they work wherever your code does. When choosing your assistant, it usually makes the most sense to look for one that scales with your workflow, not with your vendor’s ecosystem.

How to use the Oracle to find functionality regression between commits

Matt Tanner — Wed, 17 Sep 2025 20:27:35 +0000

When making changes to your codebase, it's easy to accidentally break existing functionality while implementing new features or fixing bugs. The Oracle in Amp excels at analyzing code changes and identifying potential regressions that might not be immediately obvious from a simple diff review.

This guide walks through a practical scenario where we'll make changes to a simple application, then use the Oracle to detect and fix any regressions in core functionality.

Our example application

For this walkthrough, we'll use a simple task management API. Here's the core task completion logic we want to protect:

// server.js \- Key functionality we're monitoring  
app.patch('/tasks/:id/complete', (req, res) \=\> {  
  const taskId \= parseInt(req.params.id);  
  const task \= tasks.find(t \=\> t.id \=== taskId);  

  if (\!task) {  
    return res.status(404).json({ error: 'Task not found' });  
  }  

  task.completed \= true;  
  task.completedAt \= new Date();  
  res.json(task);  
});

With the code above, let’s assume we have committed this working version as our baseline in git

## **Making changes that might introduce regressions**

As per usual development processes, we will take our baseline PoC code and begin to enhance it. Let’s say we are looking to add task editing capabilities and improve validation. Here are the key changes we might make to our completion logic:

// Enhanced validation function (NEW)  
function validateTask(title, priority) {  
  const validPriorities \= \['low', 'medium', 'high', 'urgent'\];  
  if (\!title || title.trim().length \=== 0\) return 'Title required';  
  if (priority && \!validPriorities.includes(priority)) return 'Invalid priority';  
  return null;  
}

// Updated task completion with new validation  
app.patch('/tasks/:id/complete', (req, res) \=\> {  
  const taskId \= parseInt(req.params.id);  
  const task \= tasks.find(t \=\> t.id \=== taskId);  

  if (\!task) {  
    return res.status(404).json({ error: 'Task not found' });  
  }  

  // NEW: Prevent double-completion  
  if (task.completed) {  
    return res.status(409).json({ error: 'Task already completed' });  
  }  

  task.completed \= true;  
  task.completedAt \= new Date().toISOString(); // Changed format  
  res.json(task);  
});

With these changes in place, let's commit the changes in git either manually, or via Amp:

Using Oracle to detect regressions

Now we want to ensure our changes haven't broken the core task completion functionality. Let's ask the Oracle to analyze our changes*.* Here's our prompt to Amp, which will invoke the Oracle to begin its analysis:

Use the oracle to review the last commit's changes. I want to make sure that the actual logic for task completion is unchanged and still works as it did before.

Then, in Amp, we will see the Oracle take over and begin its work.

Oracle response: No regression detected

In the case of the code we added, we know that the logic for task completion remains intact and doesn’t suffer from any regression issues. The Oracle’s output confirms this:

Oracle response: Regression detected

But what if our changes had introduced a regression? Let's instead imagine that we’ve unintentionally added this problematic change to our code base:

// Problematic version that breaks task completion  
app.patch('/tasks/:id/complete', (req, res) \=\> {  
  const taskId \= req.params.id; // BUG: Missing parseInt()  
  const task \= tasks.find(t \=\> t.id \=== taskId);  

  if (\!task) {  
    return res.status(404).json({ error: 'Task not found' });  
  }  

  task.completed \= true;  
  task.completedAt \= new Date().toISOString();  
  res.json(task);  
});

This seemingly minor change creates a critical bug that's easy to miss in code review. When a request comes in to /tasks/1/complete, req.params.id returns the string "1", not the integer 1. Our task IDs are stored as integers in the array.

The line tasks.find(t => t.id === taskId) now compares integer 1 with string "1". Due to JavaScript's strict equality (===), this comparison always returns false, making the function unable to find any task, regardless of whether it exists. Let’s use Amp to push this code up to git.

This is precisely the type of subtle regression the Oracle excels at detecting: a change that looks innocuous but completely breaks functionality through type system interactions. Without running the code and testing it, reviewers may not notice this immediately. As an extra line of regression defense, let’s once again run the Oracle to prove that it can find this type of regression:

Now, we can see that the Oracle has detected the issue and also given a suggested fix. Since the Oracle is read-only, we will need to get the Amp agent to make the fix for us.

Using Amp to fix the detected regression

When the Oracle detects a regression, we can immediately request that Amp fix it. In our particular case above, we could use the following prompt:

The oracle found a regression in task completion due to the missing parseInt(). Please fix this issue while preserving all the other improvements we made in the last commit.

From here, Amp gets to work on fixing the regression:

Amp then restores the correct type conversion, tests the fixed endpoint, and then commits the fix to git.

Then, Amp puts out a final message to us letting us know that the code is committed and fixed. This demonstrates how we can utilize the Oracle to identify potential regressions and how to utilize Amp to resolve them, ensuring everything functions as intended.

Best practices for regression detection

Although the Oracle is quite smart, there are still certain best practices when it comes to regression detection that you’ll want to keep in mind. The Oracle is most effective when you:

Be specific about what functionality to check: Instead of "check for regressions," specify "check that user authentication logic is unchanged."
Focus on critical paths: Prioritize checking core business logic, security-sensitive code, and frequently-used features
Use after significant refactoring: The Oracle excels at spotting subtle changes in complex refactoring operations
Combine with testing: Use Oracle analysis alongside your existing test suite for comprehensive coverage
Ask follow-up questions: If the Oracle finds issues, ask for specific fix recommendations or impact analysis

When to use Oracle for regression analysis

Ideal scenarios:

After large refactoring operations
When modifying shared utilities or core business logic
Before deploying critical fixes to production
When multiple developers have worked on related code

Less suitable for:

Simple, isolated changes
Purely additive features that don't touch existing code
Changes with comprehensive test coverage already

The Oracle transforms regression detection from a time-consuming manual process into a systematic analysis that catches issues before they reach production.

How to use Oracle to find and fix bugs in your code

Matt Tanner — Wed, 17 Sep 2025 20:13:56 +0000

Complex bugs often hide in the interactions between different parts of your system, making them difficult to spot through traditional debugging methods. The Oracle excels at systematically analyzing code paths, identifying issues that may only surface under specific conditions or involve subtle logic errors.

This guide demonstrates how to use the Oracle to identify and resolve a complex bug that only appears under specific circumstances.

Our example: Task API with intermittent failures

Imagine that we have a task management API, and we've been getting reports of intermittent failures when users try to complete tasks. The bug occurs only occasionally and appears to be related to concurrent usage. Here's the task completion logic that's causing issues:

// server.js \- Task completion with a subtle bug  
let tasks \= \[\];  
let completedCount \= 0;

app.patch('/tasks/:id/complete', async (req, res) \=\> {  
  const taskId \= parseInt(req.params.id);  
  const task \= tasks.find(t \=\> t.id \=== taskId);  

  if (\!task) {  
    return res.status(404).json({ error: 'Task not found' });  
  }  

  if (task.completed) {  
    return res.status(409).json({ error: 'Task already completed' });  
  }  

  // Update task status  
  task.completed \= true;  
  task.completedAt \= new Date().toISOString();  

  // Send notification (async operation)  
  sendNotification(task.userId, \`Task "${task.title}" completed\`);  

  // Update global counter  
  completedCount++;  

  // Update user's completion streak  
  await updateUserStreak(task.userId);  

  res.json(task);  
});

async function updateUserStreak(userId) {  
  const userTasks \= tasks.filter(t \=\> t.userId \=== userId && t.completed);  
  const user \= await getUserById(userId);  

  // Calculate streak: consecutive days with completed tasks  
  const today \= new Date().toDateString();  
  const yesterday \= new Date(Date.now() \- 86400000).toDateString();  

  const todayTasks \= userTasks.filter(t \=\>   
    new Date(t.completedAt).toDateString() \=== today  
  );  
  const yesterdayTasks \= userTasks.filter(t \=\>   
    new Date(t.completedAt).toDateString() \=== yesterday  
  );  

  if (todayTasks.length \> 0\) {  
    if (yesterdayTasks.length \> 0 || user.currentStreak \=== 0\) {  
      user.currentStreak \= (user.currentStreak || 0\) \+ 1;  
    }  
  }  

  await saveUser(user);  
}

The problem: Inconsistent user streaks

Users are reporting that their task completion streaks are sometimes incorrect - they complete tasks every day, but their streak counter resets unexpectedly or displays incorrect numbers. The issue occurs more frequently when multiple users attempt to complete tasks simultaneously. Let's use the Oracle to analyze the task completion logic. Here's our prompt to Amp that will invoke the Oracle to check this out:

I have a bug where user completion streaks are getting calculated incorrectly when users complete tasks. The streak counter sometimes resets to the wrong values or doesn't increment properly. This seems to happen more during peak usage times when multiple users are completing tasks simultaneously. Use the oracle to analyze the task completion logic and identify potential race conditions or logic errors that could cause streak calculations to be wrong.

Once we send the prompt through, we can see that Amp shows it is consulting the Oracle:

Oracle analysis: Identifying the root cause

The Oracle then returns with its analysis and lets us know what the issues are:

With this feedback, it’s time for Amp to get to work on fixing the issue.

Using Amp to implement the fix

Now let's ask Amp to fix the identified issues:

The Oracle identified flawed logic in a few areas of the application. Use the feedback from the Oracle to fix and test the logic to make sure it works as intended and no longer has race conditions present.

Amp then goes to work, implementing fixes for the various issues that the Oracle identified. You can see the fixes being made by checking out the updated todos:

And we can see that Amp is changing the code accordingly based on the Oracle’s feedback:

Then, Amp creates a test script to verify that its changes handle concurrent updates without race conditions.

And lastly, it gives us a final confirmation message of the fixes, including a detailed breakdown of the key fixes applied and test results:

With just a few steps, we’ve had the Oracle debug and diagnose the race condition that we inadvertently introduced into our code. Then, we used this feedback to get Amp to fix the issue and confirm it through a series of generated tests.

Oracle analysis for different bug types

The Oracle's systematic analysis approach works well for various bug categories:

Concurrency issues: Race conditions, deadlocks, shared state problems
Logic errors: Edge cases, incorrect condition handling, state machine bugs
Memory problems: Leaks, inefficient cleanup, unbounded growth
Integration bugs: API contract mismatches, timing-dependent failures
Performance issues: Inefficient algorithms, resource contention

Best practices for bug analysis with Oracle

When it comes to using the Oracle for bug analysis, there are some best practices that can help you get the most out of it. These tips include:

Be specific about symptoms: Include error messages, reproduction steps, and timing information

Provide relevant context: Share related code, configuration, and environment details where the bug occurs

Focus on complex interactions: Use Oracle for bugs involving multiple components or timing dependencies

Ask for a systematic analysis: Request a step-by-step breakdown of potential failure modes

Follow up with implementation: Use Amp to implement Oracle's recommended fixes immediately

When to use Oracle for debugging

Although you can use the Oracle whenever you want, certain scenarios make more sense than others. For example, ideal scenarios for invoking the Oracle include:

Intermittent bugs that are hard to reproduce
Issues that only appear under load or specific conditions
Complex multi-component failures
Race conditions and timing-related problems

On the other hand, bugs that include:

Simple syntax errors or obvious logic mistakes
Issues with clear stack traces pointing to specific lines
Problems that are easily reproduced and debugged locally

These are less ideal for the Oracle and can likely be handled manually or through Amp’s main agent.

By using the Oracle in Amp, you can transform mysterious, intermittent bugs into well-understood problems with clear solutions. Using this tool for complex bugs can transform hours of trial-and-error debugging into an efficient process that requires only a few minutes.

How to use the Oracle in Amp

Matt Tanner — Wed, 17 Sep 2025 19:58:49 +0000

When you're deep in a debugging session that's going nowhere, or staring at legacy code that seems to defy logic, Amp has a specialized tool that can help: the oracle. By using this tool, you get access to OpenAI's o3, one of the most capable reasoning models available today.

What is the Oracle in Amp?

The Oracle grants Amp's main agent access to advanced reasoning capabilities when the standard approach is insufficient. While Amp typically runs on Claude Sonnet 4, which is fast, efficient, and perfect for most coding tasks, sometimes you need something with deeper analytical power.

By using o3, the underlying model then trades speed and cost efficiency for reasoning depth. It's the model you want when you're dealing with complex system interactions, subtle bugs that span multiple files, or architectural decisions that could have far-reaching consequences. The Oracle operates as a read-only subagent, analyzing your code and providing detailed insights that the main agent can then act upon.

This allows the main agent to handle the implementation work with characteristic speed, while the Oracle tackles the complex reasoning that requires patience and methodical analysis.

How to enable the Oracle in Amp

The Oracle is built into Amp as a standard tool. This means that there's nothing to install or configure. However, using it effectively requires understanding how to invoke it through your prompts.

The Oracle responds to explicit requests in your messages to Amp. You can call upon it by incorporating phrases like:

"Use the oracle to analyze..."
"Have the oracle review..."
"Work with the oracle to figure out..."
"Get the oracle's assessment of..."

The key is being intentional about when you want this deeper analysis. The Oracle won't automatically jump in for routine tasks; you need to specifically request its involvement when you recognize that a problem requires its specialized approach.

What are the advantages of using the Oracle over the standard model in Amp?

The Oracle excels in scenarios where standard pattern matching isn't sufficient and you need genuine reasoning about complex interactions.

Deep debugging capabilities: When bugs involve multiple systems, race conditions, or subtle state management issues, the Oracle can trace through execution paths that other models might miss. It's particularly valuable for intermittent bugs or issues that only appear under specific conditions.

Comprehensive impact analysis: Before making significant architectural changes, the Oracle can analyze ripple effects throughout your codebase. It considers not only direct dependencies but also indirect effects that may not be immediately apparent.

Security vulnerability assessment: The Oracle approach to security analysis is methodical, considering attack vectors and edge cases that require understanding both the code's intended behavior and potential misuse.

Legacy code comprehension: When working with undocumented or poorly documented systems, the Oracle can piece together the intended logic and identify potential issues that have accumulated over time.

What are the disadvantages of using the Oracle over the standard model in Amp?

The Oracle's strengths come with clear trade-offs, making it unsuitable for everyday development tasks. These include:

Significantly slower responses: Where Amp's main agent might respond in seconds, the Oracle's deep reasoning process can take a minute or longer. This makes it impractical for iterative development, where quick feedback is necessary.

Higher operational costs: Each Oracle consultation requires more compute resources, making it expensive to use for routine questions that the main agent handles effectively.

Analysis paralysis potential: The Oracle's thoroughness can sometimes provide more detail than needed for straightforward problems, potentially slowing down simple tasks.

Limited scope: The Oracle analyzes and advises but doesn't implement. You'll still need the main agent to act on its recommendations.

Examples of using the Oracle in Amp

The Oracle proves most valuable in complex scenarios where thorough analysis prevents costly mistakes or lengthy debugging sessions.

Using the Oracle to analyze any regressions between commits

When you've made changes that might have subtle side effects, the Oracle can provide comprehensive impact analysis:

Use the oracle to examine the changes in my last three commits and identify any potential regressions in the user authentication flow. I'm particularly concerned about edge cases around session expiration.

The Oracle will systematically compare the before and after states, tracing through different execution paths to identify changes in behavior that might not be obvious from the diff alone.

Using the Oracle to help with planning complex refactoring

Large refactoring projects benefit enormously from the Oracle's ability to understand system-wide implications:

I need to refactor our payment processing module to support multiple currencies. Use the oracle to analyze the current implementation and identify all the places that will need updates, considering both direct dependencies and indirect effects on reporting and auditing.

This type of analysis helps prevent the common scenario where a refactoring project uncovers unexpected dependencies halfway through implementation.

Using the Oracle to identify and fix bugs

For bugs that have resisted standard debugging approaches, the Oracle can provide a fresh analytical perspective:

We have a memory leak that only appears in production after several hours of operation. The issue seems related to our WebSocket connection handling. Use the oracle to analyze these files and identify potential causes for gradual memory accumulation.

The Oracle's systematic approach often identifies issues that manual debugging sessions miss, particularly when bugs involve timing, concurrency, or complex state management.

Using the Oracle to scan code for known security vulnerabilities

Security analysis requires understanding both obvious vulnerabilities and subtle attack vectors:

Use the oracle to perform a security audit of our API authentication middleware. Look for common issues, such as timing attacks, privilege escalation opportunities, and input validation gaps, that could be exploited.

The Oracle's methodical analysis considers not just current vulnerabilities but also how the code might be vulnerable to future attack patterns.

Conclusion

The Oracle provides an additional tool for handling complex analysis tasks, helping to transform time-consuming puzzles into systematic problem-solving exercises. The key is recognizing when a problem requires deep reasoning rather than quick implementation and invoking the Oracle when needed. Save the Oracle for challenges that truly benefit from methodical analysis, and let Amp's main agent handle the day-to-day coding with its characteristic efficiency.

How to Install Amp in Cursor

Matt Tanner — Wed, 17 Sep 2025 19:54:47 +0000

When it comes to using Amp, there are many different IDEs that support it. For those using the latest AI coding IDEs, this means utilizing Cursor, which includes Amp support. In this tutorial, we will go over how to install Amp in Cursor. Let’s get started.

Step 1: Sign up for an Amp account

Before installing Amp, you’ll want to set up your account. To do this, visit the Amp sign-up page and complete the onboarding flow. Once completed, we can go to the next step of installing Amp in Cursor.

Step 2: Install the Amp Extension in Cursor

While logged into Amp in a browser, on the dashboard page:

Click on the Install button in the footer
Make sure Cursor is selected in the modal that appears
Click on the Install Amp for Cursor button

The browser will then likely prompt you that ampcode.com wants to open Cursor. This is expected, so you’ll need to allow this by clicking Open Visual Studio Code.

Once you’re back in Cursor, the IDE will also ask you if you want to grant permissions to open/download the extension. Once again, you’ll want to allow this so it can be installed by clicking Install Extension and Open URI in the dialog.

From here, Cursor will try to install the extension. First, though, it will ask if you trust Sourcegraph as a publisher. Here you’ll click Trust Publisher and Install.

After the extension installs, you’ll see a few things added to your Cursor instance. You’ll see the Amp chat interface on the left and the Amp extension in the left-side menu for easy access.

All of this means that you’ve successfully installed Amp in Cursor and you’re ready to start using it.

Still can’t install Amp?

If the instruction above didn’t work out quite as expected, here are a few scenarios and solutions to help you get Amp up and running successfully.

For instance, if clicking the Install Amp for Cursor button isn’t working for you, you can try to do a manual installation through the Cursor terminal. To do this, in the Amp dashboard in your browser, expand the Manual Instructions and follow the steps shown:

Alternatively, you can also go into the Extensions Marketplace in Cursor and install it directly there. Simply open up the Extensions tab and search for “amp”, then click Install.

How to Install Amp in VS Code

Matt Tanner — Wed, 17 Sep 2025 19:41:10 +0000

When it comes to using Amp, many different IDEs support it. For many, this means adding Amp to old-faithful, AKA VS (Visual Studio) Code. In this tutorial, we will go over how to install Amp in VS Code. Let’s get started.

Step 1: Sign up for an Amp account

Before you go ahead with installing Amp, you’ll want to set up your account. To do this, visit the Amp sign-up page and complete the onboarding flow. Once completed, we can go to the next step of installing Amp in VS Code.

Step 2: Install the Amp Extension in VS Code

While logged into Amp in a browser, on the dashboard page:

Click on the Install button in the footer
Make sure VS Code is selected in the modal that appears
Click on the Install Amp for VS Code button

The browser will then likely prompt you that ampcode.com wants to open VS Code. This is expected, so you’ll need to allow this by clicking Open Visual Studio Code.

Once you’re back in VS Code, the IDE will also ask you if you want to grant permissions to open/download the extension. Once again, you’ll want to allow this so it can be installed by clicking Install Extension and Open URI in the dialog.

From here, VS Code will try to install the extension. First, though, it will ask if you trust Sourcegraph as a publisher. Here you’ll click Trust Publisher and Install.

After the extension installs, you’ll see a few things added to your VS Code instance. You’ll see the Amp chat interface on the left and the Amp extension in the left-side menu for easy access.

All of this means that you’ve successfully installed Amp in VS Code and you’re ready to start using it.

Still can’t install Amp?

If the instruction above didn’t work out quite as expected, here are a few scenarios and solutions to help you get Amp up and running successfully.

For instance, if clicking the Install Amp for VS Code button isn’t working for you, you can try to do a manual installation through the VS Code terminal. To do this, in the Amp dashboard in your browser, expand the Manual Instructions and follow the steps shown:

Alternatively, you can also go into the Extensions Marketplace in VS Code and install it directly there. Simply open the Extensions tab and search for “amp”; then, click Install.