32 Useful AI Tools For Coding in 2024

Mukul Rana
9 Min Read
Useful AI Tools For Coding

The world of software development is a whirlwind of constant innovation. While staying on top of the latest frameworks and languages is crucial, AI-powered tools are here to revolutionize your coding experience. In this article, we break down 32 incredible resources that promise to streamline your workflow, boost productivity, and make coding more efficient and enjoyable.

32 Useful AI Tools For Coding

S.noAI Tools NameUsefulness
1GitHub CopilotCode completion, suggestions, pair programming assistance
2TabnineCode completion, context-aware suggestions, personalized learning
3KiteCode completion, common patterns, time-saving
4Amazon CodeWhispererCode recommendations based on comments and style (similar to Copilot)
5DeepCodeBug and security vulnerability detection, suggestions for fixes
6AlphaCode (by DeepMind)Solves coding challenges in multiple languages
7OpenAI CodexCore language model for many AI coding tools, translates natural language to code
8PylintPython code analysis for style, errors, and potential issues
9SonarQubeStatic code analysis for multiple languages, bug detection, security, maintainability
10EmboldDeep static code analysis, insights into maintainability, duplication, complexity
11CodigaCodebase analysis, refactoring suggestions, automated code changes
12ExplainCodeNatural language processing to summarize code block function
13SourcegraphUniversal code search with insights for navigating large codebases
14Diffblue CoverAutomatic unit test writing
15MablIntelligent test automation with self-healing capabilities
16FunctionizeAI-powered cloud-based platform for end-to-end functional testing
17TLDR ThisSummarizes long technical documents using AI
18QdrantSemantic search engine for concepts within your codebase
19JellyfishStreamlines engineering processes, centralizes code-related knowledge
20AISearch (by Algolia)Powerful search engine designed for codebases
21UizardTransforms hand-drawn sketches into front-end code
22KhromaHelps choose harmonious color palettes
23Remove.bgAutomatically removes image backgrounds
24Let’s EnhanceUpscales and enhances low-resolution images using AI
25WarpAI-powered terminal designed for speed and efficiency
26GitLens (VS Code extension)Supercharges Git capabilities within your editor
27LinearIssue tracking and project management with a focus on speed and design
28HeightCollaborative tool for project planning, design specs, and syncing design-development
29DebuildReverse-engineers software to recover original code (use responsibly!)
30BayesDBUses Bayesian statistics to make SQL queries smarter
31AI2sqlWrites SQL queries based on natural language questions about your database
32GalileoTracks AI model experiments and dataset lineage
32 AI For Coding

AI-Powered Pair Programmers & Autocomplete

GitHub Copilot

Description: Leverages OpenAI Codex to act as your virtual AI coding assistant, suggesting entire lines or functions in real-time within your IDE.

Link : https://github.com/features/copilot

Pros: Speed, learns your style, great for boilerplate or common patterns.

Cons: Can sometimes suggest incorrect code, reliance might affect learning for beginners.

Tabnine

Description: Intelligent code completion that understands context and tailors suggestions. Learns your individual coding patterns over time.

Link: https://www.tabnine.com/

Pros: Highly personalized, reduces repetitive typing, can discover useful APIs.

Cons: Potential slowdown on less powerful machines, some privacy considerations.

Kite

Description: Saves time by providing completions for common code patterns across numerous programming languages.

Link: https://kite.zerodha.com/

Pros: Cross-language support, easy to use, documentation lookup features.

Cons: Suggestions can be less sophisticated compared to some newer AI models.

AI for Bug Catching and Security

DeepCode

Description: Analyzes your codebase, searching for vulnerabilities, security leaks, and potentially critical bugs before they become problems.

Link: https://github.com/DeepCodeAI

Pros: Automated security, works on large projects, improves code quality.

Cons: Can sometimes produce false positives, best as part of a larger testing suite.

SonarQube

Description: Multi-language platform for static code analysis, maintaining code health, and providing insights into maintainability.

Link: https://www.sonarsource.com/products/sonarqube/

Pros: Catches diverse issues, customizable rules, integrates with pipelines.

Cons: Setup can be complex for some, more tailored tools exist for specific purposes.

AI Understanding and Explaining Code

ExplainCode

Description: Still in development, this tool uses natural language processing (NLP) to provide summaries of what specific code blocks do.

Link: https://www.figstack.com/app/explain

Pros: Potential time-saver for understanding unfamiliar code, great for onboarding new developers.

Cons: Early-stage means accuracy may vary, best for well-structured code.

Sourcegraph

Description: Universal code search that goes beyond keywords, allowing you to search by concepts and patterns to unlock insights within large codebases.

Link: https://sourcegraph.com/

Pros: Excellent for navigating complex projects, knowledge sharing within teams.

Cons: Setup can be time-consuming for self-hosting, advanced features may require the paid version.

AI for Testing

Diffblue Cover

Description: Automatically generates unit tests. Test writing can be tedious; this tool takes over that task.

Link: https://www.diffblue.com/products/

Pros: Frees up developer time, boosts test coverage, finds corner cases humans might miss.

Cons: Not a full testing replacement, best as a supplement to manual and integration testing.

Mabl

Description: AI-powered test automation platform. Visualizes changes and intelligently ‘self-heals’ broken tests.

Link: https://www.mabl.com/

Pros: Reduced test maintenance, good for those without extensive coding experience, visual interface.

Cons: May be overkill for smaller projects, less fine-grained control than purely code-based testing.

Functionize

Description: Cloud-based, intelligent platform for functional and end-to-end testing. Uses AI for efficiency and scalability.

Link: https://www.functionize.com/

Pros: Handles large-scale testing needs, minimal reliance on coding, robust reporting.

Cons: Pricing geared towards larger teams/enterprises, potential for reliance on the vendor.

AI for Documentation & Knowledge

TLDR This

Description: Summarizes technical articles and lengthy documents for quick comprehension. Great for research!

Link: https://tldrthis.com/

Pros: Time saver, breaks down complex info, useful when onboarding to new projects.

Cons: Can miss nuance, accuracy varies on topic complexity, best as a starting point.

Qdrant

Description: Semantic vector search engine specifically for understanding code relationships. Imagine ‘search your codebase’ but using concepts.

Link: https://qdrant.tech/

Pros: Powerful way to search by meaning, finding reusability, knowledge discovery.

Cons: Setup for self-hosting is non-trivial, specialized use case.

Jellyfish

Description: Streamlines engineering documentation, Q&A, and process flows, centralizing knowledge within teams.

Link: https://jellyfish.ai/

Pros: Reduces time spent hunting for information, promotes collaboration, scalable

Cons: Requires adoption to be effective, benefits heavily depend on team discipline.

AISearch (by Algolia)

Description: Advanced search built for developers, understanding code structure and semantics for precise results.

Link: https://www.aisearch.vip/

Pros: Finds exactly what you need quickly, powerful filtering, integration options.

Cons: Less common than general-purpose search, can require investment in indexing.

AI for Design Assistance

Uizard

Description: Turns your hand-drawn wireframes into HTML, CSS, and other frontend code.

Link: https://uizard.io/

Pros: Fast prototyping, bridges gap for non-designers, fun to use.

Cons: Output needs polishing, not full UI design replacement, best for quick layouts.

Khroma

Description: AI picks harmonious color palettes based on your chosen base color.

Link: https://www.khroma.co/

Pros: Saves design time, suggests unexpected combos, great if color theory isn’t your forte.

Cons: Doesn’t account for all accessibility elements, still needs human judgment.

Remove.bg

Description: One-click tool to remove backgrounds from images with great accuracy.

Link: https://www.remove.bg/

Pros: Simple, faster than manual editing, useful for quick prototyping.

Cons: May struggle with complex edges, free version has limits.

Let’s Enhance

Description: Upscales, enhances, and restores images or photos using AI.

Link: https://letsenhance.io/

Pros: Revives old assets, quick touch-ups, good for improving low-quality visuals.

Cons: Can sometimes add artifacts, results depend on initial image quality.

And so much more…

Here’s a quick list of further tools across various categories. Consider experimenting to see which ones fit your workflow best!

  • Testing: Diffblue Cover, Mabl, Functionize
  • Documentation & Knowledge: TLDR This, Qdrant, Jellyfish, AISearch (by Algolia)
  • Design Assistance: Uizard, Khroma, Remove.bg, Let’s Enhance
  • Workflow & Project Management: Warp, GitLens, Linear, Height
  • Specialty/Experimental: Debuild, BayesDB, AI2Sql, Galileo

The AI Advantage From intelligent coding assistants to automated testing and debugging, AI is rapidly making its way into the developer’s toolbox. Experimenting with these tools can help you save time, reduce errors, and discover insights hidden within your codebase. As artificial intelligence continues to evolve, this is just the beginning of how it will empower and reimagine the act of coding!

Share This Article
Leave a comment