AI CLI
Get answers for CLI commands from GPT3 directly in the terminal using AI-powered CLI tool.
Coding assistants have evolved beyond fancy autocomplete into genuine productivity multipliers. We evaluated these tools based on their ability to grasp multi-file context, refactor legacy code, and generate accurate unit tests. Whether you are debugging a race condition or scaffolding a new API, these tools aim to keep you in the flow state longer.

Anthropic's AI assistant focused on helpful, honest, and safe reasoning, strong in writing, coding, and analysis.

Google’s multimodal AI combining text, images, and data analysis for advanced reasoning.

An AI-driven design and development tool that instantly converts natural language prompts into production-ready interfaces using React, Tailwind, and Vercel’s framework. It streamlines prototyping and accelerates web app creation for developers and designers.

OpenAI’s conversational model with creative, coding, and problem‑solving abilities, versatile across topics.

Get answers for CLI commands from GPT3 directly in the terminal using AI-powered CLI tool.

Create simple but fully custom and interactive web pages just by chatting with AI.

Hands AI is an open-source platform that automates repetitive software engineering tasks, supports code generation, and aggregates verified problem sets. It fosters collaboration among users and integrates with various large language model providers for enhanced productivity.

Archie is an AI-driven platform that streamlines the software development lifecycle, automating code generation and providing a secure infrastructure. It supports various application types, aiding both startups and established businesses in efficient application creation and deployment.

AskCodi is an AI development tool that saves time and reduces redundancy by providing coding assistance, syntax generation, documentation, test creation, database queries, real-time codebase generation, and multi-language support.

Aspen is a MacOS native API testing tool with a secure, zero-trust environment. Its AI assistant streamlines testing, generates integration code, and optimizes REST API testing. Developed by Treblle, Aspen integrates efficiency and security for seamless API testing and integration processes.

Bench is a collaborative platform that streamlines hardware design reviews by allowing teams to upload CAD models and drawings, provide contextual feedback, and ensure compliance, resulting in improved clarity, accountability, and faster design cycles.
The world’s fastest AI app builder. Turn ideas into fully working apps with built-in authentication, database, edge functions, and hosting. Self-corrects errors and fixes bugs automatically. Suitable for all skill levels for rapid app prototyping and deployment.

Celerforge is an AI tool for efficient API mocking, enabling quick generation of customizable mock APIs with realistic data. It enhances frontend testing and supports integration with TypeScript and Swagger UI for streamlined development workflows.
Cerberus' Graphbook is an AI modeling aid that streamlines deep learning through visual diagramming. Its user-friendly, drag-and-drop interface minimizes errors, ensures model safety, and fosters team collaboration without the need for intricate coding.

OpenAI’s conversational model with creative, coding, and problem‑solving abilities, versatile across topics.

Anthropic's AI assistant focused on helpful, honest, and safe reasoning, strong in writing, coding, and analysis.
ClearML is an end-to-end AI platform that enables developers and data scientists to build, train, and deploy machine learning models. It features collaborative experiment management, GPU orchestration, and robust data management for scalable AI workflows.

CodeAI is helps developers optimize their development process by providing them with cutting-edge artificial intelligence. With CodeAI, users can transform ideas into written code, generate automated unit tests, and create comprehensive documentation effortlessly.

CodeDefender α is an AI tool that elevates coding quality within IDEs by detecting issues in real-time. It provides encryption, code security analysis, performance assessment, multi-language support, and documentation generation for a secure, efficient, and organized development process.

CodeGeeX is a code generation model on Hugging Face's Space that uses THUDM for generating code.
Confused about which AI model to use? Whether you're writing blogs, generating images, or conducting research, choosing which AI model is best can feel impossible. This guide explains how to choose the right AI model and directly compares top options like GPT-5.2, Claude Opus 4.5, Gemini 3 Pro, and others.
What is the best programming language to develop an AI? Choosing the best AI programming language is crucial for successful AI development. Today, Python stands out as the leading programming language for artificial intelligence thanks to its ease of use, robust libraries, and vibrant community support. Alongside Python, languages such as R, Java, C++, and Julia also play an important role in building AI systems. This guide helps you compare the top AI programming languages and frameworks for all types of AI development.
Most plugins only see open files, which limits accuracy. However, newer tools using RAG or specialized vector embeddings (like Cursor or Cody) allow for "codebase-wide" indexing. This significantly reduces hallucinations by letting the AI "see" imports and type definitions across your whole project structure, not just the current tab.
Standard enterprise tiers usually guarantee zero-data retention. However, free individual plans often use your snippets for model training. If you are working on strict NDA projects or proprietary algorithms, you must disable telemetry in settings or opt for self-hosted LLM solutions running locally to ensure total privacy.
It excels at syntax errors and explaining stack traces but struggles with deep architectural logic. Use it to brainstorm edge cases you might have missed. Do not trust it to "fix" a logic bug blindly, it often suggests plausible-looking but functionally incorrect patches that introduce subtle regressions.
Performance drops significantly outside popular stacks like Python, JavaScript, or Go. For niche languages, the training data is scarcer, leading to more syntax hallucinations. You will need to treat suggestions with higher skepticism and verify standard library usage manually, as the models often invent non-existent methods in these contexts.