Product Introduction

1. EchoComet is a desktop application designed to integrate local codebases with web-based AI platforms that support large context windows. It enables developers to systematically gather code context from projects and feed it directly to AI models like ChatGPT, Claude, and others through a streamlined workflow.
2. The core value lies in overcoming token limitations of traditional IDE-based AI tools by enabling direct interaction with LLMs capable of processing millions of tokens. This allows developers to solve complex coding problems that require extensive codebase context without manual copy-pasting or fragmented inputs.

Main Features

1. EchoComet allows users to browse and select specific files or entire folders from their codebase, eliminating manual extraction of code snippets for AI prompts. The tool aggregates selected code into a single, structured block with contextual metadata for seamless integration into LLM queries.
2. The AI-powered question improver analyzes code context to automatically enhance prompts, ensuring clarity and specificity for optimal LLM responses. This feature includes syntax-aware context tagging and dependency mapping to improve model comprehension.
3. Built-in token estimation calculates precise context window usage based on line counts, character density, and language-specific tokenization rules. Real-time analytics help users optimize input size for AI platforms while maintaining critical code relationships.

Problems Solved

1. EchoComet addresses the inability of IDE-based AI tools to handle large-scale code analysis due to restrictive token limits (typically 8k-32k tokens). It bypasses these constraints by interfacing directly with web-based LLMs supporting millions of tokens.
2. The product targets developers working on legacy systems, distributed architectures, or multi-repository projects where contextual awareness across thousands of files is essential for accurate AI assistance.
3. Typical use cases include debugging cross-module dependencies, generating architectural documentation, and implementing AI-suggested refactors that require simultaneous analysis of interconnected components across an entire codebase.

Unique Advantages

1. Unlike cloud-based alternatives, EchoComet operates entirely locally, ensuring code privacy by processing data on-device without third-party server transmission. This contrasts with tools requiring code uploads to external services for context processing.
2. The application uniquely combines file-system-level code aggregation with LLM-specific prompt engineering, automatically structuring inputs to match how AI models process hierarchical data. This includes namespace tagging and cross-file reference linking.
3. Competitive differentiation comes from its one-time purchase model with lifetime updates, multi-device licensing (3 simultaneous activations), and direct API integration with major AI providers using user-owned API keys for cost control.

Frequently Asked Questions (FAQ)

1. What AI services does EchoComet support? The tool integrates with OpenAI, Anthropic, Google Gemini, and xAI Groq through user-provided API keys, allowing flexibility in model selection and billing management through native platform dashboards.
2. How does EchoComet ensure code privacy? All processing occurs locally on the user's machine, with no data transmission to external servers. The application does not store or cache code after session termination, complying with enterprise-grade security requirements.
3. What operating systems are supported? EchoComet currently offers a macOS universal binary compatible with Intel and Apple Silicon chips, requiring macOS 10.15 (Catalina) or newer. Windows and Linux versions are planned for future updates.
4. Can I use EchoComet with private LLMs? Yes, the tool supports custom API endpoints, enabling integration with self-hosted models like Llama 3 or GPT-4 variants running on local infrastructure through its flexible configuration system.
5. How does the token estimation work? The algorithm combines GPT-4's tiktoken library with language-specific parsers to calculate exact token counts, accounting for whitespace compression, syntax structures, and embedded documentation across 48 programming languages.