I created H.L.D.R to develop a product (or proof-of-concept) that harnesses GenAI for positive impact.

I often found myself becoming overly reliant on tools that were diminishing my confidence and inducing a sense of imposter syndrome in my work and knowledge.

It became increasingly difficult to tackle challenging problems without resorting to ChatGPT for solutions.

H.L.D.R was my solution to break this dependency.

The implementation of H.L.D.R follows a streamlined three-step process:

Step 1: The user writes their code as usual, focusing on solving a problem, creating a feature, or exploring algorithms.

Step 2: The file is saved. This can happen manually (via CTRL+S) or automatically based on the user's preferences. Saving acts as the trigger for the analysis.

Step 3: H.L.D.R produces insights directly in the command line. This feedback may include error detection, suggestions for optimization, or alternative solutions tailored to the user's coding style or goals.

This approach is now enhanced to provide real-time feedback, improving accessibility and user experience.

H.L.D.R now boasts the following advanced features:

• Real-Time Feedback: Provides immediate insights as you type, reducing delays and enhancing productivity.
• Cross-Platform Compatibility: Hosted using a FastAPI backend on fly.dev, ensuring seamless access across various environments.
• Enhanced Accessibility: Streamlined setup and improved CLI interface make it more user-friendly for developers at all levels.
• Platform Independence: No longer restricted to GitHub Codespaces, allowing local development and broader IDE integration.
• Universal Theme Support: HLDR works seamlessly with any editor theme.
• Debouncing for API Requests: Optimized for environments with autosave enabled.
• Enhanced Markdown Formatting: Offers syntax highlighting, bold text, and code snippets for improved readability.
• High Performance: The backend can process up to 1000 tokens per minute, ensuring efficient handling of requests.

To build upon this foundation, I plan to implement:

• Analysis History: Implement a feature to retain the history of analyses within the current session, instead of replacing previous content with the latest response.
• Neovim Plugin: Create a plugin for Neovim leveraging Lua, featuring asynchronous communication, floating windows for insights, and configurable key mappings.
• Expanded IDE Support: Build plugins for other popular IDEs like JetBrains IntelliJ, PyCharm, and Sublime Text to make H.L.D.R widely accessible.
• Code Snippets: Allow users to select specific portions of their code for analysis, reducing token usage and improving request completion time.