LeetPilot - Safest Invisible AI for Coding Interview and Tests

What

LeetPilot is an AI assistant designed for coding interviews and programming tests. Based on the page, it runs as a background desktop app that captures problems shown on the interview computer and displays solutions on a separate device such as a phone, tablet, or laptop.

It appears positioned for candidates who want off-screen assistance during live interview or assessment settings on platforms such as LeetCode, HackerRank, and HackerEarth, as well as problems shown in meeting tools or plain text editors. Its core workflow is: capture the visible problem, generate multiple solutions with complexity notes, and support follow-up questions through a chat interface on a second device.

Features

• Window-less assistance on the interview computer — The app is described as running in the background without a visible window, with answers shown only on another device.
• Second-device answer display — Users can view outputs on a phone, tablet, or laptop, which separates the help interface from the main interview machine.
• Problem extraction via screenshots — The product can capture coding questions from the screen, either triggered from the helper device or via a shortcut on the interview device.
• Multiple solution generation — It provides more than one solution for a programming problem and includes time and space complexity information.
• Follow-up chat interface — Users can ask for modifications or clarifications through a ChatGPT-like chat workflow.
• Support for multiple programming languages — The page lists Python, C++, Java, TypeScript, Swift, C#, Go, Kotlin, Ruby, Rust, and JavaScript.

Helpful Tips

• Assess this category of tool against your interview policies and testing rules, since the page emphasizes invisibility and off-screen assistance rather than interviewer-visible collaboration.
• Validate how well screenshot-based extraction works for the specific environments you use, especially if problems appear in custom editors, remote desktops, or low-resolution video calls.
• Test language coverage on the kinds of tasks you expect, because listed language support does not by itself confirm equal depth across algorithms, debugging, and language-specific edge cases.
• Review privacy implications carefully before adoption; the page mentions screen capture and “100% Privacy,” but it does not provide technical detail on data handling in the provided content.
• If comparing alternatives, focus on latency, follow-up quality, and reliability on a second device, since those factors are central to this product’s workflow.

OpenClaw Skills

LeetPilot could likely be extended within the OpenClaw ecosystem through skills that organize problem capture, classify question type, and route prompts for faster algorithmic explanation or code refinement. A likely workflow would be an agent that receives screenshots, identifies whether the task is dynamic programming, graph traversal, SQL, or debugging, and then structures follow-up prompts to produce alternative solutions, tradeoff summaries, and concise explanation notes for the user.

More broadly, OpenClaw could support surrounding workflows for interview preparation and assessment analysis rather than only real-time solving. Likely use cases include agents that convert solved problems into reusable study guides, generate mock variants of captured questions, track weak topic areas over time, or produce language-specific rewrites. That combination could shift this kind of product from a point solution for live assistance into a broader coding-interview operations layer for candidates, coaches, or training programs, though the source page does not state any native OpenClaw integration.