AI Usage Dashboard

The description states that AI Usage Dashboard is a local-first Windows application built as a dashboard and screensaver for monitoring Codex, Claude, and DeepSeek usage. The author, Jemaine Chen, claims to have developed it during a hackathon using tools like Codex, GPT-5.6, Rust, Tauri, React, and Playwright. It is described as a product that turns an idle Surface into a glanceable command center for AI usage.

Key commercial due-diligence questions:

1. Is this a product with real market demand or just a hackathon prototype?
2. What is the actual utility of such a dashboard in practice?
3. How does it differ from existing solutions, if any?

The single most important open question: Is there evidence of traction, revenue, or customer adoption beyond the author's own development?

• The description states that AI Usage Dashboard is a "local-first Windows dashboard and screensaver"
• It presents usage data for Codex, Claude, and DeepSeek in a fullscreen interface
• It supports disabling providers, which affects credential discovery, live requests, manual refreshes, and cache presentation
• It includes a synthetic demo mode for judges that uses embedded fixtures and separate preferences
• The author built it using Tauri/Rust/React stack with Playwright and NSIS packaging

• The description states the product was inspired by the need to monitor multiple AI services without breaking focus
• It positions itself as turning an idle Surface into a "glanceable command center for Codex, Claude, and DeepSeek usage"
• The author claims it makes AI capacity feel like a system resource that is always visible instead of hidden behind multiple tools
• The evolution from v0.2 to v0.3.0 involved adding provider selection with atomic persistence, credential isolation, cache retention, and layout responsiveness

• Not evidenced. The description does not specify target customers or ideal customer profiles beyond the author's own use case.

• Not evidenced. No pricing information, revenue model, or monetization strategy is described in the self-reported content.

• Built with Tauri/Rust/React stack
• Uses Codex and GPT-5.6 for development assistance
• Includes automated tests (44 Rust tests, Playwright checks)
• Supports Windows packaging with NSIS
• Has explicit failure states and last-known-good data preservation
• Implements credential and network isolation for disabled providers
• Features intentional zero-, one-, two-, and three-panel layouts
• Includes a synthetic demo mode for judges

• Not evidenced. No customer data, revenue figures, usage metrics, or adoption indicators are provided.

• Not evidenced. No information about existing competitive products or market positioning is included in the description.

• The product appears to be a hackathon prototype with no evidence of commercial traction
• The author states it's "not only a visual prototype" but also a runnable Windows product with installer and tests, suggesting it may have reached a minimum viable product stage
• Privacy concerns are acknowledged as a challenge, particularly around credential handling and preventing leaks into webview/logs/screenshot
• Concurrency issues were noted when disabling providers during refresh operations
• The project was submitted to a hackathon (OpenAI 2026), suggesting it may not have been designed for long-term commercial viability

1. What specific problem are you solving that existing solutions don't address?
2. Have you identified any paying customers or users beyond yourself?
3. How do you plan to monetize this product, if at all?
4. What is your go-to-market strategy for reaching potential users?
5. Are there any technical limitations or scalability concerns with the current architecture?
6. How does the privacy model handle edge cases in credential management?
7. What are the actual usage patterns and frequency of checks that users perform?