dormant

Note: All claims are self-reported and unverified. No evidence of revenue, customers, funding, or adoption is present in the description.

The description states that dormant is a Rust daemon designed to blank OLED screens when presence sensors indicate the room is empty and wake them instantly upon return. It supports multiple hardware interfaces (DDC/CI, Samsung TV JSON-RPC, DPMS) and uses various strategies to preserve audio output during screen blanking.

It includes:

• A per-panel wear ledger with brightness-weighted exposure tracking
• Screensaver ladder with pixel-shift mitigation
• KDE tray and macOS menu-bar integration
• Web dashboard for monitoring
• Emergency wake functionality
• Hardware probing before implementation

The system is built using a Cargo workspace of nine crates, split into pure logic and hardware-specific controllers. It supports Linux, macOS, and Windows platforms.

Claim: dormant is a Rust daemon that blanks OLED screens when presence sensors say the room is empty and wakes them the instant you walk back in.

Evidence: Yes — from the project write-up.

Inference: The product is likely a developer tool or personal utility rather than a commercial offering.

Supporting evidence: No revenue, customer, or business model data provided.

The author positions dormant as a solution to pixel burn-in on OLED displays while preserving audio output — a niche but specific problem for users with OLED monitors and TVs.

Key claims:

• Solves the issue of screen blanking killing audio (e.g., DPMS tears down ALSA device).
• Uses mmWave radar sensors, which are less expensive than other solutions.
• Provides hardware-specific control paths to maintain audio during blanking.
• Offers wear tracking and pixel-shift mitigation features not found elsewhere.

There is no indication that dormant has evolved beyond a hackathon prototype or personal utility. The project does not reference any branding, marketing, or strategic positioning toward a broader market.

Claim: dormant prevents OLED burn-in while preserving audio output.

Evidence: Yes — from the write-up.

Inference: It is a niche tool for users with OLED screens and presence sensors.

Supporting evidence: No mention of target audience beyond personal use or home automation.

The description does not state a defined customer segment or ideal customer profile (ICP). The author describes using the system in their own home setup, but no explicit targeting of end-users is evident.

It appears to be aimed at:

• Home users with OLED displays
• Developers or tech enthusiasts who want to avoid pixel burn-in
• Users who rely on audio output from OLED screens

However, there is no evidence of market research, user personas, or segmentation data.

Claim: The target customer is likely home users or developers managing OLED displays.

Evidence: Inferred from the author’s personal use case and hardware focus.

Supporting evidence: Not explicitly stated.

There is no evidence of a business model, pricing strategy, or monetization plan in the description.

The project appears to be a self-contained tool built for personal or internal use. No mention of licensing, subscriptions, or commercial sales channels.

Claim: The product has no known business model or pricing structure.

Evidence: Not evidenced — no revenue, customer, or monetization data provided.

The project is built in Rust with a modular architecture using Cargo workspaces and traits for hardware abstraction. It includes:

• CI pipeline with 34 checks (portability, linting, stress tests)
• Support for multiple platforms (Linux, macOS, Windows)
• Release assets across six binaries, Homebrew, and AUR packages
• Use of MQTT, WebSockets, USB serial, and DDC/CI protocols

It also includes:

• Wear tracking via grid-level exposure logs
• Pixel-shift screensavers
• Emergency wake mechanisms
• Hardware probing before code implementation

Claim: The system is built with a modular Rust architecture and supports multiple platforms.

Evidence: Yes — from the write-up.

Inference: The tool is likely designed for developers or power users.

Supporting evidence: Not explicitly stated, but implied by technical depth.

There is no evidence of traction, adoption, or user base beyond the author’s own use case.

The project:

• Has shipped v0.5.0
• Includes CI and release automation
• Is dogfooded in a production environment
• Has documentation (mdBook)
• Has a documented roadmap for future features

However, no data on downloads, users, or customer engagement is provided.

Claim: The project has reached v0.5.0 with CI/CD and documentation.

Evidence: Yes — from the write-up.

Inference: It may be mature enough for internal use but lacks external validation or adoption.

Supporting evidence: Not evidenced.

The description does not mention competitors, nor does it provide any competitive analysis or positioning against existing tools.

It claims to offer:

• Audio-safe blanking
• Wear tracking
• Pixel-shift mitigation
• Multi-platform support

But no comparison to other solutions is made.

Claim: No direct competitors are named.

Evidence: Not evidenced — no mention of similar products.

Key risks and red flags:

• The project is self-reported, unverified, and lacks any third-party validation.
• No evidence of revenue, customers, or traction.
• No indication of scalability beyond personal use.
• The author is a single individual (1-person team), which may limit development velocity or long-term sustainability.
• The system is built for specific hardware protocols; generalizability to other devices is unclear.

Claim: The project lacks commercial traction and third-party validation.

Evidence: Not evidenced — no data on adoption, revenue, or partnerships.

1. What is the intended path from this hackathon prototype to a commercial product?
2. Are there any plans for monetization or distribution beyond personal use?
3. How does the system handle edge cases in real-world environments (e.g., sensor failures, network issues)?
4. Has the project been tested with other hardware vendors beyond Samsung and AOC?
5. What is the long-term vision for multi-machine coordination and presence APIs?

Note: These questions are based on the self-reported nature of the description and aim to uncover unaddressed assumptions or future plans.