Switchboard

What the company appears to be: Switchboard is a self-reported read-only command center for people operating across several ventures. The author describes it as an application that records deliberate venture transitions, estimates re-orientation costs, and helps redesign a fragmented day.

What changed: The project was submitted to the OpenAI 2026 hackathon. It includes a fictional workday fixture with reproducible results (6 venture switches, 2 cold entries, 74 estimated minutes) and is built using Next.js, React, TypeScript, and GPT-5.6 via Codex.

Single most important open question: Is there any evidence of real-world usage or adoption beyond the fictional demo? The description states no revenue, customers, or traction data are available beyond what the author reports.

The description states that Switchboard is a read-only command center for people operating across several ventures. It includes:

• An in-memory switch session
• Structured venture re-entry briefings
• A validated cross-venture priority merge
• A lower-switch venture-block planner
• Daily closeout narration
• An inspectable measurement explanation
• A fixed fictional judge workday requiring no account or credentials

The product is described as a deterministic planning model, not a scientific claim about productivity loss. It uses AI to propose complete venture orders but does not supply the success metric.

Evidence: The author's own write-up describes these features in detail.

The project’s tagline is: “Measure cross-venture context switching, explain the cost, and plan a calmer workday.”

The description states that the product was built as part of an OpenAI 2026 hackathon submission. It includes:

• A fictional workday fixture with reproducible results
• Use of GPT-5.6 via Codex for building the project
• Emphasis on deterministic behavior and transparency in AI use

There is no evidence of prior positioning or evolution beyond this single submission.

Evidence: Self-reported by the author; no external claims or historical context provided.

The description states that Switchboard targets people operating across several ventures. It does not define a specific customer segment or ideal customer profile (ICP) beyond this general use case.

Evidence: The author describes the target as "people operating across several ventures" but provides no further segmentation or persona details.

There is no evidence of any business model or pricing structure in the description. The project is presented as a hackathon submission with no indication of monetization, subscriptions, or paid features.

Evidence: Not evidenced.

The product is built using:

• Next.js 16 and React 19
• TypeScript
• Zod for validation
• OpenAI Responses API
• Vercel deployment
• Codex used in development

It includes:

• Deterministic measurement spine
• AI-assisted flows that return mock data when no API key is present
• Server routes supporting briefing, ranking, planning, and closeout narration
• Shared Read-Only Guarantee (no task completion, messaging, scheduling, deletion, or venture-data mutation)
• Public repository with tests, fixtures, and documentation

Evidence: The author describes the technical stack and architecture in detail.

There is no evidence of traction, revenue, customers, or adoption beyond the fictional demo. The project is described as a hackathon submission with no indication of real-world usage or product-market fit.

Evidence: Not evidenced.

The description does not mention any competitors or competitive landscape. It focuses only on the internal design and functionality of Switchboard.

Evidence: Not evidenced.

• The project is a hackathon submission with no evidence of real-world usage.
• No revenue, customers, or traction data are reported.
• The product is described as read-only, limiting its potential for growth or monetization.
• The use of mock data in the demo may obscure whether the AI components function properly in live scenarios.

Inference: These points are drawn from the lack of evidence and the nature of a hackathon project.

1. What is the intended path to market for this product beyond the hackathon?
2. Are there any plans to move beyond the read-only model or add user persistence?
3. How would you validate that the assumptions used in estimating switch costs align with real-world productivity loss?
4. Is there any plan to integrate actual user feedback or data into the estimator?
5. What are the next steps for product development, and how do they align with potential customer needs?