Archive position — measured, not model output
0 likes on Devpost
2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #4,908 place in the like-ranked listing is a tie-break inside that group, not a ranking.
Projects (log scale)
Likes on Devpost. ▲ marks this project's group.
Show the figures
| Likes | Projects | Share of archive |
|---|---|---|
| 0 | 5,592 | 71.2% |
| 1 | 1,758 | 22.4% |
| 2 | 285 | 3.6% |
| 3–4 | 132 | 1.7% |
| 5–9 | 75 | 1.0% |
| 10+ | 14 | 0.2% |
Executive Summary
What the company appears to be
Leaderboard Master is a self-reported macOS desktop application built by one developer (Jack Berk) for managing live-to-tape television obstacle-course competitions. It supports complex scoring workflows including qualifying leaderboards, multi-round heat racing, group stages, and elimination brackets. The tool is described as a producer-facing control-room console designed to consolidate fragmented scoring processes into one synchronized workflow.
What changed
During the OpenAI Build Week hackathon, the author used GPT-5.6 and Codex to extend an existing competition-management application into a more configurable production platform. Key additions included configurable multi-round heat racing, auto-seeding advancement workflows, and expanded scoring capabilities.
The single most important open question
Is there evidence of actual use or adoption by television producers, or has this remained a personal project with no commercial traction?
What The Product Actually Is
The description states that Leaderboard Master is a macOS desktop application built using JavaScript, HTML, CSS, and Electron, with Node.js handling integration and persistence. It supports:
- Qualifying leaderboards with configurable advancement modes
- Multi-round, three-person heat racing with previous-round Advancers, Byes, and manual entrants
- Round-robin group stages with records, standings, and wildcard advancement
- R32-through-Final elimination brackets with automatic winner propagation
- Fastest/farthest scoring, Summit Climb distance scoring, and custom tag handling
- Athlete imports, persistent local sessions, and portable session files
- Clean PDF and CSV outputs for production handoffs
- An isolated Demo Mode with fictional sample competitions
The application is described as designed for producer-facing control-room use, not public scoreboard display.
Evidence Self-reported by the author. No independent verification or demonstration of actual functionality beyond the developer's account.
Positioning & Claim Evolution
The author positions Leaderboard Master as a solution to real-time scoring inefficiencies in live-to-tape television competitions, where producers currently rely on spreadsheets and separate documents that must be reconciled during fast-paced events.
The product is framed as a single source of truth for competition management, replacing fragmented workflows with synchronized scoring, advancement, matchups, persistence, and exports within one workflow.
During OpenAI Build Week, the author claims to have significantly extended the tool's capabilities through AI-assisted development, adding features like:
- Configurable multi-round heat racing
- Auto-seeding advancement
- Expanded scoring and result-state handling
- Improved bracket workflows
Evidence Self-reported. The claim of "meaningfully extending" the application during Build Week is based on the author’s own account.
Target Customer & ICP
The description states that Leaderboard Master is built for television producers managing live-to-tape obstacle-course competitions, specifically in control-room environments where real-time scoring and standings are critical for host commentary, story development, and editorial decisions.
It targets users who need to manage complex formats such as:
- Qualifying rounds
- Multi-round heat racing
- Group stages with wildcards
- Elimination brackets from R32 through Final
The tool is described as a producer-facing console, not a public-facing scoreboard.
Evidence Self-reported. No evidence of actual customers or user feedback beyond the developer’s own experience.
Business Model & Pricing Evidence
Not evidenced.
The description does not contain any information about pricing, licensing, monetization strategy, or business model. There is no mention of revenue streams, subscriptions, or commercial sales.
Technical & Delivery Signals
The application is built using:
- Electron (desktop framework)
- JavaScript, HTML, CSS
- Node.js for desktop integration and persistence
- GPT-5.6 and Codex used during OpenAI Build Week to assist in development
Key technical claims include:
- AI-assisted engineering with GPT-5.6 and Codex
- Use of negative instructions to prevent regressions
- Deterministic Demo Mode behavior
- Refactoring shared logic across thousands of lines of code
- Integration of scoring, advancement, persistence, bracket, and export pipelines
The author notes that the tool supports persistent local sessions, portable session files, and clean PDF/CSV exports.
Evidence Self-reported. No independent technical review or demonstration provided.
Traction & Maturity Signals
Not evidenced.
There is no evidence of actual users, customers, revenue, or adoption beyond the developer’s own account. The project is described as a personal development effort with no mention of external validation, usage metrics, or market traction.
Competitive Context
Not evidenced.
The description does not provide any information about existing competitors in the live television competition scoring space, nor does it describe how Leaderboard Master compares to other tools or platforms used by producers.
Key Risks & Red Flags
- No commercial traction: The project is described as a personal development effort with no evidence of real-world use.
- Unverified claims: All features and improvements are self-reported without independent verification.
- Single-person team: Only one developer (Jack Berk) is mentioned, raising questions about scalability or long-term maintenance.
- AI-assisted development risks: The reliance on GPT-5.6 and Codex introduces uncertainty around quality control and reproducibility.
- Limited scope: The tool appears tailored to a niche market (television obstacle-course competitions), limiting potential for broader adoption.
Diligence Questions To Ask The Founders
- Has Leaderboard Master been used in any real production environments, or is it purely experimental?
- What specific television productions have used this tool, if any?
- Are there any users or customers who could provide third-party validation of its utility?
- How does the product handle edge cases or unexpected inputs during live events?
- What are the long-term plans for monetization or commercial deployment?
- Can you demonstrate how the AI-assisted development process works in practice, and what safeguards are in place to prevent unintended behavior?
Investment/Partnership Verdict
Not evidenced.
There is no evidence of revenue, customers, or traction that would support an investment or partnership decision. The project remains a self-reported personal development effort with no commercial validation. Any potential for growth depends on whether the tool finds adoption in actual television production settings — which is not yet evidenced.
Source
Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.
The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.