OpenAI 2026 hackathon

Window Swap

One spatial action to cycle stacked Windows apps.

Solo project by JC OM · 0 likes · 0 comments

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 #7,704 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

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1k
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Likes on Devpost. ▲ marks this project's group.

Show the figures
LikesProjectsShare of archive
05,59271.2%
11,75822.4%
22853.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,856 archived projects, captured when this archive was built.

Executive Summary

What the company appears to be

Window Swap is a self-reported local Windows tray utility designed to improve window-switching behavior when multiple applications are stacked in the same screen region. It was built as a hackathon project and is described as a minimal, privacy-conscious tool with no telemetry or network dependencies.

What changed

The project description reflects an evolution from prototype to a testable public release, incorporating feedback from a build week and AI assistance (Codex). The author made key product decisions to keep the utility local, avoid AI or telemetry, and preserve a deliberate interaction model.

Single most important open question

Is there any evidence of user adoption or feedback beyond the authors’ own testing? The description states no revenue, customers, or traction data are available.

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What The Product Actually Is

The description states that Window Swap is a tiny local Windows tray utility. It inspects visible top-level window geometry and, when the pointer reaches the bottom-right corner of a window region, shows a compact Swap control that brings the next window with matching bounds forward.

It supports English and Portuguese, requires no account or administrator rights, and contains no telemetry, network client, or background service. It is built using Python, Win32 APIs, Tk, and Windows tray integration.

The utility is described as a local-first tool, meaning it runs entirely on the user’s machine without cloud dependencies.

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Positioning & Claim Evolution

The author states that Window Swap was inspired by the limitations of Windows Snap when multiple apps occupy the same screen region. It aims to improve switching behavior by keeping spatial context intact.

The positioning is described as a minimal, privacy-conscious utility with no telemetry or AI. The project evolved from an earlier prototype into a testable public release during a build week, with input from Codex and real-world testing.

It does not claim to be a productivity suite or platform — it is positioned as a specific solution for window switching in stacked layouts, with no broader commercial ambitions stated.

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Target Customer & ICP

The description states that Window Swap is intended for Windows users who work with multiple applications stacked in screen regions, particularly in multi-monitor setups. It targets users who value spatial context and minimal tooling.

No explicit customer segments or personas are defined. The project is described as a local utility, not a commercial product targeting enterprise or consumer markets.

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Business Model & Pricing Evidence

Not evidenced.

The description does not state any pricing model, monetization strategy, or business model. It is described as a free, open-source tool under MIT license.

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Technical & Delivery Signals

The project is built with:

  • Python
  • Win32 APIs
  • Tk (for UI)
  • Windows tray integration

It uses GitHub Actions for CI/CD and builds one-file releases with SHA-256 digest verification. The author states that the tool supports deterministic testing, separates geometry logic from OS effects, and includes:

  • Fifteen passing tests
  • Visual testing at 150% scaling
  • Correct handling of negative coordinates
  • Duplicate-instance protection

It is described as a local-first utility with no telemetry or network dependencies.

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Traction & Maturity Signals

Not evidenced.

The description does not include any data on downloads, usage, user feedback, or adoption beyond the authors’ own testing and validation.

It was submitted to a hackathon, and the project is described as a testable public release, but no evidence of traction or growth is provided.

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Competitive Context

Not evidenced.

The description does not mention any competitors or similar tools in the market. It is unclear whether there are existing utilities with comparable functionality.

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Key Risks & Red Flags

  • No evidence of user adoption or feedback: The project is described as a hackathon submission and testable release, but no data on usage or traction exists.
  • No commercialization strategy: There is no indication that the project intends to evolve into a product with revenue or customer acquisition.
  • Limited scope and maturity: It is a minimal utility built for personal use, not a scalable or enterprise-grade solution.
  • Self-reported only: All claims are unverified and based on the author’s own account.

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Diligence Questions To Ask The Founders

  1. What was the original problem you were trying to solve, and how did you validate that it was a real pain point?
  2. Have you received any feedback from users beyond your own testing?
  3. Are there plans to expand functionality or monetize the tool?
  4. How do you intend to scale or evolve this utility if at all?
  5. What are the technical limitations of the current implementation, and how might they affect broader adoption?

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Investment/Partnership Verdict

Not evidenced.

There is no evidence of revenue, customers, traction, or commercial strategy beyond the authors’ own description. The project is described as a local utility built for personal use, not a scalable business or product with investment potential.

It is unclear whether this represents an early-stage idea or a completed tool with future plans. No indication of commercial intent or market opportunity is provided.

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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.