OpenAI 2026 hackathon

Everyday Life

Everyday Life is an ironic platformer about surviving the daily commute. Collect coffee, mute notifications, fight doomscrolling, and reclaim your attention from the screen.

Solo project by Larper Mazic · 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 #3,986 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

1
10
100
1k
10k
05,592
11,758
2285
3–4132
5–975
10+14

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

**The company appears to be a solo indie game developer working on an auto-running platformer titled Everyday Life. The project is self-reported as a playful, thematic take on digital distraction during daily life, using Godot 4 and GDScript. It is not evidenced to have any revenue, customers, or traction beyond the author’s own account.**

The single most important open question is: What is the commercial viability of this concept, and does it have any path toward monetization or user adoption?

This analysis is based entirely on the self-reported description provided by the author — no external verification or historical data are available.

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What The Product Actually Is

  • The description states that Everyday Life is an auto-running platformer.
  • It involves collecting coffee, jumping over gaps, building temporary steps, and dodging notification cards.
  • Gameplay mechanics include:
    • Second Wind (double jump),
    • Focus Mode (blocks a notification that would otherwise kill the player),
    • A friend joining via video-call bubble to mute notifications.
  • The game progresses through seven parts of the day, from Morning Rush to Off the Clock.
  • It uses procedurally generated terrain, with difficulty and obstacles changing per run.
  • Visuals are pixel-inspired, drawn directly in code using Godot’s custom drawing tools.
  • Sound effects and music are synthesized at runtime.

Inference: The game is a thematic platformer that metaphorically represents modern digital life through gameplay mechanics. It is not evidenced to be monetized or distributed beyond the author's own account.

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Positioning & Claim Evolution

  • The description states that Everyday Life is an ironic platformer about surviving the daily commute.
  • It positions itself as a playful adventure that turns ordinary life struggles — like commuting, coffee, notifications, doomscrolling — into game mechanics.
  • The author mentions shifting from a Wizard aesthetic to a more relatable everyday theme.
  • The game is described as a "coherent story about attention", where every mechanic has a visual metaphor.

Inference: The positioning evolved from a fantasy-based theme (wizard) to one grounded in real-world digital experiences. This shift suggests an intent to make the game more universally relatable, but no evidence of market testing or user feedback is provided.

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Target Customer & ICP

  • Not evidenced.

The description does not state who the intended audience is, nor does it describe any customer segmentation or ideal customer profile (ICP). No demographic or behavioral data are provided.

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Business Model & Pricing Evidence

  • Not evidenced.

There is no mention of pricing, monetization strategy, or business model in the description. The project is presented as a hackathon submission and not as a commercial product.

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Technical & Delivery Signals

  • Built with Godot 4 using GDScript.
  • Uses procedural generation for terrain and gameplay.
  • Visuals are pixel-inspired, drawn directly in code using Godot’s tools.
  • Sound and music are code-generated at runtime.
  • The author mentions challenges with rendering differences between desktop and mobile.
  • A smoke-test suite was built to cover core mechanics and terrain generation.

Inference: The technical approach is self-contained, using open-source tools (Godot) and code-based rendering. It shows some engineering sophistication but no evidence of production-grade delivery or scalability.

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Traction & Maturity Signals

  • Not evidenced.

There is no evidence of revenue, users, downloads, or adoption beyond the author’s own account. The project is described as a hackathon submission, with no mention of distribution, playtesting, or user feedback.

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Competitive Context

  • Not evidenced.

The description does not reference any competitors or market positioning in relation to other games or platforms. No competitive landscape is described.

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Key Risks & Red Flags

  • No commercial traction or monetization strategy: The project is presented as a solo hackathon effort with no evidence of a path to revenue.
  • Solo developer model: With only one team member, scalability and long-term development capacity are unclear.
  • Lack of user feedback or playtesting: No mention of external testing or community engagement.
  • Thematic depth vs. gameplay execution: While the theme is described as meaningful, there’s no evidence that it translates into engaging gameplay or player retention.

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Diligence Questions To Ask The Founders

  1. What is your plan for monetization or distribution beyond this hackathon submission?
  2. Have you conducted any user testing or playtesting with real players?
  3. How do you intend to scale the game beyond a single developer’s effort?
  4. Do you have a long-term vision for how the game could evolve into a sustainable product?
  5. What are your plans for mobile optimization, given that rendering differences were noted?

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Investment/Partnership Verdict

  • Not evidenced.

There is no evidence of any investment or partnership interest in this project. The description does not indicate any commercial intent beyond personal development or hackathon submission. No financials, funding rounds, or strategic partnerships are mentioned.

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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.