OpenAI 2026 hackathon

SpatiumTempus

SpatiumTempus is a narrated visual presentation that turns special-relativistic time dilation into a detective case to investigate twelve seconds of stolen time!

Solo project by Anoop Parayil · 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 #6,890 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

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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

SpatiumTempus is a self-reported educational tool that uses a detective-story format to teach special-relativistic time dilation. The author, Anoop Parayil, describes it as a narrated visual presentation where Inspector Holmes interrogates Mr. Einstein after he is caught with a stolen atomic clock following a high-speed journey. The project is presented as an attempt to make the concept of time dilation more accessible through storytelling and visual design.

The core commercial due-diligence question is: What is the intended audience, and how does this educational tool intend to reach them?

This analysis is based entirely on the self-reported description provided by the author. No evidence of revenue, customers, traction or market validation is present in the submission.

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What The Product Actually Is

The description states that SpatiumTempus is a narrated visual presentation that uses a detective-story format to introduce special-relativistic time dilation. It includes:

  • Character dialogue
  • Clock comparisons
  • Simple motion diagrams
  • Evidence-style visuals
  • Voice narration
  • Visual presentation design

It is built using tools such as codex, illustrated-evidence-scenes, motion-graphic, openai, scripted-dialogue, visual-presentation-design, and voice-narration.

The project is described as a "Time Thief" case involving Mr. Einstein stealing an atomic clock and fleeing Earth at 0.99c to Plasma Square Station and back, with the stolen time being twelve seconds.

It is not evidenced whether this is a standalone presentation or intended for further development into an interactive website or simulation.

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Positioning & Claim Evolution

The author claims that SpatiumTempus reframes time dilation as a memorable criminal investigation. The project positions itself as:

  • An educational tool to teach physics concepts
  • A narrative-driven approach to explaining complex science
  • A way to make the “missing time” puzzle understandable without treating it as magic

It is described as an attempt to bridge classical and modern physics through dialogue and visual storytelling.

There is no evidence of prior positioning or evolution in claims beyond this self-description. No mention of prior versions, marketing efforts, or audience feedback.

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Target Customer & ICP

The description does not state a specific target customer or ideal customer profile (ICP). It is unclear whether the tool is intended for:

  • Students
  • Educators
  • Science enthusiasts
  • General public
  • Hackathon participants

No evidence of market segmentation, user personas, or audience targeting is present.

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Business Model & Pricing Evidence

There is no evidence of a business model or pricing structure. The project is described as a hackathon submission and does not indicate any monetization strategy, licensing, or distribution plans.

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Technical & Delivery Signals

The author states that the presentation combines:

  • Character dialogue
  • Clock comparisons
  • Simple motion diagrams
  • Evidence-style visuals
  • Voice narration
  • Visual presentation design

It is built using tools like codex, openai, visual-presentation-design, and voice-narration. The project uses a physics model based on Special Relativity in flat spacetime, focusing on relative motion, acceleration, signals, and proper time.

There is no evidence of technical architecture, scalability, or delivery platform beyond the presentation format.

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Traction & Maturity Signals

The project is described as a hackathon submission to the OpenAI 2026 hackathon. No evidence of traction, adoption, or user engagement is provided.

It is not evidenced whether the tool has been used in classrooms, shared widely, or received feedback from users beyond the author’s own account.

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Competitive Context

There is no evidence of competitors or similar products in the market. The description does not reference other educational tools, physics simulators, or narrative-based learning platforms.

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Key Risks & Red Flags

  • No traction or validation: The project is a hackathon submission with no evidence of adoption or user feedback.
  • Unproven audience: No indication of target users or market demand.
  • Limited scope: The tool appears to be a one-off educational piece, not a scalable product.
  • Self-reported only: All claims are unverified and based solely on the author’s account.

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Diligence Questions To Ask The Founders

  1. What is your intended audience for SpatiumTempus?
  2. Have you tested this with students or educators? If so, what was the feedback?
  3. Are there plans to monetize or scale this beyond a hackathon submission?
  4. How do you plan to reach and distribute this educational tool?
  5. What are the key learning outcomes you expect from users?

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Investment/Partnership Verdict

Not evidenced.

The project is described as a hackathon submission with no evidence of traction, revenue, or market validation. It is not evident whether it has any commercial potential beyond its initial form. The author does not describe any plans for further development, distribution, or monetization.

This is an educational prototype with unclear commercial viability and no demonstrated path to market adoption.

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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.