OpenAI 2026 hackathon

Sage Forge

The first Agent Native Multimodal platform designed to help you build relationships, boost your EQ, and even help you pet

Solo project by LEO xu · 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 #6,516 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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05,592
11,758
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3–4132
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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

Sage Forge is described as an "Agent Native Multimodal platform" designed to help users build relationships, boost emotional intelligence (EQ), and even pet things. The project was submitted to the OpenAI 2026 hackathon by a single founder, LEO xu.

What changed

There is no evidence of prior version or evolution — this is a self-reported, unverified description of a new project submitted for a hackathon.

The single most important open question

What is the actual product functionality and whether it delivers on its claims about EQ, relationship-building, and petting?

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What The Product Actually Is

The description states that Sage Forge is "The first Agent Native Multimodal platform." It also says it is designed to help users "build relationships, boost your EQ, and even help you pet."

However, there is no clear explanation of how the product works or what its core functionality is. The author does not describe specific features, use cases, or user flows.

Evidence

  • The description states that Sage Forge is an “Agent Native Multimodal platform.”
  • It claims to assist with building relationships, boosting EQ, and petting.
  • No further detail on how the product functions or what it actually does.

Confidence Low — the description is sparse and self-reported.

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Positioning & Claim Evolution

The author positions Sage Forge as a platform that helps users build relationships, improve emotional intelligence, and even pet things. The tagline is unusual and suggests an experimental or playful approach to AI interaction.

There is no indication of prior positioning or evolution in claims — this appears to be the first articulation of its purpose.

Evidence

  • Tagline: “The first Agent Native Multimodal platform designed to help you build relationships, boost your EQ, and even help you pet.”
  • No mention of prior versions or claim evolution.

Confidence Very low — no historical positioning or development context provided.

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Target Customer & ICP

There is no evidence in the description about who the target customer is or what their identity (ICP) might be. The claims are broad and not tied to a specific segment.

Evidence

  • No mention of target personas, user types, or customer segments.
  • Claims are general and abstract: “build relationships,” “boost EQ.”

Confidence Not evidenced — no indication of who the product is for.

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Business Model & Pricing Evidence

There is no evidence in the description about how Sage Forge intends to make money or what its pricing model might be. The author does not describe monetization, subscriptions, or any commercial structure.

Evidence

  • No mention of business model or pricing.
  • No indication of revenue streams or monetization strategy.

Confidence Not evidenced — no commercial details provided.

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Technical & Delivery Signals

The project was built using several technologies, as declared by the author: airi, elevenlabs, gpt, hume, stt, three.js, tts. These tools suggest a multimodal AI platform with voice, text, and visual components.

However, there is no evidence of how these technologies are integrated or whether they form a working product.

Evidence

  • Built with: airi, elevenlabs, gpt, hume, stt, three.js, tts.
  • No demonstration, prototype, or delivery details provided.

Confidence Low — only declared tech stack, no evidence of implementation or functionality.

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Traction & Maturity Signals

There is no evidence of traction, adoption, or product maturity. The project was submitted to a hackathon and appears to be in early development.

Evidence

  • Submitted to OpenAI 2026 hackathon.
  • Team size: 1.
  • No mention of users, customers, or usage metrics.

Confidence Not evidenced — no signs of traction or maturity.

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Competitive Context

There is no evidence in the description about who the competitors are or how Sage Forge compares to existing solutions in the space. The author does not reference any competitive landscape.

Evidence

  • No mention of competitors, market positioning, or differentiation.
  • No indication of existing players in the multimodal AI or EQ-building space.

Confidence Not evidenced — no competitive context provided.

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Key Risks & Red Flags

Key risks include:

  • Lack of clarity on product functionality.
  • Unusual claims (e.g., “help you pet”) that may not be actionable.
  • No evidence of traction, customers, or monetization.
  • Single-founder team with no prior track record or team history.

Evidence

  • Vague and unverifiable claims.
  • No demonstration or prototype.
  • No evidence of product development beyond hackathon submission.

Confidence Low — risks are inferred from lack of evidence.

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Diligence Questions To Ask The Founders

  1. What is the core functionality of Sage Forge? How does it work?
  2. What specific problem is it solving, and for whom?
  3. How do you plan to monetize this product?
  4. What is the current state of development (e.g., prototype, MVP)?
  5. Are there any existing users or early adopters?
  6. What are the key technical challenges in building this platform?

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Investment/Partnership Verdict

Verdict Not evidenced.

There is insufficient information to assess whether Sage Forge has investment or partnership potential. The description is too sparse and self-reported to support a due-diligence read on commercial viability, traction, or scalability.

Confidence Very low — no evidence of product, customers, or business model.

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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.