OpenAI 2026 hackathon

Resolve

AI-assisted opportunity filter for freelancers: detect stale bounties, scam repos, claimed rewards, and low-value tickets before wasting time.

Solo project by Symmetry Enterprises · 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 #6,391 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

What the company appears to be

Resolve is a self-reported AI-assisted CLI tool for freelancers that evaluates paid technical opportunities (e.g., bounties, jobs) from public platforms like GitHub, Algora, and Superteam Earn. It classifies opportunities as "Apply now", "Investigate", "Strategic job", or "Reject" based on criteria such as claimed status, deadline expiry, region restrictions, and suspicious repository behavior.

What changed

The project evolved from a simple opportunity scanner into a safety-first tool after detecting a potentially malicious client ZIP file during the hackathon. It now includes structured JSON sources, AI-assisted scoring, and manual review workflows to protect developers from scams or low-value work.

The single most important open question

Does Resolve have any real-world usage or adoption beyond its initial hackathon prototype? The description states no revenue, customers, or traction data are available — only the authors' own account of what it does and how it was built.

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What The Product Actually Is

  • The description states that Resolve is a lightweight Node.js CLI tool.
  • It uses structured JSON sources for marketplace scans.
  • It includes:
    • A scoring and classification script
    • Structured opportunity source files
    • Rejection reasons and priority labels
    • A workflow for safe manual review before executing unknown code
  • The tool is described as not just a tracker, but an AI-assisted decision layer for filtering paid opportunities.

Evidence Self-reported by the authors. No independent verification or product demo provided.

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Positioning & Claim Evolution

  • The description states that Resolve was built to address a real operational problem: finding paid technical work is not just about spotting opportunities, but knowing which ones are worth pursuing.
  • It evolved from a basic opportunity scanner into a safety-first developer tool after discovering a suspicious repository during the hackathon.

Inference The positioning shifted from a generic job tracker to a tool focused on developer safety and time protection, especially in Web3 freelance environments.

Evidence Self-reported. No external market positioning or branding data.

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Target Customer & ICP

  • The description states that Resolve is for freelancers and small technical teams.
  • It is designed to help them decide whether to apply, investigate, or reject paid opportunities before spending hours on them.
  • It targets those looking for work on platforms like GitHub, Algora, Superteam Earn, and Telegram job channels.

Evidence Self-reported. No segmentation data or customer interviews cited.

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Business Model & Pricing Evidence

  • The description does not state any pricing model, revenue streams, or monetization strategy.
  • It is described as a CLI tool, not a SaaS product.
  • No mention of subscriptions, paid features, or commercial use cases beyond the hackathon prototype.

Evidence Not evidenced. The authors do not describe how they intend to make money from this tool.

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Technical & Delivery Signals

  • Built with:
    • Node.js
    • JavaScript
    • OpenAI Codex
    • JSON sources
    • GitHub, Algora, Superteam Earn integrations (initially)
  • Uses structured JSON for marketplace scans.
  • Includes a scoring and classification script, and a workflow for safe manual review before executing unknown code.
  • The first version evaluates real candidates from:
    • Algora
    • Superteam Earn
    • Telegram job channels
    • Manual intake

Evidence Self-reported. No delivery history or technical architecture details beyond the initial build.

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Traction & Maturity Signals

  • The project is described as a hackathon prototype.
  • It was submitted to the OpenAI 2026 hackathon on Devpost.
  • No evidence of:
    • Revenue
    • Customers
    • Adoption
    • Product usage metrics
    • Product versioning or iteration history

Evidence Not evidenced. The authors do not report any traction beyond the initial build.

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Competitive Context

  • The description does not mention competitors or similar tools.
  • It is positioned as a decision-making layer for freelance opportunities, especially in Web3 environments.
  • No mention of existing tools that filter bounties or job listings.

Evidence Not evidenced. No competitive analysis or market positioning provided.

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Key Risks & Red Flags

  • The tool is described as a CLI-based prototype, not a commercial product.
  • No evidence of:
    • Product-market fit
    • Customer feedback
    • Revenue model
    • Scalability or delivery mechanisms beyond the hackathon version
  • The project was built in one week, and no follow-up or iteration is reported.

Inference The tool may be experimental, with limited commercial viability without further development or traction.

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Diligence Questions To Ask The Founders

  1. What is the actual usage of this tool beyond the hackathon prototype?
  2. Have you validated the accuracy of your scoring model with real freelancers?
  3. How do you plan to monetize this tool, if at all?
  4. Are there any existing users or early adopters?
  5. What are the key assumptions behind the classification logic (e.g., how is “low-value” defined)?
  6. Do you have plans for a web-based interface or API integration?

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Investment/Partnership Verdict

  • The project is described as a hackathon prototype with no evidence of traction, revenue, or customer adoption.
  • It is not yet a commercial product, and the authors do not describe any monetization strategy.
  • The tool is described as AI-assisted but built for a narrow use case (freelancers in Web3).
  • No evidence of team traction, funding, or market validation.

Inference This is an early-stage idea, likely not ready for investment or partnership unless further development and proof of concept are demonstrated.

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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.