OpenAI 2026 hackathon

Git --Profile Print

An evidence-first AI learning coach that turns your public GitHub work into a personalized four-week sprint with measurable tasks and proof you can ship.

Solo project by Anuranjan Jain · 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 #4,316 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

Git --Profile Print is a self-reported AI-powered learning coach for developers that analyzes public GitHub profiles and generates personalized four-week learning sprints. The author states it uses GitHub API, OpenAI GPT-5.6, Codex, and Next.js.

What changed

The project was submitted to the OpenAI 2026 hackathon, suggesting a recent development or prototype phase. No evidence of prior traction, customers, or revenue is provided.

Single most important open question

Is there any evidence that this tool has been used by developers beyond the author's own profile? The description does not indicate whether it has been tested with others or deployed for external use.

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What The Product Actually Is

The description states:

  • Git --Profile Print analyzes a public GitHub profile.
  • It maps skills to repository evidence.
  • It identifies gaps and creates a personalized four-week learning sprint.
  • Each week ends with visible proof such as a test, benchmark, evaluation dataset, commit, or deployment.
  • The tool uses GitHub API, OpenAI GPT-5.6, Codex, and Next.js.

Inference The product appears to be a prototype or hackathon submission that leverages AI to interpret a developer’s public GitHub activity and recommend next steps in learning. It is not described as a commercial product or platform with users beyond the author.

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Positioning & Claim Evolution

The description states:

  • The tool was built because most learning roadmaps ignore what a developer has already created.
  • It aims to answer “What should I learn and prove next?” using real GitHub work.
  • It is described as an “evidence-first AI learning coach.”

Inference Positioning is centered on personalization based on existing work, not generic advice. The author emphasizes transparency in recommendations by linking them to actual repositories.

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Target Customer & ICP

The description states:

  • The tool targets developers who want to learn and prove new skills.
  • It uses public GitHub profiles as input.

Inference The primary user is likely a self-directed developer with an active public GitHub presence, possibly a junior or mid-level engineer looking to grow their skills.

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Business Model & Pricing Evidence

Not evidenced.

Explanation

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

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Technical & Delivery Signals

The description states:

  • Built with Next.js, TypeScript, GitHub API, OpenAI GPT-5.6, Codex.
  • Uses a deterministic engine to score repository evidence.
  • GPT-5.6 is used for structured recommendations.
  • Includes progress tracking and downloadable sprint briefs.
  • Ready-to-use Codex prompts are included.

Inference The tool is built with modern web stack and integrates AI APIs. It includes UI elements like progress tracking and downloadable outputs, suggesting a user-facing prototype.

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Traction & Maturity Signals

Not evidenced.

Explanation

There is no evidence of users, customers, or adoption beyond the author’s own GitHub profile. No data on usage, retention, or engagement is provided.

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Competitive Context

Not evidenced.

Explanation

No mention of competitors or market positioning is included in the description. The author does not reference existing tools for developer learning or skill tracking.

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Key Risks & Red Flags

  • Unverified claims: All descriptions are self-reported and unverified.
  • No traction: No evidence of users, customers, or revenue.
  • Prototype nature: Submitted to a hackathon; no indication of production deployment or scalability.
  • Limited scope: Only works with public GitHub profiles; may not be usable by many developers.
  • AI dependency: Relies on GPT-5.6 and Codex — both are proprietary and potentially unstable or costly.

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Diligence Questions To Ask The Founders

  1. What is the actual user base beyond your own profile?
  2. How does the tool handle private repositories or users without public GitHub profiles?
  3. Are there any plans to monetize or scale this beyond a hackathon prototype?
  4. How do you plan to validate that the learning sprints are effective for developers?
  5. What is the long-term vision for the product, and how does it differ from existing tools?

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Investment/Partnership Verdict

Not evidenced.

Explanation

There is no evidence of a business model, traction, or commercial viability beyond the author’s own use case. The project appears to be a prototype submitted to a hackathon with no indication of market readiness or scalability.

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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.