Dates

Mark your calendars for Neural Storm 2026!

Registration Deadline
November 2, 2026

Hackathon Begins
November 2, 2026 — 8:00 PM IST

Project Building Period
November 2–4, 2026 (48 Hours)

Submission Portal Opens
November 2, 2026 — 8:00 PM IST

Final Submission Deadline
November 4, 2026 — 8:00 PM IST

Important Notes

  • All times are in Indian Standard Time (IST).

  • Participants have 48 hours to build and submit their projects.

  • Submissions can be updated before the deadline.

  • Late submissions will not be accepted.

Don't wait until the last minute. Build, test, document, and submit on time!

Eligibility

Neural Storm 2026 welcomes participants who are passionate about Artificial Intelligence, Machine Learning, innovation, and solving real-world problems.

Participants must follow these requirements:

  • Register for Neural Storm 2026 through the official registration process.

  • Participate individually or as part of a team, subject to the event's team participation rules.

  • Select one of the three official challenge tracks: Future Tech, Green Planet, or Human Impact.

  • Develop an original project that aligns with the chosen track.

  • Use Python as the primary language for AI/ML components.

  • Follow the hackathon's code of conduct and maintain respectful conduct toward participants, organizers, and judges.

  • Submit the project within the official submission window.

Fair Participation

All projects must represent the original work of the participating team.

Copying another team's project is prohibited. Identical or substantially duplicated submissions may be reviewed and disqualified.

Open-source frameworks, libraries, pretrained models, and AI development assistants are permitted.

Participants must disclose any AI development tools used in their project README.

Project and Submission Requirements

Project Requirements

1. Build During the Hackathon

Projects should be developed during the official hackathon period, November 2–4, 2026. Reusing large pre-built projects is discouraged.

2. AI/ML Integration

Projects must incorporate Artificial Intelligence or Machine Learning as a meaningful part of the solution.

Python must be the primary programming language for AI/ML components.

3. Track Alignment

Each team must select one of the three official challenge tracks and develop a project relevant to that track.

Teams may choose an official problem statement or define their own specific problem within the track.

4. Originality

All submissions must represent original team work. Copying another team's project is prohibited and may result in disqualification.

5. Open-Source and AI Tools

Open-source frameworks, pretrained models, APIs, and AI coding assistants are allowed and encouraged.

Teams must disclose the AI development tools they used in their README.

Submission Requirements

Public Repository: A publicly accessible GitHub repository containing the complete source code is mandatory.

Project Documentation: The repository must include a README explaining the problem, solution, technologies, setup, execution, and results.

Demonstration: Screenshots or a demonstration GIF must be included in the README. A live deployed application is strongly recommended.

Performance Evidence: Include relevant model evaluation metrics, such as accuracy, F1-score, latency, or comparative performance, where applicable.

Submission Limit: Each team may submit one project and update it before the deadline.

Responsible AI

Projects addressing sensitive domains such as healthcare, mental wellness, and financial services should prioritize safety, explainability, privacy, and responsible use of AI.

Use appropriate disclaimers, avoid exposing personal information, and clearly communicate the limitations of your models.

Remember: A smaller, fully functional solution with measurable results is more valuable than an ambitious idea that cannot be demonstrated.

Prizes

Prize Pool - ₹20,000

Winner - ₹10,000

First Runner Up - ₹5,000

Second Runner Up - ₹3,000

Best Performer - ₹2,000

Judging Criteria and Winner Selection

Technical Implementation - 35%

Quality of code, use of AI/ML techniques, model accuracy, and overall technical depth.

Impact & Usefulness - 25%

Does the solution have real-world applicability? How significant is the problem being solved?

README & Documentation - 20%

Clarity of the README: setup instructions, problem statement, approach, results, and screenshots.

Demo & Presentation - 20%

Working demo (live or video), deployment link if available, and overall presentation quality.

Tie-breaking: If scores are equal, the team with a higher Technical Implementation score wins. If still tied, the earlier submission timestamp decides.