SnapSort 2.0

SnapSort 2.0 is a desktop application developed by a single individual (Ludmila Winckowska) that allows users to preview, organize, and safely undo photo and video sorting without overwriting files. The project was submitted as part of the OpenAI 2026 hackathon and is described as a safety-first tool built with Python and Tkinter.

The author states that SnapSort 2.0 analyzes media before making changes, supports previewing actions, prevents overwrites via SHA-256 duplicate detection, and maintains an authenticated JSON undo journal. It also includes features like cancellation support, per-file error isolation, and validation of undo journals to prevent data loss.

There is no evidence of revenue, customers, or traction beyond the author’s own description. The tool appears to be a personal project with limited commercialization intent at this stage.

The description states that SnapSort 2.0 is a desktop app designed to organize photos and videos. It operates in a preview-first mode where users can review planned actions before execution. Key features include:

• Analysis of files before any changes are made.
• Copying as the default operation; moving requires explicit confirmation.
• Prevention of overwrites through SHA-256 duplicate detection.
• Recording successful operations in an authenticated JSON undo journal.
• Validation of the undo journal and referenced paths before initiating undo.
• Refusal to modify files that have changed since sorting.
• Support for cancellation during execution.
• Per-file error isolation to avoid stopping entire analysis due to one bad file.

The tool is built using Python, Tkinter, Pillow, pytest, Git, GitHub, and was enhanced with Codex and GPT-5.6 during development.

This is a self-reported description of functionality; no independent verification or external evidence exists regarding actual performance or usage.

The author positions SnapSort 2.0 as a "safety-first" desktop app for managing media files, emphasizing its ability to preview and undo actions without overwriting data.

It claims to be an improvement over previous versions (implied by “SnapSort 2.0”) and was developed with the help of AI tools like Codex and GPT-5.6.

The evolution described is from a basic program to one with enhanced architecture, automated testing, and safety mechanisms — particularly around undo functionality and error handling.

However, this is a self-reported claim about intent and development process, not proof of traction or adoption.

The author describes working as a florist and studying Python independently. She mentions her goal to work at OpenAI one day, suggesting she may be targeting individuals who are self-taught developers or hobbyists interested in automation tools for media management.

There is no explicit mention of specific customer segments beyond the creator’s personal context. No evidence exists regarding:

• Who else uses SnapSort 2.0.
• Whether it targets professionals or casual users.
• Any defined ideal customer profile (ICP).

Thus, the target customer remains unclear and unverified.

There is no evidence of any business model or pricing strategy in the description provided.

The project appears to be a personal development effort submitted for a hackathon. No indication exists that SnapSort 2.0 is offered for sale, monetized, or even publicly released beyond its developer's own use.

The author states that SnapSort 2.0 was built using:

• Python
• Tkinter (GUI)
• Pillow (image processing)
• pytest (testing framework)
• Git / GitHub (version control and collaboration)
• Codex + GPT-5.6 (development assistance)

Key technical elements include:

• A reusable core
• Responsive Tkinter GUI
• Preview-first CLI interface
• Cancellation support
• Per-file error isolation
• SHA-256 duplicate detection
• HMAC-authenticated and checksummed undo journals
• 32 automated tests

These features suggest a structured, modular approach to development with attention to safety and robustness.

However, these are self-reported technical details, not independently verified or validated by third parties.

There is no evidence of traction, adoption, or user feedback beyond the author’s own account. The project was submitted to a hackathon and does not appear to have been released publicly for general use.

The team size is listed as one person (Ludmila Winckowska), indicating a solo development effort with no external users or customers described.

No metrics, reviews, downloads, or usage data are provided.

There is no evidence of competitors or market positioning beyond the author’s own description. The project does not reference similar tools or platforms in the media organization space.

The tool seems to be a niche solution for individuals managing large volumes of photos and videos, but no competitive landscape is described.

Several potential risks and open questions emerge from the self-reported account:

1. Single-person development: With only one developer, scalability, maintenance, and feature expansion may be limited.
2. No commercial traction or revenue: The tool appears to be a personal project without any indication of monetization or market demand.
3. Unverified safety claims: While the author describes strong safety features (e.g., HMAC-authenticated journals), these are untested in real-world conditions.
4. Limited external validation: No third-party audits, user testing, or feedback are mentioned.
5. Hackathon submission context: The project was submitted to a hackathon, which implies it may not be fully mature or production-ready.

1. Has SnapSort 2.0 been used by anyone other than yourself?
2. What real-world scenarios have you tested it in? How many files were processed?
3. Are there any known bugs or edge cases that have not yet been addressed?
4. Do you plan to release SnapSort 2.0 publicly, and if so, how?
5. Have you considered integrating with existing photo management tools or platforms?
6. What is your long-term vision for SnapSort 2.0 beyond the current scope?