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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteAppwrite AI Duplicates Detector (AADD) is a project by Devika Harshey that brings duplicate scanning and cleanup for Appwrite Storage and Databases into one workflow. Users connect an Appwrite project, choose what to scan, review similarity-based candidates, then decide whether to delete items from the source project or remove them only from AADD’s tracking list.
What AADD is designed to do
Harshey describes AADD as a full-stack web application for detecting, visualizing, and managing duplicates in Appwrite projects. Its focus is Appwrite data—not duplicate files scattered across a computer’s local drives. The case study presents the tool as a response to the effort of checking multiple storage buckets and database collections manually.
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Filename or exact-match checks can miss files that have been renamed, compressed, or slightly modified. AADD’s stated approach combines similarity analysis and perceptual hashing to surface possible matches, alongside an “AI Garden” data-health view in which an AI Gardener offers progress-based tips and encouragement. These are features described by the project’s author, not independently assessed capabilities.
How the AADD workflow works
1. Connect an Appwrite project
The connection form described in the case study asks for a project ID, API endpoint, and API key. Harshey says the key is encrypted with Fernet before being stored in the AADD Appwrite Database. That is an implementation claim, not a security audit; it does not establish how keys are managed throughout the system or whether the application is safe for a particular project.
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2. Choose storage buckets or database collections
For Storage, users can scan available buckets. For Databases, the described flow asks the user to enter a database ID, load its collections, and select particular collections or the full database. This lets the scan scope be limited to the resources the user intends to inspect.
3. Review duplicate candidates
The case study describes results with similarity scores and controls to search, filter, and sort by similarity, date, or file size. It also describes visualizations and links for viewing corresponding items in the Appwrite Console. Similarity results should be treated as candidates to review, rather than proof that two records are safe to remove.
4. Choose what happens to selected items
AADD distinguishes between deleting source data and removing an entry from its own list:
- Delete from source: removes selected files or documents from the connected Appwrite project.
- Remove from list: removes duplicate entries from AADD tracking while leaving the source data in Appwrite in place.
The author says users confirm the selected action. Because the first option affects the connected project itself, check the selected items and action carefully before confirming.
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What the reported accuracy and time savings mean
Harshey reports “85-95% similarity accuracy” and an approximately “70%” reduction in manual review effort. The case study does not provide an evaluation set, measurement method, or independent validation for either figure. They are creator-reported estimates, not verified benchmarks, and should not be used as a guarantee of results for a particular Appwrite project.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the project is built
The author lists Next.js, React, TypeScript, Tailwind CSS, shadcn/ui, and Framer Motion for the frontend, and Flask for backend API requests, Appwrite operations, and duplicate-detection logic. Appwrite is the platform whose data is scanned; Google Gemini API is identified as the service powering the AI Gardener. The case study does not provide code, architecture diagrams, deployment details, or a reproducible benchmark, so the listed technologies do not establish how the components are configured in a live deployment.
Recognition and project status
Appwrite’s announcement lists “Appwrite AI Duplicates Detector by Devika Harshey” among its five top Hacktoberfest projects (Appwrite’s Hacktoberfest 2025 announcement). Harshey’s case study calls it a Top 5 Winner in Appwrite X Hacktoberfest 2025. The case-study page displays “Posted on Sep 16” and “Edited on Sep 19” without a year; it identifies the recognition as 2025.
The author’s case study links to a live application, but its current availability and maintenance have not been independently verified. Anyone considering connecting a real project should first check that the application is currently operating and review its current security and data-handling information. The case study’s encryption description alone is not enough to assess those risks.
Who AADD may suit
AADD is aimed at people who need to review possible duplicates within Appwrite Storage, Appwrite Databases, or both, and who want scanning, result review, and cleanup in a single project-focused workflow. Its stated controls—similarity scores, filtering, sorting, visualizations, and two different cleanup actions—address that use case. It is not presented as a general local-drive duplicate finder, and the available case study does not establish independent performance or security findings.
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