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PersonalJul 28, 2025

Identify Duplicate Files in Downloads

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Identify Duplicate Files in Downloads
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Find Every Duplicate File in Your Folder — Without Opening a Single One

Most people know they have duplicate files somewhere. Copies of the same PDF with slightly different names, images saved twice from different sources, documents exported and re-exported over months of iteration. Finding and cleaning them up manually means opening everything, comparing sizes and contents, and hoping you don't accidentally delete something important.

Eigent scans your folder, identifies duplicates across all comparison dimensions, and presents the results grouped and clearly labeled — so you can decide what to keep and what to delete.

1Identify the Folder to Scan

Make sure the folder you want to scan is accessible on your desktop or within your Documents directory. For this workflow, the target is a folder named mydocs inside the Documents directory. You can adapt the prompt to any folder path on your machine.

2Write the Scan Prompt

Tell Eigent what you want it to look for:

I have a folder named mydocs inside my Documents directory. Please scan it and identify all files that are exact or near duplicates — including those with identical content, file size, or format (even if file names or extensions differ). List them clearly, grouped by similarity.

The key phrase here is "even if file names or extensions differ" — this catches cases where the same file was saved under a different name, or where a .jpg and a .jpeg are the same image.

3Eigent Scans and Compares

Eigent analyzes the folder using multiple comparison methods:

  • Exact content match: Files with identical byte-for-byte content, regardless of filename
  • File size match: Files with the same size (useful for catching likely duplicates quickly)
  • Format-based grouping: Identifying files that are the same type and likely duplicates based on content analysis

For image files, Eigent can also use perceptual hashing to detect near-identical images that were re-saved at slightly different compression levels.

4Review the Grouped Results

Eigent returns a structured list of duplicate groups. Each group shows all files that appear to be duplicates, with their full paths and relevant metadata. You can see at a glance which files are copies and where they live in your folder structure.

5Decide What to Delete

Eigent identifies the duplicates but leaves the deletion decision to you. Once you've reviewed the list, you can tell Eigent which ones to remove:

Keep the most recently modified file in each group and move the others to a folder called "Duplicates for Review".

Delete all exact-content duplicates but keep one copy of each file.

6Why This Matters

Duplicate file cleanup is one of those tasks that everybody knows they need to do and almost nobody ever gets around to. It's tedious, risky (you don't want to delete the wrong version), and hard to do systematically by hand. Eigent makes it safe by surfacing everything for review before acting — and fast by doing the comparison work automatically.

7What to Try Next

Scan my entire Downloads folder and identify all duplicate files.

Find all duplicate images in my Photos library and show me which ones are near-identical.

After cleaning up duplicates, show me the top 10 largest files remaining in this folder.

Move all duplicate files to a "Review" folder instead of deleting them.

8Tips for Better Results

  • Be specific about what counts as a duplicate. "Same content" is different from "same filename". Clarify in your prompt which criteria matter most for your use case.

  • Scan one folder at a time. For very large directories with tens of thousands of files, start with a specific subfolder to validate the results before scanning everything.

  • Ask Eigent to create a report. Adding "save the results as a CSV" gives you a persistent record of what was found, useful for auditing the cleanup later.

Other use cases

Long-Horizon Task: GLM-5.1 vs GLM-5.2 on Eigent

Long-Horizon Task: GLM-5.1 vs GLM-5.2 on Eigent

Do a deep-dive research on 26 companies in the AI infrastructure ecosystem — the most certain main thread of the entire AI value chain. Cover these 6 sub-sectors (pick representative companies in each, from large-cap leaders down to smaller players): AI Data Center (compute infrastructure / build-out); GPU / AI Chips (training & inference silicon, ASICs, IP); Servers, Networking & Optical Modules (switches, NICs, optical interconnect); Power, Liquid Cooling & Energy Storage (power supply, thermal, energy management); AI Cloud / Compute Platform (hyperscalers, GPU clouds, compute-rental platforms); Supporting Ecosystem (HBM / advanced packaging, foundry, connectors & other critical components). For each company, research: company name, sub-sector, HQ / country; core products and its specific role in the AI chain; public or private (ticker + exchange if listed; if private, note latest valuation / funding round); market cap or valuation size (used for ranking); positioning and moat in the ecosystem (1–2 sentences); key customers / competitors. Ordering: within each sub-sector, rank from largest to smallest (by market cap / valuation). Structure the whole thing top-down: from the full hardware-ecosystem landscape → down to each individual company. Output requirements: First, generate a structured data file ai_infra_data.json — containing all 26 companies with the fields above, the 6 sub-sector classifications, a public/private flag, and a cross-company comparison matrix (sub-sector × key dimensions). Then generate a polished HTML report from that JSON: include an ecosystem landscape / layered diagram, sector sections, company cards, a clear visual indicator for public vs. private (tags or color coding), a market-cap ranking chart, and a sortable/filterable comparison table. Make the design professional, information-dense, and interactive. Verify the research data for accuracy first (listing status, tickers, valuations — use the latest figures and cite sources), then generate the report. Send the task in single-agent mode.

Build 10 Chinese New Year HTML5 Games with Eigent

Build 10 Chinese New Year HTML5 Games with Eigent

Build 10 separate and COMPLETE games with topics related to 2026 Chinese New Year (Horse) in HTML, CSS and JS (no libraries). Games must be fun, original, polished, mobile-friendly. Include scoring, scaling difficulty, restart buttons, and smooth visuals. Cover: arcade, puzzle, endless runner, reaction, strategy, memory, 2-player local, idle, retro pixel, and 1 experimental game.

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Create a modern 3D side-scrolling platformer inspired by Mario, combined with Snow Bros mechanics. The player can shoot snow projectiles to freeze monsters into snowballs, then kick them to chain into other enemies. Include a scoring system, lives display, scaling difficulty, and a restart function with rich 3D layered environments.

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