Lazy Downloads:樹懶般緩慢的本機 AI,趁我午睡整理凌亂資料夾
"Lazy Downloads: A Sloth-Speed Local AI That Cleans My Messy Folders While I Nap" published: false
Lazy Downloads 是一款以本機 AI 為核心的命令列工具,會分類、重新命名並歸檔下載資料夾中的檔案。它透過 Ollama 在本機執行開放權重 Gemma,逐一處理檔案,並以 SQLite 記錄移動,方便用一條命令復原。所有檔案均在本機讀取和分類,無需互聯網;模型下載一次後,每次執行均不收費。
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
<!-- What does it do, and what effort does it save? Who gets to be a little lazier because of it? -->Lazy Downloads is a small command-line tool that sorts, renames, and archives the clutter in my Downloads folder. Every file I download ends up named Screenshot 2026-10-12 at 4.31.09 PM (3).png or final_FINAL_v2.pdf, and I never get around to cleaning it up.
It reads each new file, works out what it actually is (an invoice, a lecture note, a boarding pass, a meme), and then:
- renames it to something human, like
2026-10-electricity-bill.pdf - moves it into a sensible folder such as
Finance/Bills/orCollege/Notes/ - logs every move to a file so I can undo anything with one command
Like a sloth, it works slowly in the background: it runs as a nightly job, goes through files one at a time, and never hurries my laptop. Everyone with a chaotic Downloads folder gets to be a little lazier.
Demo
Code
How I Built It
<!-- Which open-source AI did you use (open-weight models, agent harnesses, frameworks, local inference), and how is your project built around it? -->Open pieces:
- Gemma (open-weight, Google) running locally through Ollama, with a vision-capable variant for screenshots and scanned documents
-
Python with
watchdogfor file events, plus small text extractors (PDF text, plain text, image captions) - A plain SQLite file as the move log
Why Does Open Innovation Matter?
<!-- Why does open innovation matter for what you built? What did it make possible that a closed API wouldn't? -->My Downloads folder holds bills, ID scans, bank statements, and college files. Uploading those to a closed cloud API just to rename them felt wrong, and an open-weight model made the question go away:
- Privacy: every file is read and classified on my own machine, and nothing leaves it.
- Offline: it works with no internet, so a nightly job doesn't depend on an API being up.
- Cost: after the one-time model download, each run costs nothing, which suits a job that runs every night forever.
- Control: I can swap models, edit the prompt, or fine-tune on my own folder taxonomy without waiting for a vendor to change anything.
My Agent Session
The Lazy Payoff
<!-- Optional, but this is where you can make your post stand out. How much time or effort does it actually save? Did you (or someone else) use it this week? If not, remove this section. -->- Files in my Downloads folder before: [N]
- Files correctly sorted automatically:
[N], sent to_Review/:[N] - Time to run on
[N]files on[your laptop specs]:[X minutes] - Time I would have spent sorting by hand: roughly
[X minutes]
Prize Categories
<!-- Which partner categories are you entering? List every one that applies, or remove this section. -->- Best Use of Gemma: local Gemma through Ollama is the core of the tool
-
Best Use of a .tech Domain: only if you claim a free one and host a project page there
[keep or remove] -
Best Use of GitHub Copilot: only if you actually used it to build this
[keep or remove]
<!-- Team Submissions: Please pick one member to publish the submission and credit teammates by listing their DEV usernames directly in the body of the post. -->NIL
來源:DEV Community · dev.to