I'm sure there are a ton of other projects out there that do this, but I couldn't find one that fit my needs exactly, so I threw this together in a few hours.
claude-image-renamer uses Claude Code CLI to analyze screenshots and rename them to something actually usable. It combines OCR text extraction with Claude's vision capabilities, so instead of "Screenshot 2025-12-29 at 10.03.10 PM.png" you get something like "vscode_python_debug_settings.png".
A few things it does:
Handles those annoying macOS screenshot filenames with weird Unicode characters
Uses OCR to give Claude more context for better naming
Keeps filenames clean (lowercase, underscores, max 64 chars)
Handles naming conflicts automatically
If you're on macOS, you can also set this up as a Folder Action so screenshots get renamed automatically when they are saved to a folder, typically ~/Desktop. This is useful if you take a lot of screenshots and hate digging through "Screenshot 2025-12..." files later. MetalLB - https://metallb.io/ load balancer
Traefik - https://doc.traefik.io/traefik/getting-started/quick-start-with-kubernetes/ ingress
Local Path - https://docs.apps.rancher.io/reference-guides/local-path-provisioner storage
Open to suggestions.I think about this often when discussing AI adoption with people. It's also relevant to this VS Code discussion which is tangential to the broader AI assisted development discussion. This post conflates tool proficiency with understanding. You can deeply understand Git's DAG model while never typing git reflog. Conversely, you can memorize every terminal command and still design terrible systems.
The scarce resource for most developers isn't "knows terminal commands" - it's "can reason about complex systems under uncertainty." If a tool frees up bandwidth for that, that's a net win. Not to throw shade at hyper efficient terminal users, I live in the terminal and recommend it, but it isn't going to make you a better programmer just by using it instead of an IDE for writing code. It isn't reasoning and understanding about complex systems that you gain from living in a terminal. You gain efficiency, flexibility, and nerd cred - all valuable, but none of them are systems thinking.
The auto-complete point in the post is particularly ironic given how critical it is for terminal users and that most vim users also rely heavily on auto-complete. Auto-complete does not limit your effectiveness, it's provably the opposite.
All of this is done in a Python environment with usage of Rust for speeding up critical code/computations. (The rust code is delivered as Python modules.)
The work is interesting and different challenges arise when having to process and compute datasets that are updated with 10s of TBs of fresh data daily.
About how long do these typically take to execute? Minute, Tens of Minutes, Hours?
My work if very iterative where the feedback loop is only a few minutes long.
https://github.com/sharkdp?tab=repositories&q=&type=source&l...
Birdhouse: https://img.notmyhostna.me/cRQ1gJfZCHjQKwFrgKQj
UI:
- https://img.notmyhostna.me/Hnw4qcvbg1ZQCrFxzGMn
- https://img.notmyhostna.me/62TFwSXSRRbCfxDz297h
- https://img.notmyhostna.me/40qhgHmSqQsrGr8BC7Db
- https://img.notmyhostna.me/9bgz4GYsjQH33n3MtWKp (Face labeling, so I can show thumbnails of the actual birds that visited and train a ML model on it in the future)