28 Innovative Uses for Binder Clips




Improved terminal experience over the CLIPS REPL by including several features:

  • Command Editing (moving, removing characters, etc.)
  • Command History (up/down arrow keys)
  • Clear Line (Ctrl+U) and Clear Screen (Ctrl+L)



  • Facts
  • Agenda
  • Instances

These views update their state automatically after each command.



3. DIY bookmark

It's pretty too!
It’s pretty too!

Need an emergency bookmark?

One of the best uses for paperclips is as a DIY bookmark. A bit of ribbon and a paper clip are all you need to make a really cute marker for your page.

Hello World? – Command Line

Since this framework can be framework of command line or web. Let’s start from commandline.

clips-tool begin with a simple command line tool first, so using clips-tool to write a command line program is very easy.

Here is the steps:

  1. Clone the clips-tool’s code from here
  2. Install the dependencies using composer
  3. Add the path of clips (a bash script, window user can use cygwin to run it) to PATH, or create a soft link of that file to you bin folder
  4. Run command clips version to test for it
  5. Go to any folder, run clips generate command and follow the wizzard

After the steps above, you can get a folder named commands, and a file, say HelloCommand.php.

It should be something like this:


The content of the file should be something like this:

And, yes, this is a very very simple command. But it has all the thing a command line command must have, the commandline args, and all the methods it get from base class command.

Pretty simple, say if we just want to print a welcome message (hello world). So, you can change the code to this;

Then using command clips hello to run it.

Pretty simple, huh?

Let’s begin with a little template thing.

Say you want to transfer a name to the hello world command, then you can change the code to this:

This needs some explaination.

Clips uses mustache as its default template engine. So you can just using clips_out function located in Clips namespace to use it.

But, how mustache find the template?

Clips tool uses a simple Resource scheme based resource framework to find the resource.

If there is no scheme set for clips_out, it will use tpl:// by default(which will try every template path).

And in above example, the resource scheme is string://, so mustache will just use the string as the input resource. This is a little like PHP’s resource handling frameowork, but more flexiable.

The second thing to explain is Clips\get_default.

This is a handy function, it will try the first argument (object or array), if it has the key(second argument), if so, it will return the value of that key, if not, it will return the default value (third argument).

Create a Mini Cleaner Brush


Small and delicate items, such as jewelry, electronics, and figurines, can be difficult to clean. Create your own mini cleaning brush by wrapping tissue, paper towel, gauze, felt, or cotton around a paper clip, then use the tool to gently wipe the dirty item, getting into all the nooks and crannies. The mini cleaning utensil is especially effective for clearing debris out of computer keyboards. Related: 9 Brilliant Cleaning Hacks Everyone Should Know istockphoto.com


21. Emergency hair clip

That's better!
That’s better!

We all find ourselves in a messy-hair emergency from time to time.

If you’ve turned up to work with hair everywhere, and are called in for a meeting, take yourself off to the bathroom with a few paper clips. You can slide them secretly into hair and tie back any particularly scruffy looking flyaways.

Now you’ll be thankful for the collection of paper clips you have lying around at the bottom of your drawer.

What are the royalty-free clipart images?

The Web is the place where you can easily find all kinds of royalty-free clip art images; many modern sites become some kind of trading platform for designers, photographers, and artists. Everyone can post clipart for sale here and the designers usually buy the right to use these little works of art for at the lowest prices (sometimes, one image costs only $1).

Royalty-Free clip art images are usually more object-oriented, and often they have no backgrounds. And even though there is no obvious separating line between the clip art pictures and stock images, the legal use for them stays the same.

There is a possibility to buy royalty-free clipart and stock pictures on a CD or just download and use them without any restrictions (on business cards, sites, personal scrapbooks, etc). The only purpose you can’t use these pics for is starting your clipart business; you will need the right to resell or lease them to others.

Royalty-free clipart examples:

Keep Food Fresh


Everyone wants to keep snacks and other food items fresh, but you don’t need to waste money on plastic clips to hold chip or cereal bags closed. A couple of paper clips will do the trick just fine! You can even use a paper clip in place of a twist tie to secure bread bags. istockphoto.com

Copyright issues for clip art pictures

There are paid and free clipart images.

But, like everything else in our cruel world, a high-quality clip-art will cost you some money. But still, you can find free high-quality clipart on the Web: it’s a good solution for the vendors to attract customers. This way, the user not only downloads a free image but also gets tempted to buy the paid ones.

You can download free clip art pics from the specialized websites. However, there is one nuance. Not everything that you can find and download on the Web is actually free because there are so-called content licenses. Some licenses imply the use of a clip-art only for personal (non-commercial) purposes, others mean that you can use the pics only with the copyright watermarks, etc.


We show that scaling a simple pre-training task is sufficient to achieve competitive zero-shot performance on a great variety of image classification datasets. Our method uses an abundantly available source of supervision: the text paired with images found across the internet. This data is used to create the following proxy training task for CLIP: given an image, predict which out of a set of 32,768 randomly sampled text snippets, was actually paired with it in our dataset.

In order to solve this task, our intuition is that CLIP models will need to learn to recognize a wide variety of visual concepts in images and associate them with their names. As a result, CLIP models can then be applied to nearly arbitrary visual classification tasks. For instance, if the task of a dataset is classifying photos of dogs vs cats we check for each image whether a CLIP model predicts the text description “a photo of a dog” or “a photo of a cat” is more likely to be paired with it.

CLIP pre-trains an image encoder and a text encoder to predict which images were paired with which texts in our dataset. We then use this behavior to turn CLIP into a zero-shot classifier. We convert all of a dataset’s classes into captions such as “a photo of a dog” and predict the class of the caption CLIP estimates best pairs with a given image.

CLIP was designed to mitigate a number of major problems in the standard deep learning approach to computer vision:

Costly datasets: Deep learning needs a lot of data, and vision models have traditionally been trained on manually labeled datasets that are expensive to construct and only provide supervision for a limited number of predetermined visual concepts. The ImageNet dataset, one of the largest efforts in this space, required over 25,000 workers to annotate 14 million images for 22,000 object categories. In contrast, CLIP learns from text–image pairs that are already publicly available on the internet. Reducing the need for expensive large labeled datasets has been extensively studied by prior work, notably self-supervised learning, contrastive methods, self-training approaches, and generative modeling.

Narrow: An ImageNet model is good at predicting the 1000 ImageNet categories, but that’s all it can do “out of the box.” If we wish to perform any other task, an ML practitioner needs to build a new dataset, add an output head, and fine-tune the model. In contrast, CLIP can be adapted to perform a wide variety of visual classification tasks without needing additional training examples. To apply CLIP to a new task, all we need to do is “tell” CLIP’s text-encoder the names of the task’s visual concepts, and it will output a linear classifier of CLIP’s visual representations. The accuracy of this classifier is often competitive with fully supervised models.

We show random, non-cherry picked, predictions of zero-shot CLIP classifiers on examples from various datasets below.

Show more Show less

Poor real-world performance: Deep learning systems are often reported to achieve human or even superhuman performance[1] on vision benchmarks, yet when deployed in the wild, their performance can be far below the expectation set by the benchmark. In other words, there is a gap between “benchmark performance” and “real performance.” We conjecture that this gap occurs because the models “cheat” by only optimizing for performance on the benchmark, much like a student who passed an exam by studying only the questions on past years’ exams. In contrast, the CLIP model can be evaluated on benchmarks without having to train on their data, so it can’t “cheat” in this manner. This results in its benchmark performance being much more representative of its performance in the wild. To verify the “cheating hypothesis”, we also measure how CLIP’s performance changes when it is able to “study” for ImageNet. When a linear classifier is fitted on top of CLIP’s features, it improves CLIP’s accuracy on the ImageNet test set by almost 10%. However, this classifier does no better on average across an evaluation suite of 7 other datasets measuring “robust” performance.


While CLIP usually performs well on recognizing common objects, it struggles on more abstract or systematic tasks such as counting the number of objects in an image and on more complex tasks such as predicting how close the nearest car is in a photo. On these two datasets, zero-shot CLIP is only slightly better than random guessing. Zero-shot CLIP also struggles compared to task specific models on very fine-grained classification, such as telling the difference between car models, variants of aircraft, or flower species.

CLIP also still has poor generalization to images not covered in its pre-training dataset. For instance, although CLIP learns a capable OCR system, when evaluated on handwritten digits from the MNIST dataset, zero-shot CLIP only achieves 88% accuracy, well below the 99.75% of humans on the dataset. Finally, we’ve observed that CLIP’s zero-shot classifiers can be sensitive to wording or phrasing and sometimes require trial and error “prompt engineering” to perform well.

How about configuration? – Command line

Our command is very simple for now, what if we want to connect to some database?

Or try to locate some file in some folder? How can we configure our command?

That’s the power of clips-tool.

You can add your configuration in:

  • cwd/
  • cwd/config
  • /etc/clips/
  • /etc/
  • /etc/rules

with name clips_tool.json. And if you have multiple of these, don’t worry, clips-tool will get all the configurations to you(say, you have a /etc/clips_tools.json as system wide configuration, and a project’s own configuration).

And the configuration should be something like this(a simple configuration from a demo site):

As you can see, the configuration is no more than key => value json object.

And you can access any of your configuration at anywhere using Clips\config() function.

Release Notes


Added progress bar when updating views is taking longer than usual (more than a second)

Added setting to set a custom path for the CLIPS executable

Added setting to set the default strategy used by CLIPS when each session starts

Added command to set the strategy for the current session

Added button for updating each view manually

Added setting to toggle views auto updating their state after each command

Fixed error message not showing up if the CLIPS executable is not found (in Linux)


The extension finally works on Windows :tada:

This means that issue #1 was fixed.


Fixed – Error message was not being shown when the CLIPS terminal failed to spawn.

Found issue – The CLIPS terminal does not spawn on Windows, even if the path is correct.


Fixed – Views not updating when they were hidden in a tab and then selected.


Improved the system that makes views close when CLIPS is closed.

(It is not perfect due to VSCode limitations, but it now works in more cases than before.)