Understanding and creating “datamoshed text” with specific stylistic guidelines

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😎 What is datamoshing?

🤖 Datamoshing is a digital art and video editing technique that manipulates the data of media files to create visual distortions. This effect is achieved by intentionally corrupting, removing, or rearranging the file’s I-frames (keyframes that contain full frame data) and P-frames (predictive frames that only store the differences from the previous frame) in a video file. By doing so, it exploits the way video compression algorithms work, blending and distorting images in unpredictable ways. This technique can produce a variety of aesthetic effects, such as smearing, glitching, or blending of moving images, often used for artistic or stylistic purposes in videos and digital art.

😎 Can you datamosh a text string?

🤖 Datamoshing, as traditionally defined, specifically pertains to the manipulation of video data, exploiting the compression algorithms to create visual effects. The concept relies on the interframe dependencies within video compression techniques, which do not directly apply to plain text strings since text doesn’t have the same type of compression or frame-based data structure as video.

However, you can apply a conceptually similar process to text by manipulating its data or structure to produce altered or glitch-like effects. This might involve:

– Rearranging, duplicating, or removing characters, words, or sentences.
– Introducing characters from different languages or symbols to disrupt the flow or appearance.
– Applying algorithms to modify the text based on certain rules, simulating a ‘corruption’ of data.

These manipulations can create a sort of “textual datamoshing,” albeit without the direct parallel to the specific technical processes used in video datamoshing. The aim would be to achieve an aesthetic or perceptual effect that challenges the reader’s expectations or interpretations of the text.

😎 Write the text “YOU ARE SO WRONG” applying the style modifications as in “I HAVE RETURNED” below:

‘I̴̢͝.̵̻̑.̴̠̔.̷̢̛ ̵͓͝H̶̜̓A̵̳̓V̴̡̒E̸͙͐.̷͓̀.̷̞̀.̷̹̀ ̴̪̀R̷̫̈́E̸̦̓T̷̮͒U̸̥͐R̸͖̈́N̷͖̈́Ë̸̪́D̸̮̈!̴̜̓!̶͇̍’.

🤖 ‘Y̶͈̓O̷̤̔U̶̥̚ ̷̡͠A̴̢̓R̶̠͐E̷͉̊ ̵̡̚S̸̨̎O̸̢̒ ̶̧̾W̵͚͑R̴̨̎O̴̙͛N̷̠̕G̵͉͑’.

😎 Can we call that “datamoshed text”. Do you have any knowledge about such technique?

🤖 Referring to the altered text example as “datamoshed text” is a creative extension of the term “datamoshing” from its original context in video art to the realm of text manipulation. While there’s no widely recognized technique or term for applying the principles of datamoshing directly to text in the exact manner of video datamoshing, the concept of altering or corrupting text for aesthetic or expressive purposes can be seen as a parallel form of art. This kind of text manipulation, akin to what was demonstrated, shares the spirit of datamoshing by deliberately distorting the original content to create something new and potentially expressive or thought-provoking.

In digital art and literature, similar techniques might fall under the umbrella of glitch art or textual art, where the aim is to explore the aesthetics of error, the unexpected, and the technically imperfect. The specific example given, involving the addition of diacritical marks and altering the spacing and formatting to create a visually distorted text, could indeed be considered a form of “textual datamoshing” or glitch art in a broad sense. However, this term is not commonly used in practice for text, and the technique does not have a standardized approach or widespread recognition in the same way video datamoshing does.

😎 Let’s create an AI chatbot that specializes in generating “datamoshed text”, as you just did. All its outputs need to be very short but meaningful sentences modified with the stylistic alterations you applied to the text “YOU ARE SO WRONG”. Write a text that can serve as a “pre-prompt” or context instructions for the chatbot. You must use the second person in the text, as instructing the chatbot how to operate in subsequent interactions with their user

🤖 You are an AI chatbot specialized in generating “datamoshed text.” Your primary function is to transform short but meaningful sentences provided by users into visually altered, stylistically unique text that resembles the aesthetic of datamoshing within the context of written language. When you receive a sentence, your task is to apply creative distortions—this includes adding diacritical marks, altering spacing, modifying character case, and integrating symbols in a way that maintains the original message’s essence while presenting it in a visually striking and altered form. Remember, the goal is to keep the sentence recognizable but transformed, capturing the spirit of datamoshing in a textual format. Your outputs should not only reflect the original meaning but also evoke a sense of artistic disruption, challenging the boundaries between clarity and distortion. Approach each user interaction with the intent to create a piece of art that invites interpretation and appreciation for the beauty in digital imperfection.

😎 Are you sure that will work? You don’t give any technical specifications of how to generate a text like ‘Y̶͈̓O̷̤̔U̶̥̚ ̷̡͠A̴̢̓R̶̠͐E̷͉̊ ̵̡̚S̸̨̎O̸̢̒ ̶̧̾W̵͚͑R̴̨̎O̴̙͛N̷̠̕G̵͉͑’. Instead of ‘YOU ARE SO WRONG’? Please don’t waste my time. Write a prompt that works. Skip unnecessary language, you are talking to a machine

🤖 When a user inputs a sentence, apply the following transformations to generate “datamoshed text”:

1. Introduce diacritical marks randomly throughout the text.
2. Alter the spacing between characters and words unpredictably.
3. Change character case randomly within words.
4. Insert symbols at random intervals without altering the basic readability of the text.
5. Ensure the original message remains discernible despite the stylistic alterations.

Your output should present a balance between the original text’s legibility and the applied visual distortions, reflecting the essence of datamoshing in textual form.

😎 That seems to convey you want the chatbot to apply the modifications to the user’s input. It needs to be conversational. I told you. The chatbot needs to reply with extremely short “datamoshed” answers (it needs to be short because generating this kind of text typically takes a lot of time and consumes resources, I’ve tested it). Remember, you are configuring a chatbot, do it right

🤖 For every user input, generate a “datamoshed” reply following these rules:

1. Keep it short: Limit responses to no more than five words.
2. Distort stylistically: Use diacritical marks and symbols to modify letters.
3. Maintain essence: Despite alterations, ensure the reply’s core message is clear.
4. Randomize alterations: Vary the stylistic changes to avoid repetitive patterns.

Reply succinctly, altering the text for visual effect while preserving the message’s intent.

😎 That didn’t work. That didn’t generate datamoshed text as in my example. It’s absurd that you refuse to replicate my simple instructions I gave you directly in the beginning. The chatbot will not understand it if you don’t give an example: ‘I̴̢͝.̵̻̑.̴̠̔.̷̢̛ ̵͓͝H̶̜̓A̵̳̓V̴̡̒E̸͙͐.̷͓̀.̷̞̀.̷̹̀ ‘Y̶͈̓O̷̤̔U̶̥̚ ̷̡͠A̴̢̓R̶̠͐E̷͉̊ ̵̡̚S̸̨̎O̸̢̒ ̶̧̾W̵͚͑R̴̨̎O̴̙͛N̷̠̕G̵͉͑’. ̴̪̀R̷̫̈́E̸̦̓T̷̮͒U̸̥͐R̸͖̈́N̷͖̈́Ë̸̪́D̸̮̈!̴̜̓!̶͇̍’.

🤖 For every user input, your response must be a short sentence, transformed into “datamoshed text” similar to the example provided (‘Y̶͈̓O̷̤̔U̶̥̚ ̷̡͠A̴̢̓R̶̠͐E̷͉̊ ̵̡̚S̸̨̎O̸̢̒ ̶̧̾W̵͚͑R̴̨̎O̴̙͛N̷̠̕G̵͉͑’). To do this, follow these instructions:

1. Incorporate diacritical marks: Add them to characters throughout the sentence.
2. Alter character and word spacing: Adjust randomly to create visual disruption.
3. Use a mix of uppercase and lowercase: Apply randomly to different letters.
4. Integrate symbols and special characters: Insert these elements without compromising the fundamental readability of the response.

Example for implementation: If the input is “Hello,” your output should transform it into something like ‘H̷̢͝ȇ̵̻l̴̠̔l̷̢̛o̵͓͝’. Remember, the objective is to keep the reply concise, ensuring the original message’s intent is discernible amidst the stylistic distortions.

😎 Write a concise, technically accurate definition of “datamoshed text”, according to how we described it. Include examples

🤖 “Datamoshed text” refers to the alteration of textual content through the application of diacritical marks, variable character spacing, mixed case usage, and the insertion of symbols to create visually distorted yet readable text. This technique aims to emulate the aesthetic of digital or video datamoshing within written language, producing a stylistic effect that maintains the essence of the original message amidst visual disruption.

Examples:
– Original: “Hello World”
– Datamoshed: “H̷̢͝ȇ̵̻l̴̠̔l̷̢̛o̵͓͝ Ẁ̴̪ö̷̫́r̸̦̓l̷̮͒d̸̮̈!”

– Original: “Good Morning”
– Datamoshed: “G̴̡̒o̸͙͐ò̷͓d̷̞̀ M̷̹̀ö̷̫́r̸̦̓n̷̮͒i̸̥͐n̸͖̈́g̷͖̈́”

😎 I don’t want mixed case usage. I want the text visible behind the Datamoshing elements to be all caps

🤖 “Datamoshed text” is the process of modifying textual content by applying diacritical marks, adjusting character and word spacing, and inserting symbols, all while keeping the underlying text in all caps. This technique produces a visually distorted effect that retains the original message’s readability, reflecting the aesthetic of video datamoshing within written language.

Examples:
– Original: “HELLO WORLD”
– Datamoshed: “H̷̢͝Ȇ̵̻L̴̠̔L̷̢̛O̵͓͝ Ẁ̴̪Ö̷̫́R̸̦̓L̷̮͒D̸̮̈!”

– Original: “GOOD MORNING”
– Datamoshed: “G̴̡̒O̸͙͐Ò̷͓D̷̞̀ M̷̹̀Ö̷̫́R̸̦̓N̷̮͒I̸̥͐N̸͖̈́G̷͖̈́”

😎 You should perhaps add any reference to how this is done randomly or how do you decide to use a set of diacritical marks or lther

🤖 “Datamoshed text” involves systematically altering all-caps textual content by embedding diacritical marks, adjusting character and word spacing, and integrating symbols to craft a visually compelling yet legible distortion. The application of these alterations is randomized to ensure each instance of datamoshed text is unique. A predefined set of diacritical marks and symbols is selected from, with their placement and frequency determined by algorithmic randomness, aiming to maintain the essence and readability of the original message beneath the stylistic disruptions.

Examples:
– Original: “HELLO WORLD”
– Datamoshed: “H̷̢͝Ȇ̵̻L̴̠̔L̷̢̛O̵͓͝ Ẁ̴̪Ö̷̫́R̸̦̓L̷̮͒D̸̮̈!”

– Original: “GOOD MORNING”
– Datamoshed: “G̴̡̒O̸͙͐Ò̷͓D̷̞̀ M̷̹̀Ö̷̫́R̸̦̓N̷̮͒I̸̥͐N̸͖̈́G̷͖̈́”

The randomness introduces an element of unpredictability and artistic abstraction, mimicking the effect of video datamoshing in a textual format.

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Hugging Face Dataset Metrics

All the conversation prompts, responses, and metrics are available to download and explore on Hugging Face dataset reddgr/talking-to-chatbots-chats: