The following is a purely technical solution for designing a system that automatically generates "meaningless" news text every day (i.e., the style is similar to news, but the content has no actual meaning or logic, and is only for experimental, entertainment, or technical demonstration purposes). It should be noted that the system discussed in this article is only for experimental and entertainment purposes, and should not be used to spread false information, mislead the public, or for other purposes that may cause social chaos. [1. Define goals and requirements] • Goal: Automatically generate text every day, the style of the text is similar to news, but the content is random and lacks real meaning (for example, randomly combine titles, paragraphs, and event descriptions). • Requirements: The generated content must have the layout, structure, and language style of news reports, but the subject matter and content do not need to be authentic or logical. • Disclaimer: The generated results should be accompanied by a disclaimer (such as "The content of this article is purely fictional...") to avoid misleading readers. [2. System architecture design] A. Data collection and preprocessing 1. Establish a news corpus: Collect a large amount of real news text, paying special attention to the writing style of titles, introductions, and text. 2. Data cleaning: extract structured information (such as various news templates, common sentences and formats) for subsequent template construction or model training. B. The content generation module can use one of the following methods or a combination of them: ① Template + random filling method • Design multiple news templates (such as "Company X suddenly announced...", "Y happened yesterday...", etc.), and reserve keywords or variables for each template. • Prepare a vocabulary for variables (such as names, places, numbers, company names, etc.) to achieve random combination. • Use programming languages (such as Python) to randomly select templates and fill in random words each time to generate a "meaningless" news. ② Rule-based random generation method • Write a generator: define the basic structure of a news report (title, introduction, background, details, ending) • Design random generation logic for each paragraph, such as randomly selecting modifiers, verbs, nouns, etc. to generate synthetic sentences. ③ Use deep learning language models (such as the GPT series) • Prepare labeled data: select a large amount of news text as training or fine-tuning data to enable the model to learn the writing format of news style. • Controlling generation: Using prompts and temperature parameters, the model can output relatively random text without logical constraints. Example: Input the prompt "Today's News:" and set a high temperature parameter to induce the model to generate text that is similar to news in content style but less logical. • Pay attention to filtering the output results and adding disclaimers. C. Content review and adding disclaimers • To prevent readers from misunderstanding, disclaimer text should be attached to the beginning or end of the news after generation. • If necessary, design grammar and style checking modules to adjust the results to meet the expected layout. D. Scheduled task scheduling module • Use the operating system's scheduled tasks (such as cron jobs under Linux) or other schedulers to set up automatic daily triggering of generation tasks. • The generated news text can be saved to a database, file, or automatically published to a specified platform (platform policies must be considered). E. Logs and monitoring • Record the generation log, including the generation time, template selection, and generated content, to facilitate abnormality troubleshooting or subsequent adjustments. • A simple interface can be designed for preview and manual confirmation before publishing (to avoid doubts about automatic publishing). 【3. Technology selection and implementation details】 • Programming language: Python is more suitable, with rich library support. • Template system: You can use the Jinja2 template engine to generate text based on parameters. • Data storage: You can use JSON, SQLite or other simple databases to store corpus and generate results. • Deep learning part: You can call the OpenAI API or use the Hugging Face Transformers library to call and fine-tune the model; pay attention to the model parameter adjustment (such as temperature, repeated penalties, etc.). 【4. Example process】 1. The system starts, loads the news template and vocabulary. 2. The scheduled task triggers the generator, randomly extracts a template and fills random options for each variable position. 3. Alternatively, call the pre-trained language model to generate a draft text, and then adjust the rules (such as inserting a disclaimer, formatting paragraphs). 4. The generated news is stored in a database or file, and the operation is recorded through the log module. 5. Optional: Automatically publish to a website or social media platform. 【5. Risk and ethical considerations】 • Please ensure that the generated content will not be mistaken for real news, and be sure to add a disclaimer such as "This news is purely fictional." • When publishing on the platform, relevant laws, regulations and platform regulations must be followed to avoid misunderstandings or negative social effects caused by automatically generated content. • The system is designed for legal, research or entertainment purposes only. Do not use it to create large-scale false information or disrupt public order. Summary: Designing such a system involves both text generation technology and ethical and legal risks. Technically, template random filling, rule generation or deep learning methods can be used, combined with scheduling and monitoring modules to achieve the purpose of automatic generation every day. I hope this solution can help you, and please make sure that the purpose of the project meets the corresponding specifications and requirements.