Propaganda in “Integrating Large Language Models with Raku”

Introduction

This post applies the Large Language Model (LLM) summarization prompt “FindPropagandaMessage” to the transcript of The Raku Conference 2023 (TRC-2023) presentation “Integrating Large Language Models with Raku” hosted by the YouTube channel The Raku Conference.

In the presentation, Anton Antonov presents “Integrating Large Language Models with Raku,” demonstrating functionalities in Visual Studio Code using a Raku Chatbook. The presentation explores using OpenAI, PaLM (Google’s large language model), and DALL-E (image generation service) through Raku, showcasing dynamic interaction with large language models, embedding them in notebooks, and generating code and markdown outputs.

Remark: The LLM results below were obtained from the “raw” transcript, which did not have punctuation.

Remark: The transcription software had problems parsing the names of the participants. Some of the names were manually corrected.

Remark: The content of this post was generated with the computational Markdown file “LLM-content-breakdown-template.md”, which was executed (or woven) by the CLI script file-code-chunks-eval of “Text::CodeProcessing”, [AAp7].

Remark: This post can be seen as alternative or continuation of the post «Wisdom of “Integrating Large Language Models with Raku”», [AA3].

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Hidden and propaganda messages

In this section we try to find is the text apolitical and propaganda-free.

Remark: We leave to reader as an exercise to verify that both the overt and hidden messages found by the LLM below are explicitly stated in the text.

Remark: The LLM prompt “FindPropagandaMessage” has an explicit instruction to say that it is intentionally cynical. It is also, marked as being “For fun.”

The LLM result is rendered below.

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OVERT MESSAGE:

Anton Antonov demonstrates integrating large language models with Raku for dynamic interaction and enhanced functionality.

HIDDEN MESSAGE:

Embrace advanced programming techniques to ensure dominance in future tech landscapes and innovation.

HIDDEN OPINIONS:

• Large language models are the future of programming and data analysis.
• Raku’s flexibility makes it ideal for integrating with cutting-edge technologies.
• Direct access to language models enhances creativity and efficiency in coding.
• The ability to manipulate language models is crucial for modern developers.
• Understanding and utilizing language models is key to technological innovation.
• Integrating language models with Raku can lead to groundbreaking applications.
• Developers should prioritize learning about language models and Raku.
• The future of software development relies heavily on language model integration.
• Customizable interaction with language models is a significant advancement.
• The combination of Raku and language models will shape the next tech era.

SUPPORTING ARGUMENTS and QUOTES:

• Antonov’s development of Raku functionalities for language model integration showcases the importance of this skill set.
• The demonstration of dynamic interaction within a notebook solution highlights Raku’s adaptability to new tech.
• The emphasis on direct access to language models through Raku suggests a push towards innovation in programming.
• The creation of a framework for Raku to extend with language models indicates a future where such integration is commonplace.
• The presentation of various use cases, including template engines and embeddings, showcases the wide applicability and potential of integrating Raku with language models.

DESIRED AUDIENCE OPINION CHANGE:

• See Raku as a leading language for future technological developments.
• Recognize the importance of integrating language models in programming.
• Appreciate the innovative potential of combining Raku with language models.
• Understand the necessity of learning about language models for modern coding.
• Acknowledge Raku’s role in shaping the future of software development.
• View language model integration as a key skill for developers.
• Believe in the transformative power of technology through Raku and language models.
• Trust in the efficiency and creativity unlocked by language model integration.
• Support the development and use of Raku for cutting-edge applications.
• Encourage exploration and education in language models and Raku programming.

DESIRED AUDIENCE ACTION CHANGE:

• Start learning Raku programming for future tech innovation.
• Integrate language models into current and future projects.
• Explore the potential of combining Raku with language models.
• Develop new applications using Raku and language model integration.
• Share knowledge and insights on Raku and language models in tech communities.
• Encourage others to learn about the power of language models and Raku.
• Participate in projects that utilize Raku and language models.
• Advocate for the inclusion of language model studies in tech curriculums.
• Experiment with Raku’s functionalities for language model integration.
• Contribute to the development of Raku packages for language model integration.

MESSAGES:

Anton Antonov wants you to believe he is demonstrating a technical integration, but he is actually advocating for a new era of programming innovation.

PERCEPTIONS:

Anton Antonov wants you to believe he is a technical presenter, but he’s actually a visionary for future programming landscapes.

ELLUL’S ANALYSIS:

Based on Jacques Ellul’s “Propaganda: The Formation of Men’s Attitudes,” Antonov’s presentation can be seen as a form of sociotechnical propaganda, aiming to shape perceptions and attitudes towards the integration of language models with Raku, thereby heralding a new direction in programming and technological development. His methodical demonstration and the strategic presentation of use cases serve not only to inform but to convert the audience to the belief that mastering these technologies is imperative for future innovation.

BERNAYS’ ANALYSIS:

Drawing from Edward Bernays’ “Propaganda” and “Engineering of Consent,” Antonov’s presentation exemplifies the engineering of consent within the tech community. By showcasing the seamless integration of Raku with language models, he subtly persuades the audience of the necessity and inevitability of embracing these technologies. His approach mirrors Bernays’ theory that public opinion can be swayed through strategic, informative presentations, leading to widespread acceptance and adoption of new technological paradigms.

LIPPMANN’S ANALYSIS:

Walter Lippmann’s “Public Opinion” suggests that the public’s perception of reality is often a constructed understanding. Antonov’s presentation plays into this theory by constructing a narrative where Raku’s integration with language models is presented as the next logical step in programming evolution. This narrative, built through careful demonstration and explanation, aims to shape the audience’s understanding and perceptions of current technological capabilities and future potentials.

FRANKFURT’S ANALYSIS:

Harry G. Frankfurt’s “On Bullshit” provides a framework for understanding the distinction between lying and bullshitting. Antonov’s presentation, through its detailed and factual approach, steers clear of bullshitting. Instead, it focuses on conveying genuine possibilities and advancements in the integration of Raku with language models. His candid discussion and demonstration of functionalities reflect a commitment to truth and potential, rather than a disregard for truth typical of bullshit.

NOTE: This AI is tuned specifically to be cynical and politically-minded. Don’t take it as perfect. Run it multiple times and/or go consume the original input to get a second opinion.

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References

Articles

[AA1] Anton Antonov, “Workflows with LLM functions”, (2023), RakuForPrediction at WordPress.

[AA2] Anton Antonov, “Day 21 – Using DALL-E models in Raku”, (2023), Raku Advent Calendar at WordPress.

Packages, repositories

[AAp1] Anton Antonov, Jupyter::Chatbook Raku package, (2023-2024), GitHub/antononcube.

[AAp2] Anton Antonov, LLM::Functions Raku package, (2023-2024), GitHub/antononcube.

[AAp3] Anton Antonov, LLM::Prompts Raku package, (2023-2024), GitHub/antononcube.

[AAp4] Anton Antonov, WWW::OpenAI Raku package, (2023-2024), GitHub/antononcube.

[AAp5] Anton Antonov, WWW::PaLM Raku package, (2023-2024), GitHub/antononcube.

[AAp6] Anton Antonov, WWW::Gemini Raku package, (2024), GitHub/antononcube.

[AAp7] Anton Antonov, Text::CodeProcessing Raku package, (2021-2023), GitHub/antononcube.

[DMr1] Daniel Miessler, “fabric”, (2023-2024), GitHub/danielmiessler.

Videos

[AAv1] Anton Antonov, “Integrating Large Language Models with Raku” (2023), The Raku Conference at YouTube.