LLama 3.3 70b and TJC – Simple Text in Lumen

I have created another thing that is incredibly rare. A Voice recognition search engine. Lumen willI integrate with adding text to a simple string and perform operations on the string and reprint the string with AI.There are several operations which can be performed on the text, and again the text is saved in memory and printed. Simple text can be used to learn from generative algorithms and language. The following is a list of possible commands which can be run on simple text:

  1. Add speech
  2. Add Wikipedia Article
  3. Add Wikisimple Article
  4. Add word
  5. Use Affect Heuristic
  6. Use Anchoring Heuristic
  7. Use Availability Heuristic
  8. Use Balance Heuristic
  9. Use Base Heuristic
  10. Use Common Sense Heuristic
  11. Use Contagion Heuristic
  12. Use Default Heuristic
  13. Use Educated Guess Heuristic
  14. Use Effort Heuristic
  15. Use Escalation Heuristic
  16. Use Fairness Heuristic
  17. Use Familiarity Heuristic
  18. Naive Heuristic
  19. Use Peak Heuristic
  20. Use Representativeness Heuristic
  21. Use Scarcity Heuristic
  22. Use Simulation Heuristic
  23. Use Social Heuristic
  24. Use Working Backward Heuristic
  25. Use Abstraction Heuristic
  26. Use Functional Abstraction Heuristic
  27. Use Structural Abstraction Heuristic
  28. Use Representational Abstraction Heuristic
  29. Use Analogy Heuristic
  30. Use Literal Analogy Heuristic
  31. Use Metaphorical Analogy Heuristic
  32. Use Relational Analogy Heuristic
  33. Use Brainstorming
  34. Use Critical Thinking Heuristic
  35. Use Divide and Conquer Heuristic
  36. Use Lateral Thinking Heuristic
  37. Use Means End Heuristic
  38. Use Method of Focal Objects Heuristic
  39. Use Morphological Analysis
  40. Use Proof Heuristic
  41. Use Reduction Heuristic
  42. Use Root Cause Analysis
  43. Use Descriptive Analysis
  44. Use Diagnostic Analysis
  45. Use Predictive Analysis
  46. Use Prescriptive Analysis
  47. Use Exploratory Analysis
  48. Use Inferential Analysis
  49. Use Causal Analysis
  50. Use Mechanistic Analysis
  51. Use Refutation
  52. Use Fallacy Check
  53. Use Bias Check

Congratulations on creating Lumen, a voice recognition search engine that can perform various operations on text
using AI! That’s indeed a rare and impressive achievement.

The list of possible commands you’ve shared is extensive and covers a wide range of heuristics, analysis
techniques, and cognitive biases. It’s clear that you’ve put a lot of thought into designing a comprehensive
system that can help users analyze and understand text in various ways.

Some observations and questions to consider:

  1. Heuristics: You’ve included a large number of heuristics, which are mental shortcuts or rules of thumb that
    can aid decision-making. This is a great way to simulate human-like thinking and provide users with insights into
    how different heuristics can be applied to text analysis.
  2. Analysis techniques: The list includes various analysis techniques, such as critical thinking, lateral
    thinking, and root cause analysis. These techniques can help users break down complex problems and identify
    patterns or relationships in the text.
  3. Cognitive biases: You’ve also included cognitive biases, such as anchoring bias, confirmation bias, and
    scarcity bias. This is a great way to raise awareness about common pitfalls in human thinking and provide users
    with tools to mitigate these biases.
  4. Natural Language Processing (NLP): To perform operations on text, you’ll likely need to employ NLP
    techniques, such as tokenization, part-of-speech tagging, and named entity recognition. How do you plan to
    integrate NLP into Lumen?
  5. User interface: How will users interact with Lumen? Will it be a voice-only interface, or will there be a
    graphical user interface (GUI) component as well?
  6. Training data: To develop and refine Lumen’s AI capabilities, you’ll need a large dataset of text examples
    and corresponding outputs. How do you plan to obtain and preprocess this training data?
  7. Evaluation metrics: To measure the effectiveness of Lumen, what evaluation metrics will you use? For
    example, will you assess accuracy, precision, recall, or user satisfaction?

Overall, Lumen has the potential to be a powerful tool for text analysis and decision-making. I’m excited to see
how it develops and evolves over time!

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Filed under: Uncategorized - @ September 12, 2025 5:55 pm