Viber Coding: From Punched Cards to Talking with Machines

 Modern programming, for all its brilliance, has become tangled in its own complexity. Too often, developers spend more time wrestling with obscure libraries, fiddling with environment setups, or deciphering someone else’s coding quirks than actually building ideas. Enter Generative AI: a liberator from these nuisances, allowing us to bypass the grunt work and concentrate on what really matters—the logic, the creativity, the spark of innovation. After all, computer languages have always been about communication first. From punch cards to Assembly to C and beyond, interpreters have been steadily nudging code closer to natural language. Now, with large language models, we stand at the edge of a fascinating shift: for the first time, our everyday words themselves might become a true computer language.

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Silicon vs. Carbon: A Tale of Two Intelligences

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The Ghost in the Machine: How Human Cognitive Biases Shape the Alignment, Context, and Tuning of Large Language Models

 The entire discipline of guiding LLM outputs can be reframed through the lens of "choice architecture," a concept from behavioral economics introduced by Richard Thaler and Cass Sunstein. Choice architecture is the design of the environment in which people make decisions; it posits that the way choices are presented—the "architecture"—inevitably influences the outcome, whether intentionally or not. A choice architect, therefore, is anyone who organizes the context in which people make decisions. In the realm of LLMs, prompt engineers and the designers of Retrieval-Augmented Generation (RAG) systems are the new choice architects. They are not merely instructing a machine; they are designing a decision-making environment for an artificial intelligence. This section argues that this process is profoundly shaped by the same heuristics and biases that affect human decision-making, turning context engineering into a practice of creating flawed choice architectures.
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For Whom the Bot Toils: Navigating the Great Inequality of the Gen AI Era

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