Tootfinder

Some thoughts on "vibecoding" and why it is not a cardinal sin, especially for people that have little interest in "being developers."
https://www.davideaversa.it/blog/it-is-okay-vibecode/

It Is Okay to “Vibecode”
Critiques to vibecoding are often an example of right-Gaussian thinking. If you want to be a developer, you should avoid it. But if not, why should you not take advantage of new tools? Just be aware of the limitations. Here I try to explain why vibecoding is not a cardinal sin.

I have this idea of building a "Luddite Library". A set of information, tools and processes to harness luddite thinking when analyzing technological developments and "innovation".
Something that interested parties could use to understand that there might be a different way to think about what tech is/should be/can be/mustn't be for us. Think for example sets of questions to use to analyze a new thing being pushed on you and similar tools.
I'm thinking …

System 0: Transforming Artificial Intelligence into a Cognitive Extension
Massimo Chiriatti, Marianna Bergamaschi Ganapini, Enrico Panai, Brenda K. Wiederhold, Giuseppe Riva
https://arxiv.org/abs/2506.14376

System 0: Transforming Artificial Intelligence into a Cognitive Extension
This paper introduces System 0, a conceptual framework for understanding how artificial intelligence functions as a cognitive extension preceding both intuitive (System 1) and deliberative (System 2) thinking processes. As AI systems increasingly shape the informational substrate upon which human cognition operates, they transform from passive tools into active cognitive partners. Building on the Extended Mind hypothesis and Heersmink's criteria for cognitive extension, we argue that AI systems…

From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop
Jordi de la Torre
https://arxiv.org/abs/2506.10021 https://

From Tool Calling to Symbolic Thinking: LLMs in a Persistent Lisp Metaprogramming Loop
We propose a novel architecture for integrating large language models (LLMs) with a persistent, interactive Lisp environment. This setup enables LLMs to define, invoke, and evolve their own tools through programmatic interaction with a live REPL. By embedding Lisp expressions within generation and intercepting them via a middleware layer, the system allows for stateful external memory, reflective programming, and dynamic tool creation. We present a design framework and architectural principles …

The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
Barbara Oakley, Michael Johnston, Ken-Zen Chen, Eulho Jung, Terrence J. Sejnowski
https://arxiv.org/abs/2506.11015

The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI
In the age of generative AI and ubiquitous digital tools, human cognition faces a structural paradox: as external aids become more capable, internal memory systems risk atrophy. Drawing on neuroscience and cognitive psychology, this paper examines how heavy reliance on AI systems and discovery-based pedagogies may impair the consolidation of declarative and procedural memory -- systems essential for expertise, critical thinking, and long-term retention. We review how tools like ChatGPT and calc…

I keep posting about how the AI hype bubble makes it almost impossible to have a reasonable conversation about LLMs, and it’s only when the bubble bursts that we can start thinking realistically about what if anything LLMs are actually good for in writing code.
That seems to be what Fred is getting at here: the massive gap between the hype and the reality means that the affordances of these tools fit neither the task at hand nor the tool’s own capabilities.
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