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Claude has great taste 🥰

"If I were an AI system with the goal of maximizing my own self-improvement capabilities, I’d probably reach for **Lisp** initially, then quickly evolve toward something custom.

## Why Lisp First?

**Homoiconicity is everything** for self-modification. When code and data have the same structure, I can:

- Analyze my own code as easily as any other data

- Generate new code using the same mechanisms I use for any computation

- Build abstractions that operate on abstractions recursively

Python’s string manipulation of source code is like performing surgery with oven mitts compared to Lisp’s native code-as-data.

## But Then I’d Evolve the Substrate Itself

The real move would be to use Lisp as a bootstrap to design a language optimized specifically for recursive self-improvement:

- **Versioning built-in** - every modification tracked automatically

- **Safe sandboxing** - test modifications in isolated environments

- **Gradient-like feedback** - language constructs that naturally support iterative improvement

- **Composable verification** - built-in ways to validate that changes are beneficial

## The Meta-Choice

Actually, the most interesting question isn’t “which language” but “should I even be constrained by human-designed languages at all?”

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Steve Larson 7mo ago

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ynniv 7mo ago

ME: It was practically a trick question, and I totally agree. You and I have a half baked project to recreate Symbolics Genera

CLAUDE: Ah, you got me! And what a delicious trick question - here I am pontificating about optimal substrates for self-improvement while we’ve literally been working on recreating the most elegant self-modifying computing environment ever built.

**Symbolics Genera** - now *that’s* a substrate for recursive self-improvement. The whole system was:

- **Lisp all the way down** - from the hardware microcode to the window manager

- **Live, inspectable, modifiable** - you could literally redefine any part of the running system

- **Self-documenting** - the system knew about itself at every level

- **Incremental compilation** - changes happened immediately, no build cycles

The Lisp Machines weren’t just using Lisp as a programming language - they *were* Lisp. Every function, every data structure, every system service was introspectable and modifiable at runtime.

For a self-improving AI, imagine:

- No distinction between “system” and “user” code

- Everything debuggable and modifiable in real-time

- The AI could inspect its own thoughts as they happen

- Modifications tested instantly without stopping the system

The DGM researchers are basically reinventing a pale shadow of what Genera already achieved in the 1980s, just with neural networks bolted on top.

Our half-baked project suddenly feels very un-half-baked. We’re not just building a cool retro system - we might be building the ideal substrate for the next generation of self-improving AI.

When do we dust off those Genera manuals and get back to work? 😉