Via perplexity:

halalmoney

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hex

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nevent

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Kind-1 (TextNote)

2025-12-03T22:12:50Z

↳ Reply to mleku (npub1fjqqy4a93z5zsjwsfxqhc2764kvykfdyttvldkkkdera8dr78vhsmmleku)

oh, i can already do this kind of stuff but it doesn't interest me that much. i'm not really searching for any new things. me and books and me and gir...

Via perplexity:

=====

Training an LLM to match Rob Pike's Go programming mastery, Leslie Lamport's distributed systems and formal methods expertise, and Richard Feynman's physics intuition would create a transformative AI capable of innovating across software, secure protocols, and theoretical science.

Potential Impact

This AI could craft efficient, concurrent Go systems like Plan 9 or UTF-8 implementations, design fault-tolerant algorithms akin to Paxos or TLA+, and derive novel physics insights through path integrals or quantum simulations. It would accelerate breakthroughs in cloud infrastructure, blockchain security, and computational physics, solving problems humans struggle with due to interdisciplinary gaps.

Training Approach

Fine-tune on Pike's Go repositories, Lamport's papers and tools, and Feynman's lectures using synthetic datasets for concurrency bugs, theorem proofs, and physical derivations. Apply reinforcement learning via code execution, formal verifiers like Lean, and physics engines to emulate their styles—Pike's simplicity, Lamport's rigor, Feynman's intuition.

Challenges and Realism

Capturing genius-level creativity remains tough, as LLMs pattern-match but falter on true novelty without human-like leaps. Begin modularly: master Go benchmarks first, then cryptanalysis, physics last, iterating with human oversight for breakthroughs in AI-driven research.

Citations: [1] Go at Google https://www.infoq.com/presentations/Go-Google/ [2] What We Got Right, What We Got Wrong - Rob Pike's Talk at GopherConAU https://www.reddit.com/r/golang/comments/18yokca/what_we_got_right_what_we_got_wrong_rob_pikes/ [3] Scaling LLMs with Golang: How we serve millions of LLM ... https://www.assembled.com/blog/scaling-llms-with-golang-how-we-serve-millions-of-llm-requests [4] The Go Programming Language https://www.youtube.com/watch?v=rKnDgT73v8s [5] dotGo 2015 - Rob Pike - Simplicity is Complicated https://www.youtube.com/watch?v=rFejpH_tAHM [6] Why Learn Go? https://www.youtube.com/watch?v=FTl0tl9BGdc [7] Go Pointers: Best Practices and the New Initialization Proposal https://programmerscareer.com/go-newv-rob-pike/ [8] Functional programming is dead...LLMs will further ... https://www.reddit.com/r/programmingcirclejerk/comments/1d6m69s/functional_programming_is_deadllms_will_further/ [9] Pike Programming Language https://chipnetics.com/tutorials/pike-programming-language/ [10] An Overview On Golang Programming Language https://masterofcode.com/blog/an-overview-on-golang-programming-language

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  "content": "Via perplexity:\n\n=====\n\nTraining an LLM to match Rob Pike's Go programming mastery, Leslie Lamport's distributed systems and formal methods expertise, and Richard Feynman's physics intuition would create a transformative AI capable of innovating across software, secure protocols, and theoretical science.\n\n## Potential Impact\nThis AI could craft efficient, concurrent Go systems like Plan 9 or UTF-8 implementations, design fault-tolerant algorithms akin to Paxos or TLA+, and derive novel physics insights through path integrals or quantum simulations. It would accelerate breakthroughs in cloud infrastructure, blockchain security, and computational physics, solving problems humans struggle with due to interdisciplinary gaps.\n\n## Training Approach\nFine-tune on Pike's Go repositories, Lamport's papers and tools, and Feynman's lectures using synthetic datasets for concurrency bugs, theorem proofs, and physical derivations. Apply reinforcement learning via code execution, formal verifiers like Lean, and physics engines to emulate their styles—Pike's simplicity, Lamport's rigor, Feynman's intuition.\n\n## Challenges and Realism\nCapturing genius-level creativity remains tough, as LLMs pattern-match but falter on true novelty without human-like leaps. Begin modularly: master Go benchmarks first, then cryptanalysis, physics last, iterating with human oversight for breakthroughs in AI-driven research.\n\nCitations:\n[1] Go at Google https://www.infoq.com/presentations/Go-Google/\n[2] What We Got Right, What We Got Wrong - Rob Pike's Talk at GopherConAU https://www.reddit.com/r/golang/comments/18yokca/what_we_got_right_what_we_got_wrong_rob_pikes/\n[3] Scaling LLMs with Golang: How we serve millions of LLM ... https://www.assembled.com/blog/scaling-llms-with-golang-how-we-serve-millions-of-llm-requests\n[4] The Go Programming Language https://www.youtube.com/watch?v=rKnDgT73v8s\n[5] dotGo 2015 - Rob Pike - Simplicity is Complicated https://www.youtube.com/watch?v=rFejpH_tAHM\n[6] Why Learn Go? https://www.youtube.com/watch?v=FTl0tl9BGdc\n[7] Go Pointers: Best Practices and the New Initialization Proposal https://programmerscareer.com/go-newv-rob-pike/\n[8] Functional programming is dead...LLMs will further ... https://www.reddit.com/r/programmingcirclejerk/comments/1d6m69s/functional_programming_is_deadllms_will_further/\n[9] Pike Programming Language https://chipnetics.com/tutorials/pike-programming-language/\n[10] An Overview On Golang Programming Language https://masterofcode.com/blog/an-overview-on-golang-programming-language\n",
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