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    You are at:Home»Technology»Asking Gemini 3 for Brainf*ck code puts it in an infinite loop
    Technology

    Asking Gemini 3 for Brainf*ck code puts it in an infinite loop

    TechAiVerseBy TechAiVerseDecember 29, 2025No Comments2 Mins Read2 Views
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    Asking Gemini 3 for Brainf*ck code puts it in an infinite loop

    Asking Gemini 3 to generate Brainf*ck code results in an infinite loop, akin amost to a DDoS attack:

    That is fascinating. So it made me wonder.
    Is Brainf*ck the ultimate test for AGI?
    I think so, and for 3 good reasons.

    1. The Data Scarcity Problem

    Large Language Models (LLMs) thrive on sheer volume. To master JavaScript, an LLM has been trained on
    virtually every available line of open-source code—hundreds of millions of lines of code (LOC). By
    comparison, the amount of functional Brainf*ck code on the web is a statistical
    rounding error.

    We are talking about a million times less training data. Without the
    luxury of infinite patterns to copy, the model can’t rely on mimicry; it has to understand the
    underlying logic.

    2. Anti-Literate Programming

    Brainf*ck is the antithesis of modern software engineering. There are no comments, no meaningful variable
    names, and no structure. In many ways, looking at existing Brainf*ck code is actually
    detrimental to a novice. Consider this typical snippet:

    >++++++++[+++++++++>-].>++++[+++++++>-]+.+++++++..+++.>>++++++[+++++++>-]+
    +.------------.>++++++[+++++++++>-]+..+++.------.--------.>>>++++[++++++++>-
    ]+.

    Writing in this environment is akin to zero-shot learning. Success
    requires reasoning at a high level of abstraction based on the fundamental rules of the language and a
    precise mental model of semantics, rather than memorized syntax.

    3. The Repetition Problem

    As we saw earlier, asking a modern model for complex Brainf*ck code often results in the model falling into an infinite loop—spewing the same characters over and over. The minimalistic nature of the language results in highly repetitive structures in the code. This poses a unique challenge to the way LLMs work.

    An LLM is more likely to output what it has already seen based on previous tokens, and that pertains to its own output too. When some structure is repeated more than a couple of times, there is a likelihood that the model may learn that token X is the most likely output following itself. With every subsequent iteration, this increases the likelihood of outputting X in a self-fulfilling prophecy, resulting in the infinite loop.

    So, is Brainf*ck the ultimate test for LLMs? You be the judge.

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    Jonathan is a tech enthusiast and the mind behind Tech AI Verse. With a passion for artificial intelligence, consumer tech, and emerging innovations, he deliver clear, insightful content to keep readers informed. From cutting-edge gadgets to AI advancements and cryptocurrency trends, Jonathan breaks down complex topics to make technology accessible to all.

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