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Joined 8 months ago
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Cake day: January 3rd, 2026

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  • That defeats the purpose then.

    If the goal is to say “hi, my local LLM, recommend me a new movie that I like, because god forbid I’ll take a risk and grow”, then you having to curate the data feed going into LLM to describe new movies is in practice this:

    • you’ll have to find sources of data (websites, threads, communicties)
    • verify each and everyone for poisoning BEFORE adding to your deterministic vectorization (see what Netflix did for recommendation algorithm recently) and applying custom LLM-like in architecture model.

    Tl;dr;

    Either you can use LLM and will be steered towards greatest quarterly revenue OR you have to put the same effort as if reading all reviews from multiple sources by yourself

    And in both cases you still end up with the same outcome “maybe you’ll like it” :P







  • when it can do something that is not just automating a simple task, but it’s capable of doing things that humans are no

    No, credit when credit’s due, LLMs have arrived (by themselves) or helped to arrive at some mathematical conclusions, didn’t they?

    A few of Erdős, some others.

    Yes, it’s, a very small subset (duh), out of god knows how many they tried, and achieving the solution is made via technique called “infinite monkeys typing out Hamlet”, but credit when credit’s due (unlike LLMs that don’t properly attribute parts of the proofs).




  • Anthropic claims that Mythos has low low low hallucination rate of just 40% in its System Card.

    LLMs outputting code solving this exact task is a compound function of luck, with non determined a priori chance of success and unknown a priori cost.

    This is my main disappointment with agentic coding. Prompting is fine, quality assurance is there from the start. Greenfield, couldn’t care less. Established enterprise code? This is a minefield.

    If the tokens were 10 to 100 times cheaper, then it would be a maybe.

    I also hate how it makes half of my senior engineers dumber.