Looking at screenshots of older versions gave a burst of nostalgia.

    Score: 7
    ~~~~~~
    ~····~
    ~·~~·~
    .#..#.
    ......
    ..#...
    .#H#..
    ..#...

    reachable(x,y,t) = reachable(nx,ny,t-1)

    Score: 2
    ~~~~~~
    ~....~
    ~.~~.~
    ......
    ......
    ..##..
    .#H·#.
    ..##..

See

https://gist.github.com/Macuyiko/86299dc120478fdff529cab386f...

It's fun how we are so quick to assign meaning to the way these models act. This is of course due to training, RLHF, available tool calls, system prompt (all mostly invisible) and the way we prompt them.

I've been wondering about a new kind of benchmark how one would be able to extract these more intangible tendencies from models rather than well-controlled "how good at coding is it" style environments. This is mainly the reason why I pay less and less attention to benchmark scores.

For what it's worth: I still best converse with Claude when doing code. Its reasoning sounds like me, and it finds a good middle ground between conservative and crazy, being explorative and daring (even although it too often exclaims "I see the issue now!"). If Anthropic would lift the usage rates I would use it as my primary. The CLI tool is also better. E.g. Codex with 5.1 gets stuck in powershell scripts whilst Claude realizes it can use python to do heavy lifting, but I think that might be largely due to being mainly on Windows (still, Claude does work best, realizing quickly what environment it lives in rather than trying Unix commands or powershell invocations that don't work because my powershell is outdated).

Qwen is great in an IDE for quick auto-complete tasks, especially given that you can run it locally, but even the VSCode copilot is good enough for that. Kimi is promising for long running agentic tasks but that is something I've barely explored and just started playing with. Gemini is fantastic as a research assistant. Especially Gemini 3 Pro points out clear and to the point jargon without fear of the user being stupid, which the other commercial models are too often hesitant to do.

Again, it would be fun to have some unbiased method to uncover some of those underlying persona's.

With so many different coin sizes and types in the empire, I think this makes most sense.

Wikipedia also mentions this:

> Several dodecahedra were found in coin hoards, suggesting either that their owners considered them valuable objects, or that their use was connected with coins — as, for example, for easily checking coins fit a certain diameter and were not clipped.

    #Variables: 121 (91 primary variables)
      - 121 Booleans in [0,1]
    #kLinear1: 200 (#enforced: 200)
    #kLinear2: 1
    #kLinear3: 2
    #kLinearN: 30 (#terms: 355)

    Presolve summary:
      - 1 affine relations were detected.
      - rule 'affine: new relation' was applied 1 time.
      - rule 'at_most_one: empty or all false' was applied 148 times.
      - rule 'at_most_one: removed literals' was applied 148 times.
      - rule 'at_most_one: satisfied' was applied 36 times.
      - rule 'deductions: 200 stored' was applied 1 time.
      - rule 'exactly_one: removed literals' was applied 2 times.
      - rule 'exactly_one: satisfied' was applied 31 times.
      - rule 'linear: empty' was applied 1 time.
      - rule 'linear: fixed or dup variables' was applied 12 times.
      - rule 'linear: positive equal one' was applied 31 times.
      - rule 'linear: reduced variable domains' was applied 1 time.
      - rule 'linear: remapped using affine relations' was applied 4 times.
      - rule 'presolve: 120 unused variables removed.' was applied 1 time.
      - rule 'presolve: iteration' was applied 2 times.

    Presolved satisfaction model '': (model_fingerprint: 0xa5b85c5e198ed849)
    #Variables: 0 (0 primary variables)

    The solution hint is complete and is feasible.

    #1       0.00s main
      a    a    a    a    a    a    a    a    a    a   *A* 
      a    a    a    b    b    b    b   *B*   a    a    a  
      a    a   *C*   b    d    d    d    b    b    a    a  
      a    c    c    d    d   *E*   d    d    b    b    a  
      a    c    d   *D*   d    e    d    d    d    b    a  
      a    f    d    d    d    e    e    e    d   *G*   a  
      a   *F*   d    d    d    d    d    d    d    g    a  
      a    f    f    d    d    d    d    d   *H*   g    a  
     *I*   i    f    f    d    d    d    h    h    a    a  
      i    i    i    f   *J*   j    j    j    a    a    a  
      i    i    i    i    i    k   *K*   j    a    a    a

I do agree with you that an LLM should not always start from scratch.

In a way it is like an animal which we have given the ultimate human instinct.

What has nature given us? Homo Erectus is 2 million years ago.

A weird world we live in.

What is context.

> "Good work so far, now I want to take it to another step (somewhat related but feeling it too hard): . Do you think we can do it in this conversation or is it better to start fresh? If so, prepare an initial prompt for your next fresh instantiation."

Sometimes the model says that it might be better to start fresh, and prepares a good summary prompt (including a final 'see you later'), whereas in other cases it assures me it can continue.

I have a lot of notebooks with "initial prompts to explore forward". But given the sycophancy going on as well as one-step RL (sigh) post-training [1], it indeed seems AI platforms would like to keep the conversation going.

[1] RL in post-training has little to do with real RL and just uses one shot preference mechanisms with an RL inspired training loop. There is very little work in terms of long-term preferences slash conversations, as that would increase requirements exponentially.

Much more interesting, but also with more effort required, so of course it never happens.

It would have a more beneficial societal effect, because it is this kind of article, neutrally written, deep investigation, that truly would make people capable to self-discover "maybe I should question a bit more things".

https://netron.app/?url=https://madebyoll.in/posts/world_emu...