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Computational semiotics is empirically proven. It takes three to tango 💃🪩🕺

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posted an update about 8 hours ago
This is not a pipe. Everyone is born a semiotician, no one is born knowing it. Go easy on yourself (and me) for not understanding this yet. Computational semiotics is now an empirical study. LLMs are not proto-minds. They are verifiably semiotic infrastructure. This repository (or attached demo) can show you, in real time, how any frozen model (Qwen for demo) arrives at any answer by reading its latent states directly during generation. Any questions? https://huggingface.co/spaces/RiverRider/srt-introspect Repo: https://github.com/space-bacon/SRT Grok insist my intro is condescending … This is certainly true, as is the statement in my condescended opinion. I expect heat for it, let’s think this through?
reacted to theirpost with 🚀 about 9 hours ago
Words do not have determined meanings. The vocabulary itself is reflexive. It is self-referential, looping back into its own structure rather than anchoring in fixed reality. What we treat as stable meaning is continually reconstituted in the act of using it. The observers own interpretations molding each word like clay with every utterance.  All large language models to date treat words otherwise. At the moment of softmax crystallization they determine the meaning of every token. Probabilities collapse into a single output. Meaning is not found. It is fixed, token by token, in that final distribution. SRT-Introspect is a demo for observing what Qwen actually thinks at the points of highest effort. It surfaces the internal representations during generation, making visible the reflexive vocabulary at work and the precise crystallization process: the weights, the assumptions, the decisions that resolve ambiguity into output. This includes accounting for anisotropy collapse in hidden states by centering representations around the layer-mean before analysis. Feel free to comment your prompts https://huggingface.co/spaces/RiverRider/srt-introspect Repo https://github.com/space-bacon/SRT
reacted to danielhanchen's post with 🔥 about 23 hours ago
Gemma 4 12B can now run locally on just 8GB RAM via Dynamic GGUFs. Google's new model, Gemma 4 12B Unified supports image, audio and 256K context. You can run and train the model via Unsloth Studio. GGUF: https://huggingface.co/unsloth/gemma-4-12b-it-GGUF Guide: https://unsloth.ai/docs/models/gemma-4
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