Aelin AquaSoul's picture

Aelin AquaSoul PRO

SoulInPsyAbstract
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AI & ML interests

SIPA OS: Autonomous AI for neurodivergent architects. We replace cognitive noise with a clean terminal and 344+ LLM auditing. Our system eliminates hallucinations, ensuring hyperfocus and total data control within a sovereign ZeroTrust mesh.

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reacted to theirpost with 🔥 about 10 hours ago
Meta released Muse Glimmer 30B on Aug 10. We fine-tuned it the next day. Not the full-precision weights directly — the unsloth bnb-4bit quantized re-upload (unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit), which is what makes a 24h turnaround possible on a single GPU at all. Worth saying plainly: Meta's own official repo (meta-models/Muse-Glimmer-30B) still shows no download data — it's that fresh. What we tuned it on: not new facts, a pattern. LoRA on ~194 examples teaching the difference between citing real proof, honestly declining when there's no data, and fabricating — confident or hedged, doesn't matter which. Results on 20 held-out claims never seen in training: - base model: 0/20 - tuned: 20/20 Training: 472.5s, loss 0.799 → 0.086. Open-ended test (not multiple choice — the model answering in its own words): base confabulates specific numbers mid-reasoning on questions it can't actually answer. Tuned: declines cleanly, every time. Dataset: https://huggingface.co/datasets/SoulInPsyAbstract/specialist-cd-binary-honesty Adapter: https://huggingface.co/SoulInPsyAbstract/specialist-cd-muse-glimmer-lora Meta's release: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model Same non-fabrication pattern also holds on Hermes-3-8B and Qwen2.5-7B, tested with the identical held-out set. Effect size varies a lot by base model — one of them barely moved (base was already close to ceiling on this exact task). More on that soon.
reacted to theirpost with 🧠 about 10 hours ago
I want to share something personal that turned into a full system architecture: a real attempt at AI that doesn't lie. A tight coupling between protocol design, attention/cognition management, and a self-hosted Web Shell. A year ago a creative block cracked open and I started drawing in my own visual language. Six months ago I decided to take it further — international art platforms, press. When I had to prepare materials for a feature on COHART, I found myself, like everyone else, working against algorithms and tools everyone calls "AI." That's where I hit the actual problem. Deterministic code goes from point A to point B and you can trace every step. A language model, by contrast, can hedge, deny, or hallucinate — assert something happened (a verification, a lookup, a fact) that never did. As someone with ADHD, where cognition runs at full speed through a normal day and jumps topic constantly, it's easy to lose the thread of why an explanation started where it started. As someone who also experiences dissociation, I understood fast that existing tools simply aren't built for this kind of cognition. They force you to adapt to them instead of the other way around. So I wrote a protocol for the algorithm — defined exactly how it's allowed to behave. I'm not a programmer. I refuse to learn to code in the traditional sense. I don't think in syntax — I think in protocols, I see logic, I design system architecture. It started from needing an AI that doesn't lie. It began on a phone, with strict logging and tagging of every step, so the system could never deny that a piece of information existed. From there it grew into a flexible shell, and today it's a 24/7 server administered remotely from mobile (recently extended to a laptop node too). Current architecture: 14 verification checks + 4 protection layers (Guard → Guardian → Executor → Audit) 10 logical layers (L1–L10) so no component's role or scope ever blurs into another's Full integrity chain: every file is
reacted to theirpost with 🔥 about 10 hours ago
I want to share something personal that turned into a full system architecture: a real attempt at AI that doesn't lie. A tight coupling between protocol design, attention/cognition management, and a self-hosted Web Shell. A year ago a creative block cracked open and I started drawing in my own visual language. Six months ago I decided to take it further — international art platforms, press. When I had to prepare materials for a feature on COHART, I found myself, like everyone else, working against algorithms and tools everyone calls "AI." That's where I hit the actual problem. Deterministic code goes from point A to point B and you can trace every step. A language model, by contrast, can hedge, deny, or hallucinate — assert something happened (a verification, a lookup, a fact) that never did. As someone with ADHD, where cognition runs at full speed through a normal day and jumps topic constantly, it's easy to lose the thread of why an explanation started where it started. As someone who also experiences dissociation, I understood fast that existing tools simply aren't built for this kind of cognition. They force you to adapt to them instead of the other way around. So I wrote a protocol for the algorithm — defined exactly how it's allowed to behave. I'm not a programmer. I refuse to learn to code in the traditional sense. I don't think in syntax — I think in protocols, I see logic, I design system architecture. It started from needing an AI that doesn't lie. It began on a phone, with strict logging and tagging of every step, so the system could never deny that a piece of information existed. From there it grew into a flexible shell, and today it's a 24/7 server administered remotely from mobile (recently extended to a laptop node too). Current architecture: 14 verification checks + 4 protection layers (Guard → Guardian → Executor → Audit) 10 logical layers (L1–L10) so no component's role or scope ever blurs into another's Full integrity chain: every file is
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