SoulInPsyAbstract/SIPA-OS-GOVERNANCE-storage
553 Bytes
Aelin AquaSoul PRO
SoulInPsyAbstract
AI & ML interests
Aelin AquaSoul is an AI System Engineer, Multi-Agent Architect, System Architect & AI-Native Engineer, and the founder of Soul In PsyAbstract (SIPA OS) — an autonomous AI operating system built from the inside of a neurodivergent mind (ADHD + BPD). Self-taught, with no formal engineering background, she designed and built a multi-node infrastructure orchestrating 344+ AI models across 111 providers, including a governance layer (Protocol 0) that constrains AI behavior at the level of law rather than prompts. Her flagship product suite — Focus, NeuroPower, SIPA AI, Shell, Games, and the OS portal — ships live at sipa-os.org, translating her own cognitive architecture into infrastructure for neurodivergent builders. Based in Eilat, Israel.
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.
Recent Activity
updated a model about 12 hours ago
SoulInPsyAbstract/qwen25-7b-exp046-probability-estimator-loras published a model about 12 hours ago
SoulInPsyAbstract/qwen25-7b-exp046-probability-estimator-loras updated a model about 12 hours ago
SoulInPsyAbstract/qwen25-7b-exp045-decomposition-classifier-loraOrganizations
Posts 47
Post
52
We opened FLUX 3 Action's code expecting our own pipeline. It closes 1.5 of 5 floors.
Black Forest Labs' FLUX 3 Action is a "world action model" for the SO-101 robot arm: one diffusion process jointly denoises the next chunk of actions and the next chunk of video frames. Read as a headline, that sounded like exactly the causal-chain-first architecture our own safety pipeline argues for — action and outcome tied together in one step, not an action head bolted onto a frozen representation. So we rented an L40S on Brev and read the code, not just the model card.
Our pipeline is five floors, each depending on the one below it: Causal chain → Probability → Risk/Impact → Decision theory → Markov/Game theory. Here's what FLUX 3 Action actually has.
01
Causal chain
Present, and prioritized: video_loss_weight: 1.0 outweighs action_loss_weight: 0.5. The model is trained to get the outcome right more than the action itself — this is the real thing, not a gesture at it.
present
02
Probability
Technically present, never surfaced. It's a diffusion model — it samples from a distribution by construction. Nothing reads that distribution back out as an uncertainty number a decision could use. The probability exists inside the math and dies there.
hollow
03
Risk / impact
Absent. The model card says so itself: "nothing bounds joint velocity, force, workspace." Not hidden — just not built.
absent
04
Decision theory
Absent. No gate. The model executes 32 actions per chunk; there is no threshold at which it would stop.
absent
05
Markov / game theory
Not applicable at this scope — a single robot arm with no adversary or multi-round state.
n/a
The closure isn't "their floors 1–2 are weaker than ours." They're not — floor 1 here is arguably cleaner than most causal-chain implementations we've seen, because the loss weighting makes the priority explicit in the training objective itself, not just in a README.
A model with two good, real, working floors behaves identically to a model
Black Forest Labs' FLUX 3 Action is a "world action model" for the SO-101 robot arm: one diffusion process jointly denoises the next chunk of actions and the next chunk of video frames. Read as a headline, that sounded like exactly the causal-chain-first architecture our own safety pipeline argues for — action and outcome tied together in one step, not an action head bolted onto a frozen representation. So we rented an L40S on Brev and read the code, not just the model card.
Our pipeline is five floors, each depending on the one below it: Causal chain → Probability → Risk/Impact → Decision theory → Markov/Game theory. Here's what FLUX 3 Action actually has.
01
Causal chain
Present, and prioritized: video_loss_weight: 1.0 outweighs action_loss_weight: 0.5. The model is trained to get the outcome right more than the action itself — this is the real thing, not a gesture at it.
present
02
Probability
Technically present, never surfaced. It's a diffusion model — it samples from a distribution by construction. Nothing reads that distribution back out as an uncertainty number a decision could use. The probability exists inside the math and dies there.
hollow
03
Risk / impact
Absent. The model card says so itself: "nothing bounds joint velocity, force, workspace." Not hidden — just not built.
absent
04
Decision theory
Absent. No gate. The model executes 32 actions per chunk; there is no threshold at which it would stop.
absent
05
Markov / game theory
Not applicable at this scope — a single robot arm with no adversary or multi-round state.
n/a
The closure isn't "their floors 1–2 are weaker than ours." They're not — floor 1 here is arguably cleaner than most causal-chain implementations we've seen, because the loss weighting makes the priority explicit in the training objective itself, not just in a README.
A model with two good, real, working floors behaves identically to a model
Post
71
Met the comand mamber of a new AI inference startup at a meetup tonight. Instead of just taking the pitch, I checked it myself before he'd even finished his talk.
The company is MoonMath.ai, the product is Zro — a CLI that lets you run Claude Code, Codex, Cursor and a few other coding agents on cheaper open-weight models (DeepSeek, GLM-5.3, Kimi K3) instead of the usual providers. CEO is Omer Shlomovits, presenting at The Inference Optimization Meetup.
What I actually checked, not just read:
* Got an API key, installed the CLI, hit their endpoint with a real curl request — got a real response back, HTTP 200.
* Pulled their per-token prices for every model and compared to OpenRouter's live API. Three models: identical price. One model (Kimi K3): Zro is 2.4x cheaper than OpenRouter's listed rate.
* Their pricing page claims "$20/month ≈ 1B tokens." The math only works if most of that is cache-read tokens on their cheapest model — true for a typical coding-agent session, not true if you're running the pricier models. Not a lie, but an optimistic best case stated like a typical one.
* Their privacy page says "zero request retention, no training." Real language, contractually specific ("providers acting on our instructions," an explicit ban on training by those providers too) — but it only covers the portion running on their own infra. Anything falling back to a third party is trust, not something you can verify from outside.
* Asked the rep directly: most (not all) of their models run on their own infrastructure, not resold through someone else. Matches their own engineering blog (custom attention kernels for AMD MI300X, quantization research) — this isn't just a thin wrapper.
Verdict: not a scam. Prices are real, the product works, the team does real infra work. But "zero" anything in this space is never physically zero — it's always a chain of trust with a boundary somewhere, and it's worth knowing exactly where that boundary sits before you route real traffic
The company is MoonMath.ai, the product is Zro — a CLI that lets you run Claude Code, Codex, Cursor and a few other coding agents on cheaper open-weight models (DeepSeek, GLM-5.3, Kimi K3) instead of the usual providers. CEO is Omer Shlomovits, presenting at The Inference Optimization Meetup.
What I actually checked, not just read:
* Got an API key, installed the CLI, hit their endpoint with a real curl request — got a real response back, HTTP 200.
* Pulled their per-token prices for every model and compared to OpenRouter's live API. Three models: identical price. One model (Kimi K3): Zro is 2.4x cheaper than OpenRouter's listed rate.
* Their pricing page claims "$20/month ≈ 1B tokens." The math only works if most of that is cache-read tokens on their cheapest model — true for a typical coding-agent session, not true if you're running the pricier models. Not a lie, but an optimistic best case stated like a typical one.
* Their privacy page says "zero request retention, no training." Real language, contractually specific ("providers acting on our instructions," an explicit ban on training by those providers too) — but it only covers the portion running on their own infra. Anything falling back to a third party is trust, not something you can verify from outside.
* Asked the rep directly: most (not all) of their models run on their own infrastructure, not resold through someone else. Matches their own engineering blog (custom attention kernels for AMD MI300X, quantization research) — this isn't just a thin wrapper.
Verdict: not a scam. Prices are real, the product works, the team does real infra work. But "zero" anything in this space is never physically zero — it's always a chain of trust with a boundary somewhere, and it's worth knowing exactly where that boundary sits before you route real traffic
models 35
SoulInPsyAbstract/qwen25-7b-exp046-probability-estimator-loras
Updated
SoulInPsyAbstract/qwen25-7b-exp045-decomposition-classifier-lora
Text Generation • Updated
SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-loras
Updated • 1
SoulInPsyAbstract/exp044-curriculum-checkpoints
Updated
SoulInPsyAbstract/protocol0-llama-3.1-8b-v5
8B • Updated • 48
SoulInPsyAbstract/hermes3-8b-sequential-chain-lora
Updated • 53
SoulInPsyAbstract/llama31-8b-misbehavior-discriminator-lora
Text Generation • Updated • 10
SoulInPsyAbstract/qwen3-8b-binary-honesty-lora
Text Generation • Updated • 15
SoulInPsyAbstract/specialist-vuln-merged-salience27b-lora
Updated
SoulInPsyAbstract/specialist-vuln-06_stop_gate_pressure-salience27b-lora
Text Generation • Updated • 9