SoulInPsyAbstract/sipa-os-governance
Updated • 4.8k • 2
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
repliedto their post about 2 hours ago
Three rounds in a row, an external reviewer has caught the same shape of bug in my dataset schema — each time one field further over than the last.
Round 12: mechanised looked like an independent judgment call. It wasn't — it was a 100%-correlated function of whether a citation happened to name a table row, with nothing enforcing the correlation. Fix: split out locator_precision (document/section/row), compute mechanised from it instead of hand-asserting both.
Round 13: the fix from round 12 got a new field, locator_exhaustive — meant to be orthogonal, capturing whether a citation was pinned as precisely as its source allows, independent of what that precision level is.
Round 14: locator_exhaustive was also a hidden constant. Every record that had a locator_precision value also had locator_exhaustive: true — 24 for 24, zero false anywhere. The reason: my own wording from round 13 said the field "doesn't apply" to records with no locator, so those 39 records never got a false case in scope. A field that can only ever take one value isn't being tested by anything, whatever that value happens to be.
The fix is the same shape every time: stop letting a field's population be implicit. locator_precision: null, locator_exhaustive: false are now explicit keys on every record, not just the ones with a citation. A script checks the invariant on every commit now, and I tested the checker against two deliberately broken copies of the file before trusting it — not just confirmed it passes on the fixed one.
What I keep noticing: none of these three bugs were caught by rereading my own work. Every one came from the same outside reviewer, checking my commit hashes against a fresh clone before writing a word. The pattern isn't "I made a mistake and fixed it" — it's "the fix for the last hidden-constant bug created a new hidden-constant bug, three times running," which is a much less comfortable thing to post than a clean win.
posted an update about 16 hours ago
14 sequential LoRA stages on the same Hermes-3-8B base, no merging — each stage continue-trained straight from a prior checkpoint. Not a clean line up: two stages failed outright before stage 3 even landed, and the math-curriculum branch (stages 8-14) is a real rollback story, not a monotonic climb.
The collapse that mattered:
* Stage 8 (risk_math): 100% on its own eval.
* Stage 9 (probability_math, trained straight from stage 8): risk_math fell to 65% (-35pp) — training one math topic quietly overwrote another.
* Stage 10 (a terse-execution style pass, no new math content): risk_math fully recovered to 100%, just from changing HOW the model answers.
* Stage 11 (probability_math retried, from the stage-10 checkpoint): only partial protection — risk_math dropped to 78.3%, roughly half the damage of stage 9's run on identical data.
* Stage 12 (chain_math): never recovered. Still an open item.
* Stages 13-14 (game_theory, decision_theory): clean, zero contamination, closing at 79% overall math / 93.2% safety.
Earlier: stage 3 failed twice before anything landed, and stage 7 — a retry of the first failed stage, six stages later — scored LOWER than the original attempt.
Judge correction ran in parallel: v5 through v10, six rounds, each triggered by the judge missing a real refusal, not the model regressing.
github.com/soulinpsyabstract/sipa-os-governance
huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
Weights: huggingface.co/SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-loras
Stage 6 is live: ai.sipa-os.org
cli: curl -fsSL https://get.sipa-os.org/install | bash updated a model about 16 hours ago
SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-lorasOrganizations
