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posted an update about 5 hours ago
✅ New Article: *Operating an SI-Core (v0.1)* Title: 🛠️ Operating SI-Core: Dashboards, Playbooks, and Human Loops 🔗 https://huggingface.co/blog/kanaria007/operating-si-core --- Summary: Designing an SI-Core is only half the job — the other half is *running it safely at 03:00*. This guide is a *non-normative ops runbook* for SRE/Ops teams and governance owners: what to put on the *one-page dashboard*, how to wire *alerts → actions*, when to use *safe-mode*, and how to answer the question that always arrives after an incident: > “Why did the system do *that*?” --- Why It Matters: • Turns “auditable AI” into *operational reality* (not a slide deck) • Makes *ethics + rollback* measurable, actionable, and drillable • Clarifies how humans stay in the loop without becoming the bottleneck • Provides templates for *postmortems, escalation, and regulator-grade explanations* --- What’s Inside: *Core Ops Dashboard (1 page):* • Determinism/consistency, ethics/oversight, rollback/recovery, coverage/audit — with drill-downs that reach offending decisions in *two clicks* *Alert → Runbook Patterns:* • Examples for ethics index drops and rollback latency degradation • Stabilization actions, scoped safe-mode, and governance handoffs *Human-in-Loop Operations:* • Safe-mode scopes (domain/tenant/region/risk) • “Why?” view for any effectful action (structured explanation export) *Reliability Muscle:* • Incident templates, chaos drills, on-call handoffs, and capacity planning (because SI-Core accumulates structure over time) --- 📖 Structured Intelligence Engineering Series A field manual for keeping structured intelligence upright — and explainable — under real-world pressure.
published an article about 5 hours ago
Operating an SI-Core: Dashboards, Playbooks, and Human Loops
posted an update 1 day ago
✅ New Article: *From Effect Ledger to Goal-Aware Training Data* Title: 🧾 From Effect Ledger to Goal-Aware Training Data — How SI-Core turns runtime experience into safer models 🔗 https://huggingface.co/blog/kanaria007/effect-ledger-to-training --- *Summary:* Most ML pipelines treat “training data” as an opaque byproduct of logs + ETL. SI-Core flips that: runtime experience is already structured (observations, decisions, effects, goals, ethics traces), so learning can be *goal-aware by construction* — and *auditable end-to-end*. > Models don’t just learn from data. > They learn from *traceable decisions with consequences.* --- *Why It Matters:* • *Provable lineage:* answer “what did this model learn from?” with ledger-backed evidence • *Safer learning loops:* labels come from realized goal outcomes (not ad-hoc annotation) • *Governance-native training:* ethics and risk are first-class signals, not bolt-ons • *Redaction-compatible ML:* erasure/remediation ties back to the same ledger fabric • *Real deployment gates:* rollout is constrained by system metrics, not leaderboard scores --- *What’s Inside:* • A clean mental model: *event / episode / aggregate* layers for SI-native learning data • How to define training tasks in *goal + horizon* terms (and derive labels from GCS/rollback signals) • A practical ETL sketch: extract → join → label → filter → splits (with SI-native filters like OCR) • Continual/online learning patterns with *automatic rollback on degradation* • Distributed learning with *federation + DP*, bounded by governance scopes • Lineage + audit templates: from a trained model *back to the exact ledger slices* it used --- 📖 Structured Intelligence Engineering Series A practical bridge from “structured runtime” to *goal-aware training* you can explain, govern, and repair.
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