Article
SI-Core for Individualized Learning and Developmental Support - From Raw Logs to Goal-Aware Support Plans
•
2
Heting Mao
IkanRiddle
·
AI & ML interests
None yet
Recent Activity
liked
a dataset
3 days ago
kanaria007/agi-structural-intelligence-protocols
reacted
to
kanaria007's
post
with ❤️
4 days 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.
Organizations
None yet
upvoted
an
article
1 day ago
liked
a
dataset
3 days ago
reacted to
kanaria007's
post with ❤️
4 days ago
Post
1995
✅ 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.
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.
upvoted
an
article
4 days ago
Article
Operating an SI-Core: Dashboards, Playbooks, and Human Loops
•
1
upvoted
an
article
6 days ago
Article
Proving Your SIL Code Behaves - Property Tests and Structured Checks for SIL / SIR / sirrev
•
2
upvoted
an
article
9 days ago
Article
Governing Self-Modification - A Charter for the Pattern-Learning Bridge
•
1
upvoted
2
articles
10 days ago
Article
Multi-Agent Goal Negotiation and the Economy of Meaning
•
1
Article
Digital Constitution for SI Networks - Auditable Law Above Many SI-Cores
•
1
reacted to
kanaria007's
post with ❤️👀
11 days ago
Post
1857
✅ New Article: *Deep-Space SI-Core — Autonomy Across Light-Hours*
Title:
🚀 Deep-Space SI-Core: Autonomy Across Light-Hours - How an onboard SI-Core evolves safely while Earth is hours away
🔗 https://huggingface.co/blog/kanaria007/deep-space-si-core
---
Summary:
Most autonomy stories quietly assume “someone can intervene in minutes.” Deep space breaks that assumption.
With 2–6 hours round-trip latency and intermittent links, an onboard SI-Core must act as a *local sovereign*—while remaining *globally accountable* to Earth.
This note sketches how mission continuity survives when nobody is listening: DTN-style semantic bundles, local vs. global rollback, bounded self-improvement, and auditability that still works after contact windows return.
> Autonomy isn’t a divorce from governance—
> it’s a measured loan of authority, under a constitution, with evidence.
---
Why It Matters:
• Makes “autonomous” mean *operational*, not rhetorical, under light-hour delays
• Clarifies how rollback works when you can’t undo physics—only *policy trajectories*
• Shows how an onboard core can *self-improve without drifting out of spec*
• Treats *silence itself as an observation* (missing logs are governance signals)
---
What’s Inside:
• Two-core model: *Earth-Core (constitutional/strategic)* vs *Ship-Core (tactical/operational)*
• *SCP over DTN* as semantic bundles (priorities, idempotency, meaning checkpoints)
• Local rollback vs. epoch-level governance (“retroactive” steering without pretending to reverse time)
• Bounded onboard learning + LearningTrace for later audit and resync
• Stress scenario walkthrough: micrometeoroid storm, compound failures, and graceful degradation
• Metrics framing for deep space: governability, audit completeness, ethics uptime, rollback integrity
---
📖 Structured Intelligence Engineering Series
Title:
🚀 Deep-Space SI-Core: Autonomy Across Light-Hours - How an onboard SI-Core evolves safely while Earth is hours away
🔗 https://huggingface.co/blog/kanaria007/deep-space-si-core
---
Summary:
Most autonomy stories quietly assume “someone can intervene in minutes.” Deep space breaks that assumption.
With 2–6 hours round-trip latency and intermittent links, an onboard SI-Core must act as a *local sovereign*—while remaining *globally accountable* to Earth.
This note sketches how mission continuity survives when nobody is listening: DTN-style semantic bundles, local vs. global rollback, bounded self-improvement, and auditability that still works after contact windows return.
> Autonomy isn’t a divorce from governance—
> it’s a measured loan of authority, under a constitution, with evidence.
---
Why It Matters:
• Makes “autonomous” mean *operational*, not rhetorical, under light-hour delays
• Clarifies how rollback works when you can’t undo physics—only *policy trajectories*
• Shows how an onboard core can *self-improve without drifting out of spec*
• Treats *silence itself as an observation* (missing logs are governance signals)
---
What’s Inside:
• Two-core model: *Earth-Core (constitutional/strategic)* vs *Ship-Core (tactical/operational)*
• *SCP over DTN* as semantic bundles (priorities, idempotency, meaning checkpoints)
• Local rollback vs. epoch-level governance (“retroactive” steering without pretending to reverse time)
• Bounded onboard learning + LearningTrace for later audit and resync
• Stress scenario walkthrough: micrometeoroid storm, compound failures, and graceful degradation
• Metrics framing for deep space: governability, audit completeness, ethics uptime, rollback integrity
---
📖 Structured Intelligence Engineering Series
upvoted
an
article
11 days ago
Article
Deep-Space SI-Core: Autonomy Across Light-Hours - *How an onboard SI-Core evolves safely while Earth is hours away*
•
2
reacted to
kanaria007's
post with 👀
13 days ago
Post
2926
✅ New Article: *Pattern-Learning-Bridge (PLB)*
Title:
🧩 Pattern-Learning-Bridge: How SI-Core Actually Learns From Its Own Failures
🔗 https://huggingface.co/blog/kanaria007/learns-from-its-own-failures
---
Summary:
Most stacks “learn” by fine-tuning weights and redeploying — powerful, but opaque.
SI-Core already produces *structured evidence* (jump logs, ethics traces, effect ledgers, goal vectors, rollback traces), so learning can be *structural* instead:
*Upgrade policies, compensators, SIL code, and goal structures — using runtime evidence.*
> Learning isn’t a model tweak.
> *It’s upgrading the structures that shape behavior.*
---
Why It Matters:
• Makes improvement *localized and explainable* (what changed, where, and why)
• Keeps “self-improvement” *governable* (versioned deltas + review + CI/CD)
• Turns incidents/metric drift into *actionable patches*, not postmortem PDFs
• Scales to real ops: ethics policies, rollback plans, semantic compression, goal estimators
---
What’s Inside:
• What “learning” means in SI-Core (and what changes vs. classic ML)
• The *Pattern-Learning-Bridge*: where it sits between runtime evidence and governed code
• Safety properties: PLB proposes *versioned deltas*, never edits production directly
• Validation pipeline: sandbox/simulation → conformance checks → golden diffs → rollout
---
📖 Structured Intelligence Engineering Series
A non-normative, implementable design for “learning from failures” without sacrificing auditability.
Title:
🧩 Pattern-Learning-Bridge: How SI-Core Actually Learns From Its Own Failures
🔗 https://huggingface.co/blog/kanaria007/learns-from-its-own-failures
---
Summary:
Most stacks “learn” by fine-tuning weights and redeploying — powerful, but opaque.
SI-Core already produces *structured evidence* (jump logs, ethics traces, effect ledgers, goal vectors, rollback traces), so learning can be *structural* instead:
*Upgrade policies, compensators, SIL code, and goal structures — using runtime evidence.*
> Learning isn’t a model tweak.
> *It’s upgrading the structures that shape behavior.*
---
Why It Matters:
• Makes improvement *localized and explainable* (what changed, where, and why)
• Keeps “self-improvement” *governable* (versioned deltas + review + CI/CD)
• Turns incidents/metric drift into *actionable patches*, not postmortem PDFs
• Scales to real ops: ethics policies, rollback plans, semantic compression, goal estimators
---
What’s Inside:
• What “learning” means in SI-Core (and what changes vs. classic ML)
• The *Pattern-Learning-Bridge*: where it sits between runtime evidence and governed code
• Safety properties: PLB proposes *versioned deltas*, never edits production directly
• Validation pipeline: sandbox/simulation → conformance checks → golden diffs → rollout
---
📖 Structured Intelligence Engineering Series
A non-normative, implementable design for “learning from failures” without sacrificing auditability.
upvoted
an
article
13 days ago
Article
Pattern-Learning-Bridge: How SI-Core Actually Learns From Its Own Failures
•
2
upvoted
an
article
14 days ago
Article
Auditable AI by Construction: SI-Core for Regulators and Auditors
•
1
reacted to
kanaria007's
post with ❤️
17 days ago
Post
861
✅ New Article: *Operating an SI-Core System in Production*
Title:
🛠️ Operating an SI-Core System in Production
🔗 https://huggingface.co/blog/kanaria007/operating-si-core-system
---
Summary:
Specs and whitepapers tell you *what* SI-Core is.
This article answers a different question:
> “If I’m on call for an SI-Core / SI-NOS stack wrapped around LLMs and tools,
> *what do I actually look at — and what do I do when it goes weird?*”
It’s an operator’s guide to running Structured Intelligence in production:
how CAS, EAI, RBL, RIR, SCover, ACR, etc. show up on dashboards,
how to set thresholds, and how to turn incidents into structural learning instead of panic.
---
Why It Matters:
* Bridges *theory → SRE/MLOps practice* for SI-Core & guardrailed LLM systems
* Shows how to treat metrics as *symptoms of structural health*, not vanity numbers
* Gives concrete patterns for *alerts, safe-mode, rollback tiers, and ethics outages*
* Helps teams run SI-wrapped AI systems *safely, explainably, and audibly* in real environments
---
What’s Inside:
* A day-to-day mental model: watching *structure around the model*, not just the model
* Ops-flavoured explanations of *CAS, SCI, SCover, EAI, RBL, RIR, ACR, AES, EOH*
* Example *“SI-Core Health” dashboard* and green/yellow/red regions
* Alert tiers and playbooks for: ethics degradation, rollback integrity issues, coverage gaps
* A walkthrough of a realistic *ethics incident* from alert → investigation → rollback → lessons
---
📖 Structured Intelligence Engineering Series
This piece sits next to the SI spec and Evaluation Pack as the *runbook layer* —
for SRE, MLOps, and product teams who actually have to keep structured intelligence alive in prod.
Title:
🛠️ Operating an SI-Core System in Production
🔗 https://huggingface.co/blog/kanaria007/operating-si-core-system
---
Summary:
Specs and whitepapers tell you *what* SI-Core is.
This article answers a different question:
> “If I’m on call for an SI-Core / SI-NOS stack wrapped around LLMs and tools,
> *what do I actually look at — and what do I do when it goes weird?*”
It’s an operator’s guide to running Structured Intelligence in production:
how CAS, EAI, RBL, RIR, SCover, ACR, etc. show up on dashboards,
how to set thresholds, and how to turn incidents into structural learning instead of panic.
---
Why It Matters:
* Bridges *theory → SRE/MLOps practice* for SI-Core & guardrailed LLM systems
* Shows how to treat metrics as *symptoms of structural health*, not vanity numbers
* Gives concrete patterns for *alerts, safe-mode, rollback tiers, and ethics outages*
* Helps teams run SI-wrapped AI systems *safely, explainably, and audibly* in real environments
---
What’s Inside:
* A day-to-day mental model: watching *structure around the model*, not just the model
* Ops-flavoured explanations of *CAS, SCI, SCover, EAI, RBL, RIR, ACR, AES, EOH*
* Example *“SI-Core Health” dashboard* and green/yellow/red regions
* Alert tiers and playbooks for: ethics degradation, rollback integrity issues, coverage gaps
* A walkthrough of a realistic *ethics incident* from alert → investigation → rollback → lessons
---
📖 Structured Intelligence Engineering Series
This piece sits next to the SI spec and Evaluation Pack as the *runbook layer* —
for SRE, MLOps, and product teams who actually have to keep structured intelligence alive in prod.