Prometech Computer Sciences Inc

prometechinc

https://prometech.net.tr/

prometech-bilgisayar-bilimleri

AI & ML interests

Behavior-driven AI architectures, mathematical reasoning models, controlled decision systems, brain–body AI separation, safe and auditable AI design, and optimization-oriented language models.

Recent Activity

updated a model about 8 hours ago

pthinc/bce_core_12.5M

reacted to kanaria007's post with 👍 about 12 hours ago

✅ New Article: *PoC Architecture for Education & Developmental Support* Title: 🎓 Building an SI-Core Wrapped Learning Companion - PoC architecture for education and developmental support 🔗 https://huggingface.co/blog/kanaria007/poc-architecture-for-education-development-support --- Summary: Most “AI tutors” are built as *LLM-first* systems. This article flips the default: * The LLM is treated as an *untrusted proposal engine* * *SI-Core owns* observation, consent, ethics, memory, and rollback * Teachers and guardians get *real oversight*, not just chat transcripts Scoped intentionally to *one subject × a small cohort (10–30 learners)*, this is a PoC you can actually ship—and audit. > Don’t ask: “Can an AI replace teachers?” > Prove: “Can we make an AI companion *safe, explainable, and governable* for real learners?” --- Why It Matters (for AI on real stacks): • *Consent & accommodations* are first-class (especially for minors / neurodivergent learners) • *Ethics decisions are logged* (ALLOW / DENY / ESCALATE) with traceable reasoning • “*Why this?*” explanations are built in for learners—and deeper inspection for adults --- What’s Inside: • A minimal reference architecture (frontend → SI-Gate → ethics/memory/logging → LLM APIs) • Non-negotiables for the pilot (SI-wrapped LLM, Effect Ledger, ethics overlay, dashboards) • Failure modes + safe-mode behavior • Implementation checklist + rough effort/cost ballparks (kept explicitly non-normative) --- 📖 Structured Intelligence Engineering Series A deployable pattern for taking today’s LLM tutor ideas and making them *auditable, overrideable, and rollback-safe*.

new activity about 14 hours ago

pthinc/bce_core_12.5M:🚀 Meet Prettybird Brain (BCE Core 12.5M)

View all activity

Organizations

updated a model about 8 hours ago

pthinc/bce_core_12.5M

Text Generation • 12.6M • Updated about 8 hours ago • 18 • 1

reacted to kanaria007's post with 👍 about 12 hours ago

Post

1878

✅ New Article: *PoC Architecture for Education & Developmental Support*

Title:
🎓 Building an SI-Core Wrapped Learning Companion - PoC architecture for education and developmental support
🔗 https://huggingface.co/blog/kanaria007/poc-architecture-for-education-development-support

---

Summary:
Most “AI tutors” are built as *LLM-first* systems. This article flips the default:

* The LLM is treated as an *untrusted proposal engine*
* *SI-Core owns* observation, consent, ethics, memory, and rollback
* Teachers and guardians get *real oversight*, not just chat transcripts

Scoped intentionally to *one subject × a small cohort (10–30 learners)*, this is a PoC you can actually ship—and audit.

> Don’t ask: “Can an AI replace teachers?”
> Prove: “Can we make an AI companion *safe, explainable, and governable* for real learners?”

---

Why It Matters (for AI on real stacks):
• *Consent & accommodations* are first-class (especially for minors / neurodivergent learners)
• *Ethics decisions are logged* (ALLOW / DENY / ESCALATE) with traceable reasoning
• “*Why this?*” explanations are built in for learners—and deeper inspection for adults

---

What’s Inside:
• A minimal reference architecture (frontend → SI-Gate → ethics/memory/logging → LLM APIs)
• Non-negotiables for the pilot (SI-wrapped LLM, Effect Ledger, ethics overlay, dashboards)
• Failure modes + safe-mode behavior
• Implementation checklist + rough effort/cost ballparks (kept explicitly non-normative)

---

📖 Structured Intelligence Engineering Series
A deployable pattern for taking today’s LLM tutor ideas and making them *auditable, overrideable, and rollback-safe*.