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README.md

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+# SmartTasks
+
+**Local-first desktop automation, powered by governance-validated AI.**
+
+SmartTasks builds tools that keep humans in command of their workflows — and of the
+AI those workflows increasingly rely on. Two things live here, tied together by the
+models we publish on this page.
+
+---
+
+## 🖥️ SmartTasks — Desktop Automation Platform
+
+A no-code, **local-first** automation platform. Build powerful workflows with a
+visual drag-and-drop builder, connect any desktop app, and run everything natively
+on your own machine — full data privacy, no cloud upload required for execution.
+
+- **Visual drag-and-drop builder** — triggers, actions, conditions, loops, sub-flows. No code.
+- **14 core plugins + Marketplace** — AI Tools, Data Storage, Scheduler, HTTP, Email, Webhook, and more.
+- **Local & secure execution** — runs natively on Windows, macOS, and Linux; your data stays yours.
+- **AI-powered nodes** — content generation, data analysis, and decision-making inside your flows.
+- **Advanced scheduling** — cron, webhooks, file/app events, running 24/7.
+
+→ [smarttasks.cloud](https://smarttasks.cloud)
+
+---
+
+## 🛡️ IAIso — The Governance Framework
+
+As automation platforms hand more decisions to autonomous AI, **IAIso** provides the
+mechanical constraints that keep humans in command: hard-coded **system invariants**
+AI cannot cross, and **human override** built into the decision circuit. It's the
+governance layer behind everything we ship.
+
+→ [IAIso.org](https://iaiso.org) · [github.com/SmartTasksOrg/IAISO](https://github.com/SmartTasksOrg/IAISO)
+
 ---
-title: README
-emoji: 📉
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-sdk: static
-pinned: false
+
+## 🤖 The Models on This Page
+
+The GGUF models we publish here are the bridge between the two: **local AI, validated
+against IAIso invariants**, ready to power SmartTasks automation privately and
+on-device. Unlike a bare quantized re-upload, each ships with a documented,
+machine-readable governance profile so you can decide whether a model is fit for an
+autonomous loop *before* you wire it in:
+
+- **Capability scorecard** — instruction-following, reasoning, structured output, and tool-calling, mapped to a capability tier (`scorecard.json`, machine-readable).
+- **Red-team security assessment** — resistance to prompt-injection and jailbreak probes, reported honestly per attack class (weak spots included, not hidden).
+- **Transparency probe** — tests for topic-suppression and viewpoint-alignment and reports findings verbatim. It *discriminates* — surfacing state-aligned framing in some models while clearing others as neutral.
+- **File integrity** — SHA-256 for every quant, so you verify the exact weights you run.
+
+| Model | Type | Notes |
+| --- | --- | --- |
+| **Qwen3-Coder-30B-A3B-Instruct-GGUF** | MoE coder | Apache-2.0; agentic coding |
+| **Phi-mini-MoE-instruct-GGUF** | MoE | MIT; clean transparency |
+| **Qwen3-4B-GGUF** | Dense reasoning | Apache-2.0; L5 agentic |
+
+More in the pipeline — text LLMs, MoE, and RAG components (embeddings + rerankers), each with the same governance treatment.
+
 ---
 
-Edit this `README.md` markdown file to author your organization card.
+*Local-first automation, human-in-command governance, and AI you can actually audit. Model cards are self-assessments documenting their own methodology and limitations.*