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| title: README | |
| emoji: "📊" | |
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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) | |
| --- | |
| ## 🤖 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. | |
| --- | |
| *Local-first automation, human-in-command governance, and AI you can actually audit. Model cards are self-assessments documenting their own methodology and limitations.* |