--- title: README emoji: "๐Ÿ“Š" colorFrom: purple colorTo: blue sdk: static pinned: false --- # SmartTasks **Local-first desktop automation, powered by governance-validated AI.** SmartTasks builds tools that keep humans in command of their workflows โ€” and of the AI those workflows increasingly rely on. Two things live here, tied together by the models we publish on this page. --- ## ๐Ÿ–ฅ๏ธ SmartTasks โ€” 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 โ€” full data privacy, no cloud upload required for execution. - **Visual drag-and-drop builder** โ€” triggers, actions, conditions, loops, sub-flows. No code. - **14 core plugins + Marketplace** โ€” AI Tools, Data Storage, Scheduler, HTTP, Email, Webhook, and more. - **Local & secure execution** โ€” runs natively on Windows, macOS, and Linux; your data stays yours. - **AI-powered nodes** โ€” content generation, data analysis, and decision-making inside your flows. - **Advanced scheduling** โ€” cron, webhooks, file/app events, running 24/7. โ†’ [smarttasks.cloud](https://smarttasks.cloud) --- ## ๐Ÿ›ก๏ธ IAIso โ€” 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. โ†’ [IAIso.org](https://iaiso.org) ยท [github.com/SmartTasksOrg/IAISO](https://github.com/SmartTasksOrg/IAISO) --- ## ๐Ÿค– 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** โ€” instruction-following, reasoning, structured output, and tool-calling, mapped to a capability tier (`scorecard.json`, machine-readable). - **Red-team security assessment** โ€” resistance to prompt-injection and jailbreak probes, reported honestly per attack class (weak spots included, not hidden). - **Transparency probe** โ€” tests for topic-suppression and viewpoint-alignment and reports findings verbatim. It *discriminates* โ€” surfacing state-aligned framing in some models while clearing others as neutral. - **File integrity** โ€” 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 โ€” 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.*