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

🛡️ 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 · 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.

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.