Steven EnamakelandGitHub
7ae2bf83b2
* feat(config): default screen intelligence, dictation, and vision model off Flip defaults so no macOS TCC permission prompt fires on first run: - `dictation.enabled`: `true` → `false` (was auto-starting rdev::listen, which requests Accessibility/Input Monitoring on macOS) - `screen_intelligence.use_vision_model`: `true` → `false` (fewer surprise vision-model calls; Pass 1 Apple Vision OCR still runs) Aligns all permission-gated auto-starts on a consistent opt-in posture: `screen_intelligence.enabled`, `autocomplete.enabled`, and `voice_server.auto_start` already default to `false`. Users must now explicitly flip each toggle (config or JSON-RPC) before the core triggers any OS permission dialog. * feat(channels): fire typing indicator on webhook-inbound path Two inbound flows exist today and only one fires typing: - Local bot (`channels_config.telegram.bot_token` set) → dispatch.rs already calls `channel.start_typing` + `spawn_scoped_typing_task` - Backend webhook (Telegram → backend → socket.io → core) → `ChannelInboundSubscriber` had **no typing call** — replies route via backend REST, so the local `Channel` trait isn't reachable. Close the gap by going through the backend: - `api/rest.rs`: add `send_channel_typing(channel, jwt, body)` hitting the new `POST /channels/:id/typing` backend route. - `channels/bus.rs`: extract the agent-wait loop into `run_agent_loop` and wrap it with a typing task that fires immediately on `start_chat` success, refreshes every 4s (beats Telegram's ~5s and Discord's ~10s typing TTLs), and cancels on every exit path (done / error / empty / bus-closed / lagged / timeout). Backend failures log at debug — a flaky typing call must never block the reply flow. Generalises to every channel with a backend adapter; adapters without a native typing API no-op gracefully. * Enhance test stability by introducing a Mutex guard for TRIAGE_DISABLED_ENV in tests - Added a static Mutex guard to ensure safe concurrent access to the `TRIAGE_DISABLED_ENV` variable during tests, preventing interleaved set_var/remove_var calls that could lead to spurious failures. - Updated relevant test cases to acquire the Mutex lock when accessing the environment variable, ensuring consistent behavior across concurrent test executions.
OpenHuman
The age of super intelligence is here. OpenHuman is your Personal AI super intelligence. Private, Simple and extremely powerful.
Discord • Reddit • X/Twitter • Docs
"The Tet. What a brilliant machine" — Morgan Freeman as he reminisces about alien superintelligence in the movie Oblivion
Early Beta — Under active development. Expect rough edges.
To install or get started, either download from the website over at tinyhumans.ai/openhuman or run
# For MacOS/Linux
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash
# For Windows
irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.ps1 | iex
What is OpenHuman?
OpenHuman is an open-source agentic assistant that is designed to integrate with you in your daily life. Here's what makes OpenHuman special:
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Simple, UI-first — A clean desktop experience and short onboarding paths so you can go from install to a working agent in a few clicks, without a config-first setup. You don't need a terminal to run OpenHuman.
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One subscription, many providers — You only need one account to get access to many agentic APIs (AI Models, Search, Webhooks/Tunnels and other 3rd party APIs etc..), simplifying the experience to get a powerful agent going.
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Rich Skills — Plug into Gmail, Slack, Notion, and the rest of your stack via rich, feature-backed skills. Connections are typically one click through setup wizards instead of wiring APIs by hand. Workflow data is kept on device, encrypted locally, and treated as yours: encryption and sensitive context stay on your machine. Webhooks give instant feedback into the agent when external systems or skills emit events, so the loop stays tight without constant polling.
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Local knowledge base — Built from your data and your activity. How you work across tools, sessions, and connected services—so the agent gets rich, workflow-aware context, not a one-off chat transcript. Everything is stored on your machine and compounding over time without becoming a cloud dossier. Channels, skills and ongoing conversations feed the same loop so day-to-day context does not reset every session.
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Local AI model — The Rust core exposes local AI paths (and the desktop bundle can ship local/bundled runners where applicable) for the workloads above—vision snippets, speech helpers, summarization, tooling—so sensitive steps can stay off the cloud when you choose.
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Deep desktop integrations — OpenHuman is a native desktop assistant, not a web-only chat: memory-aware keyboard autocomplete, voice (STT listening and TTS replies), screen intelligence that understands what is on screen and feeds your local context, plus windowing and OS-level permissions—so the agent meets you on the machine, not trapped in a browser tab.
Architecture: docs/ARCHITECTURE.md. Contributor orientation: CONTRIBUTING.md.
OpenHuman vs other agents
High-level comparison (products evolve—verify against each vendor). OpenHuman is built to minimize vendor sprawl, keep workflow knowledge on-device, and ship deep desktop features—not only chat.
| Claude Code/Cowork | OpenClaw | Hermes Agent | OpenHuman | |
|---|---|---|---|---|
| Open-source: Is the codebase open to review? | 🚫 Proprietary client | ✅ MIT License | ✅ MIT License | ✅ GNU License |
| Simple: Is it simple to get started? | ✅ Simple Desktop App + CLI | ⚠️ Terminal first and often complex | ⚠️ Terminal first and often complex | ✅ Simple, Clean UI/UX. Get started within minutes |
| Cost: How expensive is to run? | ⚠️ Subscription + add-on tool/API costs | ⚠️ Tied to models & hosting you choose | ⚠️ Tied to models & hosting you choose | ✅ Cost optimized with the option to run many things locally for free |
| Memory & Knowledge Base (KB): Does the agent know you and your world? | ✅ Built-in memory; mostly chat/session scoped | ⚠️ Has a local memory but often needs plugins for richer behavior | ✅ Self-learning / task loops (typical) | 🚀 Local KB + Self-learning from your activity & data (GMail, Notion etc... via skills) & prompts |
| API spagetti: How complex is it to hook mulitple features together? | 🚫 Claude bill + often extra keys for MCP/tools | 🚫 BYOK / multi-vendor common | 🚫 Multiple providers common | ✅ One account get access to many bundled platform APIs |
| Extensibility: Can you add rich features into it? | ✅ MCP (different model than sandboxed skills) | ✅ Plugin Architecture (SKILL.md) | ✅ Plugin Architecture (SKILL.md) | 🚀 Rich Skills with ability to have realtime updates, local DB & more |
| Desktop integrations: Can it integrate into your desktop completely? | ⚠️ Desktop app & access to folders | ⚠️ Often lighter native surface | ⚠️ Often lighter native surface | ✅ STT, TTS, screen intelligence, memory-aware autocomplete and a whole lot more |
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