3c247a2439 Feat/humanlike replies (#168)
* fix(chat): prevent stacked socket listeners on reconnect

subscribeChatEvents was declared async despite having no awaits, so the
cleanup function was returned in a microtask after React's synchronous
cleanup had already run. Each socket reconnect added another layer of
listeners that were never removed, causing chat:done to fire N times and
produce duplicate message bubbles.

- Remove async keyword from subscribeChatEvents; return cleanup fn directly
- Update useEffect call site to store cleanup synchronously and return it
  to React (eliminates the mounted/then race entirely)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(local-ai): add multi-turn chat via Ollama /api/chat

Expose openhuman.local_ai_chat RPC method so the UI can run full
conversation-history chat directly through the bundled Ollama model
without touching the cloud inference API.

- ollama_api.rs: add OllamaChatMessage / OllamaChatRequest / OllamaChatResponse
  types for the /api/chat endpoint
- service/public_infer.rs: add LocalAiService::chat_with_history() — sends
  multi-turn message array to Ollama, updates latency/TPS status on response
- ops.rs: add LocalAiChatMessage struct and local_ai_chat async op
- schemas.rs: register local_ai_chat controller (schema, handler, params)
  in all_controller_schemas + all_registered_controllers

Zero cloud tokens are consumed on this path; the call never reaches the
backend socket or the /openai/v1/chat/completions endpoint.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* feat(conversations): local-model chat gate with multi-bubble delivery

When Ollama is ready (isLocalModelActive), handleSendMessage bypasses
the cloud socket entirely and routes through openhumanLocalAiChat.
Response is segmented and delivered as multiple typed bubbles with
natural pauses — human-like reply behaviour at zero cloud token cost.

UI / delivery
- deliverLocalResponse(): segments full reply via segmentMessage(),
  dispatches each bubble with getSegmentDelay() pause between them;
  typing indicator (isDelivering) shows between segments
- Socket-connected guard skipped on local path so offline local use works
- Cloud socket path (chatSend → chat:done) fully unchanged

Frontend RPC
- tauriCommands: openhumanLocalAiChat(messages, maxTokens?) wraps
  openhuman.local_ai_chat via core RPC; LocalAiChatMessage type exported

Tests (402 passing)
- messageSegmentation: 4 new edge-case tests (whitespace, 80-char
  boundary, paragraph split, delay scaling)
- localChatGating (new file): 9 tests — segmentation correctness, delay
  bounds [500, 1400] ms, sender→role mapping for message history build

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix(prompts): replace vague emoji guidance with explicit contextual rules

The previous "Minimal — match the user's style" instruction was too
loose, causing the model to stack decorative emojis on every message
(e.g. "Hey! 😄 Just cooking up some AI magic! 🚀🔥").

SOUL.md — add Emoji Rules section:
- Hard cap: one emoji maximum per message; none is always acceptable
- Contextual, not decorative: emoji must reinforce the specific content
  (🔥 for exciting news, 🤔 for uncertainty,  for confirmations)
- Never open a sentence with an emoji
- Skip entirely in error/warning/technical/long responses
- Mirror the user's own emoji usage pattern
- Concrete good/bad examples so the model can calibrate

BOOTSTRAP.md — tighten the Communication Preferences entry to reference
the same rules rather than the old vague one-liner.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* chore: scope PR_DESCRIPTION.md to feat/humanlike-replies only

Remove unrelated package manager distribution content (was from a
different branch). Description now covers only the 4 commits on this
branch: socket listener fix, Rust local_ai_chat RPC, frontend local
chat gate + multi-bubble delivery, and emoji prompt rules.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* refactor(local_ai): improve code formatting and readability in chat operations

- Adjusted formatting in local_ai_chat function for better readability by adding line breaks.
- Simplified the mapping of messages to OllamaChatMessage in ops.rs.
- Streamlined the await syntax in schemas.rs for clarity.
- Enhanced formatting in public_infer.rs for consistency in API request construction.

* refactor(conversations): enhance code readability and structure in Conversations component

- Improved formatting and consistency in the Conversations component, including better alignment of dispatch calls and message handling logic.
- Removed redundant imports and streamlined the mapping of stored messages for clarity.
- Adjusted conditional rendering for improved readability in the UI logic.

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-04-01 19:43:27 +05:30
2026-03-29 10:30:18 -07:00
2026-04-01 19:43:27 +05:30
2026-03-26 17:04:46 -07:00
2026-04-01 19:43:27 +05:30
2026-02-20 13:03:15 +04:00
2026-02-20 13:03:15 +04:00

OpenHuman

The age of super intelligence is here. OpenHuman is your Personal AI super intelligence. Private, Simple and extremely powerful.

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Early Beta Platforms: desktop only Latest Release

The Tet

"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.

OpenHuman is an open-source agentic assistant that is designed to integrate with you in your daily life. Here's what makes OpenHuman special:

  • One subscription, many providers — One assistant wired to skills and backend models so you are not juggling a separate subscription stack for every integration surface.

  • Incredible memoryRust-side memory (store / recall / namespaces) plus optional TinyHumans Neocortex-backed context when configured, so the agent can retain and retrieve more than a single chat window. Channels and ongoing conversations feed the same loop so day-to-day context does not reset every session.

  • Screen intelligence — Regular screen capture (on a cadence or when triggered) feeds an on-device pipeline that understands what is on screen, distills it into memory (facts, UI state, workflows), and can propose actions the agent executes for you. OS permissions and capture APIs vary by platform; the goal is your machine first, not shipping raw frames to the cloud by default.

  • Voice & meetings — A Local-model speech stack (listen / TTS) let the assistant talk back and capture or work with meeting audio with a privacy-first default when you route inference locally. Transcripts and summaries land in the same memory + agent loop so OpenHuman can follow up: tasks, drafts, calendar nudges, or skill-backed workflows—without treating a meeting as a one-off chat.

  • Memory-aware autocompleteKeyboard autocomplete is built for right-context suggestions: it consults memory namespaces and recent context so completions stay aligned with you, your workspace, and prior sessions—not a blank model every keystroke.

  • Runs a 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.

  • Simple or advancedSkill setup wizards and defaults for common tools, with room to go deeper via settings, credentials, and core RPC when you need control and privacy.

Architecture: docs/ARCHITECTURE.md. Contributor orientation: CONTRIBUTING.md.

Download

Early Beta — Under active development. Expect rough edges.

You can download the latest desktop build from the website at tinyhuman.ai/openhuman. You can also grab it from the latest GitHub release, which includes all current artifacts (.dmg, .deb, .AppImage, .app.tar.gz, and more).

If you need an older version, browse all releases.

If you want to build from source, see docs/BUILDING.md.

Install with one command:

curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.sh | bash

On Windows, use PowerShell: irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/scripts/install.ps1 | iex

What setup does:

  • Resolves the latest stable release for your OS/arch
  • Verifies release digest when available
  • Installs locally without requiring system-wide admin rights by default
  • macOS: installs OpenHuman.app in ~/Applications
  • Linux: installs openhuman AppImage in ~/.local/bin/openhuman and creates a desktop entry
  • Windows: installs from latest release MSI/EXE in per-user mode where supported

Under the hood (Architecture)

OpenHuman is a desktop monorepo: Rust owns business logic and execution; the UI owns interaction, layout, and OS integration.

Rust (openhuman / openhuman_core). The repo root src/ crate is the brain: JSON-RPC over HTTP (core_server), domain modules (auth, config, memory, skills, channels, screen intelligence, local AI, cron, …), and a QuickJS runtime for sandboxed JavaScript skills. The openhuman binary is built and staged next to the Tauri app so the desktop shell can spawn it as a sidecar. Heavy work—SQLite, sockets, crypto, skill lifecycle—runs there under Tokio, not in the WebView.

UI (app/). Vite + React (TypeScript) implements screens, onboarding, settings, and realtime UX. Redux Toolkit holds client state; Socket.io and the MCP-style client stack stay in sync with the cores realtime surface. Tauri v2 (app/src-tauri/) is a thin Rust host: windowing, filesystem hooks where needed, and core_rpc_relay—forwarding JSON-RPC from the WebView to the openhuman process so the UI never re-implements domain rules.

Controllers and the RPC surface. Features are exposed as registered controllers: each domain declares schemas (namespace, function name, parameter shapes) and a handler. At runtime, calls are validated, dispatched by method name (e.g. openhuman.auth_get_state, openhuman.local_ai_agent_chat), and return structured outcomes. CLI and HTTP share the same controller catalog, so automation, tests, and the app all hit one contract.

What ties it together: one registry of controllers, one sidecar process for execution, Tauri IPC for shell-only capabilities, and HTTP JSON-RPC for everything else—plus skills and dual-socket behavior documented in the architecture guide.

Read more: docs/ARCHITECTURE.md · Frontend tree: docs/src/README.md · Tauri commands: docs/src-tauri/README.md

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