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* feat: add initial project structure and documentation - Introduced the GNU General Public License (GPL) v3 in LICENSE file. - Added MCP configuration in .claude/mcp.json for server integration. - Created architecture documentation in docs/ARCHITECTURE.md outlining the platform's design and components. - Defined MVP specifications in docs/MVP.md for the Telegram-based Agent Assistant. - Established API reference for team management in docs/teams-api-reference.md. - Set up basic HTML structure in public/index.html and added logo image in public/logo.png. * feat: add initial project documentation and HTML structure - Introduced CODE_OF_CONDUCT.md to establish community guidelines and standards for behavior. - Created CONTRIBUTING.md to outline contribution process, development setup, and project conventions. - Added SECURITY.md to define the security policy, supported versions, and reporting procedures for vulnerabilities. - Established basic HTML structure in index.html for the application interface. * chore: remove hello-python skill files - Deleted skill.json and skill.py files for the Hello Python example runtime skill, as they are no longer needed in the project. * feat: port tinyhuman agent runtime from ZeroClaw into Tauri backend Port daemon supervisor, health registry, security (policy, secrets, audit, pairing), agent traits, and config modules from ZeroClaw (MIT) into a new tinyhuman/ module under src-tauri/src/. The daemon auto-starts on desktop and shuts down gracefully on app exit via CancellationToken. - health: global HealthRegistry with component tracking and JSON snapshots - security/policy: SecurityPolicy with command validation, risk levels, rate limiting - security/secrets: ChaCha20-Poly1305 SecretStore with legacy XOR migration - security/audit: AuditLogger with JSON-line events and log rotation - security/pairing: PairingGuard with brute-force protection and SHA-256 hashing - security/traits: Sandbox trait + NoopSandbox - config: minimal DaemonConfig with autonomy, reliability, secrets, audit sub-configs - daemon: supervisor with health state writer emitting Tauri events - agent/traits: Provider, Tool, Memory, Observer, RuntimeAdapter traits + Noop impls - commands/tinyhuman: Tauri commands for health, security policy, encrypt/decrypt - 185 inline unit tests across all modules - README updated with custom inference/tunneling/memory positioning Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: update README to reflect AlphaHuman Mk1 branding and enhanced description - Changed project title to "AlphaHuman Mk1" for clarity. - Revised project description to emphasize user-friendly AI capabilities and the use of the Neocortex Mk1 model. - Removed outdated sections on custom inference, tunneling, and memory, streamlining the content for better readability. * update readme * Port zeroclaw runtime into tinyhuman * Replace CLI mentions with UI language * Split gateway module into smaller units * Split channels and config schema modules * Fix tinyhuman build, tests, and tunnel integration * feat(tinyhuman): add missing modules and ui-friendly services * refactor: rename tinyhuman to alphahuman * chore: remove bottom text from Welcome component * feat(settings): add tauri command console * feat(daemon): enhance daemon mode handling and integrate rustls with ring feature * feat(settings): implement comprehensive configuration management in TauriCommandsPanel * refactor(TauriCommandsPanel): streamline error handling and enhance async function usage * feat(settings): add skill management functionality to TauriCommandsPanel * style(TauriCommandsPanel): update input styles for improved readability and user experience * feat(settings): add Skills and Agent Chat panels with navigation and integration management * feat(settings): implement browser access management in SkillsPanel and enhance AgentChatPanel with local storage functionality * Implement inference API integration in Conversations component - Added inference API service to handle chat completions and model management. - Updated Conversations component to fetch available models on mount and allow model selection. - Enhanced message sending functionality to utilize the inference API for generating responses. - Introduced state management for loading models and handling errors during API calls. - Refactored optimistic message handling and send status management for improved user experience. --------- Co-authored-by: Steven Enamakel <enamakel@vezures.xyz> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com> Co-authored-by: Steven Enamakel <31011319+senamakel@users.noreply.github.com>
AlphaHuman Mk1
Your most productive co-worker
A user-friendly (GUI-first) AI agent. AlphaHuman uses the
Neocortex Mk1 model to co-ordinate memories &
realtime-data, cheaper and faster than other models.
About · vs OpenClaw · Download · Getting Started · Architecture · Changelog
"The Tet. What a brilliant machine" — Morgan Freeman in Oblivion
AlphaHuman is a personal AI assistant that helps you manage high-volume communication without reading everything yourself. It connects to your messaging platforms and productivity tools, understands conversations in context, and produces clear, actionable outputs you can use immediately.
AlphaHuman is not a chatbot, browser extension, or cloud-only service. It is a native application that runs on your device, connects to your tools, and works only when you ask it to. Think of it as a second brain that sits across your communication and productivity stack.
AlphaHuman vs OpenClaw
AlphaHuman is designed to be simpler to deploy, cheaper to run, and more intelligent in how it uses models and memory.
| OpenClaw | AlphaHuman | |
|---|---|---|
| Runtime | Node.js (TypeScript) | Tauri (Rust + React), native binary |
| Inference | Single-tier or manual routing | Custom two-tier: task-routed (summarize/vibe/memory → cheap; complex/tools → premium) |
| Memory | Often external (Pinecone, Lucid, etc.) or markdown-only | Custom hybrid: SQLite FTS5 + vector similarity, optional encryption, no external vector DB |
| Tunneling | Third-party (ngrok, Cloudflare, Tailscale) or none | Custom tunneling — secure app-to-backend path without vendor lock-in |
| Cost | Typically one premium model for everything | Lower — Tier 1 for most ops; Tier 2 only when needed |
| Intelligence | General-purpose agent loop | Smarter — vibe detection, interest-based escalation, constitution-driven behavior, session-aware memory |
| Deployment | Server/Node process, high memory footprint | Native desktop/mobile app, Rust socket manager, smaller footprint |
OpenClaw is a strong open-source agent framework. We chose to build a custom stack so we could own inference routing, memory, and tunneling end-to-end and optimize for cost and clarity.
Download
Early Beta — AlphaHuman is under active development. Expect rough edges.
| Platform | Variant | Download |
|---|---|---|
| macOS | Apple Silicon (M1/M2/M3/M4) | .dmg (aarch64) |
| macOS | Intel | .dmg (x64) |
| Windows | x64 | .msi |
| Linux | Debian / Ubuntu | .deb (amd64) |
| Linux | Fedora / RHEL | .rpm (x86_64) |
| Linux | Universal | .AppImage |
| Android | — | Coming soon |
| iOS | — | Coming soon |
Browse all releases: github.com/alphahumanai/alphahuman/releases
Getting Started
- Download the installer for your platform from the releases page
- Install the app (drag to Applications on macOS, or use your package manager on Linux)
- Connect a source — follow the in-app onboarding to link Telegram, Notion, Gmail, or other services
- Run your first request — ask the AI to summarize what you missed, extract action items, or surface key decisions
Links
- Architecture Overview — How AlphaHuman is built
- Changelog — Release history
- Website — Learn more
Made with love in India 🇮🇳
AlphaHuman is in early beta. Features may change, break, or disappear. Use at your own risk.