Steven EnamakelandGitHub
bfaabd3b86
* chore: update AlphaHuman version to 0.49.3 and configure updater plugin in tauri.conf.json - Bumped the AlphaHuman package version in Cargo.lock to 0.49.3. - Added updater configuration in tauri.conf.json to enable automatic updates with specified endpoints. * refactor: rename AlphaHuman to OpenHuman across the codebase - Updated all instances of "AlphaHuman" to "OpenHuman" in comments, tooltips, and constants to reflect the new branding. - Adjusted relevant documentation and prompts to ensure consistency with the new name. * refactor: update documentation and configurations to reflect OpenHuman branding - Replaced all instances of "AlphaHuman" with "OpenHuman" in documentation, comments, and configuration files to ensure consistency with the new branding. - Updated deep link URLs and related authentication flows to use the new "openhuman://" scheme. - Adjusted paths and references in the skills system and other related files to align with the new project name.te * refactor: standardize OpenHuman references and update configurations - Replaced all instances of "AlphaHuman" with "OpenHuman" across documentation, comments, and configuration files to maintain branding consistency. - Updated URLs and paths to reflect the new "openhuman://" scheme. - Adjusted environment variable names and related settings to align with the new project identity. - Enhanced documentation for clarity and accuracy regarding the OpenHuman framework.r * chore: update subproject commit reference in skills directory * refactor: update backend URL to reflect new service domain - Changed default backend URL from "https://api.openhuman.xyz" to "https://api.tinyhumans.ai" in both JavaScript and Rust configuration files. - Ensured consistency across the codebase regarding the new backend service endpoint. * feat: introduce identity and migration modules for OpenHuman - Added a new identity module to support AIEOS v1.1 JSON format, including structures for identity, psychology, linguistics, motivations, capabilities, physicality, history, and interests. - Implemented a migration module to facilitate data migration from OpenClaw memory, including SQLite and Markdown sources, with detailed reporting on migration statistics and warnings. - Established utility functions for handling multimodal content and image processing within the OpenHuman framework. - Enhanced the agent system with new dispatcher and classifier functionalities to improve tool management and message classification. * chore: remove Android project files and configurations - Deleted various Android project files including .editorconfig, .gitignore, build.gradle.kts, gradle.properties, and others to clean up the project structure. - Removed all related resources, layouts, and source files from the Android app directory to streamline the codebase. - This cleanup is part of a larger effort to refactor and simplify the project structure. * refactor: update login flow and remove Telegram integration - Removed the TelegramLoginButton component and its references from the OAuthLoginSection, streamlining the login options. - Updated the AppRoutes to remove the login route, reflecting changes in the authentication flow. - Enhanced the RotatingTetrahedronCanvas component with improved geometry and lighting effects for better visual presentation. - Adjusted the TypewriterGreeting component's styling for consistency. - Cleaned up the Welcome page to integrate the OAuthLoginSection directly, improving user experience. * chore: update subproject commit reference in skills directory * chore: update test configurations and improve test assertions - Modified test scripts in package.json to use a specific Vitest configuration file for consistency. - Updated assertions in loader tests to ensure loading durations are non-negative. - Enhanced tool loading tests to clarify expected behavior regarding localStorage and cache management. - Adjusted agent tool registry tests to improve error handling and ensure accurate statistics. - Refined device detection tests to reflect updated fallback URLs. * fix: enhance parameter formatting and remove unused components - Updated the `formatParameters` function to handle cases where schema properties are empty, returning a more informative response. - Deleted the `DownloadScreen` component and associated device detection utilities to streamline the codebase and remove unused functionality. - Adjusted tests to reflect changes in the tool loading and agent tool registry, ensuring accuracy in assertions. * chore: simplify Vitest configuration by removing unused include patterns - Updated the Vitest configuration to remove unnecessary test file patterns, streamlining the test setup for better clarity and maintainability. * refactor: update paths and comments for AI configuration and file watching - Modified Vite configuration to ignore only the `src-tauri` directory. - Updated logging messages to reflect the correct path for writing AI configuration files. - Adjusted fetch calls in the file watcher to use the new path for `TOOLS.md`. - Revised comments and logic in Rust code to clarify the handling of AI configuration file paths, including legacy fallback options. * chore: remove unused updater secrets from GitHub Actions workflow - Deleted UPDATER_GIST_URL and UPDATER_GIST_ID environment variables from the package-and-publish workflow, streamlining the configuration. * chore: comment out Vitest thresholds for clarity - Commented out the thresholds section in the Vitest configuration to improve clarity and maintainability, as it is currently not in use. * ran formatter * chore: update updater public key in tauri configuration - Replaced the existing public key in the updater plugin configuration with a new value to ensure proper functionality and security. * chore: update ESLint configuration and refactor components - Added `localStorage` and `sessionStorage` as readonly globals in ESLint configuration for better linting support. - Removed unused imports from `SkillsPanel.tsx` to clean up the code. - Changed the type of `watcherInterval` in `file-watcher.ts` for improved type safety. - Refactored toast management logic in `Intelligence.tsx` to enhance clarity and maintainability. - Simplified import statements in `IntelligenceProvider.tsx` for consistency. - Streamlined object property shorthand in `agentToolRegistry.ts` for cleaner code. * refactor: improve error handling and type safety in Intelligence component - Enhanced toast notification logic to defer state updates, preventing potential issues with setState in effects. - Updated the source filter dispatch to use a more specific type for improved type safety. * refactor: enhance type safety across various components and services - Updated type definitions from `any` to `unknown` in multiple files to improve type safety and prevent potential runtime errors. - Refactored state management in `TauriCommandsPanel` to use more specific types. - Adjusted context and parameters in several interfaces to ensure consistent typing. - Added ESLint directive to `polyfills.ts` for intentional global assignments. - Streamlined type handling in utility functions and API responses for better clarity and maintainability. * refactor: streamline import statements and improve code clarity - Consolidated import statements in `agentToolRegistry.ts` and `intelligenceSlice.ts` for better readability. - Simplified the `createTestStore` function in `test-utils.tsx` to enhance code conciseness. - Cleaned up the `isExecutionStepProgressEvent` function in `intelligence-chat-api.ts` for improved clarity and maintainability.
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FAQ & Troubleshooting
Frequently Asked Questions
What is OpenHuman?
OpenHuman is a personal AI assistant that connects to your communication platforms and productivity tools. It helps you manage high-volume conversations by summarizing, extracting signals, suggesting responses, and creating structured workflows all from a native app that runs on your device.
What is Neocortex
Neocortex is OpenHuman's memory engine. It is a human-like AI memory system that can work with over 1 billion tokens of data. It indexes 10 million tokens in under 10 seconds, costs $1 to index 5 million tokens, and runs on a MacBook Air CPU with no GPU required. Unlike vector databases, Neocortex understands time, entities, and relationships. It builds knowledge graphs and manages memory through tiered compression inspired by how the human brain works. Learn more in Neocortex.
What does "Big Data AI" mean?
Every AI model today is a prompt engine. You type something, it responds, and the context disappears. OpenHuman is different. It compresses your entire organizational life, messages, documents, tools, transactions, into a structured knowledge graph that persists and evolves. This is what we mean by Big Data AI: an AI that operates on months of your real data, not just the prompt you typed right now.
How is OpenHuman different from ChatGPT, Claude, or Gemini?
Those models are brilliant at reasoning and generation. But they are stateless. They know nothing about your actual life beyond what you paste into the chat window. OpenHuman is the context layer that makes those models useful. It compresses your organizational data into structured intelligence that any AI can reason over. Think of it this way: ChatGPT is the brain. OpenHuman is the memory.
How is OpenHuman different from other AI memory solutions like Mem0, SuperMemory, or MemGPT?
Most AI memory solutions use vector databases that retrieve whatever is semantically similar, but similarity alone says nothing about importance. They also cannot support consciousness-like systems or process data accurately at scale beyond 10 million tokens. Neocortex is architecturally different: it uses tiered memory, knowledge graphs, temporal weighting, and semantic deduplication. It processes 10 million tokens in under 10 seconds and supports over 1 billion tokens total. It does this with zero LLM dependency.
Is OpenHuman open source?
Yes. OpenHuman is built on the OpenClaw architecture and licensed under GNU GPL3. The full codebase is available on GitHub. Neocortex benchmarks are also open-sourced. Contributions and feedback are welcomed.
Is OpenHuman AGI?
No. OpenHuman is not AGI, and we do not claim it is. It is a meaningful architectural step closer to AGI, with innovations in memory (Neocortex) and consciousness-like processing (the subconscious system) that go beyond what existing agentic systems offer. But it operates within defined boundaries and requires human judgment.
Does OpenHuman read all my messages?
No. OpenHuman only processes messages when you ask it to and only within the scope of your request.
If you ask it to summarize a specific conversation, it reads that conversation. If you do not reference a source, it is not accessed. There is no background monitoring or continuous scanning.
Is my data safe?
Yes. OpenHuman is designed around zero retention of message content. Data is processed to produce an output and then discarded. Your conversations are never stored long-term, and they are never used to train AI models.
On desktop platforms, credentials are stored in your operating system's secure keychain. All communication between the app and OpenHuman's servers is encrypted.
Does OpenHuman store my messages?
Messages are processed only to fulfill your request. They are not permanently stored or reused beyond producing the requested output. Derived intelligence like summaries and workflow records may persist, but raw message content does not.
Can OpenHuman send messages on my behalf?
No. OpenHuman does not auto-send messages, post in your groups, or act on your behalf inside any connected platform. If a reply is suggested, you choose whether to use it.
Who is OpenHuman for?
OpenHuman is useful for anyone who:
- Manages high-volume communication across multiple platforms and groups
- Needs to stay on top of decisions, action items, and context without reading everything
- Works in distributed teams, communities, or coordination-heavy environments
- Wants structured outputs from conversations, exportable to tools like Notion or Google Sheets
You do not need to be technical to use it.
What platforms does OpenHuman support?
OpenHuman runs natively on macOS, Windows, Linux, Android, and iOS, with a web version for browser access. Your account and settings sync across all platforms.
What integrations are available?
OpenHuman currently integrates with Telegram (read and analyze conversations), Notion (export structured outputs), and Google Sheets (export tabular data and reports). More integrations are planned.
How much does OpenHuman cost?
OpenHuman offers individual and team plans with core analysis included. Deeper features are available in higher tiers. A credit system provides usage-metered flexibility, and credits can be earned through referrals.
See Pricing for details.
Troubleshooting
Summaries feel incomplete or too broad
If a summary feels incomplete, the most common cause is overly broad scope. When a request spans many conversations, long time ranges, or high-volume groups, OpenHuman prioritizes signal over detail.
Solution: Narrow the request to a specific conversation, time window, or intent. OpenHuman performs best when it knows what kind of outcome you are looking for.
Important context seems missing
OpenHuman only processes the data required to fulfill a request. If relevant context exists outside the scope of that request, it will not be included.
Solution: Expand the scope explicitly by referencing additional conversations or extending the time range.
Outputs feel incorrect or misinterpreted
OpenHuman interprets conversations probabilistically. Tone, sarcasm, and informal language can be misread, especially in fast-moving or meme-heavy discussions.
Solution: Refine the request or re-run analysis with a narrower scope. Outputs should be treated as assistance rather than ground truth.
Trust or risk signals feel inaccurate
Trust and risk intelligence is indicative, not authoritative. Signals may lag real-world changes or surface false positives.
Solution: Use these signals as inputs into your judgment rather than standalone decisions. Over time, as more outcomes accumulate within the same context, signal quality improves.
A source does not appear in analysis
OpenHuman can only analyze sources you have connected and that fall within the scope of your request.
Solution: Ensure the source is connected in your settings and explicitly referenced or selected in your request. OpenHuman does not automatically include all connected sources by default.
Integrations do not update as expected
Integrations only run when explicitly triggered by your action. OpenHuman does not continuously sync or poll external tools.
Solution: If an export fails, check that the integration is still connected and that permissions are valid. Retrying the action after resolving any permission or availability issues usually succeeds.
Performance feels slow
Response time depends on request complexity, data volume, and current system load.
Solution: Large scopes and long histories require more processing. Narrowing scope and intent improves responsiveness. During early rollout phases, performance may vary as capacity is tuned.
Revoking access and residual data
When access to a source or integration is revoked, OpenHuman immediately stops processing data from that source.
Previously exported outputs (such as summaries written to Notion or Google Sheets) remain where they were written. OpenHuman does not retain message content after revocation.
When to contact support
If an issue persists after refining scope, checking permissions, and retrying the request, support may be required.
Support is intended for system-level issues, not for disputing interpretations or outcomes. OpenHuman does not adjudicate trust or risk disagreements.