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https://github.com/ekwator/truth-training

Cross-platform project: Rust Core, Actix-web, SQLite, P2P sync (UDP+HTTP), WebSockets, OpenAPI, TypeScript/React, Tauri, Kotlin/Android, Swift/iOS
https://github.com/ekwator/truth-training

actix-web android cli cross-platform cursor cursor-ai cursor-ide http ios kotlin p2p react rust spec-kit sqlite swift tauri typescript udp websockets

Last synced: 9 days ago
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Cross-platform project: Rust Core, Actix-web, SQLite, P2P sync (UDP+HTTP), WebSockets, OpenAPI, TypeScript/React, Tauri, Kotlin/Android, Swift/iOS

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README 

          
# 🌐 Truth Training β€” The Network of Anonymous Trust  

*(Created in Cursor AI IDE)*



## πŸ” Privacy and Confidentiality



**Truth Training is built on the fundamental principle of confidentiality**: **No user actions are logged or persistently stored**. The application does not track, record, or save any user interactions, navigation patterns, clicks, or behavioral data. This ensures complete privacy and anonymity β€” users can interact with the system without leaving any trace of their actions.



**Key Privacy Guarantees:**

- βœ… **No User Action Logging**: No clicks, navigation, or interaction history is stored

- βœ… **No Persistent User Tracking**: No identifiers, session data, or behavioral analytics

- βœ… **No Telemetry Collection**: No user activity is transmitted or stored

- βœ… **Ephemeral Logs Only**: Only system-level logs (errors, sync operations) are temporarily stored for debugging purposes



This confidentiality principle is enforced across all platforms (Desktop UI, Android, Server, CLI) and is a core architectural requirement.



---



Truth Training is a decentralized communication ecosystem where **truth travels without identity**.  

Events move freely through the network β€” encrypted, verified, and echoed by others β€” creating a distributed field of awareness instead of a chain of messages.



Each reflection of an event confirms its existence; each independent echo increases its credibility.  

Like confession without a priest, users anonymously release truths into the network β€” and the collective conscience responds.



It can serve as an **alternative to voting systems**, measuring the authenticity of social signals and public sentiment not through ballots, but through shared evaluation of facts.



Unlike LoRa-based mesh systems such as **Meshtastic**, Truth Training builds a **mesh of minds, not hardware** β€” using Wi-Fi and the Internet as carriers of encrypted meaning, forming an autonomous infrastructure of human understanding.



Originally conceived to combat fraud, Truth Training evolves into a **self-learning immunity against falsehood** β€” distinguishing truth from deception through context, correlation, and collective resonance.



And beyond communication, Truth Training enables **teamwork without a team lead** β€” a coordination model where decisions arise from **collective consensus**, not hierarchy, creating a self-organizing environment for groups and projects.



Ultimately, without network connectivity, the application can serve as a **personal electronic diary** β€” a private space for individual reflection and truth-tracking.



---



## 1. Core Idea and Purpose of the System



🧠 **Truth Training β€” Operating Logic and Computational Model**

For more details see : [docs/model_core.md](docs/model_core.md)



**Truth Training** is a system for collective evaluation of events and statements, based on the principle of the *wisdom of the crowd*.  

It does not assume the presence of a central arbiter of truth and does not require expert knowledge from individual participants.



In the system, truth:



- is not declared  

- is not voted on directly  

- is not determined by authority  



Instead, it emerges statistically β€” as a stable result of independent evaluations accumulated over time.



---



## 2. Core Entity: Event (Truth Event)



An **Event** is a statement or fact that has been recorded in the system.



An event:



- appears as unverified  

- circulates within the system  

- receives independent evaluations  

- over time is either reinforced or rejected  



### Key Logic



- No event is immediately considered true or false  

- Truthfulness is a **process**, not a state  



---



## 3. Event Code (Field Code)



Each event has an **8-bit code** used not for semantic meaning, but for protocol-level propagation logic.



In the described model, the code controls:



- event transmission  

- retransmission  

- termination of propagation  



The code:



- does not directly participate in truth calculation  

- can be algorithmically modified during propagation  



This allows the system to:



- prevent infinite propagation  

- implement P2P logic without changing data structures  

- separate transport logic from evaluation logic  



---



## 4. Event Evaluation (Impact)



### What Is Impact



**Impact** is a subjective assessment of the *consequences* of an event, not an evaluation of the fact itself.



Impact answers the question:



> β€œWhat effect did (or will) this event have?”



Each Impact:



- is linked to a specific event  

- has a type (reputation, finance, emotions, etc.)  

- has a sign:

  - positive  

  - negative  

- is time-stamped  



### Key Principle



The system does not ask *β€œIs this true?”*  

It asks:



> β€œWhat happened as a result of this being accepted as true?”



## 5. Judgment Assessment (Truth Determination)



### What Is Judgment



**Judgment** is an individual user's assessment of an event's truthfulness, forming the basis for collective truth determination.



Judgment answers the question:



> "Is this event true or false based on my understanding?"



Each Judgment:



- is linked to a specific event

- represents individual truth assessment

- contributes to collective truth score

- preserves user anonymity

- is time-stamped



### Truth Calculation Based on Judgments



The truthfulness of an event emerges from the aggregation of individual judgments:



Event truthfulness β‰ˆ

(Ξ£ positive judgments βˆ’ Ξ£ negative judgments)

Γ· total number of judgments



**Key Principle:**



Truth is determined through collective independent assessments rather than direct declaration or voting.



---



## 6. Impact vs Judgment



While Impact evaluates consequences, Judgment determines truthfulness:



- **Impact**: "What effect did this have?" (consequence-focused)

- **Judgment**: "Is this true or false?" (truth-focused)



---



## 7. Truth Calculation (Implicit Truth Score)



The truthfulness of an event is not stored as a field and is not explicitly defined.



It is derived from:



- the number of Impact evaluations  

- their direction (positive / negative)  

- accumulation over time  

- stability of the result  



### Simplified Calculation Model



Event truthfulness β‰ˆ

(Ξ£ positive impacts βˆ’ Ξ£ negative impacts)

Γ· number of events  



**Important:**



- early evaluations carry less weight  

- stable evaluations gain significance over time  

- sharp changes indicate conflicting interpretations  



---



## 8. The "Wisdom of the Crowd" Principle



The system embeds key conditions for valid collective evaluation:



- **Independence of participants**  

  - participants do not see others’ evaluations  



- **Diversity of sources**  

  - different contexts, motivations, experiences  



- **Sufficient number of evaluations**  

  - the law of large numbers applies  



- **Absence of a central truth authority**  



Truth emerges as statistical equilibrium, not as a decision.



---



## 9. Evaluation Context



Each event is linked to a **context**, which defines:



- domain (social, financial, political, etc.)  

- form (truth, deception, omission)  

- cause  

- development path  

- effect  



Context:



- does not define truth  

- provides a frame for interpreting consequences  



---



## 10. Aggregated Metrics (Progress Metrics)



The system periodically computes aggregates:



- total number of events  

- ratio of positive to negative impacts  

- trend (slope of trust change)  



These metrics:



- are not used for decision-making  

- serve as system state indicators  

- allow observation of dynamics  



---



## 11. Database Schema (Semantic Overview)



### Core Tables



#### `truth_events`



Stores events:



- description  

- context  

- timestamps  

- discovery status  

- protocol propagation code  



#### `impact`



Stores impacts:



- link to event  

- impact type  

- sign (positive / negative)  

- timestamp  



#### `context`



Multifactor interpretation model:



- category  

- form  

- cause  

- development  

- effect  



#### `judgment`



Stores judgment:



- link to event

- truth assessment (true/false)

- timestamp

- user anonymity identifier



#### `progress_metrics`



Aggregated system state indicators



---



## 12. The Main Consequence



**Truth Training:**



- does not fight lies directly  

- does not require acknowledging falsehood  

- does not force truth  



Instead, the system:



- allows events to β€œlive through time”  

- records consequences  

- shows which statements remain stable over time  



Truth here is:



> that which continues to function without destroying the system



---



## 13. Why This Logic Is Resilient



- lies may be profitable in the short term  

- but their consequences accumulate  

- collective evaluation does not require trust in participants  

- only trust in statistics  



Thus, the system naturally identifies and suppresses fraud β€”  

not through control, but through observation of consequences.



---



## Release Surfaces



- **Core Library** β€” integration guide: [docs/quickstart_core.md](docs/quickstart_core.md)

- **CLI** β€” quickstart: [docs/quickstart_cli.md](docs/quickstart_cli.md), reference: [docs/CLI_Usage.md](docs/CLI_Usage.md)

- **Server** β€” quickstart: [docs/quickstart_server.md](docs/quickstart_server.md), deployment: [docs/Deployment.md](docs/Deployment.md)

- **Desktop UI** β€” quickstart: [docs/quickstart_desktop.md](docs/quickstart_desktop.md), reference: [docs/UI_Desktop.md](docs/UI_Desktop.md)

- **Android Mobile** β€” quickstart: [docs/quickstart_android.md](docs/quickstart_android.md), architecture: [docs/android_discovery_architecture.md](docs/android_discovery_architecture.md)

- **iOS Mobile** β€” quickstart: [docs/quickstart_ios.md](docs/quickstart_ios.md)



## πŸ“¦ Downloads & Pre-built Binaries



Ready-to-use binaries and installers for all platforms are available in the [GitHub Releases section](https://github.com/ekwator/truth-training/releases). Download pre-compiled executables, installers, and packages for:



- **Desktop Applications**: Linux (AppImage, DEB, RPM), Windows (MSI, EXE), macOS (DMG, PKG)

- **Android**: APK and AAB packages for direct installation

- **iOS**: IPA packages and App Store builds

- **Server**: RPM and DEB packages for Linux distributions, PKG for macOS

- **CLI Tools**: Pre-compiled `truthctl` binaries for all supported platforms

- **Core Libraries**: Static and dynamic libraries (`.a`, `.so`, `.dylib`) for integration



All releases include checksums (SHA256) for verification and are signed for security. Visit the [releases page](https://github.com/ekwator/truth-training/releases) to download the latest stable version or development builds.



## Documentation Entry Points



- [docs/README.md](docs/README.md) β€” Human-readable, narrative depth

- [spec/README.md](spec/README.md) β€” AI-focused directives and constraints

- [docs/Documentation_Refactor_Overview.md](docs/Documentation_Refactor_Overview.md) β€” Pipeline summary

- [docs/Documentation_Refactor_Inventory.md](docs/Documentation_Refactor_Inventory.md) β€” Inventory instructions

- [docs/Documentation_Refactor_Links.md](docs/Documentation_Refactor_Links.md) β€” Link validation workflow