{"id":33764026,"url":"https://github.com/ekwator/truth-training","last_synced_at":"2026-02-02T01:07:37.937Z","repository":{"id":312971757,"uuid":"1048186959","full_name":"ekwator/truth-training","owner":"ekwator","description":"Cross-platform project: Rust Core, Actix-web, SQLite, P2P sync (UDP+HTTP), WebSockets, OpenAPI, TypeScript/React, Tauri, Kotlin/Android, Swift/iOS","archived":false,"fork":false,"pushed_at":"2026-01-24T00:19:28.000Z","size":4391,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-01-24T03:27:52.995Z","etag":null,"topics":["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"],"latest_commit_sha":null,"homepage":"https://github.com/ekwator/truth-training","language":"Kotlin","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ekwator.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-01T04:21:17.000Z","updated_at":"2026-01-24T00:19:31.000Z","dependencies_parsed_at":null,"dependency_job_id":"fb46be94-e28a-48b4-850d-07a0303597df","html_url":"https://github.com/ekwator/truth-training","commit_stats":null,"previous_names":["ekwator/truth-training"],"tags_count":16,"template":false,"template_full_name":null,"purl":"pkg:github/ekwator/truth-training","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ekwator%2Ftruth-training","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ekwator%2Ftruth-training/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ekwator%2Ftruth-training/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ekwator%2Ftruth-training/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ekwator","download_url":"https://codeload.github.com/ekwator/truth-training/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ekwator%2Ftruth-training/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28998208,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-01T23:10:54.274Z","status":"ssl_error","status_checked_at":"2026-02-01T23:10:47.298Z","response_time":56,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["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"],"created_at":"2025-12-05T12:00:32.637Z","updated_at":"2026-02-02T01:07:37.932Z","avatar_url":"https://github.com/ekwator.png","language":"Kotlin","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🌐 Truth Training — The Network of Anonymous Trust  \n*(Created in Cursor AI IDE)*\n\n## 🔐 Privacy and Confidentiality\n\n**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 — users can interact with the system without leaving any trace of their actions.\n\n## 💬 Open Discussion\n\nWe are actively discussing the core assumptions of this project here:\n\n- 🧠 **What if human intelligence is fundamentally collective?**  \n  [Discussion](https://github.com/ekwator/truth-training/discussions/72)  \n\n**Key Privacy Guarantees:**\n\n\n- ✅ **No Persistent User Tracking**: No identifiers, session data, or behavioral analytics\n- ✅ **No Telemetry Collection**: No user activity is transmitted or stored\n- ✅ **Ephemeral Logs Only**: Only system-level logs (errors, sync operations) are temporarily stored for debugging purposes\n\nThis confidentiality principle is enforced across all platforms (Desktop UI, Android, Server, CLI) and is a core architectural requirement.\n\n---\n\nTruth Training is a decentralized communication ecosystem where **truth travels without identity**.  \nEvents move freely through the network — encrypted, verified, and echoed by others — creating a distributed field of awareness instead of a chain of messages.\n\nEach reflection of an event confirms its existence; each independent echo increases its credibility.  \nLike confession without a priest, users anonymously release truths into the network — and the collective conscience responds.\n\nIt 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.\n\nUnlike LoRa-based mesh systems such as **Meshtastic**, Truth Training builds a **mesh of minds, not hardware** — using Wi-Fi and the Internet as carriers of encrypted meaning, forming an autonomous infrastructure of human understanding.\n\nOriginally conceived to combat fraud, Truth Training evolves into a **self-learning immunity against falsehood** — distinguishing truth from deception through context, correlation, and collective resonance.\n\nAnd beyond communication, Truth Training enables **teamwork without a team lead** — a coordination model where decisions arise from **collective consensus**, not hierarchy, creating a self-organizing environment for groups and projects.\n\nUltimately, without network connectivity, the application can serve as a **personal electronic diary** — a private space for individual reflection and truth-tracking.\n\n---\n\n## 1. Core Idea and Purpose of the System\n\n🧠 **Truth Training — Operating Logic and Computational Model**\nFor more details see : [docs/model_core.md](docs/model_core.md)\n\n**Truth Training** is a system for collective evaluation of events and statements, based on the principle of the *wisdom of the crowd*.  \nIt does not assume the presence of a central arbiter of truth and does not require expert knowledge from individual participants.\n\nIn the system, truth:\n\n- is not declared  \n- is not voted on directly  \n- is not determined by authority  \n\nInstead, it emerges statistically — as a stable result of independent evaluations accumulated over time.\n\n---\n\n## 2. Core Entity: Event (Truth Event)\n\nAn **Event** is a statement or fact that has been recorded in the system.\n\nAn event:\n\n- appears as unverified  \n- circulates within the system  \n- receives independent evaluations  \n- over time is either reinforced or rejected  \n\n### Key Logic\n\n- No event is immediately considered true or false  \n- Truthfulness is a **process**, not a state  \n\n---\n\n## 3. Event Code (Field Code)\n\nEach event has an **8-bit code** used not for semantic meaning, but for protocol-level propagation logic.\n\nIn the described model, the code controls:\n\n- event transmission  \n- retransmission  \n- termination of propagation  \n\nThe code:\n\n- does not directly participate in truth calculation  \n- can be algorithmically modified during propagation  \n\nThis allows the system to:\n\n- prevent infinite propagation  \n- implement P2P logic without changing data structures  \n- separate transport logic from evaluation logic  \n\n---\n\n## 4. Event Evaluation (Impact)\n\n### What Is Impact\n\n**Impact** is a subjective assessment of the *consequences* of an event, not an evaluation of the fact itself.\n\nImpact answers the question:\n\n\u003e “What effect did (or will) this event have?”\n\nEach Impact:\n\n- is linked to a specific event  \n- has a type (reputation, finance, emotions, etc.)  \n- has a sign:\n  - positive  \n  - negative  \n- is time-stamped  \n\n### Key Principle\n\nThe system does not ask *“Is this true?”*  \nIt asks:\n\n\u003e “What happened as a result of this being accepted as true?”\n\n## 5. Judgment Assessment (Truth Determination)\n\n### What Is Judgment\n\n**Judgment** is an individual user's assessment of an event's truthfulness, forming the basis for collective truth determination.\n\nJudgment answers the question:\n\n\u003e \"Is this event true or false based on my understanding?\"\n\nEach Judgment:\n\n- is linked to a specific event\n- represents individual truth assessment\n- contributes to collective truth score\n- preserves user anonymity\n- is time-stamped\n\n### Truth Calculation Based on Judgments\n\nThe truthfulness of an event emerges from the aggregation of individual judgments:\n\nEvent truthfulness ≈\n(Σ positive judgments − Σ negative judgments)\n÷ total number of judgments\n\n**Key Principle:**\n\nTruth is determined through collective independent assessments rather than direct declaration or voting.\n\n---\n\n## 6. Impact vs Judgment\n\nWhile Impact evaluates consequences, Judgment determines truthfulness:\n\n- **Impact**: \"What effect did this have?\" (consequence-focused)\n- **Judgment**: \"Is this true or false?\" (truth-focused)\n\n---\n\n## 7. Truth Calculation (Implicit Truth Score)\n\n\nThe truthfulness of an event is not stored as a field and is not explicitly defined.\n\nIt is derived from:\n\n- the number of Impact evaluations  \n- their direction (positive / negative)  \n- accumulation over time  \n- stability of the result  \n\n### Simplified Calculation Model\n\nEvent truthfulness ≈\n(Σ positive impacts − Σ negative impacts)\n÷ number of events  \n\n**Important:**\n\n- early evaluations carry are a predictions of the final result and carry more weight if this prediction turns out to be correct   \n- stable evaluations gain significance over time  \n- sharp changes indicate conflicting interpretations  \n\n---\n\n## 8. The \"Wisdom of the Crowd\" Principle\n\nThe system embeds key conditions for valid collective evaluation:\n\n- **Independence of participants**  \n  - Participants see the ratings of others and can make their own assumptions based on these ratings  \n\n- **Diversity of sources**  \n  - different contexts, motivations, experiences  \n\n- **Sufficient number of evaluations**  \n  - the law of large numbers applies  \n\n- **Absence of a central truth authority**  \n\nTruth emerges as statistical equilibrium, not as a decision.\n\n---\n\n## 9. Evaluation Context\n\nEach event is linked to a **context**, which defines:\n\n- domain (social, financial, political, etc.)  \n- form (truth, deception, omission)  \n- cause  \n- development path  \n- effect  \n\nContext:\n\n- does not define truth  \n- provides a frame for interpreting consequences  \n\n---\n\n## 10. Aggregated Metrics (Progress Metrics)\n\nThe system periodically computes aggregates:\n\n- total number of events  \n- ratio of positive to negative impacts  \n- trend (slope of trust change)  \n\nThese metrics:\n\n- are not used for decision-making  \n- serve as system state indicators  \n- allow observation of dynamics  \n\n---\n\n## 11. Database Schema (Semantic Overview)\n\n### Core Tables\n\n#### `truth_events`\n\nStores events:\n\n- description  \n- context  \n- timestamps  \n- discovery status  \n- protocol propagation code  \n\n#### `impact`\n\nStores impacts:\n\n- link to event  \n- impact type  \n- sign (positive / negative)  \n- timestamp  \n\n\n#### `context`\n\nMultifactor interpretation model:\n\n- category  \n- form  \n- cause  \n- development  \n- effect  \n\n#### `judgment`\n\nStores judgment:\n\n- link to event\n- truth assessment (true/false)\n- timestamp\n- user anonymity identifier\n\n#### `progress_metrics`\n\nAggregated system state indicators\n\n---\n\n## 12. The Main Consequence\n\n**Truth Training:**\n\n- does not fight lies directly  \n- does not require acknowledging falsehood  \n- does not force truth  \n\nInstead, the system:\n\n- allows events to “live through time”  \n- records consequences  \n- shows which statements remain stable over time  \n\nTruth here is:\n\n\u003e that which continues to function without destroying the system\n\n---\n\n## 13. Why This Logic Is Resilient\n\n- lies may be profitable in the short term\n- but their consequences accumulate\n- collective evaluation does not require trust in participants\n- only trust in statistics\n\nThus, the system naturally identifies and suppresses fraud —\nnot through control, but through observation of consequences.\n\n---\n\n## 14. Participant Reputation Model\n\nThe system implements a reputation model to track participant accuracy and influence:\n\n- **Reputation Calculation**: Based on historical accuracy of participant's impact and judgment assessments\n- **Accuracy Tracking**: Monitors how well participant's predictions align with collective outcomes\n- **Dynamic Adjustment**: Reputation scores evolve based on continued performance\n- **Weighted Influence**: Higher reputation participants have proportionally greater impact on collective assessments\n- **Anonymous Identity**: Reputation is tied to cryptographic keys, preserving participant anonymity\n\nThis model ensures that consistently accurate participants gradually gain more influence in the system while maintaining privacy.\n\n---\n\n## 15. Consequence Prediction Mechanism\n\nThe system incorporates predictive capabilities for anticipating event consequences:\n\n- **Prediction Modeling**: Participants can forecast potential outcomes of events\n- **Accuracy Assessment**: Predictions are evaluated against actual outcomes over time\n- **Temporal Horizon**: Predictions include timing estimates for when consequences may manifest\n- **Probability Weighting**: Confidence levels are assigned to different prediction scenarios\n- **Learning Feedback**: Prediction accuracy contributes to participant reputation\n\nThis mechanism enables proactive assessment of potential future impacts rather than solely reactive evaluation.\n\n---\n\n## 16. Temporal Axis\n\nTime plays a crucial role in the system's evaluation process:\n\n- **Event Timeline**: Each event has associated temporal boundaries and duration\n- **Consequence Timing**: Impacts may manifest with delays relative to event occurrence\n- **Truth Evolution**: Event truthfulness may change as more temporal data accumulates\n- **Stability Detection**: Events are evaluated for temporal consistency over time\n- **Decay Functions**: Older assessments may have reduced influence through temporal decay\n\nThe temporal dimension allows the system to capture delayed consequences and evolving understanding of events.\n\n---\n\n## 17. Anti-Manipulation Protection\n\nThe system implements multiple layers of protection against manipulation attempts:\n\n- **Behavioral Analysis**: Monitors for suspicious assessment patterns or coordinated activity\n- **Trust Limiting**: Caps maximum influence any single participant can have on outcomes\n- **Anomaly Detection**: Identifies unusual correlation patterns that may indicate manipulation\n- **Decentralized Control**: No single authority can override collective assessments\n- **Transparency**: All assessments and their origins remain traceable for verification\n\nThese mechanisms maintain system integrity even when faced with deliberate attempts to skew results.\n\n---\n\n## Release Surfaces\n\n- **Core Library** — integration guide: [docs/quickstart_core.md](docs/quickstart_core.md)\n- **CLI** — quickstart: [docs/quickstart_cli.md](docs/quickstart_cli.md), reference: [docs/CLI_Usage.md](docs/CLI_Usage.md)\n- **Server** — quickstart: [docs/quickstart_server.md](docs/quickstart_server.md), deployment: [docs/Deployment.md](docs/Deployment.md)\n- **Desktop UI** — quickstart: [docs/quickstart_desktop.md](docs/quickstart_desktop.md), reference: [docs/UI_Desktop.md](docs/UI_Desktop.md)\n- **Android Mobile** — quickstart: [docs/quickstart_android.md](docs/quickstart_android.md), architecture: [docs/android_discovery_architecture.md](docs/android_discovery_architecture.md)\n- **iOS Mobile** — quickstart: [docs/quickstart_ios.md](docs/quickstart_ios.md)\n\n## 📦 Downloads \u0026 Pre-built Binaries\n\nReady-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:\n\n- **Desktop Applications**: Linux (AppImage, DEB, RPM), Windows (MSI, EXE), macOS (DMG, PKG)\n- **Android**: APK and AAB packages for direct installation\n- **iOS**: IPA packages and App Store builds\n- **Server**: RPM and DEB packages for Linux distributions, PKG for macOS\n- **CLI Tools**: Pre-compiled `truthctl` binaries for all supported platforms\n- **Core Libraries**: Static and dynamic libraries (`.a`, `.so`, `.dylib`) for integration\n\nAll 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.\n\n## Documentation Entry Points\n\n- [docs/README.md](docs/README.md) — Human-readable, narrative depth\n- [spec/README.md](spec/README.md) — AI-focused directives and constraints\n- [docs/Documentation_Refactor_Overview.md](docs/Documentation_Refactor_Overview.md) — Pipeline summary\n- [docs/Documentation_Refactor_Inventory.md](docs/Documentation_Refactor_Inventory.md) — Inventory instructions\n- [docs/Documentation_Refactor_Links.md](docs/Documentation_Refactor_Links.md) — Link validation workflow\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fekwator%2Ftruth-training","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fekwator%2Ftruth-training","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fekwator%2Ftruth-training/lists"}