{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_ai-diary","slug":"ai-diary","name":"AI Diary","type":"product","url":"https://aidiary.io","page_url":"https://unfragile.ai/ai-diary","categories":["app-builders"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_ai-diary__cap_0","uri":"capability://text.generation.language.voice.to.text.diary.entry.capture","name":"voice-to-text diary entry capture","description":"Converts spoken audio input into structured diary entries using automatic speech recognition (ASR) with real-time transcription. The system likely processes voice through a cloud-based ASR engine (possibly Google Speech-to-Text, Azure Speech Services, or similar), then stores the transcribed text as a diary entry with automatic timestamp and metadata attachment. The implementation appears to handle variable audio quality and ambient noise through preprocessing before transcription.","intents":["I want to quickly capture my thoughts by speaking instead of typing while commuting or busy","I need to journal hands-free during activities like walking or exercising","I want to reduce friction in my journaling habit by eliminating the typing barrier"],"best_for":["busy professionals and students with limited typing time","users with accessibility needs who prefer voice input","people building habit-formation apps that need low-friction capture"],"limitations":["ASR accuracy degrades in noisy environments (coffee shops, public transit); background noise may corrupt transcriptions","Requires internet connectivity for cloud-based ASR processing; no offline fallback mentioned","Language support limited to whatever the underlying ASR provider supports; multilingual switching may require manual selection","Real-time transcription latency typically 2-5 seconds depending on audio chunk size and network conditions"],"requires":["microphone hardware on device (smartphone, tablet, or computer)","active internet connection for ASR API calls","user account with AI Diary service","browser or mobile app with audio capture permissions"],"input_types":["audio/wav","audio/mp3","audio/m4a"],"output_types":["text (transcribed diary entry)","structured JSON (entry with timestamp, metadata)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-diary__cap_1","uri":"capability://data.processing.analysis.ai.powered.mood.detection.and.emotional.analysis","name":"ai-powered mood detection and emotional analysis","description":"Analyzes diary entry text (from voice or manual input) using NLP/sentiment analysis models to extract emotional state, mood intensity, and emotional themes. The system likely uses transformer-based models (BERT, RoBERTa, or fine-tuned variants) to classify mood categories (happy, sad, anxious, etc.) and extract emotional intensity scores. Results are stored as structured mood metadata linked to each entry, enabling temporal mood tracking and pattern detection across multiple entries.","intents":["I want to understand my emotional patterns over time without manually tagging every entry","I need AI to identify recurring emotional triggers or mood cycles from my journal entries","I want actionable insights about my emotional state, not just raw mood scores"],"best_for":["individuals seeking data-driven emotional self-awareness","mental health app developers building mood-tracking features","teams building wellness platforms that need automated emotional analytics"],"limitations":["NLP models may misinterpret sarcasm, cultural context, or irony in personal writing; false positives/negatives in mood classification are likely","Emotion detection is probabilistic and culturally biased toward training data (typically Western emotional expression norms)","No real-time feedback loop to correct misclassified moods; users cannot easily retrain the model on their personal emotional language","Requires sufficient entry volume (10+ entries) for meaningful pattern detection; sparse journaling yields unreliable insights"],"requires":["diary entry text (minimum 50 characters for reliable analysis)","access to NLP model inference (cloud-based or on-device)","user account with historical entry data for trend analysis"],"input_types":["text (diary entry content)"],"output_types":["structured JSON (mood classification, intensity score 0-100, emotional themes array)","time-series mood data for visualization"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-diary__cap_2","uri":"capability://text.generation.language.ai.generated.reflective.prompts.and.emotional.insights","name":"ai-generated reflective prompts and emotional insights","description":"Generates contextual follow-up prompts and reflective questions based on detected mood and entry content using a large language model (likely GPT-3.5, GPT-4, or similar). The system chains mood analysis results and entry text into a prompt template, then uses the LLM to generate personalized reflection questions or insights designed to deepen emotional processing. Responses are presented as suggestions rather than directives, maintaining user agency over their journaling narrative.","intents":["I want AI to suggest deeper reflection questions based on what I've written, not just classify my mood","I need help exploring emotional patterns through guided prompts tailored to my specific situation","I want AI insights that help me understand my emotions better without imposing a particular narrative"],"best_for":["reflective individuals seeking guided emotional exploration","therapy-adjacent app builders wanting to add depth to journaling features","users who struggle with self-reflection and benefit from structured prompts"],"limitations":["LLM-generated insights may be generic or miss personal context despite prompt engineering; risk of one-size-fits-all emotional narratives","Potential for AI responses to inadvertently pathologize normal emotions or suggest inappropriate coping strategies","No human therapist oversight; generated insights should not be treated as clinical guidance","Prompt injection risk if user entries contain adversarial text designed to manipulate LLM output","Latency typically 2-5 seconds per insight generation due to LLM inference time"],"requires":["completed mood analysis on diary entry","access to LLM API (OpenAI, Anthropic, or similar)","entry text with sufficient detail (100+ characters) for meaningful prompt generation"],"input_types":["text (diary entry)","structured JSON (mood classification, emotional themes)"],"output_types":["text (reflective prompts, insights, suggestions)","structured JSON (prompt category, confidence score)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-diary__cap_3","uri":"capability://data.processing.analysis.temporal.mood.trend.visualization.and.analytics","name":"temporal mood trend visualization and analytics","description":"Aggregates mood classifications across multiple diary entries over time and generates visual representations (charts, graphs, heatmaps) showing emotional patterns, cycles, and trends. The system stores mood data in a time-series database or indexed structure, then applies statistical aggregation (daily/weekly/monthly mood averages, standard deviation, trend lines) and renders interactive visualizations using charting libraries (likely D3.js, Chart.js, or Plotly). Users can filter by date range, mood category, or emotional theme to explore specific patterns.","intents":["I want to see my mood patterns over weeks or months to identify cycles or triggers","I need to visualize whether my emotional state is improving or deteriorating over time","I want to correlate my mood with specific events or time periods to understand emotional drivers"],"best_for":["individuals tracking mental health trends for personal insight or therapy discussions","wellness app developers building analytics dashboards","researchers studying emotional patterns in journaling populations"],"limitations":["Visualization accuracy depends on entry frequency; sparse journaling (1-2 entries/week) creates gaps that distort trend analysis","Statistical methods assume mood data is normally distributed, which may not hold for individual emotional patterns","Correlation analysis (mood vs events) is suggestive only; cannot establish causation without controlled data collection","Privacy risk: mood trend data is highly sensitive; visualization export/sharing features must be carefully gated"],"requires":["minimum 10-15 diary entries with mood classifications for meaningful trend analysis","historical mood data spanning at least 2-4 weeks for pattern detection","client-side charting library or server-side visualization rendering capability"],"input_types":["structured JSON (mood classifications with timestamps)"],"output_types":["interactive charts (line graphs, bar charts, heatmaps)","statistical summaries (average mood, mood variance, trend direction)","exportable reports (PDF, CSV)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-diary__cap_4","uri":"capability://safety.moderation.secure.cloud.storage.with.encryption.for.diary.entries","name":"secure cloud storage with encryption for diary entries","description":"Stores diary entries and mood data on cloud infrastructure with encryption at rest and in transit. The system likely implements end-to-end encryption (E2EE) where entries are encrypted on the client device before transmission, with decryption keys managed by the user or derived from user credentials. Transport uses TLS 1.3 for in-flight encryption. Server-side storage likely uses AES-256 encryption with key management via a KMS (Key Management Service). However, the editorial summary notes that specific encryption standards and data retention policies are unclear.","intents":["I need my diary entries encrypted so that even the service provider cannot read them","I want assurance that my sensitive personal data is protected if the cloud infrastructure is breached","I need to understand the data retention and deletion policies for my diary content"],"best_for":["privacy-conscious individuals journaling about sensitive topics","teams building HIPAA-compliant or GDPR-compliant journaling features","users in jurisdictions with strict data protection regulations"],"limitations":["Encryption implementation details are not publicly documented; unclear whether E2EE or server-side encryption is used","Data retention policies are not clearly stated; unclear how long deleted entries are retained in backups","If E2EE is implemented, account recovery becomes difficult if user loses encryption keys; no key escrow mechanism mentioned","Encryption adds computational overhead (~50-100ms per entry encryption/decryption on typical devices)","Server-side mood analysis requires decryption on the server, creating a window where plaintext entries exist in memory"],"requires":["HTTPS/TLS 1.3 capable client and server","cryptographic library for client-side encryption (if E2EE is implemented)","key management infrastructure (KMS) for server-side key storage","user account with secure credential management"],"input_types":["plaintext diary entries (before encryption)"],"output_types":["encrypted ciphertext (stored on server)","decrypted plaintext (returned to authorized client)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-diary__cap_5","uri":"capability://automation.workflow.freemium.subscription.tier.management","name":"freemium subscription tier management","description":"Implements a freemium pricing model with feature gating based on subscription tier. The system likely uses a subscription management service (Stripe, Paddle, or similar) to track user tier status, enforce feature limits (e.g., free tier: 5 entries/month, premium: unlimited), and manage billing/renewal. Feature access is gated at the API level, with client-side UI reflecting available features based on user tier. Tier upgrades are handled through a payment flow integrated with the app.","intents":["I want to try the app with basic features before committing to a paid subscription","I need to understand what features are locked behind the paywall and what the upgrade cost is","I want to manage my subscription, upgrade, downgrade, or cancel without friction"],"best_for":["freemium app builders seeking low-friction user onboarding","teams building consumer wellness apps with monetization requirements","users evaluating tools before purchase commitment"],"limitations":["Free tier feature limits may be too restrictive (e.g., 5 entries/month) to provide genuine value, limiting conversion to paid","Specific free vs premium feature breakdown is not documented; unclear what functionality is gated","Subscription management adds complexity: billing failures, churn, refund handling require robust infrastructure","Free tier users may experience degraded performance or slower inference if resources are prioritized for paying customers"],"requires":["payment processor integration (Stripe, Paddle, etc.)","subscription state tracking in user database","feature flag system to gate functionality by tier","billing/renewal automation"],"input_types":["user tier status (free, premium, etc.)","payment method (credit card, etc.)"],"output_types":["subscription status (active, expired, etc.)","feature access permissions (boolean flags per feature)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-diary__cap_6","uri":"capability://automation.workflow.multi.device.entry.synchronization","name":"multi-device entry synchronization","description":"Synchronizes diary entries and mood data across multiple devices (smartphone, tablet, desktop) using a cloud-based sync engine. The system likely implements operational transformation (OT) or conflict-free replicated data types (CRDTs) to handle concurrent edits across devices, with a central server as the source of truth. Sync is triggered on entry creation/modification and uses incremental sync (delta sync) to minimize bandwidth. Offline entries are queued and synced when connectivity is restored.","intents":["I want to start a journal entry on my phone and continue editing on my desktop","I need my mood data and insights to be available across all my devices","I want offline journaling capability with automatic sync when I reconnect"],"best_for":["users with multiple devices who want seamless journaling across platforms","mobile-first app builders adding desktop/web support","teams building cross-platform wellness apps"],"limitations":["Sync latency typically 1-5 seconds depending on network conditions; real-time sync is not guaranteed","Conflict resolution (if user edits same entry on two devices simultaneously) may result in data loss or manual merge required","Offline-first sync requires local storage on each device; storage limits on mobile devices may constrain entry history","Sync bandwidth overhead adds ~10-20% to total data usage per entry"],"requires":["cloud backend with sync API (REST or WebSocket)","local database on each device (SQLite, Realm, etc.)","network connectivity for sync (though offline mode is supported)","user account with device registration"],"input_types":["diary entry modifications (text, mood data, metadata)"],"output_types":["synchronized entry state across devices","sync status indicators (synced, syncing, pending)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-diary__cap_7","uri":"capability://text.generation.language.conversational.ai.chat.interface.for.diary.reflection","name":"conversational ai chat interface for diary reflection","description":"Provides a chat-based interface where users can have multi-turn conversations with an AI assistant about their diary entries, moods, and emotional patterns. The system likely uses a conversational LLM (GPT-3.5, GPT-4, or similar) with conversation history management and context injection from the user's diary data. Each conversation turn is processed through a prompt template that includes relevant diary entries, mood data, and conversation history to maintain context. Responses are generated in real-time and streamed to the user.","intents":["I want to have a conversation with AI about my emotional patterns and get personalized advice","I need to explore my feelings through dialogue rather than reading static insights","I want to ask follow-up questions about my mood trends and get contextual answers"],"best_for":["users seeking conversational emotional support and reflection","wellness app builders adding AI coaching features","individuals who prefer dialogue-based learning over static dashboards"],"limitations":["Conversational AI lacks clinical training and should not be treated as therapy; responses may be inappropriate or harmful if user is in crisis","Conversation history is stored on the server, creating privacy risk for sensitive emotional discussions","LLM responses are probabilistic and may hallucinate facts about the user's mood or suggest inappropriate coping strategies","Latency per response typically 2-5 seconds; slower than human conversation, which may disrupt conversational flow","No ability to escalate to human support if user expresses suicidal ideation or other crisis indicators"],"requires":["access to conversational LLM API (OpenAI, Anthropic, etc.)","conversation history storage (database or vector store)","context injection mechanism to include diary data in prompts","user account with diary data"],"input_types":["text (user message in conversation)","structured JSON (diary entries, mood data for context)"],"output_types":["text (AI response, streamed in real-time)","structured JSON (conversation metadata, confidence scores)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-diary__cap_8","uri":"capability://data.processing.analysis.emotion.based.entry.tagging.and.categorization","name":"emotion-based entry tagging and categorization","description":"Automatically assigns semantic tags and categories to diary entries based on detected emotional content and themes. The system likely uses NLP techniques (named entity recognition, topic modeling, or LLM-based classification) to extract key emotional themes, people, places, and situations mentioned in entries. Tags are stored as metadata and indexed for search and filtering. Users can also manually add custom tags, which are incorporated into the tagging model over time.","intents":["I want to automatically tag my entries by emotion and theme so I can find related entries later","I need to search my diary by emotional topic (e.g., 'anxiety about work') without manually tagging everything","I want to see which people, places, or situations are associated with specific emotions"],"best_for":["users with large diary archives who need searchable organization","individuals tracking emotional patterns across specific life domains (work, relationships, health)","app builders adding semantic search to journaling platforms"],"limitations":["Automatic tagging may be inaccurate or miss subtle emotional nuances; false positives/negatives in theme extraction","Named entity recognition (NER) may fail to identify people/places mentioned by nickname or context-dependent reference","Tag proliferation: without deduplication, similar tags may be created separately, fragmenting search results","Privacy risk: extracted entities (people names, locations) are stored as metadata, increasing exposure if database is breached"],"requires":["NLP model for theme/entity extraction (spaCy, BERT, or LLM-based)","tag storage and indexing infrastructure (database with full-text search)","user account with diary entries"],"input_types":["text (diary entry content)"],"output_types":["structured JSON (tags array, entities array, theme classifications)","indexed tag metadata for search"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_ai-diary__cap_9","uri":"capability://data.processing.analysis.privacy.preserving.on.device.mood.analysis.optional.local.inference","name":"privacy-preserving on-device mood analysis (optional local inference)","description":"Optionally performs mood detection and analysis on the user's device rather than sending entries to cloud servers, using on-device ML models. The system likely includes lightweight transformer models (DistilBERT, MobileBERT, or quantized variants) that can run on mobile/desktop hardware with acceptable latency. Users can toggle between on-device and cloud-based analysis. On-device analysis eliminates the need to transmit sensitive diary content to external servers, though it requires more device storage and computational resources.","intents":["I want mood analysis without sending my diary entries to cloud servers","I need privacy-first journaling where my entries never leave my device","I want to use AI features offline without internet connectivity"],"best_for":["privacy-maximalist users unwilling to trust cloud infrastructure with diary data","users in regions with strict data localization requirements","teams building privacy-first wellness apps"],"limitations":["On-device models are smaller and less accurate than cloud-based models; mood classification accuracy may drop 5-15%","Device storage overhead: on-device models require 50-500MB depending on model size; significant for mobile devices","Computational overhead: mood analysis on older devices may take 5-10 seconds per entry, degrading UX","Model updates require app updates; users may run outdated models if they don't update the app","Conversational AI and advanced insights still require cloud processing, so true privacy requires disabling those features"],"requires":["on-device ML runtime (Core ML on iOS, TensorFlow Lite on Android, ONNX on desktop)","quantized or distilled model weights (50-500MB)","device with sufficient RAM (2GB+) and storage"],"input_types":["text (diary entry content)"],"output_types":["structured JSON (mood classification, intensity score)","local storage of mood metadata (no cloud transmission)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["microphone hardware on device (smartphone, tablet, or computer)","active internet connection for ASR API calls","user account with AI Diary service","browser or mobile app with audio capture permissions","diary entry text (minimum 50 characters for reliable analysis)","access to NLP model inference (cloud-based or on-device)","user account with historical entry data for trend analysis","completed mood analysis on diary entry","access to LLM API (OpenAI, Anthropic, or similar)","entry text with sufficient detail (100+ characters) for meaningful prompt generation"],"failure_modes":["ASR accuracy degrades in noisy environments (coffee shops, public transit); background noise may corrupt transcriptions","Requires internet connectivity for cloud-based ASR processing; no offline fallback mentioned","Language support limited to whatever the underlying ASR provider supports; multilingual switching may require manual selection","Real-time transcription latency typically 2-5 seconds depending on audio chunk size and network conditions","NLP models may misinterpret sarcasm, cultural context, or irony in personal writing; false positives/negatives in mood classification are likely","Emotion detection is probabilistic and culturally biased toward training data (typically Western emotional expression norms)","No real-time feedback loop to correct misclassified moods; users cannot easily retrain the model on their personal emotional language","Requires sufficient entry volume (10+ entries) for meaningful pattern detection; sparse journaling yields unreliable insights","LLM-generated insights may be generic or miss personal context despite prompt engineering; risk of one-size-fits-all emotional narratives","Potential for AI responses to inadvertently pathologize normal emotions or suggest inappropriate coping strategies","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.72,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.132Z","last_scraped_at":"2026-04-05T13:23:42.562Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ai-diary","compare_url":"https://unfragile.ai/compare?artifact=ai-diary"}},"signature":"bOWro+DIx8AbbLnHnX4T8QIlAv5RB8VpkX9emyi9uRTeuSkX3KE1UjbVTogdj9UbhSzs+9khhobbQBatX/BfCA==","signedAt":"2026-06-20T02:21:46.609Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ai-diary","artifact":"https://unfragile.ai/ai-diary","verify":"https://unfragile.ai/api/v1/verify?slug=ai-diary","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}