{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_charlie","slug":"charlie","name":"Charlie","type":"product","url":"https://charlie.news","page_url":"https://unfragile.ai/charlie","categories":["text-writing"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_charlie__cap_0","uri":"capability://memory.knowledge.interest.based.news.feed.personalization","name":"interest-based news feed personalization","description":"Charlie implements a collaborative filtering and content-based recommendation engine that learns user reading patterns over time to surface relevant stories. The system tracks article engagement signals (clicks, dwell time, completion rates) and maps them against user-declared interests and implicit preference signals to rank and filter incoming news stories from partner sources. This creates a dynamically-weighted feed that adapts as reading behavior evolves, rather than applying static keyword matching or manual curation rules.","intents":["I want my news feed to show only stories relevant to my specific interests without manually managing subscriptions","I need the system to learn what I actually care about by observing what I read, not just what I claim to care about","I want to reduce cognitive load by filtering out irrelevant news while discovering unexpected relevant stories in my interest areas"],"best_for":["Individual news readers seeking hyper-personalized curation without algorithmic manipulation","Users willing to invest 2-4 weeks of reading history to bootstrap accurate personalization","Readers who value discovery within their interests over comprehensive news coverage"],"limitations":["Cold-start problem: new users receive generic recommendations until sufficient reading history accumulates (typically 20-50 articles)","Preference drift detection is reactive rather than proactive—algorithm may lag behind genuine interest shifts by 1-2 weeks","Personalization quality degrades for niche or emerging topics with limited coverage in partner network","No explicit feedback mechanism (thumbs up/down) to accelerate preference learning—relies entirely on implicit signals"],"requires":["User account with email verification","Minimum 2-3 days of active reading to establish baseline preferences","JavaScript-enabled browser for engagement tracking (dwell time, scroll depth)"],"input_types":["user reading history (implicit: clicks, time-on-page, scroll position)","user interest declarations (explicit: topic tags, category selections)","article metadata from partner sources (title, category, publication date, author)"],"output_types":["ranked feed of news articles (JSON with score, relevance explanation optional)","personalized story recommendations (push notifications or in-app suggestions)","interest profile summary (user-facing: topics, confidence scores)"],"categories":["memory-knowledge","personalization-recommendation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_charlie__cap_1","uri":"capability://data.processing.analysis.curated.partner.source.network.integration","name":"curated partner source network integration","description":"Charlie maintains a vetted network of news sources (publications, wire services, independent outlets) from which it aggregates stories. The integration layer normalizes article metadata (title, byline, publication date, category tags) across heterogeneous source APIs and feeds (RSS, JSON APIs, web scraping) into a unified internal schema. Source quality and coverage diversity are managed through editorial curation rather than algorithmic inclusion, ensuring baseline journalistic standards while limiting the breadth of available sources.","intents":["I want to read news from quality, trustworthy sources without manually managing dozens of RSS feeds or subscriptions","I need stories aggregated from multiple sources so I can see different perspectives on the same event","I want to avoid misinformation and low-quality clickbait by relying on editorially-vetted sources"],"best_for":["Readers prioritizing source credibility and editorial standards over comprehensive coverage","Users who want aggregation without managing individual subscriptions or feed readers","Audiences in English-language markets with established news infrastructure"],"limitations":["Limited source diversity: curated network is smaller than aggregators like Google News or Apple News, potentially missing niche, independent, or international outlets","Source addition is manual and editorial, not algorithmic—new sources may take weeks to integrate","Geographic bias toward English-language sources; minimal coverage of non-English news","Dependency on partner API stability and rate limits; source outages directly impact feed freshness","No user control over source inclusion/exclusion—cannot blacklist or prioritize specific publications"],"requires":["Active partnerships with news sources (RSS feeds, APIs, or web scraping agreements)","Content normalization pipeline (ETL) to map heterogeneous source schemas to internal article format","Duplicate detection system to identify same-story coverage across multiple sources"],"input_types":["RSS feeds (XML)","JSON APIs from news sources","Web-scraped HTML (for sources without structured feeds)"],"output_types":["normalized article objects (title, byline, publication date, category, source attribution, content excerpt)","deduplicated story clusters (same event covered by multiple sources)","source metadata (publication name, category, reliability score)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_charlie__cap_2","uri":"capability://text.generation.language.distraction.free.reading.interface.with.engagement.tracking","name":"distraction-free reading interface with engagement tracking","description":"Charlie provides a minimal, ad-free reading interface that prioritizes article content over navigation chrome, ads, or recommended-content sidebars. The interface silently tracks engagement metrics (scroll depth, time-on-page, reading speed, completion status) via client-side JavaScript instrumentation without explicit user action, feeding these signals back to the personalization engine. The design philosophy prioritizes reading experience over monetization, with no interstitial ads, paywalls, or tracking pixels from third parties.","intents":["I want to read news without visual clutter, ads, or distracting recommendations interrupting my focus","I need the system to understand my reading behavior (what I finish vs. skim) without asking me to rate articles","I want a reading experience optimized for comprehension and retention, not engagement metrics"],"best_for":["Readers with ad fatigue from traditional news sites and apps","Users who value reading experience and focus over feature richness","Audiences comfortable with implicit engagement tracking for personalization purposes"],"limitations":["Minimal customization: no dark mode toggle, font size adjustment, or reading-mode options (as of current version)","No offline reading capability—requires active internet connection to load articles","Engagement tracking relies on JavaScript; users with JS disabled or privacy extensions (uBlock Origin, Privacy Badger) may not contribute signals to personalization","No article save/bookmark feature for later reading; ephemeral feed design encourages immediate consumption","Mobile interface may not be optimized for all screen sizes; responsive design limitations on older devices"],"requires":["Modern web browser with JavaScript enabled (Chrome 90+, Firefox 88+, Safari 14+)","Active internet connection for real-time article loading","Cookies enabled for session persistence and engagement tracking"],"input_types":["article HTML/text content from partner sources","user interaction events (scroll, click, focus, time-on-page)"],"output_types":["rendered article view (HTML/CSS optimized for readability)","engagement telemetry (JSON: scroll_depth, time_on_page, completion_status, reading_speed)","user session data (article IDs viewed, timestamps, engagement scores)"],"categories":["text-generation-language","user-experience-design"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_charlie__cap_3","uri":"capability://memory.knowledge.interest.based.content.categorization.and.tagging","name":"interest-based content categorization and tagging","description":"Charlie allows users to declare and manage interest categories (e.g., 'Technology', 'Climate', 'Local Politics') which serve as explicit preference signals for the personalization engine. The system maps incoming articles to these user-defined categories using NLP-based topic classification (likely keyword matching, TF-IDF, or lightweight ML models) and uses category-level preferences to weight feed ranking. Users can adjust interest weights (e.g., 'Technology: high priority', 'Sports: low priority') to directly influence feed composition without relying solely on implicit reading signals.","intents":["I want to explicitly declare what topics I care about so the system has a clear starting point for personalization","I need to adjust how much weight different topics get in my feed (e.g., prioritize tech news, deprioritize sports)","I want to discover new stories in my interest areas without manually searching or subscribing to specific sources"],"best_for":["Users who prefer explicit control over feed composition rather than pure algorithmic learning","Readers with clearly-defined interest areas who want quick onboarding without weeks of training data","Audiences who want to adjust personalization weights dynamically based on current priorities"],"limitations":["Category taxonomy is predefined by Charlie; users cannot create custom categories or hierarchies","Topic classification accuracy depends on article metadata quality from partner sources; poorly-tagged articles may be miscategorized","Interest weights are global, not contextual—cannot specify 'high priority for tech news from specific sources' or time-based rules","No negative interest filtering (e.g., 'never show me sports news')—only positive weighting available","Category-level personalization may be too coarse-grained for nuanced preferences (e.g., 'AI ethics' vs. 'AI safety' within Technology)"],"requires":["User account with onboarding flow to select initial interests","Predefined category taxonomy (likely 15-30 top-level categories)","NLP-based article classification pipeline to map stories to categories"],"input_types":["user interest selections (category IDs, priority weights)","article metadata (title, content excerpt, source-provided tags)"],"output_types":["user interest profile (JSON: category_id, weight, last_updated)","article-to-category mappings (article_id, category_id, confidence_score)","feed ranking adjustments (category-weighted scores)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_charlie__cap_4","uri":"capability://data.processing.analysis.real.time.feed.freshness.and.story.deduplication","name":"real-time feed freshness and story deduplication","description":"Charlie continuously polls partner news sources (via RSS, APIs, or scheduled scraping) to ingest new articles, typically with a refresh cadence of 15-60 minutes depending on source priority. The system implements duplicate detection (likely using content hashing, title similarity, or URL canonicalization) to identify when multiple sources cover the same story, clustering them together and attributing coverage to all sources. Feed freshness is maintained by prioritizing recent articles in ranking, ensuring users see breaking news and developing stories without stale content dominating the feed.","intents":["I want to see breaking news and developing stories as they happen, not hours-old articles","I need to understand when a story is covered by multiple sources so I can see different perspectives","I want the feed to stay current without manual refresh or checking multiple news sites"],"best_for":["Readers who want real-time or near-real-time news updates without manual polling","Users interested in seeing how different sources cover the same story","Audiences who value feed freshness and breaking news over comprehensive historical coverage"],"limitations":["Refresh latency: stories appear 15-60 minutes after publication depending on source polling schedule; breaking news may lag behind real-time platforms like Twitter/X or news wire services","Duplicate detection is imperfect: similar stories about the same topic may not be clustered if they use different angles or headlines; conversely, unrelated stories may be incorrectly grouped","Source outages or API rate limits can cause feed staleness for affected sources","No user control over refresh frequency or freshness preferences; feed update cadence is system-wide","Historical article retention is limited; older stories (>30 days) may be archived or removed from feed, limiting ability to browse past coverage"],"requires":["Continuous polling infrastructure (scheduled jobs, message queues, or webhooks from partner sources)","Duplicate detection algorithm (content hashing, fuzzy string matching, or ML-based similarity)","Article storage and indexing system to track publication timestamps and source attribution","Feed ranking algorithm that weights recency alongside personalization signals"],"input_types":["article feeds from partner sources (RSS, JSON APIs, HTML)","article metadata (title, URL, publication date, content)"],"output_types":["deduplicated article objects with multi-source attribution","story clusters (same event, multiple source perspectives)","feed ranking with recency weighting","freshness metadata (last_updated, source_count, coverage_timeline)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_charlie__cap_5","uri":"capability://memory.knowledge.user.preference.persistence.and.profile.management","name":"user preference persistence and profile management","description":"Charlie maintains a persistent user profile that stores interest declarations, engagement history, and personalization weights across sessions. The profile is stored server-side (likely in a relational database) and synchronized with client-side session state, allowing users to maintain consistent personalization across devices and sessions. Profile data includes interest categories, reading history (article IDs, timestamps, engagement metrics), and derived preference weights that feed the ranking algorithm. Users can view and manually adjust their profile (interests, weights) to correct or refine personalization.","intents":["I want my personalization to persist across devices and sessions so my feed is consistent whether I read on mobile or desktop","I want to see my reading history and understand what the system has learned about my preferences","I want to manually adjust my interests or reset my profile if my preferences change significantly"],"best_for":["Users who read across multiple devices (phone, tablet, desktop) and expect consistent personalization","Readers who want transparency into how the system models their preferences","Audiences who want control to adjust or reset personalization without starting from scratch"],"limitations":["Profile data is centralized on Charlie's servers; no local-first or offline-first option for privacy-conscious users","Reading history is retained indefinitely (or for extended periods); no granular user control over data retention or deletion","Profile export/portability is likely unavailable; users cannot easily migrate preferences to other news apps","No collaborative profiles or family sharing; each user maintains a separate, isolated preference model","Profile synchronization across devices may have latency (seconds to minutes) before changes propagate"],"requires":["User account with email authentication","Server-side database to store user profiles (relational DB like PostgreSQL or NoSQL like MongoDB)","Session management and authentication tokens (JWT, OAuth, or session cookies)","Client-side state management to sync profile data with server"],"input_types":["user interest selections (category IDs, weights)","reading history (article IDs, timestamps, engagement metrics)","profile adjustment requests (interest updates, weight changes, history deletion)"],"output_types":["user profile object (interests, weights, reading history, derived preferences)","profile summary view (user-facing: interests, reading stats, preference weights)","profile export (likely JSON or CSV for data portability, if available)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["User account with email verification","Minimum 2-3 days of active reading to establish baseline preferences","JavaScript-enabled browser for engagement tracking (dwell time, scroll depth)","Active partnerships with news sources (RSS feeds, APIs, or web scraping agreements)","Content normalization pipeline (ETL) to map heterogeneous source schemas to internal article format","Duplicate detection system to identify same-story coverage across multiple sources","Modern web browser with JavaScript enabled (Chrome 90+, Firefox 88+, Safari 14+)","Active internet connection for real-time article loading","Cookies enabled for session persistence and engagement tracking","User account with onboarding flow to select initial interests"],"failure_modes":["Cold-start problem: new users receive generic recommendations until sufficient reading history accumulates (typically 20-50 articles)","Preference drift detection is reactive rather than proactive—algorithm may lag behind genuine interest shifts by 1-2 weeks","Personalization quality degrades for niche or emerging topics with limited coverage in partner network","No explicit feedback mechanism (thumbs up/down) to accelerate preference learning—relies entirely on implicit signals","Limited source diversity: curated network is smaller than aggregators like Google News or Apple News, potentially missing niche, independent, or international outlets","Source addition is manual and editorial, not algorithmic—new sources may take weeks to integrate","Geographic bias toward English-language sources; minimal coverage of non-English news","Dependency on partner API stability and rate limits; source outages directly impact feed freshness","No user control over source inclusion/exclusion—cannot blacklist or prioritize specific publications","Minimal customization: no dark mode toggle, font size adjustment, or reading-mode options (as of current version)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"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":"2026-05-24T12:16:29.716Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=charlie","compare_url":"https://unfragile.ai/compare?artifact=charlie"}},"signature":"7uBPhTIOV8wn2KveOT9anikwbQZfq6fOwbIAlw8tA5CtO/Fl9NFw6JJ23+7TUT5fi5vpJ3hwq+QWg4pkTKmCBg==","signedAt":"2026-06-22T07:56:55.948Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/charlie","artifact":"https://unfragile.ai/charlie","verify":"https://unfragile.ai/api/v1/verify?slug=charlie","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"}}