{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_onesub","slug":"onesub","name":"OneSub","type":"webapp","url":"https://onesub.io","page_url":"https://unfragile.ai/onesub","categories":["research-search"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_onesub__cap_0","uri":"capability://search.retrieval.multi.source.news.aggregation.with.perspective.diversity","name":"multi-source news aggregation with perspective diversity","description":"Crawls and indexes news articles from a curated set of diverse source feeds (spanning different editorial positions, geographic regions, and publication types), then groups semantically similar stories across sources using NLP-based topic clustering and entity matching. The system maintains source metadata (publication bias indicators, geographic focus, editorial stance) to enable perspective-aware ranking and presentation rather than simple recency or popularity sorting.","intents":["I want to read about a breaking news story and immediately see how different outlets are covering it","I need to understand multiple viewpoints on a controversial topic without manually visiting 5+ news sites","I want to discover stories that mainstream algorithms might suppress or amplify based on engagement metrics"],"best_for":["intellectually curious readers seeking media literacy","educators designing curricula around critical news consumption","researchers studying media bias and coverage patterns"],"limitations":["No transparent documentation of source selection criteria — unclear how 'diverse' sources are chosen or weighted","Semantic clustering may conflate distinct stories with similar keywords, creating false equivalence between unrelated events","Real-time aggregation latency unknown — may lag breaking news by minutes to hours depending on feed update frequency","No API or programmatic access documented, limiting integration into third-party research or educational tools"],"requires":["Active internet connection for real-time feed polling","JavaScript-enabled browser for interactive perspective comparison UI","No authentication required (free tier)"],"input_types":["RSS/Atom feeds from news sources","Web-scraped article content (headlines, body text, publication metadata)"],"output_types":["Grouped story clusters with perspective variants","Structured article metadata (source, publication date, bias indicators)","Rendered HTML UI with side-by-side article comparison"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_onesub__cap_1","uri":"capability://data.processing.analysis.editorial.perspective.classification.and.labeling","name":"editorial perspective classification and labeling","description":"Assigns editorial stance labels to each news source and article variant (e.g., 'left-leaning', 'center', 'right-leaning', or domain-specific labels like 'pro-business', 'environmental-focus') using a combination of historical editorial analysis, source metadata, and potentially ML-based text classification on article framing. These labels are then displayed alongside articles to help readers contextualize the source's likely bias before consuming content.","intents":["I want to know the editorial bias of a news source before reading an article from it","I need to understand how a story's framing differs across sources with different political or ideological positions","I want to filter or weight articles based on source credibility or bias indicators"],"best_for":["media literacy educators teaching students to identify bias","researchers analyzing how editorial stance affects coverage","readers with strong priors seeking to challenge their own viewpoints"],"limitations":["No public documentation of labeling methodology — unclear if labels are manually curated, crowdsourced, or algorithmically derived","Binary or ternary political spectrum (left-center-right) may oversimplify complex editorial positions that don't map to traditional political axes","Labels may become stale if source editorial positions shift over time without regular recalibration","Risk of false labeling creating new biases (e.g., mislabeling a centrist source as left-leaning due to algorithmic error)"],"requires":["Source metadata database with historical editorial analysis","Optional: ML model trained on labeled article samples (training data sources unknown)"],"input_types":["Article text and metadata (headline, body, publication date, source)","Source publication history and editorial guidelines (if available)"],"output_types":["Perspective labels (text strings: 'left', 'center', 'right', or domain-specific)","Confidence scores for labels (if probabilistic model used)","Visual indicators in UI (color coding, badges, or text labels)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_onesub__cap_2","uri":"capability://data.processing.analysis.semantic.story.clustering.and.deduplication","name":"semantic story clustering and deduplication","description":"Groups articles covering the same underlying news event across multiple sources using NLP-based similarity matching on article headlines, body text, and extracted entities (people, places, organizations). The system likely uses embeddings-based retrieval (sentence transformers or similar) to compute semantic similarity, then applies clustering algorithms (k-means, hierarchical clustering, or graph-based methods) to group related articles while filtering near-duplicates from wire services (AP, Reuters).","intents":["I want to see all coverage variants of a single news story in one place without scrolling through duplicate headlines","I need to understand how the same event is being reported differently across sources with different editorial angles","I want to reduce cognitive load by seeing one story cluster instead of 20 near-identical headlines"],"best_for":["busy professionals who want news summaries without redundancy","researchers studying media coverage patterns and narrative divergence","educators teaching students to identify how framing affects perception of events"],"limitations":["Semantic clustering may incorrectly group unrelated stories with similar keywords (e.g., multiple 'tech CEO' stories conflated into one cluster)","Clustering latency unknown — real-time updates to story clusters may lag by minutes, creating stale groupings","No documented handling of evolving stories (e.g., a breaking news event that develops over hours) — unclear if clusters are merged, split, or updated dynamically","Deduplication may remove legitimate variations in reporting (e.g., different angles on the same event from different regions)"],"requires":["NLP embeddings model (likely pre-trained transformer like BERT, RoBERTa, or sentence-transformers)","Clustering algorithm implementation (computational cost scales with number of articles)","Real-time or near-real-time article ingestion pipeline"],"input_types":["Article text (headline, body, metadata)","Named entity extraction (people, organizations, locations mentioned in articles)"],"output_types":["Story clusters (grouped articles with cluster ID and similarity scores)","Cluster summaries or representative headlines","Metadata indicating primary vs. secondary sources within cluster"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_onesub__cap_3","uri":"capability://text.generation.language.balanced.perspective.presentation.and.comparison.ui","name":"balanced perspective presentation and comparison ui","description":"Renders a user interface that explicitly juxtaposes articles from sources with different editorial perspectives on the same story, using visual layout (side-by-side panels, tabs, or carousel) to facilitate direct comparison. The UI likely highlights key differences in framing, emphasis, and factual claims across variants, potentially using visual annotations (highlighting, callouts) to surface divergent narratives or interpretations of the same events.","intents":["I want to read the same story from a left-leaning and right-leaning source side-by-side to see how framing differs","I need to identify which claims are consistent across sources vs. which are disputed or emphasized differently","I want a distraction-free interface that prioritizes content over ads, engagement metrics, or algorithmic recommendations"],"best_for":["readers actively seeking to challenge their own viewpoints","educators using OneSub as a classroom tool for media literacy","researchers studying how editorial perspective affects narrative construction"],"limitations":["Side-by-side comparison UI may overwhelm users with too much information, reducing comprehension vs. reading one article at a time","No documented accessibility features (screen reader support, keyboard navigation, color-blind friendly design) — may exclude users with disabilities","UI design assumes users want to compare perspectives; users seeking a single authoritative source may find the interface confusing or frustrating","No personalization or filtering options documented — users cannot adjust perspective balance (e.g., 'show me 3 left, 1 right' vs. equal split)"],"requires":["Modern web browser with CSS Grid/Flexbox support","JavaScript enabled for interactive comparison features","Responsive design for mobile and tablet viewing"],"input_types":["Article content (text, headlines, images, metadata)","Perspective labels and source metadata","User interaction events (clicks, scrolls, article selections)"],"output_types":["Rendered HTML UI with comparison layout","Interactive controls for switching between articles or perspectives","Visual indicators of perspective diversity (badges, color coding)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_onesub__cap_4","uri":"capability://data.processing.analysis.source.credibility.and.fact.check.integration","name":"source credibility and fact-check integration","description":"Integrates credibility indicators and fact-check information from external databases (e.g., Media Bias/Fact Check, Snopes, PolitiFact) to display alongside articles, showing whether claims in articles have been fact-checked, disputed, or verified. The system likely queries fact-check APIs or maintains a curated database of fact-checks linked to article claims, then displays credibility badges or warnings alongside relevant content.","intents":["I want to know if the claims in an article have been fact-checked before sharing it","I need to identify which sources have a track record of accuracy vs. misinformation","I want to see fact-checks for specific claims mentioned in articles without manually searching for them"],"best_for":["readers concerned about misinformation and seeking to verify claims","educators teaching students to evaluate source credibility","researchers studying the relationship between source bias and factual accuracy"],"limitations":["Fact-check coverage is incomplete — most articles won't have associated fact-checks, limiting the utility of this feature","Fact-check databases themselves have editorial biases and may rate claims differently based on methodology","No documented mechanism for handling conflicting fact-checks from different sources (e.g., PolitiFact vs. FactCheck.org disagree on a claim)","Credibility indicators may create false confidence in sources that haven't been fact-checked (absence of evidence is not evidence of absence)"],"requires":["Integration with external fact-check APIs or databases (Media Bias/Fact Check, Snopes, PolitiFact, etc.)","Claim extraction and linking logic to match article text to fact-checks","Regular updates to fact-check database to maintain freshness"],"input_types":["Article text and extracted claims","Source metadata and historical credibility ratings","Fact-check database entries (claims, verdicts, sources)"],"output_types":["Credibility badges or ratings (text or visual indicators)","Fact-check links and summaries","Source credibility scores (if aggregated from multiple databases)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_onesub__cap_5","uri":"capability://planning.reasoning.personalized.perspective.balance.configuration","name":"personalized perspective balance configuration","description":"Allows users to customize the ratio and types of perspectives shown in their news feed (e.g., 'show me 50% left, 30% center, 20% right' or 'prioritize sources with high factual accuracy over perspective diversity'). The system likely stores user preferences in a profile, then weights article ranking and clustering based on these preferences while still surfacing some opposing viewpoints to maintain the core value proposition of perspective diversity.","intents":["I want to see mostly sources aligned with my views, but with some opposing perspectives to challenge my thinking","I want to adjust the balance of perspectives based on the topic (e.g., more diverse perspectives on politics, less on sports)","I want to prioritize factual accuracy over editorial diversity when reading about scientific or health topics"],"best_for":["users who want perspective diversity but not to the point of cognitive dissonance","educators customizing OneSub for classroom use with specific pedagogical goals","researchers studying how users engage with opposing viewpoints when given control"],"limitations":["No documented evidence that this feature exists — may not be implemented at all","Allowing users to customize perspective balance risks recreating echo chambers (users could set 90% preferred perspective, 10% opposing)","Per-topic customization would require topic classification and separate preference storage, adding complexity","Unclear how system handles edge cases (e.g., user requests 100% left-leaning sources, contradicting OneSub's core mission)"],"requires":["User authentication and profile storage","Preference management UI","Ranking algorithm that incorporates user preferences alongside perspective diversity"],"input_types":["User preference settings (perspective ratios, topic-specific preferences, accuracy vs. diversity tradeoffs)","User interaction history (articles read, sources followed, preferences adjusted)"],"output_types":["Customized news feed with user-specified perspective balance","Preference confirmation or adjustment UI"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_onesub__cap_6","uri":"capability://data.processing.analysis.topic.based.news.filtering.and.categorization","name":"topic-based news filtering and categorization","description":"Organizes news stories into topic categories (politics, technology, business, health, science, etc.) using NLP-based text classification or manual tagging, allowing users to browse news by topic rather than chronologically. The system likely uses pre-trained text classifiers (e.g., zero-shot classification with transformers) to assign articles to topics, then presents topic-specific feeds with perspective diversity maintained within each topic.","intents":["I want to read only about technology news, but still see diverse perspectives on tech stories","I need to quickly find coverage of a specific topic without scrolling through unrelated news","I want to understand how different topics are being covered across the political spectrum"],"best_for":["readers with specific topical interests who want depth without breadth","professionals monitoring industry-specific news (e.g., tech workers following technology coverage)","researchers studying how coverage of specific topics varies by source perspective"],"limitations":["Topic classification may be inaccurate for articles covering multiple topics or niche subjects not well-represented in training data","No documented taxonomy of topics — unclear how granular categorization is (e.g., 'technology' vs. 'artificial intelligence' vs. 'large language models')","Topic-based filtering may reduce exposure to important cross-cutting stories (e.g., a story about AI regulation that spans technology, politics, and business)","Maintaining perspective diversity within small topic categories may be difficult if few sources cover niche topics"],"requires":["Text classification model (pre-trained transformer or custom-trained classifier)","Topic taxonomy definition","Article metadata enrichment pipeline"],"input_types":["Article text (headline, body, metadata)","Topic labels (manual or automatic)"],"output_types":["Topic-categorized article feeds","Topic-specific story clusters with perspective diversity","Topic browsing UI (category navigation, topic-specific search)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_onesub__cap_7","uri":"capability://automation.workflow.real.time.news.feed.updates.and.notifications","name":"real-time news feed updates and notifications","description":"Continuously polls news source feeds and updates the OneSub feed in real-time, with optional push notifications for breaking news or user-specified topics. The system likely uses a background job scheduler (cron, message queue, or event-driven architecture) to fetch new articles from source feeds at regular intervals, then re-clusters and re-ranks them based on recency and user preferences. Push notifications may be triggered by story importance (e.g., breaking news from major sources) or user-specified keywords.","intents":["I want to be notified immediately when a major news story breaks","I want my news feed to update automatically without manually refreshing","I want to receive notifications only for topics I care about, not all breaking news"],"best_for":["news junkies and professionals who need real-time information","users on mobile devices who want push notifications","researchers studying how real-time news feeds affect user engagement and perspective exposure"],"limitations":["Real-time updates may create notification fatigue, especially for breaking news that develops over hours","No documented notification customization — unclear if users can filter notifications by topic, source, or perspective","Feed update latency unknown — may lag breaking news by minutes to hours depending on source feed update frequency","Push notification delivery depends on user device and OS (iOS, Android, web) — may not work reliably across all platforms"],"requires":["Background job scheduler or message queue (e.g., Celery, RabbitMQ, Kafka)","Push notification service (Firebase Cloud Messaging, Apple Push Notification service, or web push API)","Real-time database or cache for feed updates (Redis, Memcached)","User device registration for push notifications"],"input_types":["News source feeds (RSS, Atom, or API)","User notification preferences (topics, sources, frequency)","Breaking news detection signals (source importance, article velocity)"],"output_types":["Updated news feed with new articles","Push notifications (text, title, link)","In-app notification badges or indicators"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_onesub__cap_8","uri":"capability://data.processing.analysis.source.feed.curation.and.editorial.selection","name":"source feed curation and editorial selection","description":"Maintains a curated list of news sources spanning different editorial perspectives, geographic regions, and publication types (mainstream media, independent outlets, international sources, etc.). The system likely uses manual editorial review to select sources, with periodic audits to ensure continued diversity and quality. Source selection criteria may include editorial stance, factual accuracy track record, geographic coverage, and audience reach, though the exact methodology is not publicly documented.","intents":["I want to trust that OneSub is sourcing from legitimate, diverse news outlets rather than fringe sources","I want to understand which sources OneSub includes and why they were selected","I want to suggest new sources to be added to OneSub's feed"],"best_for":["users concerned about source quality and legitimacy","researchers studying media diversity and representation","educators evaluating OneSub for classroom use"],"limitations":["No public documentation of source selection criteria or methodology — users cannot verify that sources are truly diverse or balanced","Source list may become stale if editorial positions shift or outlets change ownership without recalibration","Manual curation is labor-intensive and may not scale to include truly comprehensive source coverage","No documented mechanism for users to suggest new sources or challenge source selection decisions","Unclear how OneSub handles sources that change editorial stance or are acquired by larger media companies"],"requires":["Editorial team to review and select sources","Source metadata database with editorial stance, accuracy ratings, and geographic focus","Periodic audits to ensure continued source quality and diversity"],"input_types":["News source information (URL, RSS feed, publication metadata)","Editorial analysis and bias assessment","User feedback and source suggestions"],"output_types":["Curated source list (public or internal)","Source metadata and editorial stance labels","Source inclusion/exclusion decisions"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active internet connection for real-time feed polling","JavaScript-enabled browser for interactive perspective comparison UI","No authentication required (free tier)","Source metadata database with historical editorial analysis","Optional: ML model trained on labeled article samples (training data sources unknown)","NLP embeddings model (likely pre-trained transformer like BERT, RoBERTa, or sentence-transformers)","Clustering algorithm implementation (computational cost scales with number of articles)","Real-time or near-real-time article ingestion pipeline","Modern web browser with CSS Grid/Flexbox support","JavaScript enabled for interactive comparison features"],"failure_modes":["No transparent documentation of source selection criteria — unclear how 'diverse' sources are chosen or weighted","Semantic clustering may conflate distinct stories with similar keywords, creating false equivalence between unrelated events","Real-time aggregation latency unknown — may lag breaking news by minutes to hours depending on feed update frequency","No API or programmatic access documented, limiting integration into third-party research or educational tools","No public documentation of labeling methodology — unclear if labels are manually curated, crowdsourced, or algorithmically derived","Binary or ternary political spectrum (left-center-right) may oversimplify complex editorial positions that don't map to traditional political axes","Labels may become stale if source editorial positions shift over time without regular recalibration","Risk of false labeling creating new biases (e.g., mislabeling a centrist source as left-leaning due to algorithmic error)","Semantic clustering may incorrectly group unrelated stories with similar keywords (e.g., multiple 'tech CEO' stories conflated into one cluster)","Clustering latency unknown — real-time updates to story clusters may lag by minutes, creating stale groupings","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"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":"2026-05-24T12:16:31.859Z","last_scraped_at":"2026-04-05T13:23:42.560Z","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=onesub","compare_url":"https://unfragile.ai/compare?artifact=onesub"}},"signature":"fsLkU2whHyGla1Ay4yFvK9/PjnbED2et1oYz61bMlK9/ufa1IsQx+4cGWj70fuc+Q5vmQS9FUzAofeYBc8GAAQ==","signedAt":"2026-06-21T04:28:29.667Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/onesub","artifact":"https://unfragile.ai/onesub","verify":"https://unfragile.ai/api/v1/verify?slug=onesub","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"}}