{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-teleprompter","slug":"teleprompter","name":"Teleprompter","type":"agent","url":"https://github.com/danielgross/teleprompter","page_url":"https://unfragile.ai/teleprompter","categories":["chatbots-assistants"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-teleprompter__cap_0","uri":"capability://text.generation.language.real.time.speech.to.text.transcription.with.meeting.context.awareness","name":"real-time speech-to-text transcription with meeting context awareness","description":"Captures and transcribes live audio from meetings using on-device speech recognition, maintaining a rolling context window of the conversation to understand speaker intent and topic flow. The system processes audio streams locally without sending raw audio to external services, enabling low-latency transcription that feeds into suggestion generation pipelines.","intents":["I need to capture what's being said in my meeting in real-time without cloud latency","I want transcription that understands the meeting context to generate relevant suggestions","I need privacy-preserving speech recognition that doesn't leak meeting content to third parties"],"best_for":["remote workers in sensitive meetings (legal, financial, medical)","teams prioritizing data privacy and on-device processing","meeting participants who need low-latency transcription for live suggestions"],"limitations":["On-device speech recognition accuracy varies by language and audio quality; background noise degrades transcription","Context window is limited by device memory; very long meetings may lose early conversation context","Requires sufficient CPU/GPU resources on the host device; may drain battery on mobile devices"],"requires":["Microphone access and OS-level audio permissions","On-device speech recognition engine (e.g., Whisper, native OS APIs)","Minimum 4GB RAM for smooth real-time processing"],"input_types":["audio stream (PCM, WAV, or native OS audio buffer)","meeting metadata (participants, topic if available)"],"output_types":["text transcription","timestamp-aligned segments","speaker identification (if supported by underlying engine)"],"categories":["text-generation-language","audio-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teleprompter__cap_1","uri":"capability://text.generation.language.contextual.quote.suggestion.generation.with.charisma.scoring","name":"contextual quote suggestion generation with charisma scoring","description":"Analyzes the live meeting transcript and speaker intent to generate relevant, contextually appropriate quotes or talking points that enhance communication impact. Uses language model inference to score suggestions by charisma metrics (engagement, relevance, tone-match) and ranks them for presentation to the speaker, operating entirely on-device to minimize latency.","intents":["I want real-time suggestions for what to say next that sounds more compelling","I need quotes or talking points that match the current conversation topic and tone","I want to improve my communication impact without breaking my train of thought"],"best_for":["executives and presenters in high-stakes meetings","sales professionals who need persuasive talking points on-the-fly","non-native speakers seeking communication enhancement in real-time"],"limitations":["Suggestion quality depends on meeting context accuracy; poor transcription leads to irrelevant suggestions","Charisma scoring is heuristic-based and may not align with individual communication style or cultural context","On-device LLM inference adds 500ms-2s latency per suggestion batch; may feel slow for rapid-fire conversations","Limited by the size of on-device model; smaller models trade accuracy for speed"],"requires":["On-device language model (quantized or distilled for local inference)","Real-time transcription output from speech-to-text capability","Minimum 2GB VRAM for LLM inference on GPU, or 8GB RAM for CPU inference"],"input_types":["meeting transcript (text)","speaker intent/topic context","conversation history (rolling window)"],"output_types":["ranked list of quote/suggestion candidates","charisma score per suggestion (0-100)","explanation of why each suggestion is relevant"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teleprompter__cap_2","uri":"capability://memory.knowledge.meeting.context.window.management.with.sliding.buffer","name":"meeting context window management with sliding buffer","description":"Maintains a fixed-size rolling buffer of recent meeting transcript and speaker turns to provide context for suggestion generation without storing entire meeting history. Implements a sliding window strategy that prioritizes recent exchanges while allowing the system to reference earlier key points, enabling efficient memory usage on resource-constrained devices.","intents":["I need the system to remember what was said earlier in the meeting to make relevant suggestions","I want context-aware suggestions that reference earlier discussion points without storing the entire meeting","I need efficient memory usage so the agent doesn't slow down my device during long meetings"],"best_for":["users on laptops or tablets with limited RAM","long-running meetings (1+ hours) where full history storage is impractical","privacy-conscious teams that want to minimize meeting data retention"],"limitations":["Context window size is fixed; very important early discussion points may be evicted from memory","No persistent memory between meetings; context resets when a new meeting starts","Sliding window strategy may miss important context if window size is too small relative to meeting complexity"],"requires":["Configurable buffer size parameter (typically 2-10 KB of text)","Timestamp tracking for each transcript segment","Memory management system to handle buffer eviction"],"input_types":["streaming transcript segments with timestamps","speaker identification"],"output_types":["current context window (text)","metadata about evicted segments (for logging/debugging)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teleprompter__cap_3","uri":"capability://planning.reasoning.speaker.intent.detection.and.topic.tracking","name":"speaker intent detection and topic tracking","description":"Analyzes the meeting transcript in real-time to identify the current speaker's intent (e.g., persuading, explaining, questioning, negotiating) and track the primary topic being discussed. Uses linguistic patterns and conversation flow analysis to classify intent and maintain a topic state machine, enabling suggestions that align with the speaker's communicative goal rather than just the surface content.","intents":["I want suggestions that match what I'm trying to accomplish (persuade, explain, etc.) not just the topic","I need the system to understand if I'm asking a question vs making a statement vs negotiating","I want suggestions that maintain coherence with the meeting's evolving discussion thread"],"best_for":["sales and negotiation professionals who need intent-aligned talking points","presenters who want suggestions that match their rhetorical strategy","teams with complex multi-topic meetings where topic switching is frequent"],"limitations":["Intent detection relies on linguistic patterns; sarcasm, irony, and cultural context may be misclassified","Topic tracking may drift in meetings with rapid context switches or ambiguous discussion threads","Requires sufficient transcript history to establish intent; first few turns in a meeting may have low confidence"],"requires":["Real-time transcript with speaker turns clearly marked","Intent classification model (rule-based or small neural model)","Topic ontology or dynamic topic state tracking"],"input_types":["meeting transcript with speaker labels","conversation history (recent turns)"],"output_types":["detected speaker intent (category: persuade/explain/question/negotiate/etc.)","confidence score for intent classification","current topic label or topic vector","topic coherence score"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teleprompter__cap_4","uri":"capability://automation.workflow.low.latency.suggestion.delivery.with.ui.integration","name":"low-latency suggestion delivery with ui integration","description":"Delivers generated suggestions to the user interface with minimal latency (target <1s from speech end to suggestion display) through optimized inference batching and asynchronous processing. Integrates with native OS notification systems or in-app UI overlays to present suggestions non-intrusively, allowing the speaker to glance at options without breaking focus on the meeting.","intents":["I need suggestions to appear fast enough that they're useful during the meeting, not after","I want suggestions displayed in a way that doesn't distract me from the meeting","I need to quickly accept or dismiss suggestions without interrupting my speech"],"best_for":["real-time meeting participants who need sub-second suggestion latency","users on high-performance laptops or desktops with GPU acceleration","meeting platforms where UI integration is feasible (Zoom, Teams, Google Meet plugins)"],"limitations":["Latency increases significantly on CPU-only devices or with large context windows; may exceed 2-3s on older hardware","UI integration depends on meeting platform support; some platforms may not allow overlays or notifications","Suggestion batching may delay individual suggestions if the system waits for multiple candidates; trade-off between latency and quality"],"requires":["GPU acceleration (NVIDIA CUDA, Apple Metal, or Intel Arc) for <1s latency; CPU inference acceptable for 2-3s latency","Native OS notification API or meeting platform plugin SDK","Asynchronous inference queue with priority scheduling"],"input_types":["ranked suggestion list from suggestion generation capability","user interaction signals (accept/dismiss/ignore)"],"output_types":["UI notification or overlay with suggestion text","user action (accept/dismiss/ignore) with timestamp"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teleprompter__cap_5","uri":"capability://safety.moderation.privacy.preserving.on.device.processing.with.no.cloud.transmission","name":"privacy-preserving on-device processing with no cloud transmission","description":"Ensures all processing (speech recognition, transcription, suggestion generation, context management) occurs entirely on the user's device without transmitting meeting audio, transcript, or context to external servers. Implements local-only inference pipelines using quantized or distilled models that fit within device memory constraints, with optional user-controlled logging for debugging.","intents":["I need to use an AI meeting assistant without my meeting content leaving my device","I want to comply with data privacy regulations (GDPR, HIPAA, etc.) that restrict cloud transmission","I need assurance that my sensitive meeting discussions aren't stored or analyzed by third parties"],"best_for":["enterprises in regulated industries (finance, healthcare, legal)","teams handling confidential or proprietary information","users in jurisdictions with strict data residency requirements"],"limitations":["Model quality is constrained by on-device memory and compute; smaller models trade accuracy for speed","No ability to leverage cloud-scale training or fine-tuning; models are static and pre-trained","Debugging and error analysis are limited without cloud telemetry; troubleshooting requires local logs","No cross-device learning or personalization based on aggregate user data"],"requires":["Quantized or distilled language models that fit in device memory (typically 500MB-2GB)","On-device speech recognition engine (Whisper, native OS APIs, or similar)","Local storage for model weights and optional meeting logs","No internet connectivity required for core functionality (optional for updates)"],"input_types":["audio stream from microphone","optional: meeting metadata (topic, participants) entered locally"],"output_types":["suggestions displayed locally","optional: local log files for debugging (user-controlled)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teleprompter__cap_6","uri":"capability://text.generation.language.multi.language.support.with.language.detection","name":"multi-language support with language detection","description":"Automatically detects the language being spoken in the meeting and adapts speech recognition and suggestion generation to that language. Supports multiple languages through language-specific models or multilingual model variants, enabling the system to work in non-English meetings while maintaining suggestion quality and relevance.","intents":["I need the system to work in my native language, not just English","I want automatic language detection so I don't have to configure language settings","I need suggestions in the same language I'm speaking"],"best_for":["global teams with multilingual meetings","non-English speakers who want to use the tool in their native language","organizations operating in multiple countries with different primary languages"],"limitations":["Language detection may fail or be ambiguous in code-switching scenarios (mixing multiple languages)","Suggestion quality varies significantly by language; languages with smaller training datasets have lower quality","On-device models for multiple languages increase total model size; may require more device storage/memory","Charisma scoring and intent detection may be less accurate in non-English languages due to linguistic differences"],"requires":["Language detection model (lightweight, <50MB)","Language-specific speech recognition models for each supported language","Language-specific or multilingual suggestion generation model","Supported language list (e.g., English, Spanish, French, German, Mandarin, Japanese, etc.)"],"input_types":["audio stream in any supported language","optional: language hint from user"],"output_types":["detected language code (ISO 639-1 or similar)","transcription in detected language","suggestions in detected language"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-teleprompter__cap_7","uri":"capability://memory.knowledge.user.feedback.loop.for.suggestion.refinement","name":"user feedback loop for suggestion refinement","description":"Captures user interactions with suggestions (accept, dismiss, ignore, edit) to build a local feedback signal that can be used to refine suggestion generation over time. Implements a lightweight on-device learning mechanism that adjusts suggestion ranking, intent detection, or topic tracking based on user behavior patterns, without requiring cloud synchronization or external training.","intents":["I want the system to learn my communication style and preferences over time","I want suggestions to improve based on which ones I actually use","I want to personalize the system without sharing my data with the vendor"],"best_for":["frequent meeting participants who want personalized suggestions","users who want to improve system accuracy without cloud data sharing","teams with consistent communication styles that benefit from local adaptation"],"limitations":["On-device learning is limited by device compute; can't implement complex fine-tuning algorithms","Feedback signal is sparse (user may ignore suggestions without explicit feedback); requires heuristics to infer preference","No cross-device or cross-user learning; each device learns independently","Feedback data is lost if the user uninstalls or resets the application"],"requires":["Local storage for feedback logs (user interaction history)","Lightweight adaptation mechanism (e.g., suggestion ranking weights, intent classifier calibration)","User consent for feedback collection and local storage"],"input_types":["user interaction signals (accept/dismiss/ignore/edit)","suggestion metadata (intent, topic, charisma score)","timestamp and context of interaction"],"output_types":["updated suggestion ranking weights","personalization profile (user communication style preferences)","optional: local feedback report for user review"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["Microphone access and OS-level audio permissions","On-device speech recognition engine (e.g., Whisper, native OS APIs)","Minimum 4GB RAM for smooth real-time processing","On-device language model (quantized or distilled for local inference)","Real-time transcription output from speech-to-text capability","Minimum 2GB VRAM for LLM inference on GPU, or 8GB RAM for CPU inference","Configurable buffer size parameter (typically 2-10 KB of text)","Timestamp tracking for each transcript segment","Memory management system to handle buffer eviction","Real-time transcript with speaker turns clearly marked"],"failure_modes":["On-device speech recognition accuracy varies by language and audio quality; background noise degrades transcription","Context window is limited by device memory; very long meetings may lose early conversation context","Requires sufficient CPU/GPU resources on the host device; may drain battery on mobile devices","Suggestion quality depends on meeting context accuracy; poor transcription leads to irrelevant suggestions","Charisma scoring is heuristic-based and may not align with individual communication style or cultural context","On-device LLM inference adds 500ms-2s latency per suggestion batch; may feel slow for rapid-fire conversations","Limited by the size of on-device model; smaller models trade accuracy for speed","Context window size is fixed; very important early discussion points may be evicted from memory","No persistent memory between meetings; context resets when a new meeting starts","Sliding window strategy may miss important context if window size is too small relative to meeting complexity","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.050Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=teleprompter","compare_url":"https://unfragile.ai/compare?artifact=teleprompter"}},"signature":"LGEkT00pfqApqG/MTdMTic7UFzgHIzjX1t8go21mYSGmH9WgPPAR1YwwosNAh3wEmEKqLz0meMDkjRAuZtdOCA==","signedAt":"2026-06-22T01:10:39.680Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/teleprompter","artifact":"https://unfragile.ai/teleprompter","verify":"https://unfragile.ai/api/v1/verify?slug=teleprompter","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"}}