{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pieces-for-developers","slug":"pieces-for-developers","name":"Pieces for Developers","type":"product","url":"https://pieces.app","page_url":"https://unfragile.ai/pieces-for-developers","categories":["code-editors"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pieces-for-developers__cap_0","uri":"capability://memory.knowledge.os.level.passive.context.capture.with.automatic.enrichment","name":"os-level passive context capture with automatic enrichment","description":"Runs a background daemon (PiecesOS) that monitors OS-level events across all applications in real-time, capturing code snippets, browser tabs, chat messages, documents, and highlights without user intervention. The Workstream Pattern Engine ingests millions of micro-events and routes them through on-device classification models (TF-IDF, SVMs, LSTMs, RNNs) to automatically detect code, extract metadata, flag sensitive data (PII/credentials), and associate context (source app, timestamp, related files/tabs). Captured data is stored locally in a proprietary database with optional cloud sync via Pieces Drive.","intents":["I want to save code snippets without manually bookmarking or copying them","I need to automatically capture context around my work (which files, tabs, chats were open when I wrote this)","I want sensitive data like API keys automatically flagged when I save code","I need my entire development workflow preserved without interrupting my focus"],"best_for":["Individual developers who context-switch frequently across IDEs, browsers, and chat tools","Teams building on shared codebases who need activity history","Developers doing research or debugging who capture code from multiple sources"],"limitations":["Hard retention limit of 9 months — older memories are automatically deleted or archived (mechanism unclear)","Sensitive data is flagged but not automatically redacted; user must manually delete flagged items","No real-time collaboration on captured context — team sharing mechanism is unclear (likely eventual consistency, not real-time)","Performance degradation likely at scale (9 months of continuous capture = millions of events; search latency unknown)","Disk space requirements for full activity capture not documented","On-device models (TF-IDF, SVM, LSTM) are lightweight but less capable than modern transformers for semantic understanding"],"requires":["Windows, Linux, or macOS desktop OS","Desktop app installation (no web-only option)","Sufficient disk space for 9 months of activity (size unknown)","Optional: API key for cloud sync (Pieces Drive)"],"input_types":["code text (direct paste, IDE capture, OCR from images)","browser tabs and URLs","chat messages","documents","highlights and keywords","OS-level screen activity"],"output_types":["structured code snippets with metadata (language, source app, timestamp, associated context)","flagged sensitive data alerts","indexed vector embeddings for search"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_1","uri":"capability://search.retrieval.natural.language.search.across.9.month.memory.with.time.based.filtering","name":"natural language search across 9-month memory with time-based filtering","description":"Indexes all captured snippets, documents, and activity with vector embeddings, enabling semantic search via natural language queries. Users can search across 9 months of personal context and filter by time-based queries (e.g., 'code I wrote last Tuesday', 'snippets from the past week'). The search engine ranks results by relevance and associates results with the 'bigger picture' — implied relationship graph linking snippets to related chats, tabs, and documents. Queries are processed locally by default; optional cloud search available via Pieces Drive.","intents":["I need to find a code snippet I saved weeks ago but don't remember the exact name or syntax","I want to search my personal context, not just what a generic LLM knows","I need to find all code related to a specific feature or bug from a particular time period","I want to understand the context around a snippet (which files, chats, tabs were related)"],"best_for":["Developers with large codebases or long project histories who need to recall past work","Teams collaborating on shared memory who need to search team context","Developers doing research or debugging who need to correlate code with activity history"],"limitations":["Search latency for 9 months of continuous capture is unknown; likely increases with memory size","Maximum context window for search results when injected into LLM is unknown — may truncate results","Time-based queries are supported but granularity (day, hour, minute) is not specified","Relationship graph linking snippets to related context is implied but mechanism is undocumented","No full-text search mentioned; appears to be semantic/vector-based only"],"requires":["At least one saved snippet or captured context","Desktop app with local database populated","Optional: Pieces Drive enabled for cloud search"],"input_types":["natural language query (e.g., 'Python function for parsing JSON')","time-based filter (e.g., 'last week', 'Tuesday')","keyword or phrase"],"output_types":["ranked list of code snippets with metadata","associated context (related chats, tabs, documents)","relevance scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_10","uri":"capability://memory.knowledge.local.first.data.storage.with.optional.cloud.sync","name":"local-first data storage with optional cloud sync","description":"All captured context and snippets are stored in a local, proprietary database on the user's machine by default. Cloud sync via Pieces Drive is optional and user-controlled — users can enable/disable sync at any time. No data is transmitted to cloud unless explicitly enabled. Local storage uses vector embeddings for semantic search and supports 9 months of retention with automatic deletion of older data.","intents":["I want my code and context to stay on my machine, not in the cloud","I need to work offline without losing access to my saved snippets","I want to control when and if my data is synced to the cloud"],"best_for":["Individual developers with privacy concerns","Teams with data residency requirements","Developers in regulated industries (finance, healthcare, government)","Developers working offline or with unreliable internet"],"limitations":["Local storage is proprietary format — no standard export to JSON, CSV, or other formats documented","Vendor lock-in: switching to competitor requires manual re-capture or data loss","9-month retention limit — older data is automatically deleted (no option to extend)","Disk space requirements for 9 months of continuous capture not documented","No encryption of local database documented — data may be readable if device is compromised","No backup mechanism documented — local database loss means data loss","Cloud sync (Pieces Drive) mechanism is undocumented — unclear if incremental, full, or real-time"],"requires":["Desktop app installed","Sufficient local disk space (size unknown)","Optional: Pieces Drive account for cloud sync"],"input_types":["code snippets, documents, activity (captured automatically)"],"output_types":["local database (proprietary format)","vector embeddings (for search)","optional: cloud sync to Pieces Drive"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_11","uri":"capability://data.processing.analysis.hardware.accelerated.on.device.ml.inference.for.real.time.classification","name":"hardware-accelerated on-device ml inference for real-time classification","description":"Uses hardware acceleration (GPU, NPU, or CPU optimization — specific method undocumented) to run on-device ML models (TF-IDF, SVM, LSTM, RNN) in real-time as context is captured. Models classify code, detect language, associate context, and flag sensitive data without cloud transmission. Hardware acceleration enables low-latency inference on millions of micro-events per day.","intents":["I want real-time classification of captured code without cloud latency","I need sensitive data detection to run instantly as I save code","I want context association to happen automatically without user input"],"best_for":["Developers with high-volume code capture (100+ snippets/day)","Teams needing real-time classification without cloud dependency","Developers in air-gapped or offline environments"],"limitations":["Hardware acceleration method is undocumented — unclear if GPU, NPU, or CPU optimization","Model accuracy is undocumented — on-device models (TF-IDF, SVM, LSTM) are lightweight but less capable than modern transformers","False-negative rate for sensitive data detection unknown","Performance on older hardware (no GPU/NPU) unknown — may fall back to CPU with latency","No option to use larger, more accurate models (e.g., transformers) due to hardware constraints"],"requires":["Desktop app installed","Optional: GPU or NPU for hardware acceleration (CPU fallback available)"],"input_types":["code snippets (captured automatically)"],"output_types":["classification results (language, type, sensitivity)","context associations","sensitive data flags"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_12","uri":"capability://data.processing.analysis.automatic.language.detection.and.code.metadata.extraction","name":"automatic language detection and code metadata extraction","description":"On-device models automatically detect programming language, framework, and code type (function, class, snippet, etc.) from captured code. Extracted metadata is stored with the snippet and used for search, filtering, and context association. Detection runs in real-time without user input or cloud transmission.","intents":["I want my code automatically tagged with language and type without manual labeling","I need to filter saved snippets by language or code type","I want search to understand code semantics (e.g., 'Python functions' vs 'JavaScript classes')"],"best_for":["Developers working across multiple languages","Teams with diverse codebases","Developers who want automatic organization without manual tagging"],"limitations":["Language detection accuracy is undocumented — likely high for common languages, lower for obscure or mixed-language code","Code type detection (function, class, etc.) is undocumented — may be limited to basic patterns","No custom language or type definitions — detection is fixed to built-in models","Metadata extraction is basic (language, type) — no semantic analysis (complexity, dependencies, etc.)"],"requires":["Desktop app with context capture enabled"],"input_types":["code snippets (captured automatically)"],"output_types":["language tag (e.g., 'Python', 'JavaScript')","code type tag (e.g., 'function', 'class')","framework tag (if detected, e.g., 'React', 'Django')"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_13","uri":"capability://memory.knowledge.pieces.drive.cloud.sync.with.optional.team.collaboration","name":"pieces drive cloud sync with optional team collaboration","description":"Optional cloud sync service (Pieces Drive) that synchronizes local memory to cloud storage for backup, multi-device access, and team collaboration. Users can enable/disable sync at any time. Sync mechanism (incremental, full, real-time) is undocumented. Team collaboration via Pieces Drive enables shared memory across team members with role-based access control.","intents":["I want to access my saved snippets on multiple devices","I need a backup of my local memory in case my device fails","I want my team to have access to shared context and snippets"],"best_for":["Developers using multiple devices (laptop, desktop, etc.)","Teams needing shared memory and collaboration","Developers wanting cloud backup of local memory"],"limitations":["Sync mechanism is undocumented — unclear if incremental, full, real-time, or on-demand","Sync latency unknown — may introduce delays between local and cloud state","Conflict resolution for simultaneous edits is undocumented","No version history or rollback capability documented","Cloud storage limits unknown — unclear if unlimited or tiered by plan","Encryption in transit and at rest not documented","No option to sync only specific snippets or categories — all-or-nothing sync"],"requires":["Pieces Drive account (free or paid tier)","Internet connection for sync","Desktop app with Pieces Drive enabled"],"input_types":["local memory (code snippets, documents, activity)"],"output_types":["cloud-synced memory (accessible from other devices or team members)","backup of local memory"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_2","uri":"capability://tool.use.integration.context.aware.copilot.with.multi.llm.backend.selection","name":"context-aware copilot with multi-llm backend selection","description":"Provides an AI copilot that accepts user queries and automatically injects personal context (saved snippets, activity history, related documents) before routing to a user-selected LLM backend. Supports Claude (4 Sonnet, Opus), Gemini 2.5, OpenAI models, and Ollama (local). The copilot 'knows what you know, not just what the LLM knows' — meaning it personalizes responses based on your saved code, patterns, and project context. Integrates via MCP (Model Context Protocol) server built into PiecesOS, enabling direct injection into Claude, GitHub Copilot, Cursor, and Goose.","intents":["I want AI assistance that understands my codebase and past work, not generic LLM knowledge","I need to ask questions about my own code and get contextual answers","I want to use my preferred LLM (Claude, OpenAI, Gemini) with my personal context injected","I need to integrate AI assistance into my existing IDE or copilot workflow"],"best_for":["Individual developers using Claude, OpenAI, or Gemini who want personalized AI assistance","Teams using GitHub Copilot, Cursor, or Goose who want to inject shared team context","Developers who want to switch LLM providers without losing context integration"],"limitations":["Context window management when personal memory exceeds LLM token limits is undocumented — likely truncates or samples context","Hallucination rate inherits from selected LLM; Pieces does not reduce LLM hallucinations","MCP integration limited to Claude, GitHub Copilot, Cursor, Goose — other IDEs/tools not supported","No code execution capability — copilot generates code but cannot run or test it","Accuracy of context injection (which snippets are actually relevant to the query) depends on on-device classification models, which are lightweight (TF-IDF, SVM, LSTM)"],"requires":["API key for selected LLM (Claude, OpenAI, Gemini) OR local Ollama instance","Desktop app with populated context memory","For MCP integration: Claude, GitHub Copilot, Cursor, or Goose installed","Pro plan for Claude 4 Sonnet/Opus and Gemini 2.5 (free tier LLM options unknown)"],"input_types":["natural language query","code snippet or file reference","question about saved context"],"output_types":["LLM-generated response with injected personal context","code suggestions or explanations","references to related saved snippets"],"categories":["tool-use-integration","memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_3","uri":"capability://code.generation.editing.code.snippet.transformation.and.language.conversion","name":"code snippet transformation and language conversion","description":"Accepts saved code snippets and applies transformations: change programming language, improve readability, optimize performance, or refactor for specific patterns. Transformations are executed by the selected LLM with personal context injected, enabling suggestions that align with your coding style and project patterns. Output can be previewed, edited, and re-saved to memory.","intents":["I have a Python function but need it in JavaScript for a web project","I want to refactor old code to follow modern best practices","I need to optimize a snippet for performance or readability","I want to convert code between frameworks or libraries"],"best_for":["Developers working across multiple languages or frameworks","Teams standardizing code style or patterns","Developers refactoring legacy code"],"limitations":["Transformation quality depends on selected LLM; Pieces does not guarantee correctness","No code execution or testing — transformed code is not validated","Context injection may not capture all relevant patterns if memory is large (9 months of capture)","Language support limited to languages the selected LLM understands (typically 50+ languages, but not exhaustive)"],"requires":["Saved code snippet in Pieces memory","API key for selected LLM","Pro plan for premium LLMs (Claude 4 Sonnet/Opus, Gemini 2.5)"],"input_types":["code snippet (text)","transformation request (natural language, e.g., 'convert to Python', 'optimize for performance')"],"output_types":["transformed code snippet","explanation of changes","option to save to memory"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_4","uri":"capability://tool.use.integration.shareable.snippet.links.and.github.gist.export","name":"shareable snippet links and github gist export","description":"Generates custom shareable links for individual code snippets, enabling developers to share saved code with teammates or in documentation without exposing the full Pieces memory. Supports export to GitHub Gists for version control integration and public sharing. Links are generated on-demand and can be revoked. Export to Gist includes snippet metadata (language, description, tags).","intents":["I want to share a code snippet with a teammate without giving them access to my entire memory","I need to export code to GitHub Gist for version control and public documentation","I want to create a shareable link for a code example in a pull request or documentation"],"best_for":["Individual developers sharing code with teammates","Teams documenting code examples","Developers publishing code snippets publicly"],"limitations":["Export limited to GitHub Gists only — no bulk export to JSON, CSV, or other formats documented","Shareable links are Pieces-specific URLs — no standard format or portability","Gist export is one-way (Pieces → GitHub); no sync back to Pieces if Gist is updated","No access control on shareable links — links are either public or private, no granular permissions","Metadata exported to Gist is limited (language, description, tags); full context (related chats, tabs) not included"],"requires":["Saved code snippet in Pieces memory","For Gist export: GitHub account and authentication"],"input_types":["code snippet (from Pieces memory)"],"output_types":["shareable URL (Pieces-hosted)","GitHub Gist link","snippet metadata (language, description, tags)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_5","uri":"capability://memory.knowledge.workflow.history.and.activity.summaries","name":"workflow history and activity summaries","description":"Automatically organizes captured activity into workflow summaries — high-level overviews of work done during a session or time period. Summaries are generated from the captured context (code, chats, tabs, documents) and indexed for smart search. Users can review what they worked on, when, and with what context. Summaries are stored in memory and linked to related snippets.","intents":["I want to see a summary of what I worked on today or this week","I need to understand the context and timeline of a project or feature","I want to search across my activity history to find when I worked on something specific"],"best_for":["Developers tracking their own productivity and work patterns","Teams reviewing activity history for project context","Developers writing status reports or retrospectives"],"limitations":["Summary generation mechanism is undocumented — unclear if automated or LLM-generated","Accuracy of summaries depends on quality of captured context (which may be incomplete or noisy)","Summaries are read-only — no editing or manual annotation","Granularity of summaries (hourly, daily, weekly) is not specified","No export of summaries to external formats (PDF, Markdown, etc.) documented"],"requires":["Active context capture enabled","At least one work session with captured activity"],"input_types":["captured activity (code, chats, tabs, documents)"],"output_types":["workflow summary (text)","linked snippets and context","timestamp and duration"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_6","uri":"capability://safety.moderation.sensitive.data.detection.and.flagging","name":"sensitive data detection and flagging","description":"On-device classification models (TF-IDF, SVM, LSTM) automatically detect sensitive data patterns (API keys, credentials, PII, passwords) in captured code and flag them for user review. Flagged items are marked in the UI but not automatically redacted or encrypted. Users must manually review and delete flagged items. Detection runs in real-time as code is captured, without cloud transmission.","intents":["I want to be alerted if I accidentally save code with API keys or credentials","I need to prevent sensitive data from being shared or synced to the cloud","I want to audit my saved code for security risks"],"best_for":["Individual developers working with credentials and API keys","Teams with security policies requiring sensitive data detection","Developers in regulated industries (finance, healthcare) needing compliance"],"limitations":["Flagging is passive — sensitive data is marked but not automatically deleted or redacted","User must manually review and delete flagged items; no bulk deletion or automatic remediation","Detection accuracy depends on on-device models (TF-IDF, SVM, LSTM) which are lightweight; false negatives likely","No encryption of flagged data — flagged items remain in plaintext in local database","No audit log of flagged items or deletion history","Detection patterns are not customizable — users cannot add custom sensitive data patterns"],"requires":["Desktop app with context capture enabled","No additional configuration required"],"input_types":["code snippets (captured automatically)"],"output_types":["flagged item alert (UI notification)","list of flagged snippets","suggested action (delete, redact, etc.)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_7","uri":"capability://memory.knowledge.team.shared.memory.with.role.based.access","name":"team shared memory with role-based access","description":"Enables teams to share a collective memory of code snippets, documents, and activity across team members. Team members can search and access shared context, with role-based access control (mechanism unclear — likely admin/member roles). Shared memory is synced across team members via Pieces Drive (cloud sync). Teams plan includes priority support and custom LLM integration (bring-your-own or OpenAI/Anthropic/Ollama).","intents":["I want my team to have access to shared code snippets and context","I need to search across my team's collective knowledge, not just my own","I want to onboard new team members with access to team context and patterns"],"best_for":["Small to medium teams (2-50 developers) sharing codebases","Teams with shared patterns or libraries","Teams needing collective knowledge management"],"limitations":["Shared memory mechanism is undocumented — unclear if real-time sync or eventual consistency","Role-based access control is mentioned but not detailed — unclear what roles exist or what permissions they grant","No conflict resolution for simultaneous edits or deletions","Pricing is custom quote — no transparent per-seat or flat-rate pricing","Minimum team size unknown","No mention of audit logs or activity tracking for team members","Shared memory retention (9 months) applies to all team members — no per-member history"],"requires":["Teams plan (custom pricing)","Pieces Drive enabled for cloud sync","Team members with Pieces accounts","Admin setup and role assignment (mechanism unclear)"],"input_types":["code snippets (captured by any team member)","documents, chats, tabs (captured by any team member)"],"output_types":["shared memory accessible to team members","search results across team context","activity history (team member attribution unclear)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_8","uri":"capability://tool.use.integration.ide.and.browser.plugin.integration","name":"ide and browser plugin integration","description":"Provides plugins for VS Code and Chrome (and other IDEs/browsers, specific list unclear) enabling one-click saving of code snippets directly from the editor or browser. Plugins integrate with the desktop app via local IPC, capturing code, file context, and browser tab context automatically. Plugins also enable quick access to saved snippets and copilot assistance from within the IDE/browser.","intents":["I want to save code from my IDE without switching to another app","I need to access my saved snippets from within VS Code or Chrome","I want to use the copilot from my IDE with my personal context injected"],"best_for":["VS Code users (primary IDE support)","Chrome users (primary browser support)","Developers using other IDEs with plugin support (list unclear)"],"limitations":["Plugin support list is incomplete — only VS Code and Chrome explicitly mentioned; support for other IDEs/browsers unclear","Plugin functionality is limited to save, search, and copilot — no advanced features (transformation, export) from plugin UI","Plugins require desktop app running — no standalone plugin functionality","No plugin for other popular IDEs (JetBrains, Vim, Emacs) documented","No plugin for other browsers (Firefox, Safari, Edge) documented"],"requires":["VS Code 1.80+ (version requirement inferred, not stated)","Chrome (version requirement unknown)","Desktop app installed and running","Plugin installed from VS Code Marketplace or Chrome Web Store"],"input_types":["code selection in IDE","browser tab or URL","natural language query (from plugin UI)"],"output_types":["saved snippet (to Pieces memory)","search results (from plugin UI)","copilot response (in plugin panel)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__cap_9","uri":"capability://tool.use.integration.mcp.model.context.protocol.server.for.copilot.integration","name":"mcp (model context protocol) server for copilot integration","description":"Pieces includes a built-in MCP server that exposes personal context (saved snippets, activity history) to MCP-compatible tools: Claude, GitHub Copilot, Cursor, and Goose. The MCP server runs on the local machine and injects context into copilot queries without cloud transmission. Enables seamless integration of personal context into existing copilot workflows without switching tools.","intents":["I want to use Claude with my personal context injected","I want GitHub Copilot to understand my codebase and saved snippets","I want to use Cursor or Goose with my personal context"],"best_for":["Developers using Claude, GitHub Copilot, Cursor, or Goose","Teams using MCP-compatible tools","Developers who want to avoid vendor lock-in to a single copilot"],"limitations":["MCP integration limited to Claude, GitHub Copilot, Cursor, Goose — other tools not supported","MCP server runs locally — requires desktop app running for copilot to access context","Context injection mechanism is undocumented — unclear how context is selected and prioritized","No conflict resolution if MCP server is unavailable (e.g., desktop app crashes)","MCP protocol version and compatibility with future tool versions unknown"],"requires":["Desktop app running with MCP server enabled","Claude, GitHub Copilot, Cursor, or Goose installed and configured","MCP configuration in copilot tool (mechanism varies by tool)"],"input_types":["copilot query (from Claude, Copilot, Cursor, or Goose)"],"output_types":["copilot response with injected personal context","references to related snippets"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pieces-for-developers__headline","uri":"capability://code.generation.editing.ai.powered.developer.productivity.tool","name":"ai-powered developer productivity tool","description":"An AI-driven tool designed to enhance developer productivity by automatically capturing, enriching, and reusing code snippets, integrating seamlessly with IDEs and browsers.","intents":["best AI code productivity tool","AI tool for managing code snippets","developer productivity tool for IDEs","how to reuse code snippets effectively","top tools for developer productivity"],"best_for":["software developers","full-stack developers","DevOps engineers"],"limitations":["may not provide advanced reasoning capabilities","performance may vary based on hardware"],"requires":["integration with IDEs or browsers"],"input_types":["code snippets","documents","chat messages"],"output_types":["context-aware insights","enriched code snippets","search results"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Windows, Linux, or macOS desktop OS","Desktop app installation (no web-only option)","Sufficient disk space for 9 months of activity (size unknown)","Optional: API key for cloud sync (Pieces Drive)","At least one saved snippet or captured context","Desktop app with local database populated","Optional: Pieces Drive enabled for cloud search","Desktop app installed","Sufficient local disk space (size unknown)","Optional: Pieces Drive account for cloud sync"],"failure_modes":["Hard retention limit of 9 months — older memories are automatically deleted or archived (mechanism unclear)","Sensitive data is flagged but not automatically redacted; user must manually delete flagged items","No real-time collaboration on captured context — team sharing mechanism is unclear (likely eventual consistency, not real-time)","Performance degradation likely at scale (9 months of continuous capture = millions of events; search latency unknown)","Disk space requirements for full activity capture not documented","On-device models (TF-IDF, SVM, LSTM) are lightweight but less capable than modern transformers for semantic understanding","Search latency for 9 months of continuous capture is unknown; likely increases with memory size","Maximum context window for search results when injected into LLM is unknown — may truncate results","Time-based queries are supported but granularity (day, hour, minute) is not specified","Relationship graph linking snippets to related context is implied but mechanism is undocumented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":null,"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=pieces-for-developers","compare_url":"https://unfragile.ai/compare?artifact=pieces-for-developers"}},"signature":"gqi4tCv+V8QN4i6juqXvV/dN1iR4uzFzqsx89hUrOi50hCqgOEEc7or/hRlmdFgclpdrP/F203p8d424e+E6AA==","signedAt":"2026-06-21T06:17:58.725Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pieces-for-developers","artifact":"https://unfragile.ai/pieces-for-developers","verify":"https://unfragile.ai/api/v1/verify?slug=pieces-for-developers","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"}}