{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-turbopilot","slug":"turbopilot","name":"TurboPilot","type":"repo","url":"https://github.com/ravenscroftj/turbopilot","page_url":"https://unfragile.ai/turbopilot","categories":["code-editors"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-turbopilot__cap_0","uri":"capability://code.generation.editing.local.codebase.aware.code.completion","name":"local-codebase-aware code completion","description":"Generates code completions using the Salesforce Codegen 6B model running locally via llama.cpp's quantized inference engine. The model processes the current file context and cursor position to predict the next tokens, with completions streamed back to the editor without sending code to external servers. Uses memory-mapped model weights and CPU/GPU acceleration to maintain sub-second latency on commodity hardware.","intents":["Get real-time code suggestions while typing without exposing code to cloud services","Run a copilot-like experience on a laptop with only 4GB of available RAM","Maintain code privacy by keeping all model inference local to the development machine","Reduce latency for code completion by eliminating network round-trips to remote APIs"],"best_for":["Solo developers and small teams with privacy-critical codebases","Developers in environments with restricted internet or air-gapped networks","Engineers building on resource-constrained machines or embedded systems","Organizations with compliance requirements prohibiting cloud code transmission"],"limitations":["Model quality is lower than GPT-3.5 or Claude — Codegen 6B has ~70% accuracy on code tasks vs 85%+ for larger models","Completion quality degrades significantly for languages outside the training distribution (Go, Rust, Kotlin have lower accuracy than Python/JavaScript)","No fine-tuning or adaptation to project-specific patterns — uses base Codegen weights only","Inference speed varies dramatically by hardware — 4GB RAM constraint means CPU-only or minimal GPU acceleration on most machines","Context window is limited to ~2048 tokens, so multi-file context awareness is minimal"],"requires":["4GB RAM minimum (8GB+ recommended for comfortable use)","Python 3.7+ for the TurboPilot server","Editor integration via LSP or direct API calls (requires custom plugin for most editors)","llama.cpp compiled for your CPU architecture (x86_64, ARM64, etc.)","Quantized Codegen model weights (~2.5GB disk space for 6B parameter Q4 quantization)"],"input_types":["source code (any programming language)","cursor position and line context","file path and project metadata (optional)"],"output_types":["text tokens (code completion suggestions)","streaming token stream via HTTP or LSP protocol"],"categories":["code-generation-editing","local-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-turbopilot__cap_1","uri":"capability://tool.use.integration.lsp.based.editor.integration","name":"lsp-based editor integration","description":"Exposes code completion capabilities via the Language Server Protocol (LSP), allowing TurboPilot to integrate with any LSP-compatible editor (VS Code, Vim, Neovim, Emacs, JetBrains IDEs). The server listens on a local socket or TCP port, receives textDocument/completion requests from the editor, and returns completion items with insertion text and metadata. Handles incremental document synchronization to maintain accurate context for the model.","intents":["Integrate TurboPilot into my existing editor without writing custom plugins","Use the same code completion experience across multiple editors","Maintain document state synchronization between editor and inference server","Receive completions with metadata (documentation, type hints) formatted for editor display"],"best_for":["Developers using VS Code, Vim, Neovim, or other LSP-compatible editors","Teams standardizing on LSP for tool interoperability","Users who want plug-and-play integration without custom development"],"limitations":["LSP protocol overhead adds ~50-100ms per request compared to direct API calls","No support for non-LSP editors (Sublime Text, older IDE versions) without custom adapters","Document synchronization can lag if editor sends rapid edits — may cause stale context for completions","LSP textDocument/completion request/response cycle is synchronous, blocking editor UI if inference takes >1 second"],"requires":["LSP client support in your editor (VS Code 1.40+, Neovim 0.5+, etc.)","TurboPilot server running and accessible on localhost or network address","LSP client configuration file (launch.json for VS Code, init.lua for Neovim, etc.)"],"input_types":["LSP textDocument/completion requests with document URI, position, and context"],"output_types":["LSP CompletionItem objects with label, insertText, documentation, and sortText"],"categories":["tool-use-integration","editor-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-turbopilot__cap_2","uri":"capability://code.generation.editing.quantized.model.weight.loading.and.inference","name":"quantized model weight loading and inference","description":"Loads pre-quantized Codegen model weights (typically Q4 or Q5 quantization) using llama.cpp's mmap-based weight loader, which memory-maps the model file to avoid loading the entire model into RAM at once. Inference runs on CPU with optional SIMD acceleration (AVX2, NEON) and can offload layers to GPU if available. Token generation uses sampling strategies (temperature, top-p) to balance quality and diversity.","intents":["Run a 6B parameter model on machines with only 4GB of RAM","Achieve reasonable inference speed (50-200 tokens/second) without dedicated GPU hardware","Load model weights efficiently without duplicating them in memory","Support both CPU and GPU inference paths depending on available hardware"],"best_for":["Developers on laptops, desktops, or servers without high-end GPUs","Organizations wanting to avoid GPU infrastructure costs","Embedded systems or edge devices with limited memory"],"limitations":["Quantization reduces model accuracy by 5-15% compared to full-precision weights — larger impact on complex reasoning tasks","CPU inference is 10-50x slower than GPU inference — a 4-token completion takes 0.5-2 seconds on CPU vs 50-100ms on GPU","Memory-mapped loading adds ~100-200ms startup latency as the OS pages in model weights","SIMD acceleration (AVX2, NEON) requires specific CPU features — older CPUs fall back to scalar operations with 2-3x slower inference","No support for dynamic quantization or mixed-precision inference — model quantization is static at load time"],"requires":["llama.cpp compiled and available in PATH or as a library","Pre-quantized Codegen model weights in GGML format (~2.5-4GB depending on quantization level)","CPU with AVX2 or ARM NEON support for reasonable performance (older CPUs will be very slow)","Optional: CUDA 11.0+ or Metal support for GPU acceleration"],"input_types":["quantized model weights file (GGML format)","prompt text (code context + prefix to complete)"],"output_types":["token stream (raw token IDs and decoded text)","completion text with sampling metadata (temperature, top-p applied)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-turbopilot__cap_3","uri":"capability://code.generation.editing.streaming.token.generation.with.configurable.sampling","name":"streaming token generation with configurable sampling","description":"Generates code completions token-by-token using configurable sampling strategies (temperature, top-p, top-k) to control output diversity and quality. Tokens are streamed back to the client (editor or API consumer) as they are generated, enabling real-time display of suggestions. Supports early stopping based on token limits or end-of-sequence markers.","intents":["Display code completions in real-time as they are generated, not waiting for the full completion","Control the creativity vs determinism of completions via temperature and top-p parameters","Limit completion length to avoid generating overly long suggestions","Stop generation when a natural code boundary is reached (e.g., end of function)"],"best_for":["Interactive editor integrations where real-time feedback improves UX","Applications where completion quality varies by task (lower temperature for deterministic code, higher for creative suggestions)","Scenarios where completion length is unpredictable and needs runtime control"],"limitations":["Streaming adds complexity to error handling — if inference fails mid-stream, partial completions may be displayed","No beam search or re-ranking — once a token is generated and streamed, it cannot be revised","Sampling strategies are applied at generation time, not post-hoc — cannot adjust temperature after generation","Early stopping heuristics (e.g., detecting end-of-function) are language-specific and imperfect — may cut off valid code or continue past natural boundaries"],"requires":["Streaming HTTP or WebSocket connection from client to server","Support for Server-Sent Events (SSE) or chunked transfer encoding","Client-side token buffering to handle variable-rate token arrival"],"input_types":["prompt text (code context)","sampling parameters (temperature: 0.0-2.0, top_p: 0.0-1.0, top_k: 0-100)","max_tokens limit (integer)"],"output_types":["streaming token stream (text chunks via HTTP streaming)","completion metadata (finish_reason: length, stop_sequence, etc.)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-turbopilot__cap_4","uri":"capability://code.generation.editing.multi.language.code.context.parsing","name":"multi-language code context parsing","description":"Extracts relevant code context from the current file and optionally nearby files to construct a prompt for the model. Uses language-specific parsing (regex or simple AST analysis) to identify the current function, class, or scope, and includes preceding lines of code to provide semantic context. Handles indentation and formatting to match the project's code style.","intents":["Provide the model with enough context to generate contextually appropriate completions","Automatically extract the current function or class definition for scope awareness","Include imports, type hints, and other metadata that influence code generation","Preserve code formatting and indentation in the prompt"],"best_for":["Developers working in dynamically-typed languages (Python, JavaScript) where context is critical","Projects with consistent code style and structure","Scenarios where multi-file context is not necessary (single-file completions)"],"limitations":["Language-specific parsing is limited — regex-based extraction works for simple cases but fails on nested scopes, complex syntax, or edge cases","No multi-file context awareness — cannot pull in imports or type definitions from other files","Indentation handling is simplistic — may not preserve complex formatting or mixed tabs/spaces","No semantic understanding of code — cannot distinguish between variable declarations, function calls, and other constructs","Context window is limited to ~2048 tokens, so large files are truncated, losing important context"],"requires":["File path and content from the editor","Language detection (inferred from file extension or explicit parameter)","Optional: Language-specific configuration (indentation style, comment syntax)"],"input_types":["source code (full file or snippet)","cursor position (line and column)","language identifier (python, javascript, etc.)"],"output_types":["extracted context string (code snippet with surrounding context)","prompt text ready for model inference"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-turbopilot__cap_5","uri":"capability://tool.use.integration.http.api.server.for.remote.inference","name":"http api server for remote inference","description":"Exposes the inference engine via a simple HTTP API, allowing remote clients (editors, IDEs, custom applications) to request completions over the network. Implements endpoints for completion requests (POST /complete) and model status (GET /status). Handles request parsing, model inference, and response serialization. Supports both synchronous and streaming responses.","intents":["Run TurboPilot on a central server and share it across multiple developer machines","Integrate TurboPilot into custom applications or build tools via HTTP","Monitor model status and inference metrics via a simple API","Support both blocking and streaming completion requests depending on client needs"],"best_for":["Teams wanting to share a single TurboPilot instance across multiple developers","Custom tool builders integrating code completion into non-standard environments","Scenarios where the inference server runs on a different machine than the editor"],"limitations":["Network latency adds 10-50ms per request compared to local inference","No authentication or authorization — API is open to any client on the network (requires firewall or reverse proxy for security)","Single-threaded or limited concurrency — multiple simultaneous requests may queue, increasing latency","No request queuing or priority handling — all requests are processed FIFO","Streaming responses require persistent HTTP connections — may not work through all proxies or firewalls"],"requires":["HTTP server library (Flask, FastAPI, or similar in Python)","Network connectivity between client and server","Server running on a machine with sufficient resources to handle inference"],"input_types":["JSON request body with prompt, sampling parameters, and max_tokens"],"output_types":["JSON response with completion text and metadata","streaming response with chunked transfer encoding (for streaming endpoint)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["4GB RAM minimum (8GB+ recommended for comfortable use)","Python 3.7+ for the TurboPilot server","Editor integration via LSP or direct API calls (requires custom plugin for most editors)","llama.cpp compiled for your CPU architecture (x86_64, ARM64, etc.)","Quantized Codegen model weights (~2.5GB disk space for 6B parameter Q4 quantization)","LSP client support in your editor (VS Code 1.40+, Neovim 0.5+, etc.)","TurboPilot server running and accessible on localhost or network address","LSP client configuration file (launch.json for VS Code, init.lua for Neovim, etc.)","llama.cpp compiled and available in PATH or as a library","Pre-quantized Codegen model weights in GGML format (~2.5-4GB depending on quantization level)"],"failure_modes":["Model quality is lower than GPT-3.5 or Claude — Codegen 6B has ~70% accuracy on code tasks vs 85%+ for larger models","Completion quality degrades significantly for languages outside the training distribution (Go, Rust, Kotlin have lower accuracy than Python/JavaScript)","No fine-tuning or adaptation to project-specific patterns — uses base Codegen weights only","Inference speed varies dramatically by hardware — 4GB RAM constraint means CPU-only or minimal GPU acceleration on most machines","Context window is limited to ~2048 tokens, so multi-file context awareness is minimal","LSP protocol overhead adds ~50-100ms per request compared to direct API calls","No support for non-LSP editors (Sublime Text, older IDE versions) without custom adapters","Document synchronization can lag if editor sends rapid edits — may cause stale context for completions","LSP textDocument/completion request/response cycle is synchronous, blocking editor UI if inference takes >1 second","Quantization reduces model accuracy by 5-15% compared to full-precision weights — larger impact on complex reasoning tasks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.050Z","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=turbopilot","compare_url":"https://unfragile.ai/compare?artifact=turbopilot"}},"signature":"vDNk5pPuCcISF+U8E0g+7e/YL2RaCyf6L7LqdET8ZZKAxaLeNnpyR7V1JM8tkwTfp9Y4LAfVWwqZM2zwis5TBA==","signedAt":"2026-06-22T05:18:55.970Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/turbopilot","artifact":"https://unfragile.ai/turbopilot","verify":"https://unfragile.ai/api/v1/verify?slug=turbopilot","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"}}