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Pieces for Developers is an AI-powered developer productivity tool created by Mesh Intelligent Technologies, Inc. The software provides code snippet management, an AI copilot with long-term memory, and on-device processing capabilities across desktop and IDE environments. Founded in 2020 and headquartered in Cincinnati, Ohio, the company has raised $26.1 million in funding and reports over 100,000 users across its desktop application and plugin ecosystem [1].
Pieces differentiates itself from other AI coding assistants through its local-first architecture. Rather than routing all processing through cloud servers, the tool runs a background service called PiecesOS on the user's machine, handling data capture, snippet enrichment, and machine learning inference locally. This approach allows developers to use AI features offline while keeping their code and workflow data on their own hardware [2].
Mesh Intelligent Technologies, Inc. was founded in 2020 by Tsavo Knott and Mack Myers. Knott, who graduated from Miami University in 2018 with degrees in Game and Interactive Media Design and Computer Science, serves as CEO. Myers, who graduated from Miami University in 2016 with degrees in Professional Writing and Entrepreneurship, serves as Chief Product Officer [3]. Mark Widman joined as the company's founding engineer and later became Chief Technology Officer [4].
Before founding Pieces, Knott had co-founded Accent.ai, a language learning platform. His earlier companies, Runtime and MeshMyCampus, were open-sourced to Google Chrome and acquired by Idera, respectively [3].
The company initially focused on building a productivity suite to reduce context switching for developers. The early product centered on saving, organizing, and reusing code snippets across different development tools [5].
In June 2021, Pieces raised $8 million in seed funding led by Drive Capital, a Columbus, Ohio-based venture capital firm. The company also secured $3 million in venture debt from Silicon Valley Bank. Chris Olsen from Drive Capital joined the board of directors. At the time of the raise, Knott stated that the funding would be used to expand the team, invest in product development, and scale the platform to reach developers globally [6].
By 2022, the company had grown to nearly 30 employees distributed across multiple countries. That year, Pieces won the Best Tech category in Cincy Inno's Fire Awards [7]. During this period, the company developed its Workstream Pattern Engine, a system designed to passively capture context from a developer's workflow across applications without disrupting performance. The team also built on-device machine learning models using TF-IDF, support vector machines, LSTMs, and recurrent neural networks for automatic context classification [4].
On July 10, 2024, Pieces announced a $13.5 million Series A round, again led by Drive Capital with participation from Cintrifuse Capital, RedHawk Ventures, and other investors. The total funding raised reached $26.1 million across all rounds. At the time of the announcement, the company reported over 100,000 users with access to Pieces Copilot+ through its desktop application and integrations with VS Code, Visual Studio, JetBrains IDEs, Google Chrome, Obsidian, and other tools [1].
The funding was directed toward accelerating development of next-generation workflow copilot technology and scaling the productivity suite to teams and enterprises.
On March 4, 2025, Pieces launched Copilot+ with the Live Context feature on Product Hunt and secured the #1 Product of the Day ranking. The launch emphasized the product's on-device, privacy-first approach to AI-assisted development [8].
PiecesOS is the core background service that runs locally on the user's machine. It operates as an HTTP service on localhost, scanning ports 39300 through 39399 for availability. PiecesOS handles local data processing, manages the on-device machine learning models, and exposes API endpoints that other Pieces products (desktop app, IDE plugins, browser extensions) connect to [2].
The system supports three processing modes:
| Mode | Description |
|---|---|
| Local | All processing happens on-device; no data leaves the machine |
| Blended | Combines on-device processing with optional cloud model access |
| Cloud | Uses cloud-hosted LLMs for generation tasks |
Users can control which data sources PiecesOS tracks through granular enable/disable settings, and can pause or resume the Workstream Pattern Engine at any time [2].
Pieces uses a collection of task-specific "nano-models" rather than relying solely on large general-purpose language models. These lightweight models handle classification, span extraction, normalization, enrichment, relevance scoring, and formatting entirely on-device. The company describes this approach as building "a mesh of task-specific nano-models: fast, lightweight, and precise, designed for reflexes rather than reasoning" [9].
Two specific nano-models introduced with LTM-2.5 illustrate this approach:
Temporal Intent Classifier: This model determines whether a user's query requires temporal memory access. It classifies intent across six categories: Content Retrieval, Action/Scheduling, Future Information/Planning, Current Status, Temporal-General, and Non-Temporal. According to Pieces' benchmarks, the model achieves 99.30% accuracy (compared to 82.41% for Gemini and 86.34% for GPT-4o), processing 544 samples per second at roughly 23 milliseconds of latency [10].
Temporal Span Predictor: This model extracts precise time ranges from natural language queries, handling five range types: point-in-time, explicit period, implicit period, relative recent, and fuzzy historical. Pieces reports an E.C.O. Rate of 94.50% (versus approximately 20% for cloud LLMs), an average Intersection over Union of 92.01% (versus approximately 18% for cloud models), and throughput of 785 samples per second at 88 to 108 milliseconds of latency [10].
These nano-models were created through knowledge distillation from larger foundation models, then quantized and pruned to run on consumer hardware. They replaced a previous pipeline that required 8 to 11 preprocessing tasks and 2 to 4 post-processing tasks running on cloud-hosted LLMs, reducing time-to-first-token from seconds to milliseconds while eliminating per-query API costs that had ranged from $0.018 to $1.90 per cloud run [10].
Pieces supports both cloud-hosted and locally downloadable language models. Local models can be downloaded through the Copilot LLM selector in any Pieces product, enabling fully offline AI generation [11].
| Provider | Cloud models | Local models |
|---|---|---|
| OpenAI | GPT-4, GPT-4o | N/A |
| Anthropic | Claude Sonnet, Opus, Haiku | N/A |
| Gemini Pro, Flash | Gemma, Code Gemma | |
| Meta | N/A | LLaMA, CodeLLaMA |
| Mistral AI | N/A | Mistral, Mixtral |
| IBM | N/A | Granite (Code, Dense, MoE variants) |
| Microsoft | N/A | Phi |
| Alibaba | N/A | Qwen, QwQ Coder |
| BigCode | N/A | StarCoder |
Pieces Copilot is the AI assistant component that provides conversational code assistance within the desktop app and IDE plugins. Unlike standalone chatbots, the Copilot draws context from the user's captured workflow history, open files, and saved snippets in addition to the selected LLM's training data. Users can add files and folders as explicit context, and the Copilot can reference out-of-IDE context when the Long-Term Memory Engine is enabled [12].
The Copilot supports multiple use cases: explaining code, generating documentation, refactoring, debugging errors, summarizing recent work sessions, and preparing standup reports. Users can switch between different cloud and local LLMs within the same conversation [12].
The Long-Term Memory Engine (LTM-2.7) tracks a developer's workflow context over a rolling period of up to nine months. It monitors activity at the operating system level, capturing data from IDEs (file changes, commits), browsers (tabs, links, documentation pages), and collaboration tools (Slack, Google Chat, Microsoft Teams). All captured data is stored locally on the user's device [2].
LTM enables temporally grounded queries, meaning developers can ask questions tied to specific time periods. Examples include "What was I working on yesterday afternoon?" or "What documentation did I read about authentication last week?" The system uses on-device ML algorithms to filter out sensitive information and secrets before storing captured data [2].
The company describes the LTM system as reaching "a 380% increase in recall accuracy" while reducing resource usage by 14 times compared to earlier implementations, achieved through reinforcement and decay models that distinguish valuable context from noise [4].
Live Context, introduced alongside Pieces Copilot+ in 2024, provides real-time workflow awareness to the AI assistant. It is powered by the Workstream Pattern Engine, which operates at the OS level across macOS, Windows, and Linux. The engine captures activity from browsers, IDEs, and collaboration tools, then processes the data with on-device algorithms to identify and retain the most relevant information [13].
Knott described Live Context at launch as "the first AI assistant with live context across your entire operating system" [1].
The Workstream Pattern Engine learns continuously from previous Copilot conversations and applies reinforcement and decay models (modeled after REM sleep patterns, according to the company) to link memories across time and topics [4].
Pieces Drive is the code snippet management component. It allows developers to save code from IDEs, web browsers, screenshots, and files. Saved snippets are automatically enriched with AI-generated tags, descriptions, related links, and language detection. Users can transform snippets by refactoring, changing programming languages, or adding inline comments [12].
Snippets can be shared via custom links or exported as GitHub Gists. The system supports full-text search across all saved materials [12].
The Timeline interface displays captured workflow activity in 20-minute blocks called LTM Roll-Ups. Each block contains summaries of activities, referenced links, file changes, and other captured context. Users can search through Timeline entries using natural language through Conversational Search [2].
Deep Study provides comprehensive analysis of a user's recent workstream activities. Unlike standard workflow summaries, Deep Study generates detailed reports about development patterns, project progress, and code changes. Reports typically take 10 to 20 minutes to generate and run on dedicated cloud LLMs (currently Google models). Deep Study is available for Pieces Pro users [2].
Pieces provides a Model Context Protocol (MCP) server, enabling external AI tools to access the user's local context and long-term memory without custom integrations. The MCP server exposes two primary tools [14]:
| Tool | Function |
|---|---|
ask_pieces_ltm | Retrieves historical and contextual information based on a question, with parameters for keywords, open files, application sources, and the active LLM |
create_pieces_memory | Captures new memories with summary descriptions, project paths, file locations, external links, and client information |
Supported MCP clients include Cursor, Claude, Perplexity, Goose, ChatGPT, and VS Code [14].
Pieces runs on macOS, Windows, and Linux. The core PiecesOS service must be installed on the user's machine, and individual plugins connect to it locally [2].
| IDE | Features |
|---|---|
| VS Code | Copilot chat, snippet saving, AI Quick Actions, snippet sharing via links |
| JetBrains (IntelliJ, PyCharm, WebStorm, etc.) | Copilot chat, Pieces Drive, AI Quick Actions, Search Everywhere integration |
| Visual Studio | Copilot chat and snippet management |
| JupyterLab | Notebook-aware copilot and snippet tools |
| Platform | Type | Description |
|---|---|---|
| Google Chrome | Browser extension | Save snippets from web pages, capture browsing context for LTM |
| Obsidian | Plugin | Access Pieces Drive and Copilot within the note-taking workspace |
| Slack | Workflow capture | LTM captures context from Slack conversations |
| Microsoft Teams | Workflow capture | LTM captures context from Teams conversations |
| Google Chat | Workflow capture | LTM captures context from Google Chat conversations |
Pieces publishes an open-source Pieces OS Client SDK, available through NPM, Maven, and PyPI. The SDK exposes over 50 endpoints for building applications on top of PiecesOS, including access to local language model capabilities. The company's open-source repositories are hosted at github.com/pieces-app [15].
Pieces uses a freemium pricing model.
| Plan | Price | Features |
|---|---|---|
| Free | $0 | Copilot, Pieces Drive, full local memory, chat history, local LLM access |
| Pro | $18.99/month | Advanced cloud LLMs (Claude Sonnet and Opus, Gemini 2.5), Deep Study, additional features |
| Team | Contact sales | Enterprise features and team collaboration |
The company has stated it plans to always maintain a free tier [16].
Pieces occupies a distinct position in the AI developer tools market. While GitHub Copilot, Tabnine, and Sourcegraph Cody focus primarily on inline code completion and generation within the editor, Pieces emphasizes workflow memory, snippet management, and cross-application context awareness [17].
| Tool | Primary focus | On-device option | Snippet management | Workflow memory |
|---|---|---|---|---|
| Pieces for Developers | Workflow context and snippet management | Yes (full local processing) | Yes (Pieces Drive) | Yes (LTM, 9-month rolling) |
| GitHub Copilot | Inline code completion and chat | No | No | No |
| Tabnine | Code completion with privacy focus | Yes (enterprise self-hosted) | No | No |
| Sourcegraph Cody | Code search and context from repositories | No | No | Repository-level indexing |
| Cursor | AI-native code editor | No | No | Codebase indexing |
Pieces' competitive advantage lies in its on-device architecture and long-term memory system. Most competing tools send code to cloud servers for processing, while Pieces can operate entirely offline. The LTM feature, which captures workflow context across IDEs, browsers, and chat tools over months, is not replicated by other tools in the category [17].
At the same time, Pieces does not provide the deep inline code completion capabilities of GitHub Copilot or Tabnine. The company has positioned itself as complementary to code generation tools rather than a direct replacement, describing Pieces as "an AI-Teammate to code generation tools" [17].
On G2, Pieces for Developers holds a 4.5-star rating from 81 verified reviews. Users frequently praise the ease of setup, broad IDE integrations, offline functionality, and privacy-first approach. Common criticisms include occasional slow performance, inconsistent context understanding, and intermittent issues with the chat interface [18].
On Product Hunt, Pieces has earned the #1 Product of the Day designation for its Copilot+ launch. Reviewers on the platform noted the unique combination of snippet management and AI copilot features in a single tool [8].
| Name | Role | Background |
|---|---|---|
| Tsavo Knott | CEO and Co-Founder | Miami University '18 (Game and Interactive Media Design, Computer Science); previously co-founded Accent.ai, Runtime, and MeshMyCampus |
| Mack Myers | Chief Product Officer and Co-Founder | Miami University '16 (Professional Writing, Entrepreneurship) |
| Mark Widman | Former CTO and Founding Engineer | Led infrastructure development and ML/product collaboration; second company and third major project [4] |
The company operates as a remote team with its legal headquarters at 1311 Vine St, Suite 301, Cincinnati, OH 45202 [19].