Tom Langridge
Co-founder of Fastio and VividEngine
Tom Langridge is the co-founder of Fastio and VividEngine. He has spent 20 years building technology companies and products used by more than 100 million people. At Fastio, he focuses on shared workspaces where people and AI agents can securely store, search, share, and collaborate on files.
Latest guides by Tom Langridge
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Box AI MCP: Connecting AI Agents to Enterprise Box Storage
A Box AI MCP server exposes Box content platform and Box AI capabilities to external agentic clients via the Model Context Protocol. This guide explains how to connect agents to enterprise Box storage, evaluate native connector latency and context bottlenecks, and bridge corporate files into indexed workspaces using Cloud Sync.
8 Best ChatGPT Detector Tools in 2026
GPTZero reported 99.5% accuracy when it re-ran a benchmark published by University of Chicago Booth researchers, while a March 2026 Kinja roundup of independent tests put its accuracy between 62% and 88%. That gap between lab benchmarks and actual writing defines AI text detection in 2026. This guide compares 8 ChatGPT detector tools by their documented features, free limits, and pricing, explains where every classifier fails, and shows how to use detection reports in an editorial workflow.
ChatGPT Projects Context Window: Token Limits, Hidden Context and MCP
The ChatGPT Projects context window provides a 128,000-token working memory buffer shared across custom instructions, retrieved file snippets, and active conversation turns. Because project files do not expand native model memory, heavy file uploads trigger retrieval dilution and accelerate context truncation. Decoupling document storage into persistent workspaces connected through the Model Context Protocol keeps conversation context lean while unlocking external knowledge.
ChatGPT Projects File Size Limit: Document Caps and Large-Corpus Search
The ChatGPT Projects file size limit enforces a 512MB and 2 million token cap per text document, a 50MB ceiling on spreadsheets, and a 20MB limit on images. Because project knowledge bases rely on retrieval rather than unbounded model memory, uploading massive files leads to parsing timeouts and context exhaustion. Decoupling storage into an external workspace lets ChatGPT query large document collections via MCP on demand.
LangChain Google Drive Loader: Querying Drive Files Without API Bottlenecks
The LangChain Google Drive loader (GoogleDriveLoader) extracts files from Google Drive directories and converts them into Document objects for retrieval pipelines. In production environments, recursive folder traversal triggers API rate limits, file export ceilings, and memory exhaustion. Synchronizing Drive folders to an external workspace and querying indexed files via Model Context Protocol (MCP) bypasses these bottlenecks without custom batch scripts.
LangChain SharePoint Connector: Ingesting Enterprise Libraries via MCP
A LangChain SharePoint connector enables autonomous agents to query enterprise document libraries, but direct batch loading quickly triggers Microsoft Graph API rate limits. Instead of pulling raw files across the network, teams can synchronize SharePoint libraries to an intelligent workspace and query indexed documents through MCP. This architecture enables hybrid semantic search while avoiding local parsing overhead, tenant scope creep, and request throttling.
Law Firm Workflow Automation: Practical Systems for Modern Legal Teams
Law firm workflow automation is the application of structured processes and standardized digital workspaces to automate repetitive administrative handoffs, document routing, and client interactions across legal matters. Instead of deploying complex legacy enterprise suites or unverified chatbots, legal teams build structured intake portals, automated metadata extraction, and defensible file closeout procedures. This guide details practical systems for automating core legal workflows.
LlamaIndex Google Drive Connector: Cloud Sync and MCP Indexing
A LlamaIndex Google Drive connector links Google Drive storage to LlamaIndex vector indexes, enabling retrieval-augmented generation pipelines to query enterprise documents. While native GoogleDriveReader handles simple folders, large collections trigger Google Drive API rate limits and memory bottlenecks. Syncing Google Drive folders to indexed Fast.io workspaces lets LlamaIndex agents execute fast hybrid search over remote Model Context Protocol connections without downloading whole files.
Unraid Rclone Google Drive: Cloud Sync vs AI Agent Workspaces
Mounting Google Drive on an Unraid server with rclone provides local file access, but AI agents scanning recursive VFS mounts trigger API rate limits and storage cache exhaustion. Pairing Google Drive with cloud synchronization into an intelligent workspace eliminates scanning bottlenecks. Agents query indexed content through remote Model Context Protocol connections without mounting drives or managing local cache pools.
eDiscovery File Transfer: Secure Delivery of Load Files and Production Sets
eDiscovery file transfer demands strict data integrity, cryptographic hash verification, and structured packaging to prevent load file corruption. Transmitting production sets across litigation teams requires moving multi-gigabyte collections of images, natives, and database files without dropping delimiters or breaking Bates number sequences. This guide covers load file mechanics, hash verification protocols, transfer method tradeoffs, and operational procedures for legal teams.
Gemini 2.5 Output Token Limit: Max Generation & Thinking Limits
The Gemini 2.5 output token limit defines the maximum number of response tokens generated in a single completion (reaching 8,192 or 65,536 tokens depending on API configuration), which includes both internal thinking tokens and visible output. When reasoning models spend budgets on internal thoughts, visible text truncates with a MAX_TOKENS finish reason. Setting token headroom and offloading reference files to external storage prevents generation failures.
Managing the GitHub Copilot Chat Token Limit in Large Codebases
The GitHub Copilot Chat token limit restricts active model context to budgets between 8,000 and 128,000 tokens for standard sessions, with extended context modes scaling up to one million tokens. When large repositories or attached files exceed these input ceilings, Copilot truncates reference material without explicit warnings. Connecting your editor to an indexed external workspace over the Model Context Protocol provides semantic code retrieval without consuming conversation memory.