ChatGPT Plugins and MCP (Model Context Protocol) are both ways to extend AI assistants with external tools and data, but they were built with fundamentally different goals and architectures. ChatGPT Plugins were designed as a consumer marketplace — a curated store of capabilities users could add to ChatGPT. MCP is designed as a developer protocol … Read more
MCP servers communicate with clients over one of two transport mechanisms: stdio (standard input/output) or SSE (Server-Sent Events over HTTP). The choice of transport determines how the server is deployed, how clients connect to it, and what operational complexity it introduces. Most developers start with stdio for local development and migrate to SSE or HTTP … Read more
MCP servers expose three capability types to clients: tools, resources, and prompts. These three types are not interchangeable — they represent fundamentally different interaction patterns, and choosing the right one for each capability you want to expose is a core design decision when building an MCP server. Getting this wrong leads to servers that are … Read more
MCP (Model Context Protocol) and function calling both let AI models use external tools and data, but they solve the problem at different layers. Function calling is an API feature — a capability built into specific model providers that lets the model request structured function invocations within a single conversation. MCP is a protocol — … Read more
Processing PDFs with a vision LLM means treating each page as an image and passing it to a multimodal model rather than extracting the text layer first. This approach handles PDFs that break standard text extraction: scanned documents with no text layer, PDFs with complex layouts where text extraction produces garbled output, files with important … Read more
Extracting text from images with a language model means using a vision-capable LLM as an OCR engine — passing an image to the model and asking it to return the text it contains. For many use cases this works better than traditional OCR: it handles handwriting, low-quality scans, unusual fonts, mixed languages, and complex layouts … Read more
Speech-to-text models have diversified rapidly since Whisper’s release. In 2026, practitioners choosing a local STT model face a genuine selection problem: there are fast models and accurate models and streaming models and multilingual models, and the best choice depends on the specific deployment scenario. This guide cuts through the options with a task-first framework — … Read more
Video understanding with local LLMs means running a multimodal model on your own hardware to analyse, describe, or answer questions about video content — without sending frames to a cloud API. The capability became practical for local deployment in 2025 as models like LLaVA-NeXT-Video, InternVL2-Video, and Qwen2-VL made video understanding available in open-weight form. Local … Read more
Analysing charts with a vision LLM means asking a multimodal model to read, interpret, and reason about visual data — bar charts, line graphs, scatter plots, pie charts, heatmaps — without converting them to numbers first. The use case is practical and common: extracting values from a chart image in a PDF report, comparing trends … Read more
Multimodal RAG extends retrieval-augmented generation beyond text to include images, charts, figures, and tables embedded in documents. A standard text RAG pipeline loses everything visual — diagrams in technical manuals, charts in financial reports, figures in research papers — because it can only index and retrieve text. Multimodal RAG preserves these visual elements and makes … Read more
