The MCP Database Server connects AI assistants directly to your database, letting them query data, inspect schemas, and answer questions about your data in natural language without requiring you to write SQL or export data manually. Instead of asking “how do I write a query to find all users who signed up last month and … Read more
browser-use is an open-source Python library that connects AI agents to a real web browser, letting them navigate websites, fill forms, click buttons, extract information, and interact with web applications through natural language instructions. Unlike web scraping libraries that parse static HTML, browser-use controls an actual browser — it handles JavaScript-rendered content, authentication flows, multi-step … Read more
The MCP Filesystem Server gives AI assistants like Claude direct access to read and write files on your local machine or a specified directory. Rather than copying file contents into a chat manually, you connect the server once and the AI can browse directories, read files, create new ones, and make edits — all through … Read more
GPT-4o’s vision capabilities allow you to send images alongside text prompts and receive detailed, contextually aware responses about what the model sees. Released as part of GPT-4o in mid-2024, the vision API accepts standard image formats, handles multiple images per request, and produces responses that range from simple descriptions to complex reasoning about visual content. … Read more
Google’s Gemini models are natively multimodal — they were trained from the ground up to understand text, images, audio, video, and documents together rather than having vision bolted on as a separate capability. The Gemini API exposes this through a unified interface where any combination of these modalities can appear in the same request. For … Read more
Building a voice assistant with local LLMs means combining three components: a speech-to-text model that transcribes your voice, a language model that understands and responds, and a text-to-speech model that speaks the response aloud. None of these need to touch external servers — Whisper handles transcription locally, Ollama runs the LLM, and tools like Piper … Read more
Transcribing and analysing video with AI combines two capabilities — speech-to-text for the audio track and vision or language understanding for the content — to produce searchable transcripts, summaries, topic extractions, and structured insights from video at scale. The tooling for this has matured significantly: Whisper handles transcription reliably, and frontier LLMs handle analysis of … Read more
An image question-answering pipeline lets you send an image and a natural language question to a vision-capable LLM and receive a structured, actionable answer. The use cases range from product quality control and document processing to accessibility tooling and visual search. Building one well requires thinking through model selection, prompt design, how to handle batches … Read more
Writing tests with an AI assistant removes most of the friction that causes developers to skip or defer testing. Tests are repetitive, edge-case enumeration is tedious, and the boilerplate for mocks and fixtures looks nearly identical across hundreds of test files. AI handles all of this well. The challenge is knowing how to direct it … Read more
Building a full application with an AI coding assistant is a different experience from using AI for isolated tasks. The challenge is not getting the AI to write code — it will do that readily — but keeping the architecture coherent, the codebase understandable, and the quality high across a multi-session project that grows in … Read more
