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Versions used in this post — read this first: Python 3.12.3 · FastAPI 0.161.1 · uvicorn 0.54.0 · pypdf 6.21.2 · google-genai 2.33.0 · python-multipart 0.0.32 · Model: gemini-3.8-flash . Everything runs from one file, app.py , and the code below is the exact code that was run and tested for this post. The direct answer Yes — one small FastAPI file is enough. Upload a PDF and the server extracts its text with pypdf, splits it into overlapping chunks, and stores them in memory. Ask a question and it picks the chunks sharing the most words with it, sends only those to Gemini with a strict instruction to answer from them alone, and returns the answer with the token counts the API reported and what they cost. No vector database, nothing on top of FastAPI — and if no chunk matches, the app does not call the model at all. This is the first episode of Small AI Apps : complete, runnable applications built with FastAPI and Gemini, each small enough to finish in under an hour and honest ab...

Versions used in this post — read this first: Python 3.12 · MCP Python SDK mcp 2.3.0 (specification revision 2026-07-28) · Transport: stdio · No API key, no account. Version note that matters: SDK v2 renamed the class most older tutorials use — code importing FastMCP was written for v1 and will not run on v2. This post uses MCPServer throughout. The direct answer MCP — the Model Context Protocol — is a standard way for an AI application to discover and call tools that live outside the model. Instead of every framework inventing its own plugin format, a tool is packaged once as an MCP server, and any MCP-aware host can list its tools, read their input schemas, and call them over a defined protocol. In the previous post, the tools lived inside the same file as the agent. That works for one program. MCP is for when a tool should outlive it — usable by a second agent, a different host, or next month's project. Host, client, server — in plain words Three roles appear in every ...

  Versions used in this post — read this first: Python 3.12 · google-genai 1.35.0 · model gemini-2.5-flash · No framework, no LangChain. Everything below runs from one file. If a newer SDK changes a method name, the loop in Step 4 stays the same — only the call syntax moves. The direct answer An AI agent is not a special kind of model. It is a normal program that does three things in a loop: it sends your question and a list of tools to a model, it runs whichever tool the model asks for, and it sends the tool result back so the model can answer — or ask for another tool. Execution stays with your program from start to finish — the model only ever asks. This post builds that loop by hand, on purpose. Frameworks hide the loop, and the loop is the part you need when something breaks in production. What you will build A small command-line agent with two tools: Weather lookup — gets the current temperature and wind for a city from the free Open-Meteo service, which needs no...

After completing the development of your desktop Application. The next phase is to deployment or installation of that application. For this you need to create a setup of your project, then you just need to give the setup file to the users or your client so they can install and use your application. In Visual Studio 10 or prior edition, has built in functionality to create setup file using Installer Projects. But in Visual Studio 13 and next edition you need to install an extension first and then you can create.

Android is a mobile operating system developed by Google. It is primarily designed for Touchscreen mobile devices such as smartphone and tablets. As we know, a large number  of  audience is using Android smartphone. So you have a huge market to publish your android apps on play store.

In my previous post, I have shared the basic introduction of base64 encode and decode and it uses. I have also shared the code How you can base64 encode and decode a string in C#, Java, and Object-c . I have built a simple java script based tool,  which online converts the string to Base64 encode and base64 decode. At the end of this post will get the source code of this tool and the code snippet  to share this tool on your own site.