A non-technical individual shares their journey of building and deploying their first AI Agent API, "Agentic Finance Beast," using Python, FastAPI, and Mistral AI. This project marks Day 4 of their AI Engineer journey, with future plans including RAG systems and multi-agent financial research.
This article details a full-stack AI-powered system designed to automate event feedback collection and reporting. Utilizing Gemini 2.5, FastAPI, and Supabase, it transforms raw feedback into branded PDF reports with AI-generated sentiment analysis and improvement suggestions, delivered directly to organizers.
This post describes a Zero Trust AI architecture built with Google Gemini, React, and FastAPI to automate logistics dispatch chats while mitigating data leaks and AI hallucinations. The system ensures LLM isolation via Pydantic schemas, includes a human-in-the-loop for critical cases, and deanonymizes data only at the backend.
This article details the creation of a voice-controlled AI agent, enabling users to interact with their computer for tasks like file creation or code writing. It covers the system's architecture, model selection, and a three-stage process involving Groq Whisper for STT and Groq Llama-3.1-8b for intent classification, all built with FastAPI and Streamlit.
The content details how to deploy Llama 3.2 1B using TinyLLM and FastAPI on a $5/month DigitalOcean Droplet, achieving sub-100ms latency inference. This setup enables production-grade real-time AI inference, drastically cutting costs and avoiding vendor lock-in.
JobSync is an AI-powered semantic job matching system that moves beyond keyword matching by using vector embeddings and dual encoders to understand the meaning of job descriptions and candidate profiles. Built with FastAPI and Qdrant, it effectively connects candidates with roles by recognizing closely related concepts.
The content outlines an AI workflow automation system architecture using Make.com, FastAPI, OpenAI, and Monday CRM, designed to handle production challenges like AI inaccuracies and human approval needs. It aims to reduce repetitive manual review while ensuring controlled, traceable, and practical daily business operations.
This guide provides a step-by-step tutorial for beginner and intermediate developers on how to build an AI-powered Learning Management System from scratch. It leverages technologies like Ollama, FastAPI, and React.
FastAPI has emerged as a top choice for serving machine learning models, favored for its lightweight, fast, and user-friendly nature.
An automation engineer shares their first AI testing project, building a FastAPI service with a local LLM (Ollama/llama3.2) and a pytest suite, prompted by a recruiter's job offer. The goal was to understand the nuances of AI/LLM testing compared to traditional UI/API testing, with the suite's initial success making the learning experience challenging.
The author details their six-month struggle to master LangGraph, realizing they were misusing tools and concepts. A methodical approach, understanding LangGraph's graph-based core, and leveraging tools like Pinecone and FastAPI marked the turning point to functional models.
This tutorial demonstrates how to build, test, deploy, and monitor a FastAPI Cloud application. It guides users through creating a simple live gold and silver dashboard.
This content details the development of Rutiq, a system designed for last-mile orchestration. It highlights the integration of modern technologies like FastAPI, Supabase, and Telegram, alongside applied artificial intelligence, to build a robust solution.
This content lists 10 essential GitHub repositories to master FastAPI. It covers various topics such as examples, guides, authentication tools, microservices, and machine learning projects.
This article analyzes why Vercel, despite technically supporting Python via serverless functions, is not ideal for complex Django, FastAPI, or Flask applications. It highlights limitations with persistent processes, background tasks, and WebSockets.
This definitive guide presents 5 practical strategies to optimize the performance of FastAPI applications using Python and Vue.js. It covers topics from correct event loop usage to lazy loading, including code examples and common error analysis.