#FastAPI AI
124 related articles
·3 min
Multi-Agent AI Paper Writing System: A Full-Stack Breakdown of FastAPI + Vue3
A deep dive into an AI paper writing system built with FastAPI + Vue3, covering multi-agent collaboration, RAG, streaming output, and full academic workflow automation.
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·2 min
FastAPI Fundamentals: Frontend-Backend Separation and RESTful Design Explained
Learn the core concepts behind FastAPI: frontend-backend separation, API interface design, and RESTful specification. Master resource-oriented design before writing your first line of code.
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·2 min
FastAPI Fundamentals: Frontend-Backend Separation and RESTful API Design Explained
A comprehensive introduction to FastAPI's core concepts: frontend-backend separation, RESTful API design, JSON data exchange, HTTP methods, and idempotency explained clearly.
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·3 min
CrewAI + FastAPI in Practice: Building a Multi-Agent Collaborative Application
Learn CrewAI's core concepts (Agent, Task, Process, Crew, Pipeline) and how to wrap a multi-Agent service with FastAPI. Covers GPT, Qwen, and Ollama local model integrations with real benchmark comparisons.
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·2 min
FastAPI Fundamentals: Web Development Patterns, API Interfaces, and RESTful Standards Explained
Learn FastAPI fundamentals: frontend-backend separation, what API interfaces really are, and how RESTful standards work. Essential groundwork for Python backend development.
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Tutorials·3 min
Practical Guide to Building Multi-Agent Collaborative Applications with CrewAI + FastAPI
Learn how to build a multi-Agent collaborative system with CrewAI and FastAPI. Covers Agent, Task, Crew concepts, GPT/Tongyi Qianwen/Ollama integration, with complete code examples and model comparisons.
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Tutorials·2 min
FastAPI + Next.js + Supabase: A Practical Guide to Full-Stack AI Application Architecture
A practical guide to building full-stack AI apps with FastAPI, Next.js, and Supabase, covering async task processing, Celery queues, and data flow architecture with code examples.
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Tutorials·3 min
FastAPI Beginner's Guide: Four Core Advantages and ASGI Architecture Explained
Deep dive into FastAPI's four core advantages: high-performance async IO, type hints, automatic API docs, and ease of use. Learn the CGI to WSGI to ASGI protocol evolution.
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Product Reviews·3 min
Skill-Agent: A Production-Grade AI Agent Framework Built on FastAPI with 100+ LLM Support
Deep dive into Skill-Agent, an open-source FastAPI framework integrating 100+ LLM providers, MCP tool protocol, multi-agent collaboration, RAG knowledge base, and sandbox execution for enterprise AI Agent development.
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·3 min
Machine Learning Project Portfolio: A Complete Guide to Building Resume-Worthy ML Projects
From project selection to deployment, learn how to build resume-worthy ML projects. Covers end-to-end workflows, tiered project recommendations, and practical tips for ML learners transitioning from beginner to intermediate.
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·3 min
persistent-inference: Solving TF/Keras Cold Start Problems with Just Two Files
Deep dive into the persistent-inference open-source project: solve TF/Keras cold start problems with just two files by keeping models resident in memory, eliminating reload overhead.
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·4 min
From Zero to AI Engineer: A Real Growth Path from a Student's Resume
A CS student went from Python basics to model deployment in 3-4 months, building an AI portfolio through three real projects. This article breaks down the learning path, project value, and resume optimization strategies.
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·3 min
Which ML Projects Will Actually Help You Land a Job Offer?
Ditch overused tutorial projects. Learn what hiring managers actually look for in ML portfolios: LLM apps, Agent systems, MLOps practices, and real-world solutions.
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·3 min
Practical Guide to Setting Up a Local AI Coding Environment on MacBook Pro M4
Complete guide to setting up a local AI coding environment on MacBook Pro M4, covering Ollama, MLX, Continue, Qwen3-Coder 30B configuration, and performance optimization strategies for 32GB RAM.
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·3 min
Jina v3 Matryoshka Embeddings in Practice: Cut Vector Storage Costs by 75%
Use Jina v3 Matryoshka embeddings to truncate 1024-dim vectors to 256-dim, cutting Pinecone storage costs by 75% while maintaining retrieval quality with task-specific LoRA adapters and circuit breakers.
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·3 min
Jina v3 Matryoshka Embeddings in Practice: Cut Vector Storage Costs by 75%
Use Jina v3's Matryoshka embeddings to truncate 1024-dim vectors to 256-dim, cutting Pinecone storage costs by 75%. Covers dimension truncation, task-specific LoRA adapters, and circuit breakers for reliable RAG systems.
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·4 min
A Guide to Full-Stack Machine Learning Project Repository Structure: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and externalized configuration to help ML developers move from experimental code to production-grade engineering standards.
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·4 min
Full-Stack Machine Learning Project Repository Structure Guide: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and configuration externalization to help ML developers move from experimental code to production-grade engineering.
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·3 min
From Demo to Production: The Real Challenges of Deploying LLM Systems
Deep analysis of deploying LLM systems from prototype to production: a real-world AI incident investigation assistant case revealing critical engineering challenges beyond the model.
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·3 min
From Demo to Production: The Real Challenges of Deploying LLM Systems
Deep analysis of deploying LLM systems from prototype to production: a real-world AI incident investigation assistant case study revealing key engineering challenges beyond the model.
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