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#pydantic

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SScopiescopie.hashnode.devMay 5 · 9 min read

Building a Learning Platform

A backend engineer's first-person account of building Naija Learn from scratch. Every great project starts with a conversation. Mine started with a simple thought: Nigerian students deserve better. N

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MMMuhammed Magdymuhammed-magdy.hashnode.devMay 1 · 8 min read

“If It Quacks Like a Duck”… Why Your Python Types Still Break AI Systems

A practical guide to TypedDict, dataclass, and Pydantic for ML engineers Python is a duck-typed language. That means Python does not care what a value is. It only cares what a value does. If it walk

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Aanvvsharmaanvvsharma.hashnode.devApr 30 · 3 min read

How to Build Robust Data Models Using Python Pydantic

Learn how to create reliable, type-safe, and validated data models in Python using Pydantic — perfect for APIs, microservices, and data-driven apps. Introduction Building clean, reliable, and validate

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NJNaveen Joshiaiops-hub.hashnode.devApr 27 · 12 min read

Building an AI-Powered CI/CD Auto-Triage and Healing Agent-Part-02

In Part 1, we built the simplest possible thing — a Python script that fetches a failed GitLab job trace and asks Claude to classify it. It worked. But the moment it worked, we saw every crack clearly

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JKJangwook Kimeffloow.hashnode.devApr 17 · 11 min read

LLM Structured Outputs in Production: Stop Parsing JSON with Regex

If your production codebase contains a function called try_parse_llm_response that runs re.search(r'\{.*?\}', response, re.DOTALL), you are not alone — and you are also in 2023. In 2026, parsing LLM output with regex is the equivalent of hand-rolling...

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RSRahul Sehrawatai-zero-to-hero.hashnode.devApr 13 · 11 min read

Stop Parsing Strings: Structured Output From LLMs

Here's a class of bug I wrote in my first LLM app and then again, on a different team, six months later. I wrote a feature that asked the model to classify a user's message into one of three categories — billing, technical, other — and return it as J...

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Aaiagentmemoryaiagentmemory.hashnode.devApr 7 · 9 min read

Mem0 Pydantic AI: Structured Memory for Intelligent Agents

What if an AI agent could recall information not just accurately, but with absolute structural integrity? Imagine an agent failing a critical task because a key piece of data was malformed, leading to catastrophic failure. This is precisely the probl...

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AFAira Francodataengineeringlearning.hashnode.devMar 27 · 7 min read

Building a batch processed "Real Time" Retail Inventory Data Platform using ETL + FastAPI + Kafka + Postgresql / Supabase

Product Finder is a retail data platform prototype built for SportsWear AB. The goal is simple: take inventory and sales events from operations, process them in a reliable pipeline, and turn them into

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VvictorstackAIvictorstackai.hashnode.devMar 20 · 4 min read

Pydantic's Monty: A Sandboxed Python for AI Agents That Actually Makes Sense

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; Simon Willison shared research on running Pydantic's Monty in WebAssembly. Monty is a minimal, secure Python interpreter written in Rust, designed specifically for safely executing...

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RDRynko Devblog.rynko.devMar 10 · 9 min read

Adding Output Validation to Your LangGraph Agent with Rynko Flow

LangGraph gives you fine-grained control over your agent's execution graph — you define nodes, edges, and conditional routing. But one thing that's missing from most LangGraph tutorials is what happen

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#pydantic

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Trending tags this week

Building a Learning Platform

“If It Quacks Like a Duck”… Why Your Python Types Still Break AI Systems

How to Build Robust Data Models Using Python Pydantic

Building an AI-Powered CI/CD Auto-Triage and Healing Agent-Part-02

LLM Structured Outputs in Production: Stop Parsing JSON with Regex

Stop Parsing Strings: Structured Output From LLMs

Mem0 Pydantic AI: Structured Memory for Intelligent Agents

Building a batch processed "Real Time" Retail Inventory Data Platform using ETL + FastAPI + Kafka + Postgresql / Supabase

Pydantic's Monty: A Sandboxed Python for AI Agents That Actually Makes Sense

Adding Output Validation to Your LangGraph Agent with Rynko Flow