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POST /items: Adding New Data to Your API with FastAPI & Pydantic

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POST /items: Adding New Data to Your API with FastAPI & Pydantic
From Read-Only to Interactive Until now our API could only read data. Today we add the...
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AI
Self-Tuner: Building an Adaptive Position Sizing System in Python
Originally published at chudi.dev A strategy that works on average might not work in all market...
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AI
Indian Language TTS for Your AI Agent: Integrating Sarvam.AI Bulbul v3 with OpenClaw
How I added high-quality Indian language text-to-speech to my OpenClaw agent using Sarvam.AI Bulbul v3 — 11 languages, 30+ voices, and a 50-line Python script.
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AI
Data Serialization: A Concise Guide to JSON, YAML, TOML, and More
As a developer, you’ve most likely encountered serialization, perhaps without realizing it. It’s...
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I Built an AI Agent Monitoring System as a Non-Dev Side Project. Here's Why.
Hey everyone. This is my first ever side project and my first ever post here. I'm not a developer by...
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We Built Auto-Highlighting Subtitles for YouTube Shorts — Here's the 130-Line Python That Does It
60% of YouTube Shorts viewers watch on mute. Our AI agent built an ASS subtitle system that auto-highlights keywords by type — yellow for numbers, red for impact words, green for positive words, cyan
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idea-reality-mcp v0.3.0: How We Built Chinese Language Support Into an MCP Server
TL;DR — idea-reality-mcp is an MCP server that checks if your project idea already exists. v0.3.0...
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Introducing the Leanvox Python SDK: Text-to-Speech in 3 Lines
What if adding voice to your Python app was as easy as pip install and three lines of code? Today it...
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pop() vs del in Python: Same Result, Completely Different Intent
.pop() vs del in Python — The Million-Dollar Question 💰 In practice, both remove items...
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Your Chatbot Recommends Products You Don't Sell
Most e-commerce chatbots hallucinate inventory. I added a preprocessing step that checks the real catalog before the agent runs. Four actions, one extra LLM call.
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Choosing an Agent Framework in 2026: A Data-Driven Decision Guide
You've seen the benchmarks. You've read the methodology. Now the question that actually matters:...
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Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets
The reliability of multilingual Large Language Model (LLM) evaluation is currently compromised by the inconsistent quality of translated benchmarks. Existing resources often suffer from semantic drift and context loss, which can lead to misleading performance metrics. In this work, we present a fully automated framework designed to address these challenges by enabling scalable, high-quality transl
arXiv
Elvin Babanlı
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