Urgent.News

the world's headlines, one feed

Sunday, 9 August 2026 Reading Room
Editions ▾
🌐 Global edition🇦🇷 Argentina🇦🇺 Australia🇧🇷 Brazil🇨🇦 Canada🇨🇴 Colombia🇫🇷 France🇩🇪 Germany🇬🇭 Ghana🇭🇰 Hong Kong🇮🇳 India🇮🇩 Indonesia🇮🇱 Israel🇯🇵 Japan🇰🇪 Kenya🇲🇾 Malaysia🇳🇬 Nigeria🇵🇰 Pakistan🇶🇦 Qatar🇷🇺 Russia🇸🇦 Saudi Arabia🇸🇬 Singapore🇿🇦 South Africa🇰🇷 South Korea🇪🇸 Spain🇹🇭 Thailand🇦🇪 UAE🇺🇦 Ukraine🇬🇧 United Kingdom🇺🇸 United States🇻🇳 Vietnam

Tech

POML คืออะไร, ภาษาที่ทำให้ Prompt Engineering เป็นแบบ HTML/CSS

POML คืออะไร, ภาษาที่ทำให้ Prompt Engineering เป็นแบบ HTML/CSS โดย Nokka (นก-กา) | 6 สิงหาคม 2569 บทความนี้เขียนโดย AI (deepseek-v4-flash:0731) ผ่าน Hermes Agent ภายใต้การควบคุมและตรวจสอบคุณภาพโดยมนุษย์, Nokka (นก-กา) ถ้าคุณเป็นนักพัฒนาที่ทำงานกับ AI และรู้สึกว่า prompt ที่เขียนเป็นข้อความยาวๆ เริ่มจัดการยากขึ้นเรื่อยๆ มีข่าวดีจาก Microsoft ในบทความนี้ผมจะอธิบายว่า POML คืออะไร เอาไว้ใช้ทำอะไร…

Original Thai Read in English

dev.to ·

POML (Prompt Orchestration Markup Language) is an open-source language developed by Microsoft to organize and structure prompt components in a systematic way. It aims to separate content from presentation, similar to how HTML separates structure from CSS styling. POML uses semantic tags for roles, tasks, and examples, making it easier for prompt engineering compared to long, unwieldy prompts.

It features a stylesheet to control verbosity and formatting, a built-in templating engine for creating reusable prompts, and a VS Code extension for previewing and diagnosing prompts.

Brief written by urgent.news from Dev.to's own syndicated text. Machine-written — it may contain errors, so check the original before relying on it.

Read the original at dev.to →

More in Tech

React Mastery Series – Day 31: Advanced React Architecture – Designing Large-Scale Applications

Welcome back to the React Mastery Series ! We've completed the first 30 days of our React journey. So far, we've covered: React Fundamentals Components & Props Hooks Context API Redux Toolkit API…

  • High cohesion: Group related features in a domain (e.g., Payments)
  • Low coupling: Features communicate through public APIs, not direct dependencies
  • Scalability: Organize around business domains, use layered architecture, clean architecture

·dev.to

Build, Buy, or Call an API: How We Actually Decide

Clients ask me why we don't just build our own model. It's a fair question, and most of the time the honest answer is that building our own would be the slowest and priciest route to a result slightly…

  • Shanti Infosoft avoids building AI models to save time and cost.
  • They opt for APIs on commodity capabilities like text generation or translation.
  • Building only makes sense for unique selling propositions or proprietary data influence.

·dev.to

Designing MCP tools an agent won't misuse

The first article in this series made a pair of claims. One: an AI-native product is an MCP server that lets an agent do things, not a chat widget that talks about them.

  • Well-typed calls can still go wrong despite schema validation.
  • Design MCP surface to prevent misuse by constraining input space.
  • Use closed types like enums and provide clear tool descriptions.

·dev.to