I downgraded to free Gemini and ChatGPT for a week, and only one subscription is worth keeping

Learn Vector Databases for Beginners – Embeddings, Similarity Search & ChromaDB

A vector database stores data as lists of numbers called embeddings, which lets it search by meaning instead of matching exact keywords. It's the piece behind a lot of recommendation systems, RAG apps

RAG vs. Vectorless RAG, clearly explained!

...explained visually (with tradeoffs in production).

DeepSeek Super-Agent Architecture Explained

How DeepSeek Harness's Super-agent works: a Router, isolated Workers, event triggers and a Watchdog let one AI agent run many tasks. Diagrams inside.

Don't Write Markdown Files for Your Coding Agent. Write Scripts.

If you want agents to do what you want, you need to learn how to box them into deterministic actions.... Tagged with ai, programming, devops, productivity.

The End of AI Frontiers

Large language models remain far less efficient than biological brains at learning, representing knowledge, and planning, the post argues. A child hears roughly one token per second, reaching a billion tokens only after 31 years, while GPT-4-class models reportedly use four orders of magnitude more data. The Phi model series, trained on 10 trillion tokens, still trails GPT-4 along most axes. A Portia spider plans prey-catching behavior with a brain a million times smaller than a human's, and squirrels recall cached food after months, suggesting that existing models may be far less representationally and memory efficient.

When the Political Shift Hits the Cluster: What the 2026 Midterms Mean for AI Infra and Code

If you’ve been watching the tech sector and macroeconomic feeds over the last 48 hours, you’ve... Tagged with discuss, ai, webdev, programming.

Gemini 4 Carbon: Google’s New AI Takes the #1 Spot, But You Can’t Use It Yet

Gemini 4 Carbon: Google’s New AI Takes the #1 Spot, But You Can’t Use It Yet Just two weeks after Gemini 4 Argon, Google announced Gemini 4 Carbon on October 28, 2026, calling it its strongest …

Claude Code's creator says better prompts need just 3 things, so I tested his approach

Bối cảnh của bài viết khám phá liệu chúng ta có đang quá tư duy hóa việc tạo prompt hay không. Nguyên nhân kỹ thuật được nêu ra là một phương pháp đơn giản chỉ cần 3 yếu tố: context, instruction, và input, được đề xuất bởi nhà sáng tạo Claude Code. Hệ quả khi áp dụng phương pháp này là việc tạo prompt hiệu quả trở nên dễ dàng hơn nhiều so với những cách phức tạp trước đây. Điều đáng học là ngay cả những AI model mạnh nhất hiện nay như Claude 3 hay GPT-4 vẫn phản ứng tốt với prompt có cấu trúc đơn giản này.

Bài viết này giúp bạn hiểu ra cách đơn giản hóa việc tạo prompt hiệu quả chỉ với ba yếu tố cơ bản.