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In my previous article - https://hackernoon.com/your-ai-agent-should-not-be-talking-to-all-mcp-tools, I talked about exposing repeatable sequences as domain tools. The main LLM should not have to choose among APIs, queries, and Model Context Protocol (MCP) capabilities. This is a continuation of the article where I will explain internal orchestration and go
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Let's learn about Documentation via these 95 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the Learn Repo or LearnRepo.com to find the most read blog posts about any technology. Documentation is the process of creating written or visual materials that explain how
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Traditional CRUD APIs were designed around resources and operations such as create, read, update, and delete. That model remains effective for browsers, mobile applications, and service-to-service integrations where developers understand the domain and explicitly control the call sequence. AI agents operate differently. An agent receives a goal, selects tools dynamically,
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The most dangerous AI productivity metric is the one that is easiest to screenshot. Prompts sent, tokens spent, suggestions accepted, lines of code generated. These numbers look impressive on a dashboard, but they often mask a more pointed question: “Did our software actually get better?” We’re now using AI
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In the summer of 2026, the hottest thing in Germany wasn't the weather. It was a box. Specifically, a 32-kilogram white box on wheels, manufactured in China, that could be wheeled into a Berlin apartment and switched on without a single hole drilled in a single wall. Its
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TL;DR: Quaternion, octonion, sedenion, and Clifford-algebra neural networks keep appearing in the literature with claims of parameter efficiency and special expressivity. We spent two months building them, benchmarking them — and honestly trying to make them win. Whenever we added one properly matched real-valued baseline, the advantage evaporated. Every time.
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Picture a support agent in production. It receives a user message, calls an internal knowledge base, decides it needs account data, calls a second tool, then drafts a reply and sends it. Five model and tool calls, start to finish, in about two seconds. Now picture a thousand of those
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What building and publishing XPilot taught me about Manifest V3, OAuth, and the gap between “it works on my machine” and “it works in the Chrome Web Store.” I just shipped my first Chrome extension. Not a tutorial toy. Not a “hello world” that never left chrome://extensions. A real
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Every RAG tutorial starts the same way: spin up a Pinecone index, generate some embeddings, wire it into LangChain, and call it a day. That's exactly what I did for a customer-support search tool I built last year until I realized I was paying to sync data between
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A solo dev, a plastic-welding day job, and the cleanup that a Microsoft Store listing forced on a script that had quietly become real software. I Had a 6,533-Line Python File. Then My App Hit the Microsoft Store. That evening is PC Workman, a Windows system monitor with an
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Overview Mage-Flow-Edit-Turbo is a compact 4-billion-parameter image editing model built by microsoftthat applies text-guided edits to images through a shared architecture designed for both text-to-image generation and instruction-based editing. The model operates using a two-component stack: Mage-VAE (a lightweight latent tokenizer using one-step diffusion with anchor-latent KL regularization) and NR-MMDiT
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Overview KAT-Coder-V2.5-Dev is a Mixture-of-Experts (MoE) coding model built by Kwaipilot for autonomous software engineering tasks within executable repositories. The model contains 35B total parameters with 3B activated per token, operating as a text-only model compatible with Hugging Face Transformers, vLLM, SGLang, and KTransformers. Built on the Qwen3.6-35B-A3B
