Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT - WSJ
Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT WSJ
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How to build an MCP server from scratch (Python, 2026 guide)
If you're using Claude Code, Cursor, or any MCP-compatible AI tool, you can extend its capabilities by building custom MCP servers. This guide walks through building one from zero. What's an MCP server? Model Context Protocol (MCP) servers are tools that AI coding assistants can call. Think of them as plugins — each server exposes a set of tools (functions) that the AI can invoke during conversation. When you say "get me the current Bitcoin price" in Claude Code, an MCP server handles the actual API call and returns structured data. Prerequisites Python 3.10+ uv package manager (or pip) Claude Code, Cursor, or any MCP client Step 1: Project setup mkdir my-mcp-server cd my-mcp-server uv init uv add mcp httpx Create the structure: my-mcp-server/ ├── pyproject.toml ├── src/ │ └── my_mcp_serve
5 AI Side Hustles That Generated $1,000 in 3 Months for a Beginner Like Me
5 AI Side Hustles That Generated $1,000 in 3 Months for a Beginner Like Me I turned my first AI-powered side hustle attempt into a $1,000/month income stream in just three months. Here’s how I did it with five different models, including the exact tools and steps I took. 1. AI Video Creation: From Script to Short Video in 10 Minutes Tool: InVideo.io First Try Code Block (Steps as Markdown) # Fifteen-Second Product Intro Video 1. **Signup Upload** : InVideo.io → Free Account → Upload 3 phone-shot clips of a coffee mug. 2. **AI Features** : - **Auto Captions** : "This is my daily coffee mug" - **Smart Cut** : Auto-beat alignment - **Background Removal** : Replace with a plain white background 3. **Export** : - Resolution: 1080p - Platform: Instagram Reels - Hashtags: #coffeemug #AIshort Outc
5 Key Insights That Cut My AI Wearable Development Time by 40%
5 Key Insights That Cut My AI Wearable Development Time by 40% I was wrong about how complex building AI wearables could be. Turns out, focusing on the right hardware and software strategies can significantly reduce development time. Here's what I learned: 1. Choosing the Right Development Board: ESP32-S3 The ESP32-S3 stands out for AI wearables due to its: Built-in AI accelerator 8 MB PSRAM Dual-mode Wi-Fi/Bluetooth Actionable Step : Get an ESP32-S3 DevKit. Ensure it has a PCB antenna or external-antenna hole for easier testing. # Example: Verify ESP32-S3 board in Arduino IDE Board: "ESP32-S3 DevKit" CPU Frequency: 240 MHz 2. Evaluating Hardware Needs with a Checklist Before buying, assess: | Checklist Item | Why It Matters | Example Check | |----------------|----------------|------------
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5 Key Insights That Cut My AI Wearable Development Time by 40%
5 Key Insights That Cut My AI Wearable Development Time by 40% I was wrong about how complex building AI wearables could be. Turns out, focusing on the right hardware and software strategies can significantly reduce development time. Here's what I learned: 1. Choosing the Right Development Board: ESP32-S3 The ESP32-S3 stands out for AI wearables due to its: Built-in AI accelerator 8 MB PSRAM Dual-mode Wi-Fi/Bluetooth Actionable Step : Get an ESP32-S3 DevKit. Ensure it has a PCB antenna or external-antenna hole for easier testing. # Example: Verify ESP32-S3 board in Arduino IDE Board: "ESP32-S3 DevKit" CPU Frequency: 240 MHz 2. Evaluating Hardware Needs with a Checklist Before buying, assess: | Checklist Item | Why It Matters | Example Check | |----------------|----------------|------------
A domestic shortage of electrical equipment such as transformers and switchgear is forcing the US to rely on Chinese imports, delaying data center construction (Bloomberg)
Bloomberg : A domestic shortage of electrical equipment such as transformers and switchgear is forcing the US to rely on Chinese imports, delaying data center construction The struggle to manufacture transformers, switchgear and batteries domestically has forced the US to rely on imports, delaying data center construction.
Semantic Evolution over Populations for LLM-Guided Automated Program Repair
arXiv:2604.02134v1 Announce Type: new Abstract: Large language models (LLMs) have recently shown strong potential for automated program repair (APR), particularly through iterative refinement that generates and improves candidate patches. However, state-of-the-art iterative refinement LLM-based APR approaches cannot fully address challenges, including maintaining useful diversity among repair hypotheses, identifying semantically related repair families, composing complementary partial fixes, exploiting structured failure information, and escaping structurally flawed search regions. In this paper, we propose a Population-Based Semantic Evolution framework for APR iterative refinement, called EvolRepair, that formulates LLM-based APR as a semantic evolutionary algorithm. EvolRepair reformula
APITestGenie: Generating Web API Tests from Requirements and API Specifications with LLMs
arXiv:2604.02039v1 Announce Type: new Abstract: Modern software systems rely heavily on Web APIs, yet creating meaningful and executable test scripts remains a largely manual, time-consuming, and error-prone task. In this paper, we present APITestGenie, a novel tool that leverages Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and prompt engineering to automatically generate API integration tests directly from business requirements and OpenAPI specifications. We evaluated APITestGenie on 10 real-world APIs, including 8 APIs comprising circa 1,000 live endpoints from an industrial partner in the automotive domain. The tool was able to generate syntactically and semantically valid test scripts for 89\% of the business requirements under test after at most three attempts.
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