Build AI-Enhanced Web Apps: http://
amzn.to/4bgux6d
———
Amazon Summary: "This book shows you step-by-step and example-by-example how to build sites and applications that take advantage of large language models (LLMs) like GPT, Claude, and Llama. Written especially for web
CODE
-
Educational guide on building AI-enhanced web applications with LLMs
By
–
-
Learning Model Context Protocol for Building Agentic Systems
By
–
Learn Model Context Protocol [MCP] with Python — Build Agentic Systems in Python with the new standard for AI Capabilities: http://
amzn.to/4njfsVM by @chris_noring v/ @PacktDataML 𝓦𝓱𝓪𝓽 𝓨𝓸𝓾 𝓦𝓲𝓵𝓵 𝓛𝓮𝓪𝓻𝓷:
Understand the MCP protocol and its core components -
How to Build Structured AI Agents
By
–
How to build AI agents • Define scope
• Structure inputs
• Add tools & reasoning
• Orchestrate agents
• Add memory & context Smart agents are structured systems, not just prompts. Via Giuliano Liguori (
@ingliguori
) #AI #AIAgents #GenAI -
Codex preferred over CC with more context and better taste
By
–
yeah, and codex already is really really good. I even prefer it over CC most of the times. More context + better taste and its GOAT
-
Claude has taste and context; Codex will overcome when it gains both.
By
–
Claude is lazy, but has taste and context (no talking about 4.7 tho) Codex is eager, but still lacks some taste and context. Once Codex gets both, it’s over.
-
Building Complex Multi-Agent Systems for Automation
By
–
🚨 AGENT SWARMS – BUILD COMPLEX APPS AND AUTOMATIONS
— Abacus.AI (@abacusai) 16 mai 2026
Combine Gemini 3.1 Pro, Opus 4.7 and GPT 5.5 to create complex multi-agent systems
Each agent excels at a particular task – coding, testing, mobile app, research and monitoring
Master agent orchestrates worker agents pic.twitter.com/mLvu0sNKjwAGENT SWARMS – BUILD COMPLEX APPS AND AUTOMATIONS Combine Gemini 3.1 Pro, Opus 4.7 and GPT 5.5 to create complex multi-agent systems Each agent excels at a particular task – coding, testing, mobile app, research and monitoring Master agent orchestrates worker agents
-
AI bypasses Apple’s M5 security in a week
By
–
Three researchers used Anthropic's Mythos to build a working macOS kernel exploit that bypasses Apple's M5 Memory Integrity Enforcement, a security system Apple spent five years and billions of dollars building.
— Chubby♨️ (@kimmonismus) 16 mai 2026
Bug found April 25. Working exploit May 1. Walked into Apple Park… https://t.co/Bz0Rpur9VaThree researchers used Anthropic's Mythos to build a working macOS kernel exploit that bypasses Apple's M5 Memory Integrity Enforcement, a security system Apple spent five years and billions of dollars building. Bug found April 25. Working exploit May 1. Walked into Apple Park
-
Debugging AI Tool Usage via Command Line
By
–
Mind running /usage and pasting the output here? Happy to debug
-
AI Agents Hermes and OpenClaw Compared on GitHub History Analysis
By
–
Atomic Bot put Hermes and OpenClaw head-to-head on the exact same task, running the same model (Qwen 3.6 35B) with the same goal: analyzing GitHub history, mapping growth spikes, and shipping a live dashboard in the browser.
— 🚨 AI News | TestingCatalog (@testingcatalog) 15 mai 2026
Key metrics to watch for 👀
> Time to complete the… https://t.co/VReoAL9Taz pic.twitter.com/GgYHjaO30CAtomic Bot put Hermes and OpenClaw head-to-head on the exact same task, running the same model (Qwen 3.6 35B) with the same goal: analyzing GitHub history, mapping growth spikes, and shipping a live dashboard in the browser. Key metrics to watch for > Time to complete the
-
Software development with unlimited tokens and 100 codex in cloud
By
–
People freaking out over my AI spend. What nobody sees: Part of what excites me so much about working on OpenClaw is that I'm trying to answer the question: How would we build software in the future if tokens don't matter? We constant run ~100 codex in the cloud, reviewing