BiggB

Building AI Agents with Skills vs without Skills

🚨 CEO of Nvidia: "I'd hire the graduate who's expert in AI over the one who isn't. Every time." two types of people right now: type 1: uses the chat window. copy-pastes answers. type 2: knows the 25 tricks, and shortcuts most users have never seen. type 1 gets replaced. type 2 gets hired at $350K. I wrote a full guide on how to start. zero experience needed.

Someone open-sourced the 17 Claude skills behind serious social media growth. Tired of generic AI content that doesn’t sound like you? charlie947 dropped social-media-skills: a complete set of 17 specialized skills that make any AI agent a pro-level social media operator. Everything stays on-brand because it all starts with a single voice-builder skill that defines your personality and story. Then post-writer, carousel maker, reels scripter, analytics tool, and more all follow that voice perfectly. Key skills included: - Voice & profile setup - Hook & post generation with scoring - Carousels, infographics, reels scripts - Thumbnail ideas, graphic prompts - Niche research, content planning - Analytics and performance insights Works seamlessly with Claude Code and similar agents. One-time install, then prompt in plain English and get platform-ready content. If you’re building in public, running a newsletter, or growing any social presence, this repo will save you hours every week while improving quality. MIT License. Actively updated.

🚨 BREAKING: Anthropic just released their official prompt engineering course and it's free. Interactive Jupyter notebooks covering: → Basic to advanced prompting techniques → Chain-of-thought and tool use → Real agent patterns from the Claude team 12,200 stars (+2,459 this week). The only prompt engineering course you actually need

CLAUDE CODE MASTERCLASS 4 HOURS: Build & Sell (2026) Credit : Michele Torti

This 185-page book unlocks the secrets of deep learning. drive.google.com/file/d/188pV6F… Foundations > machine learning basics > computation efficiency > training methodologies Deep Models > activation functions > pooling > dropout > normalization > attention Architectures > MLPs > CNNs > attention Applications > image classification > object detection > speech recognition > reinforcement learning The Compute Schism > prompt engineering > quantization > adapters > model merging

Stop letting Claude Code read files that have nothing to do with your task! Every time you ask Claude Code to review a change, it scans your entire codebase. Most of those files are irrelevant. You are paying for tokens that add no value. code-review-graph fixes this by building a persistent knowledge graph of your codebase using Tree-sitter. It maps every function, class, import, and dependency, then at review time computes the exact set of files Claude actually needs to read. Here's how it works: • Parses your codebase into an AST and stores it as a graph of nodes and edges in SQLite • On every file save or git commit, a hook fires and re-parses only what changed • At review time it traces the blast radius of your change: every caller, dependent, and test that could be affected • Claude reads only those files instead of scanning the whole project Real results across 6 open-source repositories: 6.8x fewer tokens on code reviews, up to 49x on daily coding tasks. Supports 23 languages including Python, TypeScript, Rust, Go, Java, Vue, Solidity, and Jupyter notebooks. Works with Claude Code, Cursor, Windsurf, and any MCP-compatible agent. It's 100% open source. Link to the GitHub repo in the comments!

Tremendo post de Daniel sobre cómo usar subagents Esto él lo ha centrado en Claude Code pero en realidad esto se extrapola a cualquier cliente que permita usar subagents de esta forma En OpenCode yo tengo un setup muy parecido. Casi siempre tengo entre 2 - 6 subagents corriendo

EXERCISES FOR VISUAL ACUITY Eye exercises work wonders if done regularly for 10 minutes a day. 1. Blink frequently for two minutes. 2. Slant your eyes to the right and then move your gaze in a straight line. Do the same in the opposite direction. 3. Move your gaze in different directions: right-left, up-down, circle, figure eight. 4. Close your eyes for 3-5 seconds, then open your eyes. Repeat 7 times. 5. Press on the upper eyelids with your fingers, but without much effort, hold in this position for about two seconds. Perform in series - 4-5 times each. 6. Stand near a window, focus on an object located in the immediate vicinity (a point on the glass), and then shift your gaze to a distant object. Repeat 10 times. 7. Close your eyes and slowly move your eyeballs up and down 5-10 times. 8. With open eyes draw in the air first simple geometric figures, and then complex objects and large-scale compositions.

New release from @PacktDataML available at: amzn.to/40Sp4O9 "Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Python-based Agentic AI Framework with tool use, memory, and multi-agent workflows" Table of Contents: 🟠Introduction to Generative AI and AI agents ⚫️Understanding How AI Agents Work 🟠A Hands on Walk-Through of a Simple AI Agent ⚫️Building a Tool-Based Agentic AI Framework 🟠Implementing Custom Tools ⚫️Creating Chat Interfaces Using Slack and Chainlit 🟠Integrating with the Model Context Protocol Ecosystem ⚫️Designing Multi-Agent Systems 🟠Implementing Multi-Agent Systems with A2A ⚫️Testing, Debugging, & Troubleshooting Multi-Agent Systems 🟠Deploying Multi- Agent Systems ⚫️Advanced Topics and Future Directions

Instead of just watching an hour of Netflix, why not dive into this 2-hour Stanford lecture on AI careers? It’s packed with insights that could give you a real edge in the AI race, more than all the AI content you’ve seen this year.

Local AI for #PowerBI medium.com/microsoft-powe… #MachineLearning #ChatGPT

30 Agents Every AI Engineer Must Build — Build production-ready agent systems using proven architectures and patterns: amzn.to/41ckg6z v/ @PacktDataML — What you will learn: 1️⃣Deploy production-ready agent systems that scale securely and reliably 2️⃣Use LangChain and LangGraph to build autonomous agents with modular architectures 3️⃣Implement agents with sophisticated memory, planning, and reasoning capabilities 4️⃣Seamlessly integrate tools, APIs, and external data into agent workflows 5️⃣Establish robust evaluation frameworks to measure and optimize agent performance 6️⃣Implement guardrails and explainability features to ensure ethical and safe deployment 7️⃣Build multi-agent systems for complex, collaborative task orchestration 8️⃣Apply specific agent architectures across healthcare, finance, and legal domains

DevOps Journey – From Easy to Extreme 🔥 • 🐧 Linux Basics → 🟢 Easy • 🌿 Git & GitHub → 🟢 Easy • 📦 Package Managers → 🟢 Easy • ⚙️ Basic Scripting (Bash) → 🟢 Easy • 🔁 CI/CD Pipelines → 🟡 Easy–Medium • 🐳 Docker → 🟡 Easy–Medium • ☁️ Cloud Basics → 🟡 Easy–Medium • 🔐 Basic Security Practices → 🟡 Easy–Medium • 🌐 Networking Concepts → 🟠 Medium • 📊 Monitoring (Logs/Metrics) → 🟠 Medium • ⚖️ Load Balancing → 🟠 Medium • 🏗️ Infrastructure as Code (Terraform) → 🟠 Medium • ☸️ Kubernetes → 🔴 Hard • 🔑 IAM & Access Control → 🔴 Hard • 🔄 High Availability Systems → 🔴 Hard • 🧠 System Design → 🟣 Very Hard • 🔍 Debugging Distributed Systems → 🟣 Very Hard • ☠️ Production Outage Handling → 💀 Extreme Most people learn tools. Top 1% understand systems. #DevOps #Cloud #SRE

Building Enterprise-Level RAG Systems with #Azure From Blob to AI Search levelup.gitconnected.com/building-enter… #MachineLearning #ChatGPT