Google AntiGravity is amazing. In less than 2.5 hours, I've built this Solar System Explorer playground with Gemini 3.1 Pro and 0 lines of code. Cherry on the cake, you can chat with Gemini Live API as you're exploring the solar system and get fun facts about each planet and

Anthropic dropped a 33 page cheat sheet for using Claude to build and design anything. resources.anthropic.com/hubfs/The-Comp…

SOMEONE CREATED A GITHUB REPO WITH AN ENTIRE SETUP FOR AN AI AGENCY Engineers, designers, growth marketers, product managers. Broken down how even a rookie could understand. It has over 10K stars in 7 days GitHub: github.com/msitarzewski/a…

Google just dropped a new Generative AI Python library for SQL Databases. Introducing Google GenAI Toolbox. This is what you need to know:

How to become AI engineer in next 6 months: By the end, you want to be able to: - build LLM apps end-to-end - use APIs from OpenAI / Anthropic / open-source stacks - design prompts and context properly - add tool calling and structured outputs - deploy real projects So, let’s

This diagram shows what a production-grade agentic AI system actually looks like. Study it. But don't stop there:

Thank you Offerzen OfferZen

Claude Code project structure

Stop burning tokens on Claude Code. Use this instead 👇 A free GitHub repo (80K⭐) that turns your CLI into a high-performance AI coding system. Link → github.com/affaan-m/every… Why it’s different: → Token optimization Smart model selection + lean prompts = lower cost →

This AI System Design guide teaches RAG better than most courses. And I'm giving it away for free (Only for First 4500) Inside: • RAG fundamentals & chunking strategies • Hybrid retrieval (BM25 + vector search) • Production-level RAG architecture • Evaluation & RAGAS

A senior Google engineer just dropped a 421-page doc called Agentic Design Patterns. Every chapter is code-backed and covers the frontier of AI systems: → Prompt chaining, routing, memory → MCP & multi-agent coordination → Guardrails, reasoning, planning This isn’t a blog

How do I transition from Data engineer to AI engineer? Here's a structured 6-step transition path: You already have the hardest part: engineering fundamentals + production mindset. The goal is to add the AI layer on top. Step 1: Work inside an AI-flavored data pipeline -

Meet Gemma 4: our new family of open models you can run on your own hardware. Built for advanced reasoning and agentic workflows, we’re releasing them under an Apache 2.0 license. Here’s what’s new 🧵

Available in four sizes: 🔵 31B Dense & 26B MoE: state-of-the-art performance for advanced local reasoning tasks – like custom coding assistants or analyzing scientific datasets. 🔵 E4B & E2B (Edge): built for mobile with real-time text, vision, and audio processing.

Build autonomous agents that plan, navigate apps, and execute multi-step tasks – like searching databases or triggering APIs – with native tool use. With up to 256K context, it can analyze full codebases and retain complex action histories without losing focus.

Start building with Gemma 4 now in Google AI Studio. You can also download the model weights from @HuggingFace, @Kaggle, or @Ollama. Find out more → goo.gle/41IC3lY

You can now fine-tune Gemma 4 (and 500 other open source models) in a free Google Colab 🔥 1. Open the Colab notebook below 2. Run the blocks to launch Unsloth Studio 3. Choose a model and dataset 4. Hit 'Start Training' And you're done!