Started Working on a New Project using GibsonAI Memori, Composio , Nebius AI Studio & ScrapeGraphAI Will be sharing more details soon!

Awesome AI Apps is at 4.9k Stars! Can we get 5k by this week?

After months on Cursor, I just switched back to VS Code 👀 Why? Hugging Face’s Copilot Chat extension. It lets you use open-source models like Kimi.ai K2 & Qwen3 from Nebius AI Studio , right inside your editor. Here’s how to set it up 👇

AI Agents aren’t just another workflow tool! They flip control logic on its head! Instead of rigid programs, agents reason, adapt, and act dynamically. That’s why they’re perfect for messy, real-world problems where fixed workflows break

Finally Langchain launched their New Docs! And believe me, It's much cleaner than the previous versions! Mintlify did the magic again ✨

Agents don’t need to forget. With GibsonAI Memori's Auto Ingest Mode: - Each query runs a memory search - Finds the most relevant facts (up to 5) - Injects them into the LLM call Dynamic context retrieval in real time 👇

Been Exploring Amazon Web Services Strands Agents lately 👀 It’s actually pretty fun, spins up agents fast, and the Agent Loop is slick. So I recorded a quick tutorial: • How Strands works • Launching your first agent • Agent Loop in action Check it out 👇

Learn How to Build Docs QnA Agent in 5mins! Full Video: youtu.be/lZRKKd7MC5A

Been struggling with messy PDFs and broken OCR? Same here 👀 IBM just dropped Granite Docling It's a compact AI model that converts PDFs & images into clean, structured text while keeping tables, math & layouts intact. I tried it on Hugging Face Spaces here’s the demo 👇

Databases have powered the internet for 50 years. Now they might power AI memory too.

The agentic loop is what makes Strands agents so smart. Instead of hardcoding every step, Strands lets the model: 1. Perceive the situation 2. Think about options 3. Act with the right tool It’s a continuous cycle, so your agent can handle complex tasks on its own

Awesome AI Apps now features top agent frameworks: → Google ADK → OpenAI Agents SDK → LangChain → LlamaIndex 🦙 → Agno → CrewAI → AWS Strands SDK → Pydantic AI Been fun curating this. What should we add next? Drop your favs 👇

It takes just one line to crawl entire websites with AI ✨ ScrapeGraphAI SmartCrawler can: - Traverse multiple pages & follow links - Extract structured data with LLMs - Convert HTML → clean markdown (80% cheaper) Here’s how 👇

Context Engineering >> Prompt Engineering! Totally agree with Jeff Huber

GitHub just dropped Copilot CLI 👀 With it, you can use Copilot directly in your terminal: - Access repos, issues, and PRs with natural language - Build, edit, debug, and refactor code with AI - MCP-powered, fully controllable Install via npm & code smarter ⚡

Just tried the new GitHub Copilot CLI 👀 And wow… It’s quite good. I asked it to fetch issues, explain code, and even suggest project ideas. The coolest part? It actually scanned my repo and gave me solid ideas based on them! This looks Promising!

I’ve been experimenting a lot with AI agents while building prototypes for clients and side projects, and one lesson keeps repeating: Sometimes a single agent works fine, but for complex workflows, a team of agents performs way better. To relate better, you can think of it like

Don’t Just Build Agents. Build Memory-Augmented AI Agents! Two essays shook up the AI world recently. Anthropic wrote about building multi-agent research systems: a coordinated team of agents, each specializing in a piece of the puzzle, passing context back and forth. Then