Those building AI coding agents will eventually hit the... same wall: No matter how smart the model is, the moment you ask it to write production-ready code with the latest libraries, it starts hallucinating. Everyone is building coding agents these days, but most of them run into the same issue: They're coding with outdated documentation. The root cause isn't the model. It's the retrieval layer. If you are building an AI agent that needs information from specific domains such as code, law, academia, and finance Sign up, connect it to your agent, and you'll see the difference immediately.show more

Most AI coding agents can write code. But what... happens when you give one agent an entire project and tell it to figure everything out? I put EvoX Agent to the test. Here’s what happened ↓show more

Meet Muse Spark 1.2 and Muse Code, the agent... built to run it against your coding challenges. Now in beta. Together they take a task from idea to production, and deploy teams of agents that plan, write and refine across your repos. Start building now:show more

AI coding isn't just “building apps faster.” Sometimes it... feels closer to drawing with logic. Everyone has their own timeline. Everyone has their own shape. You write a prompt, adjust the motion, bend the layout, and slowly the thing starts to breathe. I don't think this is beneath AIGC. If image generation is painting with pixels, AI coding is painting with behavior. Which one feels more like art to you?show more

AI coding agents are moving past “write me code.”... They can now build, review, and improve the same project. So we connected Claude Code to Step 3.7 flash and gave it a real task.show more

Issue 4 of Interfaces just released! This issue is... about building an animated sign-in dialog. It covers everything that goes into building it, from the animations to the small but important details. It's one of the most in-depth issues yet, full of code snippets and demos.show more

NO WAY. Someone is basically building a MANAGER for... your AI agents. You give it one objective. It briefs Claude, Codex + the agents you choose, keeps them on the same context and coordinates the work. This is Jarvix. And I had to dig into this: 🧵 #JarvixOSshow more

AI agents are quickly becoming one of crypto’s biggest... narratives in 2026 🤖 Coinbase calls it the future. Jensen Huang says they will “change everything.” At Bluwhale, you can already create your own AI agent with Bluprint ⚔️ No coding required. Start building 👇 👇 Sourceshow more

How do you build AI agents that remember and... learn from past conversations? Watch this episode of The Agent Factory for a deep dive into building agents with memory, including the fundamentals of agent memory, Vertex AI Memory Bank, and more →show more

Excited to launch a new way to upskill with... AI agents. This is how we are making it possible for anyone to learn to build with coding agents. To start, we are launching 4 new hands-on labs on the following topics: - Agent Skills - Agentic Image Generation - 30 Days of Hermes Agents - Prompt Engineering with Agents I am confident that with our new DAIR.AI platform, anyone can learn to become a top AI builder by building and acquiring highly-demanded AI skills. And there is a lot more landing in the coming weeks.show more

Building AI agents is finally simple — and Airia... is leading the way. I’ve been testing Airia AI , enterprise AI orchestration platform that unifies every model, workflow, and data source into one secure environment. Whether you’re a developer, analyst, creator, or enterprise leader, Airia makes it incredibly easy to build powerful AI agents — without wrestling with multiple tools or complex integrations. Using the no-code builder, you can drag-and-drop actions, connect data, choose your LLM, and launch an agent in minutes. Then run it live, publish it, and even share it with the Airia Community, home to 2,500+ pre-built agents you can use or remix. If you want to automate workflows, prototype faster, or explore real enterprise AI use cases, Airia is the place to start. 👉 Build your first agent today: 👉 Explore the community: #Airia #AgenticAI #AIOrchestration #AIAgents #AIWorkflow #DigitalTransformationshow more

LangGraph. CrewAI. Agno. Which one to pick? The good... news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.show more

Seems like Visual Studio Code is starting to tell... you: your agent primitives need to move. There is now a new migration banner in the Chat panel, and it is part of a much bigger change happening under the hood: the move from the old Local harness to the new Agent Host architecture built around AHP. This is not just about moving where an agent runs. The old model was very VS Code-centric: prompts, custom agents, instructions and skills could live in VS Code-specific locations and the agent runtime lived inside the extension host. The new Agent Host separates the agent runtime from the editor. Sessions can keep running when the window closes, be shared across VS Code windows, run remotely, and support different harnesses such as Copilot, CLI and Copilot Desktop App through a common session layer. And that means some of our primitives need to move too. Prompt files are being deprecated for Agent Host and migrated to Skills. User-level agents and instructions that lived in VS Code profile storage need to move to harness-supported locations. Even the old location settings are being deprecated. The new migration experience can detect these things and guide you through moving or converting them, while keeping the originals unless you explicitly remove them. The new banner is basically the first visible sign that this migration is becoming a real product workflow. Basically telling us - it's time to move on!!!! If you have accumulated a lot of prompts, custom agents, instructions and skills over the last year, now is probably a good time to understand where they actually live and which harness owns them. To summarize the shift - it isn't just: VS Code Chat → Agent Host It is: VS Code-specific primitives → harness-native primitives. And I think this is going to become increasingly important as agents stop being features inside an IDE and become runtimes that multiple clients can connect to. Go run your migrations now 🏃‍♀️show more

Agents are the future of AI creative tools. And... the new Grok Imagine Agent is a great one! It feels like a smart partner that you can brainstorm and iterate with - it does the heavy lifting of writing prompts + building out the canvas. Watch me build a froyo brand 👇show more

It's true, most AI coding tools make you do... the grunt testing and debugging work. That's why we invested in researching and building a state-of-the-art app testing tool. Replit Agent 3 will spin up a browser and test your app end-to-end.show more

whoever leaked this is f*cking crazy someone mapped 300+... AI agents into one GitHub repo - and basically leaked the shortlist of what’s actually worth trying. coding agents. browser agents. memory. voice. research. multi-agent teams. local models. sandboxes. evals. all sitting on one page. and once you start opening the categories, you realize how much stuff people are still building from scratch for absolutely no reason. want an AI that works inside your codebase? -> Aider / Continue want agents working together like a tiny company? -> CrewAI / AutoGen / MetaGPT want your agent to actually remember you tomorrow? -> Mem0 want it running code somewhere that isn’t your laptop? -> E2B want models running locally for $0/token? -> Ollama want to see what your agent actually did after it inevitably does something weird? -> AgentOps and that’s barely scratching it. there are 300+ projects across ~25 categories. the real cheat code isn’t downloading all of them. it’s opening the repo before you build anything and asking: “has someone already solved this?” because there’s a pretty good chance the answer is yes - and the open-source version already has thousands of people testing it for you. I went through the map and pulled out the agents I’d actually start with + my pick for every major part of the stack. full 300+ AI agent list belowshow more

THE LLM WORLD ISN’T JUST CHATGPT ANYMORE. It’s an... entire ecosystem. Think of it like a tech stack: → Models GPT Claude Gemini Llama → Frameworks LangChain LlamaIndex DSPy → Tools MCP Vector databases Web search Code execution → Applications AI agents Coding assistants RAG systems AI automation And they all connect. Model → Context → Tools → Memory → Actions That’s why building with AI is becoming less about “which model is best?” And more about: How do all these pieces work together? The model is only one part of the system. ------- Check out these AI resources to get started. Inside you'll find: • 1,000+ AI prompts • Curated AI tools • Automation workflows • Productivity systems • Real-world use cases and examples Download here: -------show more

‼️found a site that ends AI design slop. superrr... is an MCP server that gives your coding agent taste. they measured 132 real landing pages, verified 123 of them and pulled out 17 design directions with exact tokens. the agents still writes the code. superrr picks the direction, hands it the structure then grades what it built by scanning the live page after. no more generic purple gradient landing pages. their landing page was one-shotted with it. three lines to connect to Claude. link:show more

Today, AI can generate tons of code—but how do... we know if it's good? That's why we built Sculptor: the first coding agent environment. Sculptor helps you catch issues, write tests, and improve your code—all while you work in your favorite editor.show more