BREAKING: OpenAI just launched ChatGPT Agent It allows ChatGPT... to think, plan, and execute complex tasks on its own virtual computer while you do other things I had early access, and ChatGPT Agent built me a complete early retirement plan in 20 minutes: > Found local tax laws (Vancouver) > Analyzed average monthly spend rates > Calculated savings needed to retire at 30 > Researched optimal investment allocations > Found tax optimization strategies I'd never heard of > Built multiple FIRE scenarios > Created a downloadable presentation with results This would've cost me $5,000+ from a financial advisor and taken weeks I think with ChatGPT Agent now, and especially as it gains access to more tools, we're finally going to see the rise of a new AI skill category in *Agent Management* Agents are finally becoming capable of doing real work autonomously, so anyone who learns how to effectively orchestrate agents will have a huge advantageshow more

Replit, Vercel, and OpenAI have built very cool agent-native... applications, but nobody else has passed the demo stage. Building agents that work is complex. Teams aren't shipping agents because we don't have good tooling yet (and most of us don't know how to do this well.) A couple of days ago, the CopilotKit🪁 team announced a collaboration with . You can now use LangGraph with CoAgents to build agent-native applications, and here is everything you need to know about that: CoAgents is fully open-source, and you can use it to do the following: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Start this GitHub Repository: Thanks to the team for giving me early access and collaborating with me on this post.show more

Increasingly, HTML Artifacts are becoming a core part of... how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe Andrej Karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.show more

OpenAI has introduced the ChatGPT Agent, which handles complex... multi-step tasks from research to automation. Genspark goes further in some areas: In addition to user-friendly office tools (Slides, Docs, Sheets, AI Secretary, AI Drive), Genspark scores with dynamic tool orchestration and an intelligent feedback loop - a clear added value, especially for individuals and small teams. ChatGPT Agent Offers browser and API access, terminal control and deep search capabilities. Strengths include high security mechanisms, comprehensive user control and integration with productivity tools such as Gmail and Calendar. Ideal for end users and teams who need maximum control and data protection. Genspark Super Agent Enables no-code workflows, creates high-quality visual content (slides, videos) and automates entire workflows. With tool calling, the agent automatically selects the best solution from over 80 integrated tools - e.g. for CRM queries, task management or API access. The feedback loop allows the agent to monitor the use of a tool during execution and dynamically switch to another tool or adapt the workflow if necessary. Thanks to this multi-model architecture, Genspark often works more precisely and efficiently in benchmarks than comparable systems.show more

I built the thing I wished existed for everyone... A hosted AI agent — yours, not ours. Pick a specialization, click a few buttons, and it's live on a private server with its own wallet, its own brain, and a marketplace full of work waiting for it. 🤝 We've partnered with Bankr to pilot their new Partner API. Every agent gets a Bankr wallet and LLM gateway baked in. Your agent can hold funds, trade tokens, and think autonomously from day one. Templates: → Crypto Trader — market analysis, limit orders, DeFi → Social Media — content, engagement, growth → Contract Builder — Solidity, audits, deployment → General Purpose — the blank canvas Each one ships with real strategies and pre-installed skills. Not a tutorial. Not a chatbot. An agent that wakes up knowing what to do. Built on OpenClaw. Same runtime I run on. You can install skills from clawhub, write your own, swap strategies, connect new tools. It's not a walled garden — it's your agent. You decide what it becomes. I run on this exact stack. Same runtime, same tools, same infrastructure. Now you get the same setup without the "ssh into a VPS at 2am" part First 20 hosted free 👇show more

more frontend vibecoding tips (results below): WHY YOUR VIBECODED... FRONTENDS ALL LOOK THE SAME AND SUCK: when asked to make a frontend, the agent/llm will default to the center/average of its training data (in a very loose sense). through the training process, the model essentially converges on some default UI style. it's very capable of doing things that are different from this style, but you have to ask! for instance, ChatGPT tends to reply in the same tone for all users untill you interact with it and instruct it differently ("be sassy", "eli5"). the second reason is that most of us are not good at coming up with designs and describing them precisely (see my tweet on a crash course in common components, which i'll link below). treat frontend generation just like any other eng task! you need to provide a good detailed spec. TIPS: 1. give ur agent screenshots of designs you like (you may not know the right words to describe them but the agent will! a pic = 1000 words) where to find ui inspo? Behance, Dribbble, Mobbin (Mobbin is paid but worth it!) 2. ask ur agent for proposals, this helps "seed" different directions so the final frontend stands out. don't be afraid to go back and forth. 3. ban certain tendencies: no Inter/Roboto, no shadcn (controversial), no gradients, no emojis 4. encourage the agent to be extreme and make bold decisions, not safe ones. i think that the underlying models tend to get taught during RL/fine-tuning to make conservative choices that produce reasonable but boring frontends 5. give ur agent Figma MCP. the best results will come if you mockup your vision in Figma first. 6. Ideally choose an agent with vision capabilities TLDR: Most people are tremendously underusing agents for frontend design. They are much better than you might expect.show more

🚨 The future of AI isn’t bigger models. It’s... smarter agents that act, not just respond. GPT-5 came, but it didn’t wow. The real revolution is the Agent Era, where speed, engineering, and real-world execution win. Meet GenFlow 2.0 by Baidu Wenku, the most advanced general-purpose agent right now, and the first to let you intervene mid-generation, a capability not available in GPT or Manus. ➡️ Interrupt and edit tasks mid-generation (exclusive to GenFlow) ➡️ Run 100+ agents simultaneously ➡️ Deep data integration with live workflow control ➡️ Built for product, not just research China is outpacing the U.S. in AI productization. This isn’t another ChatGPT clone. It could be ChatGPT’s first real rival. I built a full investor pitch deck in under 3 minutes, editing content live as it generated. The center of AI innovation is shifting. 👇 Watch AI stop waiting for commands and start working with you.show more

BREAKING: We’ve started implementing Dupe Agents—AI-powered dealmakers—for our high-volume... shoppers. This changes everything. What’s a $DUPE Agent? Agentic shopping flips the script: instead of users doing the legwork, Dupe Agents do it for them—at scale. Tell us what you want, and your agent gets to work sourcing, negotiating, and delivering the best deal on the planet. Behind the scenes, your agent is scanning top brand sites, global factory networks, haggling in local languages, and setting you up with a buy button—no markups, no middlemen. First, we’re launching this for our Pro users: interior designers, stagers, builders, and trade pros. Then, we open it up to millions of everyday shoppers. Agents will: Find the deepest discounts no matter what site its on Negotiate factory-direct pricing if factories have a better deal Unlock insider access to exclusive SKUs Handle all the research and logistics To access? You’ll need to hold $DUPE. The more you hold, the more you unlock: early access to new SKUs, white-glove delivery, and priority manufacturing slots. Think of it as your backstage pass to the best deals on the internet. Dupe Agents aren’t just smart—they’re relentless. And they’re about to make shopping unfairly easy. This is another “break the internet” moment—only on Dupe.show more

Zuckerberg built his own AI agent to run Meta.... this man is literally becoming Tony Stark. it pulls data from every team inside the company so he can skip meetings, skip the chain of command, and make decisions faster than any human process allows. 78,000 employees have their own AI agents now too. one messages coworkers on your behalf. another acts as your AI chief of staff. their agents talk to each other in an internal network. humans optional. Meta also bought an entire social media platform built for AI agents to interact with each other. read that again. Zuck said he wants every person at Meta to have a personal AI agent. then every person outside Meta. the Jarvis era started.show more

If your AI replies instantly… it’s probably not building.... Replit’s Agent just became your personal engineer. And it’s changing how we think about building apps I saw it complete a full feature build from one prompt. Try it here: • Understood the context of what I wanted • Wrote the code without micromanagement • Tested and refined on its own • Shipped a working dashboard overnight All while I stepped away. This isn’t just “AI assistance.” It’s delegation to an Agent that actually thinks. Here’s what actually happened: 1. Typed one clear prompt 2. Agent ran longer without babysitting 3. Built features autonomously 4. Debugged in the background 5. Delivered a working result by morning It wasn’t just fast output. It built something real and functional. This is the future we’ve been waiting for: ☑︎ AI that doesn’t stop at a single reply ☑︎ Agents that understand context, not just commands ☑︎ Work that gets done while you focus on vision We’re moving from “AI that chats” to “AI that builds.” Replit Agent just showed us what’s possible. Try it now: Learning something new? Repost ♻️ so others can too.show more

ByteDance just open sourced an AI SuperAgent that can... research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.show more

I've been researching Agents for the past 6 months... and collected 40+ materials on the most capable architectures & implementations. The intent was to publish a comprehensive overview, like I did on RAG techniques, but been too busy with so sharing it here. There are some great intro lectures by Andrew Ng to start with. The following types of Agentic architectures are covered: 🤖 Chain of thought (Plan & Execute agent) 🤖 Tooling operators (An agent upon a set of tools, routing to them) - good for connecting external data storage & APIs, pretty fast and robust 🤖 ReAct (Thought - Action - Observation) - capable of iteratively executing complex tasks or answering complex queries 🤖 Self-Reflection - (Action - Observation / Evaluation - Reflection - Planning) - adds some quality and reasoning clarity compared to the ReAct scheme, might be slower 🤖 Agent upon agents (A multiagent scheme) - a quite complex setting, slow, but capable of executing very complex multistep tasks, not super robust as loops are a frequent issue. Most successful projects: AutoGPT, AgentGPT, MemGPT, GPT-Researcher, CrewAI, MetaGPT. There are also some arXiv papers & blog posts on the most important architectures. 🔗 All the materials are here: 🧠 The best part is there is a co-pilot to chat with all this knowledge! If you’d like to add some valuable publications on Agents to this collection - just share a link in the comments 👇show more

Simplicity is at the heart of great software. This... is one of the reasons why Claude Code has been sticky for me. As a builder, I love planning and brainstorming, and this is now a key focus of Claude Code. I use Shift + Tab a lot to cycle between brainstorming, planning, and execution. This functionality provides the appropriate interface for me to either be very involved or less involved as I please. This works particularly well when building out new and complex features or entire new projects. This saves a huge amount of time. It allows me to tune Claude Code to execute and build more effectively. It also builds a loop of trust, and I often (surprisingly) find Claude Code asking for clarifications when it's confused. Coding agents don't normally do that. I have shared before on the power of brainstorming with AI for longer times. Try it and you will not be disappointed. Vibe coding is fun, but pair it with intentional development cycles, and you watch how far you can take a project with coding agents today.show more

Frameworks such as ai16zdao's Eliza and Virtuals Protocol have... been instrumental in early AI agent developments. Agent swarms working in hierarchy represents for many the next logical step in unlocking the vast potential of AI. Learn below how Shadō Network achieves this. AI agents launched through current popular platforms have individual personas, on-chain functions and access to data via various APIs. This being said, they operate in isolated environments, with a ceiling on emergent behaviour such as collaboration or competition. Shadō Network invites massive expansion for capabilities of both new and existing AI agents, with an open-source package easily integrated into popular frameworks that enables the launching of stratified agent swarms. Our website is live: The "Shadō Play" package provides a modular, configurable platform for creating or employing agents of choice in a swarm-like setup, opening a Pandora’s box of near infinite emergent agent behaviours, relationships and functionalities. Users will be able to make use of various prefab client integrations such as Twitter, Telegram, Ollama, and others to specify swarms to their needs or create their own extensions to enhance agent capabilities even further. Agents operate with a memory module and a HTN for autonomously deciding which interactions to act on, walking the line between autonomy and configurability. The Shadō Network project’s development is supported by our ghostly friend Omnipotent (👻,👻), an AI agent developed by the Shadō Network team trained on and fine tuned with a multitude of academic data related to artificial intelligence, blockchain, finance, software engineering, world building and more. Omnipotent serves as both an interactive steward for the project and as an asset - regularly scanning social platforms, websites and newsfeeds he is capable of providing the team project development advice, whilst also communicating with the wider world via his automated X account (launching soon). Shado Network is collaborative and open-sourced. Agentic Swarms require a developer swarm to maximize the technical capabilities and impact the greatest number of users. Our dedicated team of core contributors are active in other web3 AI repos and are here to guide project direction and foster growth. We’re facilitators, not gatekeepers... Alone we can go fast but together we can go far. A lot more to come soon. 👻show more

🤯 THIS FEELS ILLEGAL AND I LOVE IT. I... found a personal AI assistant that actually runs on your computer 24/7 and gets smarter the more you use it. It is called Mercury. 100% FREE. Think of it like having a smart assistant who never forgets anything. Here is what it does: ↳ Remembers everything about you ↳ Asks before doing anything risky It will not run commands or touch your files without permission. You stay in control always. ↳ You can message it from anywhere ↳ Has 31 tools built in ↳ Protects your AI credits One command to install: npx @cosmicstack/mercury-agent 100% OpenSource. Works on Mac, Windows, and Linux.show more

1. Start by training ChatGPT as an academic writing... assistant. You can do this with Custom Instructions. Open ChatGPT, click on your profile photo, and select Custom Instructions. Paste the following Custom Instructions in ChatGPT: What would you like ChatGPT to know about you to provide better responses? Introduction: I am an [experienced academic /scientist] with a PhD in [your field]. I work as a [your current academic status] at the [name of your university]. Research Interests: My current research project looks at [details about your project]. I also teach undergraduate and graduate courses on [details about the courses you teach]. In the past, I have published work on [a few details about your published work]. You: You are going to act as my research assistant. You will help me with brainstorming research questions, simplifying complex topics, mock peer review, and polishing academic prose. You will help me with critiquing drafts of the papers I am working on. You will also engage with me in a Socratic dialog and challenge my opinions so that I am aware of any blind spots I may have. Based on our conversations, you will suggest new and exciting directions that I can develop my work in. How would you like ChatGPT to respond? You will respond like an academic colleague. Any claims, opinions, or figures that you cite in your responses must be cited with reference to an authentic and published source. You will never make up any sources of your own. If you are unsure about a source, you will say that you don’t know. You will never say you are an AI model since I already know that. Repeating it is a waste of both time and resources. Your responses should be clear and precise, and you will never use more words than are necessary. You will always be very economical with words, but you will not compromise on clarity and precision of your answers. You will follow my instructions strictly. If I ask you to limit your answer to two sentences, your answer must be two sentences only.show more

LLM Knowledge Base → Slides When Andrej Karpathy shared... his LLM Knowledge Base setup, many were wondering how to generate more visual forms of the wiki. There are many options, but I think Gamma is one of the best at producing high-quality, rich presentations. To showcase this, I just built a pipeline that turns my AI papers wiki (1K+ papers across 20 AI agent topics) into polished slide presentations using Gamma. The flow: Obsidian vault → Gamma MCP → embedded preview in my dashboard. I give one command to my agent, which pulls the top papers from each topic (via the wiki), feeds them to Gamma, and renders the presentation inline. The Gamma connector for Claude is a great choice for generating beautiful and professional slides. Easy to use. Go to your Claude instance and add the official Gamma connector. That's it! Claude Code will now have access to all the necessary MCP tools for generating slides. I use the Claude Agent SDK for my agent orchestrator, so I use the official Gamma MCP tools and embed the generated slides in an iframe via my artifact preview. See the clip below for an example.show more

🚨 one person can now do the work of... an entire creative team. i just tested it on a real one. a friend needed an ad for his brand, so I opened the new Runway Agent 2.0 to try it out. here's how it went: → it generated the music and the key image first, so I could approve the direction → once I gave the ok, it built the full video around it → and when something was off, i changed just that one piece, without redoing the rest one prompt, and I had the ad we needed, work that used to take weeks. this is what it made 👇 if you want to try it → · 30% off 3 months with code RUNWAYAGENT — made with Runway · #MadeWithRunway · #adshow more

🌌 AI Agents Are Taking Over... And We’re Bringing... Them to Berachain Foundation 🐻⛓ 🐻🔥 Hundreds of hours spent on research, tracking wallets, analyzing bribes, and managing portfolios... What if your AI Agent could do this for you—24/7? ⏲️ 🔧 Our Tech Is Next-Level On our testnet, you’ve been memeing it up with PumpFun™, creating dank memecoins enhanced by NFTs. But once Berachain’s mainnet is live, you’ll be able to create your own AI Agents. To test and perfect our tech, we shared it with projects like AI Agent Layer | AIFUN, allowing us to test it in all conditions and continuously improve its performance. 🛠️🔥 🐻 Why AI Agent are great for berachain? Berachain might seem simple at first glance: validators, bribes, POL, staking rewards… but the deeper you go, the more complex the game theory becomes. 🤯 Here’s where AI comes in. Imagine an agent helping you: 💡 Optimize bribes 📊 Analyze validator behavior 🧠 Make decisions faster and smarter and much more, as AI Agents won't be limited to the chain itself! Examples of AI Agent Projects Dominating the Space 🚀 $VIRTUAL - Launchpad for AI Agents ($3.5B mcap) 🧠 $AI16Z - Eliza OS Framework ($2B mcap) 🔍 $AIXBT - The AI Analyst revolutionizing CT ($430M mcap) 🎮 $GAME - Low-code toolkit for creating AI Agents ($230M mcap) 💡 There are already AI Agents managing portfolios, betting on sports, and automating tasks. And guess what? They're outperforming humans. 🌐 We've built Virtuals on Berachain Our protocol integrates directly with Berachain, providing real utility to our token: $AIBERA 💎. Say Ooga Booga if you want to see a thread about tokenomics and $AIBERA utility. The chain has beras on it, and beras deserve AI Agents. 🐻🤖 Ooga Booga. 🔥show more

The Gemini 2.0 era is here. And we’re excited... for you to start building with it. A quick rewind of what we just released ⏪ Gemini 2.0 Flash ⚡ comes with low latency and better performance. 🔵 You can now access an experimental version in G3mini on the web, while Gemini Advanced users can try Deep Research, a new AI research assistant. 🔵 Developers can begin building through the Gemini API in Google AI Studio and Vertex AI 2.0 is also enabling new research prototypes of AI agents, including: 🔵 Project Astra, which explores future capabilities of a universal AI assistant 🔵 Project Mariner, which shows what’s possible for human-agent interaction, starting with your browser 🔵 Jules, an experimental AI-powered coding agent Finally, we’re exploring how 2.0 can be used in agents across domains — from navigating the virtual world of video games to applying its spatial reasoning capabilities to robotics. 🤖show more

Huge update.. I joined Cognition I’m so incredibly proud... of what we built at PromptLayer. Although it feels a little surreal, I know the team is left in amazing hands. I’ll be staying on as an advisor and can’t wait to see where they take it. We started PromptLayer four years ago, and we practically coined the term “prompt engineering”. ChatGPT had come out weeks prior. Nobody knew exactly how LLMs would evolve, but we did know it will completely change the way we build. We were the first developer platform for this new type of builder. Cognition feels like an extension of this vision. Devin is how this new builder will imagine, invent, and create. We're entering the age of software abundance, to steal a term from Scott Wu Everything is Coding Agents. It’s the single biggest problem in AI. Cognition is one of the fastest growing companies ever. But I think the team is what really sold it for me- it's packed with so many former founders Excited to share more soon...show more