🚨BREAKING: Non-coders are shipping AI apps right now HowOneAI just silently dropped the 10-minute app builder of 2026. I just shipped a real app in 10 minutes with it: Upload image → 3D miniature transform → cinematic animated video - with no coding Here’s the breakdown: 🧵

Google’s new AI marketing tool Pomelli is on fire lately 🔥 …but here’s the twist: you can build the same kind of platform on @HowOneAI in just 10 minutes ⚡️💡 Try it yourself 👉 apn3vfy-6ff205.howone.app/ #Google #HowOneAI #NoCode #AIagents #MarketingTech #Pomelli

HowOne Beta is officially live. A new way to build real Agentic Apps — no code, no flowcharts, no glue tools. But let me ask you something: Still spending hours learning “vibe coding”? Still stitching together pieces generated by different AI tools just to make one product? Still wiring model keys and configuring APIs for every single workflow? You don’t need to do that anymore. With one prompt, HowOne generates: frontends backends databases multiple agentic workflows…and even self-evolving agents — all deployable in minutes. We’re opening our first wave of beta access. If you want to become an Agentic App Builder, join early and create tools that think, act, and improve — this is your moment. Beta users get: • Free credits • Workflow evolution features • Visual workflow editing • Support to find real use cases & customers • Priority in our upcoming builder marketplace 👇 Join the beta (invite link in comments) The next generation of apps won’t be coded — they will be evolved. #AgenticApps #AIApps #IndieHackers #BuildInPublic #AIStartup

HowOne AI Alpha Test Begins NOW! You don’t need a team to build your AI startup anymore. With HowOne AI, you can turn your idea into a fully functional AI app—in minutes. Build → Launch → Earn—all in one platform. 🎥 Here’s how it works 👇 #Solopreneur #AIapps #HowOneAI #NoCode #AgenticAI

Self-Evolving AI Agents are about to blow up 🚀🔥 New Survey Alert 🚨 From static LLMs ➡️ lifelong, adaptive agents 🌱🤖 A Comprehensive Survey of Self-Evolving AI Agents... — your roadmap to the future of AI that learns & evolves after deployment! 💡 arxiv.org/pdf/2508.07407

New EvoAgentX Talk coming this Sunday! 🎙️ Session Overview: Large language models are evolving fast—but how do we move beyond “text generators” to create true autonomous agents? Join our upcoming EvoAgentX Talk and follow Guibin Zhang, PhD candidate at the National University of Singapore (NUS), to dive into: ✨ How Agentic RL Transforms LLMs into Automated Agents He’ll explore: 1. Why traditional supervised learning can’t unlock the full potential of LLMs on complex tasks 2. How Reinforcement Learning (RL) enables long-term goal optimization and dynamic interaction 3. Key techniques and challenges of Agentic RL 4. How multi-agent systems, memory mechanisms, and self-evolving paradigms build truly autonomous LLM-based agents 📅 Date & Time: Sep 28 2025, Sunday, 9:30 AM (London Time) 📍 Location: Zoom Online 🔗 Join Here: Zoom short-url.org/1fZsg or Meeting ID: 862 8273 5663, Passcode: 396325 👤 Speaker Bio: Guibin Zhang 🎓 PhD Candidate, School of Computing, NUS (Advisor: Prof. Shuicheng Yan) 🔬 Research: Multi-Agent Systems, Agent Memory, Self-Evolving Agents 📚 Publications: 10 papers in NeurIPS, ICML, ICLR, KDD, TKDE 🌟 Representative Work: Graph for Agent series G-Designer (ICML 2025 Oral) G-Safeguard (ACL 2025) G-Memory (NeurIPS 2025 Spotlight) 🤝 About EvoAgentX Talk EvoAgentX Talk is a recurring online series from the EvoAgentX open-source community, bringing together global researchers and developers to discuss cutting-edge topics such as Agentic AI, RAG, workflow automation, agent evaluation & evolution, and Agentic Memory. Community members can: Stay updated on the latest research and technology Connect with leading researchers and developers Gain inspiration and contribute to the open-source ecosystem 🌟 Join Us & Get Inspired Click the Zoom link above to participate, and follow EvoAgentX to catch future talks. Let’s witness together how Agentic RL can drive LLMs to become self-directed intelligent agents. 💡 Interested in sharing your own research or project? Reach out to the EvoAgentX team to be our next speaker! By joining our Discord channel: discord.gg/XWBZUJFwKe or following us on Linkedin: evoagentx Github: github.com/EvoAgentX/EvoAgen… #EvoAgentX #AgenticRL #LLM #AutomatedAgents #ReinforcementLearning #MultiAgent #OpenSource #AIResearch #AICommunity #NUS

Hello X! 🚀 EvoAgentX v0.1.0 is live — and it redefines how agentic AI gets built, optimized, and shipped. Stop hand-wiring brittle pipelines. With EvoAgentX, agents build themselves from a single goal, then evaluate and evolve to keep getting better. * What’s inside v0.1.0 : 1. Auto-construction from one prompt Describe your outcome, and EvoAgentX generates a task-aware multi-agent graph you can run immediately. Visualize the graph, persist it, reload it, and iterate—no custom scaffolding needed. 2. A rich, ready-to-ship tool arsenal Out of the box, your agents can: • run code (Python, Docker) • search the web & query APIs (Google, Wikipedia, arXiv, SerpAPI/Serper, RSS) • work with files & shells • talk to databases (MongoDB, PostgreSQL, FAISS) • analyze/generate images • automate browsers (low-level & LLM-driven) Everything is modular and plug-in simple, so you ship real capabilities on day one. 3. Memory that compounds Built-in short-term and long-term memory let agents remember context, reflect across steps, and improve over sessions—so results keep getting sharper instead of starting from zero. 4. Human-in-the-Loop, by design Add approval gates and data-collection checkpoints anywhere. Pause an action, review, approve/reject, then continue—perfect for compliance, safety, and production controls. 5. Plug-and-play LLMs (cloud or local) Use OpenAI or Qwen directly—or route to Claude, DeepSeek, Kimi via LiteLLM, Siliconflow, or OpenRouter. Prefer local? LiteLLM helps you run models on your own hardware. Choice and portability are built-in. 6. Tool-enabled workflow generation Hand EvoAgentX a list of toolkits; it auto-wires the right tools to the right agents when generating the workflow (e.g., research with an arXiv toolkit). Less glue code, more outcomes. 7. Battle-tested architecture & evolution A five-layer modular design (components → agent → workflow → evolving → evaluation) keeps systems clean and extensible. Built-in optimizers (TextGrad, AFlow, MIPRO) refine prompts, tool configs, and even workflow topology—optimize once, keep improving. 8. Proven results without the laundry-list Benchmarks across multi-hop QA, code generation, reasoning, and real-world agent tasks show consistent, measurable gains—the payoff of self-evolving workflows. * See it in action Watch financial-analysis and research-summarizer demos, including an arXiv-powered agent using MCP-style tooling—great blueprints for your own production workflows. * Get started in minutes pip install evoagentx and you’re off. Explore the quickstart, tutorials, and examples to go from idea → running agent → evolving system. MIT-licensed, community-driven. 👉 GitHub (stars 1.4k ), Docs, and Community are all here—jump in and help shape the future of self-evolving agents. #AI #OpenSource #LLM #MultiAgent #SelfEvolving #AIagents #WorkflowAutomation #EvoAgentX