🚀 New OpenClaw Update + Qwen3 Coder + RentAHumanAI — What You Really Need to Know Hey Builders — this video breakdown covers the latest developments in OpenClaw, Qwen3 Coder, and a wild new frontier in AI agents hiring humans for real-world tasks. If you didn’t catch the live or want a clean summary, here’s the gist and why it matters to everyone in this community. 🦞 1. OpenClaw Has Evolved Fast OpenClaw isn’t “just another agent.” It’s an open-source, locally-run AI assistant that can live in WhatsApp, Telegram, Slack, Discord, Signal, and more — all on your machine under your control, with your data, your rules. (Medium) This means: - It doesn’t depend on cloud servers for most functionality - It integrates with messaging platforms you already use - It can automate tasks, run code, use plugins/skills, control browsers, and adapt over time. (Medium) - 🛠 2. What’s New in the Latest OpenClaw Release Without a transcript but based on context and ecosystem moves, this update likely includes: - Expanded model support and performance improvements - Better stability and tools intuition - Enhanced channels and usability UX - Security hardening and more plugins/skills out of the box This matches broader growth signals from the project’s rapid community and star count on GitHub. (GitHub) 🧠 3. Qwen3 Coder — A Beast for Coding & Agents “Qwen3 Coder” refers to the latest generation in the Qwen model family — a high-performing coding and reasoning LLM that’s state-of-the-art across benchmarks. (arXiv) In simple terms: - It’s designed to think deeply and reason multi-step - It’s significantly stronger for code generation and planning workflows - Agents like OpenClaw can leverage it to automate technical tasks more effectively -

The "Personal AI Operating System" era is officially here. Tools like OpenClaw (formerly Moltbot) are letting AI handle our emails, calendars, and digital lives. But there’s a massive problem: If you’re using closed-source APIs, you don't actually own your OS. 🧠 Most people are piping their most private data into "black box" providers. You lose control, you risk data logging, and you’re locked into one company’s roadmap. I just read a great breakdown from Fireworks AI on how to fix this. Here is the TL;DR on why you should switch your agent's brain to Fireworks: 1. Privacy First 🔒 Fireworks is US-based and has a Zero Data Retention default. They don’t use your inference data to train their models. If your AI has access to your messages and files, this isn't a "nice-to-have"—it's a requirement. 2. Up to 10x Cheaper 💸 Running an agent 24/7 to monitor notifications adds up. Fireworks lets you use efficient models for routine tasks and swap to heavy-hitters only when needed. It makes a personal AI economically sustainable. 3. Access to SOTA Models (Like Kimi K2.5) ⚡ You aren't stuck with one "flavor" of intelligence. You get Day-0 access to the best open-source models like Llama, DeepSeek, and Kimi K2.5. 🛠 How to make the switch (The 30-Second Version): 1. Get your Key: Sign up at Fireworks AI to get your API key. 2. Configure: Run this in your terminal to set up the Fireworks config: curl -fsSL https://storage.googleapis.com/fireworks-public/openclaw/setup-fireworks.sh | bash -s -- YOUR_API_KEY 3. Onboard: Run openclaw onboard --install-daemon and choose "Keep Current" for the model to use the Fireworks setup. The Question: Are you guys still using closed-source APIs for your agents, or have you started moving toward "owning" your AI with open models? Let’s discuss in the comments! 👇

After kilocode now opencode gives you FREE kimi 2.5 for a limited time... go and build you dream bot using Open Claw and Kimi 2.5🧑‍🍳

Here’s a secure-by-default OpenClaw setup that matches “reviewer-based” norms (the bot can propose + prepare, but a human must approve + execute anything risky). I’ll structure this as a checklist you can implement in order. (References: skill structure + safety patterns , workflows + reviewer gating , and diagnostic commands .) 1) Decide your security posture (default: “read-mostly, write-by-approval”) Default policy (recommended): - ✅ Read access: OK (calendar read, repo read, docs read, web research) - ⚠️ Write access: allowed only with explicit approval steps - ❌ Destructive actions: never without “type-the-confirmation-phrase” approval This mirrors the “submit PR → wait for human approval → deploy” pattern described in the autonomous dev workflow. 2) Isolate the environment (separation prevents “oops” from becoming “breach”) Minimum isolation: - Run OpenClaw on a dedicated machine / VM (don’t co-mingle with your personal daily-driver). - Use a separate OS user account for the bot. - Keep a clean boundary between: This aligns with the “dedicated accounts + limited permissions + approval workflows” guidance. Network hygiene (secure defaults): - Prefer outbound-only connectivity. - If you need inbound control UI access, restrict by VPN / allowlist. - Turn on OS firewall; block unnecessary ports. 3) Use dedicated accounts + least privilege everywhere Create dedicated “bot” identities: - Email account for the bot (no access to your personal inbox) - GitHub user/service account (scoped to only necessary repos) - Separate API keys per integration (don’t reuse your personal keys) Permissions: - Start with read-only scopes. - Add write scopes only after the bot proves reliability on a narrow workflow. - For GitHub: prefer “PR creation” over “push to main”; require reviews for merges. This matches the security section (dedicated accounts, limit sensitive info, approvals). 4) Secrets management: “no secrets in prompts, logs, or skill files”

Security-hardened. Production-ready. Deploy in minutes, not days. What you get out of the box: - Container isolation — Agent crashes don't take down your server - Authentication by default — Unique gateway tokens, no exposed endpoints - Hardened configuration — Firewall, non-root execution, fail2ban - Private access controls — Only approved devices connect - TLS-secured — All traffic encrypted end-to-end No more: - Manual security hardening nightmares - "Is my gateway exposed?" anxiety - 40-hour DevOps learning curves - Fragile laptop-dependent setups The real story: DigitalOcean (10M+ developers) just validated OpenClaw as production-grade. That's huge. Best for: → Client deployments that need security compliance → Always-on agents (actual uptime) → Going from experiment → production without rebuilding everything How to get started: DigitalOcean Marketplace → Search "OpenClaw" → 1-Click Deploy What questions do you have about the security setup? Let's talk about how this changes your deployment strategy. Who's spinning one up? 👇 I will review the deployed config and run my custom gpt to validate the DO build #OpenClaw #Moltbot #AIAgents #DigitalOcean #DevOps