I didn't want to talk about it until it was real. It's real now. I launched last week. Haven AI — the first voice-based AI guide for freelancers that remembers your whole journey. You talk to Ariel, your AI guide. She uses Socratic questioning to help you see things you can't see on your own. She doesn't give advice. She asks the questions you didn't know you needed to be asked. The problem it solves: AI is creating an identity crisis for freelancers. Not "my rates are dropping" — deeper than that. "If AI can do what I do, what am I?" We analyzed 13,700+ freelancer quotes across every corner of freelancing and 13 coaching modules to understand how this actually shows up. The patterns are brutal. Nobody's helping them navigate it. What I think this community will find interesting — the build: - Ariel runs on Claude, with a Socratic coaching framework layered on top. She doesn't just respond. She coaches through stages. - Streaming voice pipeline: Deepgram STT → Claude streaming → ElevenLabs WebSocket. The user hears Ariel thinking out loud in real time. First audio in ~2 seconds. Latency and cost were constant battles — every millisecond and every token mattered. - Persistent memory: Ariel maintains a "User Story" — a structured narrative of your journey that carries across every session. She doesn't forget who you are, or what you told her three weeks ago. - 8 occupation families instead of rigid personas — Creative, Content, Marketing, Technical, Healthcare, Business, Service, General. A palliative care nurse and a UX designer both get coaching that speaks to their world. - The full VOC research base (13,700+ quotes) feeds both the coaching and content marketing. The same data that trains Ariel powers the blog. - Infrastructure: AWS end-to-end — ECS Fargate, CloudFront, ElastiCache, Cognito, SES. Non-coder. No backend team. Just me and Claude. Happy to answer anything about the build, the architecture, or the 847 things that broke along the way.

yesterday my AI agent (OpenClaw) couldn't click on my Mac. Now it can. And it just shipped the plugin so yours can too. Here's what happened: I gave my agent a goal: drive my Mac, click buttons, type stuff, take screenshots. macOS Tahoe immediately broke the standard playbook (it silently rejects cliclick from the Accessibility panel, fun discovery). So we figured it out live. Routed everything through Hammerspoon, a signed .app that Tahoe trusts. Wrapped it in a clean mac CLI: marius click X Y marius type "hello world" marius screenshot marius focus "Safari" Then I said: "package this so every Mac-based agent on Earth can install it in 30 seconds." It did. We pushed it. The last thing the agent did before publishing was use the tool it had just built. On itself. Computer-use is no longer a research preview locked behind a Pro plan. It's a git clone away. Super Marius: open source, MIT, works with Claude Code, Cowork, and OpenClaw: https://github.com/autosolutionsai-didac/super-marius you've got a Mac and an agent, go give it hands.

What if a skill could improve itself? I built one. Then let it loose on its own source code for 10 rounds. The result: 52 versions. 109 experiments. 100 adversarial eval prompts conquered. Zero discards. (I'm not making this up, check in comments) Meet the Autoresearch Loop skill. Based on Karpathy's autoresearch methodology, it systematically improves anything measurable: other skills, n8n workflows, system prompts, SOPs, business processes. The loop is simple: Define "better" → Lock everything else → Change one thing → Test → Keep or discard → Repeat The execution is not. Why is it so good? We ran it against itself across 10 rounds. Each round we wrote 10 new, harder eval prompts targeting gaps the skill didn't cover yet. Each round it started near 0% and climbed to 100%. By the end it handles things like: • Emergency production hotfixes mid-loop • Multi-stakeholder metric conflicts • LLM-as-judge scoring drift • Artifact forking when scope grows too broad • Campaign cost/ROI tracking across months • When to abandon a loop entirely vs retire an artifact 462 lines. Every single one battle-tested against a prompt specifically designed to break it. This is the skill that makes other skills better. The proof is in the methodology: it improved itself 52 times, until we ran out of ways to challenge it. Download it at give it a try with any of your current skills!

Here is the security guide I promised, packed with everything I've learned over the last few weeks about safely deploying and managing your own OpenClaw instance. Enjoy! https://is-your-openclaw-safe-he-qjg7nak.gamma.site/

Hey everyone 👋 I owe you an apology. I've been MIA for a while and I know it. Honestly? I've been deep in the 8th dimension building with OpenClaw and completely lost track of time. You know how it goes. You start setting up one AI agent, then it's two, then you're orchestrating a team of eight and suddenly two months have disappeared. But I'm back. And I'm bringing everything I learned with me. I'm starting a new series here where I'll share everything I've picked up (and everything I'm still figuring out) about setting up OpenClaw instances from scratch. The real stuff. Not the "it works in the docs" version, but the "I broke it six times before it worked" version. Things like: 🧠 How to give your agents actual memory that survives restarts ⚡️ Running everything on a $5/month server (no GPU needed) 🤖 Orchestrating multiple agents that actually talk to each other 🔧 The gotchas nobody warns you about 💰 Keeping costs stupid low while running production agents I already published my first resource — a full memory system setup you can install on any OpenClaw agent in 5 minutes: https://clawhub.ai/autosolutionsai-didac/agent-memory-setup More coming soon. If you're building with OpenClaw or thinking about it, stick around. This is going to be a fun ride. Let me know in the comments what you'd want me to cover first 👇