Google just opened the floodgates. They've released their Data Commons through MCP—billions of verified data points from governments, the UN, authoritative sources. This isn't just about better training data for AI, this is about fundamentally different AI capabilities that will separate winners from losers. https://developers.googleblog.com/en/datacommonsmcp/ Here's what most business leaders are missing while they're focused on model selection and prompt engineering: The AI reliability crisis has potentially been solved—but only for organizations that understand how to leverage authoritative data sources. You now have: Billions of verified data points from authoritative sources including governments and the UN Data Commons MCP Server providing direct access to structured, context-rich datasets Universal data integration through Anthropic's Model Context Protocol standard Hallucination reduction through grounding AI outputs in verified sources Competitive advantage for enterprises that integrate authoritative data into AI systems But here's what makes this strategically transformative: This isn't just about accessing better data. It's about building AI systems that deliver reliable, verifiable outputs that create sustainable competitive advantages. Want to learn by doing and get practical experience with live expert help and support? Looking to learn more about Agentic AI? Consider joining us in the AI Leadership Institute. Our next masterclass in just a few weeks away!

I am considering exploring using Replit ( https://replit.com/ ) Agent 3 as a tool for vibe coding and building production-quality code. I'm wondering if anyone else is already using Replit or something else, and your experiences with the code being generated? I have looked at Claude Code, but am unhappy with the token limits that Anthropic enforces.

If you are vibe coding, which model do you find produces the most efficient code which aligns closest to your communicated objectives? Which requires the least iterations to achieve code Nirvana? I’m using GPT-5 and Codex. Open to hearing other perspectives and recommendations.

If you’re moving your prototype (especially one built quickly with Supabase, Lovable, Bolt, or similar platforms) toward a production-grade app, security must be your top priority. Follow these practical steps and use the hands-on audit prompt below to elevate your app’s defenses. Proven Security Tips 1. Lock Down Your Backend (Supabase/Firebase Policies) Most vibe-coded apps leave their backend wide open. Anyone who can find your endpoint URL might access or modify sensitive data such as user accounts, subscriptions, or payment info. - How: Don’t rely on default settings. In Supabase, navigate to your Auth Policies and set everything to “deny all” by default. Only permit authenticated users to access their own data. - Why: Even if your frontend looks secure, an exposed backend lets anyone directly access your database. - Learn more: Supabase Row-Level Security Docs 2. Never Trust the Frontend Alone No-code and rapid-build tools sometimes generate apps that do important checks (like upgrades or edits) only in the UI. - What to do: Always assume users can inspect, alter, and resend requests. Validate every action on the backend: check logged-in status, roles, and permissions. - Why: Frontend logic can easily be bypassed. Without backend validation, anyone could break or exploit your app. 3. Keep Secrets... Secret! One common mistake is leaking environment variables or keys (accidentally committing them to Git, misconfiguring servers, etc.). - How: Restrict access to env files and secrets, especially if deploying on your own server. - Why: Exposure of these can lead to total compromise of your stack. 4. Leverage Automated Security Audits Ask AI-powered coding tools or assistants to generate an actionable security audit checklist for your exact stack, then implement and verify each suggested fix. 5. Be Wary of Platform Defaults After reviewing many apps, open REST endpoints were most prevalent with Lovable (less so with Bolt, which configures Supabase rules by default). Always double-check the security settings, even if your platform claims to “secure by default.”

𝗔𝗜 𝗰𝗮𝗻 𝘄𝗿𝗶𝘁𝗲 𝗰𝗼𝗱𝗲. 𝗕𝘂𝘁 𝗶𝘁 𝗰𝗮𝗻’𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘂𝘀𝗲𝗿𝘀. I’ve been hands-on with Vibe Coding using ChatGPT. The results are revealing, and a bit surprising. 🤔 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴? Coined by Andrej Karpathy in 2025, Vibe Coding is a new approach where you describe a problem conversationally to an AI and let it generate code. You then guide, test, and refine by accepting suggestions in a creative loop rather than writing every line yourself. Sounds futuristic. Sometimes it even feels like magic. But here’s what I’ve observed firsthand: ✦ AI can accelerate early drafts and boilerplate, but without direction, it drifts. ✦ It confidently delivers solutions that might be incomplete, misaligned, or detached from user needs. ✦ And while many developers feel more productive, controlled studies show experienced engineers using AI assistants actually take 19% longer on complex tasks (Source: Stanford HAI). ✦ Yes, junior developers and rapid prototyping gain speed. But the gap between perception and reality is real (Source: GitHub Copilot Research, Mimo). 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀𝗻’𝘁 𝗶𝗻 𝗹𝗲𝘁𝘁𝗶𝗻𝗴 𝗔𝗜 𝗿𝘂𝗻 𝗳𝗿𝗲𝗲. 𝗜𝘁’𝘀 𝗶𝗻 𝗵𝗼𝘄 𝘄𝗲𝗹𝗹 𝘆𝗼𝘂 𝗴𝘂𝗶𝗱𝗲 𝗶𝘁. 🎯 ✅ 𝙔𝙤𝙪 𝙙𝙚𝙛𝙞𝙣𝙚 the North Star, the real-world outcomes that matter. ✅ 𝙔𝙤𝙪 𝙘𝙡𝙖𝙧𝙞𝙛𝙮 standards, structure prompts, and question assumptions. ✅ 𝙔𝙤𝙪 𝙖𝙘𝙩 as the architect, not just a collaborator. 𝗔𝗜 𝗶𝘀 𝗮 𝗷𝘂𝗻𝗶𝗼𝗿 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗲𝗿. It’s fast, helpful, and tireless, but it still needs direction, mentorship, and guardrails. 🛠️ Have you been experimenting with Vibe Coding? What are you seeing in your workflow? Let’s discuss in the comments 👇