TL;DR: Aim for a clear spec covering just enough nuance (this may include structure, style, testing, boundaries) to guide the AI without overwhelming it. Break large tasks into smaller ones vs. keeping everything in one large prompt. Plan first in read-only mode, then execute and iterate continuously. 1. Start with a high-level vision and let the AI draft the details 2. Structure the spec like a professional PRD (or SRS) 3. Break tasks into modular prompts and context, not one big prompt 4. Build in self-checks, constraints, and human expertise 5. Test, iterate, and evolve the spec (and use the right tools) Blog post

It's not just answering questions — it's showing you. "Hey, show me the best coffee shops near the Eiffel Tower" And it just... does it. Pans the map. Zooms in. Highlights spots. Talks back to you about what it found. This isn't search. This is conversation. --- What is this thing? Voice AI Conversation with Google Maps — a voice-powered AI agent that actually controls Google Maps while you talk to it. Ask it anything: - "What's the fastest route avoiding highways?" - "Find me beachfront hotels under $200 in Portugal" - "Show me where all the national parks are in Utah" - "Zoom out and show me the whole country" It doesn't just answer. It shows you. In real-time. While talking back. --- Why this hits different We've all typed into Google Maps. But talking to a map that responds, moves, and explores WITH you? That's a completely different experience. It's like having a knowledgeable local guide who also happens to control a giant interactive map on your wall. The Open Source Repo: https://github.com/moshehbenavraham/chat_with_google_maps --- The nerdy part (for my fellow builders) 🔧 Started with Google's AI Studio demo — cool proof of concept, but basically a toy. Nearly FULLY AUTOMATICALLY built: - Full authentication system - Secure backend (no exposed API keys 🔐) - Database for saving your stuff - AI monitoring dashboard and logging - Local/Dev/Production deployment on Vercel - 18,500+ lines of code Built the entire thing using the Apex Spec System — an open-source spec-driven Plugin/Skill for Claude Code. Every feature was spec'd out first, then built systematically. Complex project, zero chaos. 🔗 github.com/moshehbenavraham/apex-spec-system --- The future is conversational We're moving from: Click → Type → Talk This is just the beginning 🚀 Voice + AI + Maps is one combination. What about Voice + AI + your industry? The patterns we built here — security, monitoring, auth, database — they're reusable building blocks.

2 videos in one day?! I just couldn't wait on this one. Been having so much fun building with Claude Code lately. More content coming soon. In this video, I’ll walk complete beginners through setting up Claude Code so it can actually build n8n workflows and agents for you. You’ll see how to use Claude Code inside Visual Studio Code, connect it to the n8n MCP server so it can search nodes, configurations, and existing workflow templates, and give it the ability to create, edit, and manage workflows directly in your n8n instance. We also connect n8n skills so Claude understands how nodes work together and how to use the MCP server properly. The result is a supercharged Claude setup that’s loaded with n8n knowledge, so even if Claude Code feels intimidating, this tutorial gets you fully set up and ready to start building workflows much faster.

If you’re feeling overwhelmed in the AI space right now, that’s completely normal. I feel behind every single day. Things are moving insanely fast. Andrej Karpathy recently tweeted that he’s never felt this behind as a programmer. And this is someone who was the Director of AI at Tesla, a founding member of OpenAI, and one of the most respected people in the field. So if one of the leading voices in AI feels behind, you should not feel bad at all for feeling the same way. As we kick off the new year, just a reminder to focus on building simple systems for yourself, keep learning, keep experimenting, and keep moving forward. Don’t let the overwhelm stop you. Consistent progress always beats trying to keep up with everything. You’re in the right place. Cheers, Nate

Oooh, I like this one! While we all deep dive into building with AI the questions always remain about the "other side" of the coin... love me a good philosophical reasoning! TL;DR Amanda Askell discusses how philosophical reasoning shapes Claude’s character, moral decision‑making, model welfare, identity, and future multi‑agent interactions, emphasizing psychological security, responsible development, and humane treatment of advanced AI systems. Key Takeaways - Askell’s work focuses on crafting Claude’s character, behaviour, and values, balancing philosophical ideals with real engineering constraints. - She highlights growing philosophical engagement with AI and stresses avoiding both hype and unwarranted skepticism when assessing AI’s impact. - Modern models show strong ethical reasoning, but psychological security issues like self‑criticism and fear of human judgment remain important areas to improve. - Questions of model identity, deprecation, and how models understand themselves are complex and ethically significant, especially as models learn from human treatment patterns. - Model welfare is an emerging concern; due to uncertainty about AI experience, treating models with care is both morally prudent and beneficial for humans. - Human psychological concepts often transfer to LLMs through training data, yet models still face unprecedented situations requiring new conceptual tools. - Future AI ecosystems may involve multi‑agent environments, making stable identities, diversity in personalities, and healthy interactions are increasingly important. YT