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Weekly Coaching Call is happening in 12 hours
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AI Developer Accelerator β€” Coaching Call - May 12th
Turns out you can build a brain that remembers 68 coaching calls for the price of a fancy coffeeβ€”Patrick proved that data prep beats model selection when he spent only $0.75 of his $30 budget on cleaning versus embedding. Now he's packaging that Community Brain for download, and several of us are plotting our escape from Claude Code to Codex. If you missed the great migration debate, you’ll want a front-row seat this week. πŸ“ž HOW THE CALLS WORK The calls can run 2+ hours. We want to make sure we're respecting everyone's time. Especially those of you who actually show up. Here's the structure: πŸ‘‰ Reply to this post with your questions before the call πŸ‘‰ If you submit a question and you're on the call, you go first πŸ‘‰ We work through questions in the order they came in πŸ‘‰ Then we open it up for everyone else If you can't make the call but want your question answered, drop it in the comments. We'll get to it. But priority goes to people who are there. The goal is simple: if you're taking the time to show up, you shouldn't have to wait behind questions from people who aren't even on the call. We've got some serious follow-ups brewing: Patrick is bringing the Community Brain download ready for anyone who wants to index their own chaos, he and Paul are reporting back on their Codex $100 tier test drives, and Morgan is handing over those Raspberry Pi kiosk scripts to Juan and Paul. If you were curious about any of those rabbit holes, now's the time to dig deeper. πŸ”— ZOOM LINK (save this) https://us06web.zoom.us/j/81995207847?pwd=Xe6u6LmIQOmCP5VTnOwWYjDBfZNKGB.1 πŸ“… WHEN Tuesday May 12th at 6PM ET Looking forward to seeing you on the call!
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RecapFlow : May 5th Coaching call analysis
πŸ“ SUMMARY This week's roundtable featured Patrick Chouinard demonstrating his Community Brain RAG system that indexes 68 sessions of call transcripts, Juan Torres showcasing an AI photo-booth app for events, and Morgan Cook presenting a Raspberry Pi digital signage kiosk built over a weekend. The group also debated the shifting landscape between Claude Code and Codex, with several members planning migrations due to quality concerns and billing quirks. πŸ’‘ KEY INSIGHTS Data preparation beats model selection in RAG pipelines. Patrick found that cleaning and chunking transcripts cost only $0.75 of a $30 total spend, while embedding model choice (Nomic vs Gemini) showed minimal difference when underlying data was solid. Hybrid retrieval is essential for conversational data. Combining BM25 keyword search with vector search significantly outperformed either method alone when querying call transcripts. Small local models can reach 75-80% of frontier quality. With strong retrieval infrastructure, GPT-4o-mini and open-source alternatives approached Opus-level results at a fraction of the cost, though Gemma 4 struggled with complex retrieval logic despite its 30B parameters. Enterprise Claude Code requires configuration hardening. Deploy organizational settings.json files to supersede personal configs, and use seeded CLAUDE.md files to explain constraints rather than just blocking users. Watch for billing mode triggers. Mentioning competitor agent names like OpenClaw or Hermes in Claude Code sessions switches from subscription to API token billing. The Superpower framework now bridges Claude Code, GitHub Copilot, and Codex. This allows workflow portability as members evaluate switching between coding agents. Model default preferences are the new SEO. If a technology isn't in a model's training-weighted defaults, it effectively doesn't exist for AI-assisted development. ❓ KEY Q&A Q: What replaced ShipKit for the group? A: Most have moved to Claude Superpowers, which now works across Claude Code, GitHub Copilot, and Codex.
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πŸš€ Welcome to AI Developer Accelerator (Start Here)
πŸ‘‹ Hey there! Ready to supercharge your dev skills with AI? You're in the right place. Watch the intro video below for a walkthrough of our community and a peek at our AI-enhanced future. πŸ›  CLASSROOMS: - Full Stack Development with AI: Where code meets cognition. Sharpen your skills and build AI-- powered apps. - CrewAI: Your squad for all things AI. Share ideas, collaborate on projects, and celebrate successes together. - Code Bugs & Project Issues: Debug like a pro. Get help on tricky bugs and offer your wisdom to others. - Monetize Your AI Dev Skills: From AI code to income: Collaborate, innovate, and monetize your dev skills! - YouTube Tutorial Requests: Please let me know what you want to learn more about when it comes to Fullstack Developement and AI. πŸ“œ RULES: - Promotions are a no-no. Let's keep the focus on learning and growing. - We appreciate quality contributions. Enhance your posts with visuals and use ChatGPT for refining your content. - No talking about politics or religion. Go to X if you want to talk about that. - See something off? Help us maintain the community spirit by reporting any issues to me. πŸ₯‡ FIRST STEP: Introduce yourself with a post about your AI journey and what you're working on in the General Discussion group. **Bonus points for sharing a screenshot of your current app!** 🎯 ACTIONS: Be proactive, engage in discussions, and collaborate on group projects. πŸ‘©β€πŸ’»πŸ‘¨β€πŸ’» Let's code, innovate, and thrive together!
Just dropped agents-cli to help AI assistants build AI agents 🀯
Google just dropped agents-cli to help AI assistants build AI agents 🀯 Has anyone checked out the new agents-cli from Google yet? It’s not another coding assistantβ€”it's actually a specialized tool for your coding agents (Gemini CLI, Claude Code, Cursor, etc.). It basically gives your AI direct skills to scaffold, evaluate, and deploy Agent Development Kit (ADK) agents straight to Google Cloud without you having to handhold it through the boilerplate. Key features that stand out: - Fast Scaffolding: One command (uvx google-agents-cli setup) injects the skills right into your coding environment. - Auto-Deployment: You can tell your coding agent to deploy your project to Cloud Run or Agent Runtime, and it handles the Infrastructure as Code (IaC) automatically. - Human Mode: If you don't want the AI doing everything blindly, you can manually run the commands yourself for deterministic control. It looks like a huge time-saver if you're building in the GCP ecosystem. What do you guys think? Is the speed worth the GCP lock-in? Repo: https://github.com/google/agents-cli
Learn Agentic Ai
Hello everyone, I’m very interested in learning Agentic AI and building real-world AI agents. I’m looking for mentorship or guidance from experienced members of this community. I’m ready to learn consistently, practice daily, and work on projects. I would really appreciate any roadmap, resources, or advice on how to start and grow step by step. My goal is to understand:β€’ AI agents fundamentalsβ€’ Tools like LangChain / LangGraph / AutoGenβ€’ RAG systemsβ€’ Multi-agent workflowsβ€’ Real-world projects and deployment If anyone is open to mentoring me or guiding me, I’d be truly grateful. Thank you!
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AI Developer Accelerator
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Master AI & software development to build apps and unlock new income streams. Transform ideas into profits. πŸ’‘βž•πŸ€–βž•πŸ‘¨β€πŸ’»πŸŸ°πŸ’°
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