VIEW RECORDING - 145 mins (No highlights) Key Takeaways - Brandon pre-launched ShipKit, a comprehensive AI development toolkit with templates and resources - Several members reported progress on client projects and personal AI initiatives - Discussions covered cloud deployment, database scaling, and AI personality development - The group shared insights on tools like N8N, LangExtract, and various cloud services Topics ShipKit Launch - Brandon announced the imminent launch of ShipKit, a toolkit for AI application development - Includes templates for chat, RAG, and agent applications in simple and SaaS versions - Features AI-assisted setup and development processes - Aims to simplify the journey from idea to production-ready AI applications Client Projects and Networking - Al Cole secured gigs through networking at business events, focusing on AI automation for marketing agencies - Juan Torres presented at a conference in Istanbul and is working on RAG systems for clients - Jake Maymar is developing projects under NDA, concerned about scaling from 100 to 10,000 users

Here's what I was reading, from the link at the bottom: Why CLI > MCP Agents are really, really good at calling CLIs (actually much better than calling MCPs), so you don’t have to clutter up your context and you can use all the features that Peekaboo has on demand, no installation required. Just add a note in your project’s CLAUDE.md or agent instructions file that “peekaboo is available for screenshots”, or simply mention peekaboo whenever your current context requires visual debugging. As Armin Ronacher perfectly articulates in “Code Is All You Need”, CLIs offer composability, reliability, and verifiability that complex tool interfaces can’t match. CLIs work for both humans and AI agents – we can run, debug, and understand them. Once a CLI command works, it can be executed hundreds of times without requiring additional inference or context. This mechanical predictability makes CLIs the universal, composable interface that bridges human and AI interaction. I’m not saying all MCPs are useless - for example Microsoft’s Playwright MCP for browser automation is great. However, they also built an MCP for GitHub, which is simply a lesser version of the existing gh cli which does the same thing. If this got you thinking, watch Manuel Odendahl’s excellent “MCPs are Boring” talk from AI Engineer. https://steipete.me/posts/2025/peekaboo-2-freeing-the-cli-from-its-mcp-shackles#why-cli--mcp I'm not sure where I fall on this yet, but it sounds perfectly reasonable at a glance.

I have been kicking the tires on a few different coding agents, but it's a pain to move the rules around. This utility is super handy for that: https://github.com/intellectronica/ruler Supports: - Github Copilotf - Cursor - Windsurf - Claude Code - Jules - Kilo - Aider And on and on and on. You can can make a global starter config to easily create global rules for all projects, and populate them into your project with `ruler init --global`

Anyone tried Kiro (Kiro: The AI IDE for prototype to production) from Amazon? They talk about spec-driven AI development and it sounds a lot like the foundation behind ShipKit from @Brandon Hancock . What Brandon is creating is a lot more feature rich but I thought the underlying concept of vibe specing was similar. Anyway would love to know the opinions of anyone who tried it.

Hi, first time here and just getting started with GenAI and agentic AI. Wondering how can I leverage AI to process log files. Identify patterns, issues etc. Challenge here is the volume of log data and I don't want to break the bank or wait endlessly for the response.. thanks