Hey everyone! Really looking forward to tonight’s session — we’re leveling up! Last week we broke down how AI actually works under the hood: LLMs, prompt engineering, and why the way you talk to AI changes everything. Tonight, we’re taking that foundation and running with it. Here’s what’s on deck: Context engineering — the secret sauce behind great AI output (hint: it’s not just about better prompts) Context windows & RAG — how AI pulls in fresh, relevant information instead of relying on what it was trained on Agent memory — how AI remembers what matters and how you can set up simple file-based memory systems yourself The big takeaway? When you build a better environment around AI, you get dramatically better results. Think of it like this — a great chef needs a great kitchen, not just a great recipe. Missed last week? No stress at all — I’ll kick things off with a quick recap so everyone’s on the same page. By the end of tonight, you’ll walk away knowing how modern AI tools actually work and how to start designing your own workflows to make AI sharper, more personal, and way more useful. See you there!

Claude’s new model so far seems to be living up to its hype. At least inside claude code, it is faster and runs more efficiently than Opus 4.8. I haven’t of course run any personal evals on the model yet but I can notice some performance bump. Have you tried it? How was your experience?

I watched a few videos mentioning one on one time to get things rocking can someone elaborate on how I can access that?

I'm attempting to connect my Quickbooks to my OpenClaw. Its been a struggle. Any tips ? Perhaps someone has a skill to share ?

Hey folks, I’m planning to start a practical AI learning series for our community focused on understanding how modern AI actually works and how people are building with it today. This won’t be a super academic or overly technical course. The goal is to make AI concepts approachable, practical, and useful for developers, founders, students, creators, and curious builders. Some of the areas I’m planning to cover across the series: - LLMs and transformers - Prompt engineering - AI memory and RAG - Context engineering - Software development with AI - AI-native workflows and tooling This will be a 3-part series from my side that runs every Friday: Class 1: LLMs and Prompt Engineering Class 2: AI memory, RAG, and Context Engineering Class 3: Software Development in AI era I think there’s a huge gap right now between using AI tools casually and actually understanding how AI systems work under the hood and how to build effectively with them. Hoping to help bridge that gap a little. Please let me know your thoughts on this. I can start this Friday if we have enough interest.