TL;DR Notion is my shared memory for ChatGPT. I keep a single, simple table of pages with clear titles and short summaries, so the assistant can pull the right page, use it to answer, and show me the source. I add something once and every future chat can reuse it, so ideas stay connected across projects without me reexplaining things. How it works, with examples Structure. There is one master database. Titles are slugged in lowercase with hyphens. Each page has natural language aliases and a short description of three sentences placed directly under the title. There is a status field for draft, evergreen, or archive, a last reviewed date, a single collection, a single function, and whatever tags are useful. Because the aliases and descriptions are written the way a person would search, recall is predictable without semantic search. Example [USER]: You know how we talked about cars from 1993, spin up a notion note on that. Expected behavior: the assistant returns a paste ready Markdown block that follows the master parsing guide, with the slug "cars-from-1993", status set to draft, a sensible collection and function based on context, six to twelve aliases, three to six tags, the three sentence description under the title, a minimal content scaffold, a see also section, and an initial updates line. Modes. The assistant uses two gears. Quick search is for speed. It matches on alias and description, gives a small preference to evergreen pages, opens the top note, and if confidence is not high it glances at a neighbor before answering. Deep search is for precision. It filters by collection, function, and tag, includes date filters if I specify them, prefers evergreen pages, sorts by last reviewed, then reads the best one or two before answering. This is simple enough to be consistent and strict enough to be reliable. Example, quick search [USER]: Search for "aws iam" in Notion Expected behavior: the assistant opens the most relevant evergreen IAM note (and one neighbor if needed), then answers strictly from those pages with inline links back to those notes.

Ever wished you could bring your wildest ideas to life in real time? This one-of-a-kind NO COST public workshop fuses AI, art, and imagination — designed for creators, and entrepreneurs who want to move faster without losing their magic or their human touch. Live Demos: Watch as I turn audience ideas, real-time, into concepts & high impact event production. I will also be teaching about the dangers of sycophancy & how to activate "Unhinged Mode" inside of your Chat GPT. Personal Myth: Rose Wild will guide you to uncover the story that defines your creative & personal identity — with AI as your co-writer. Come ready to play, create, and leave with your own personal myth & AI-generated art. Bring your imagination. Leave with momentum. 📍Oct 15 | 6PM | Scottsdale IN PERSON 🎟 RSVP: https://luma.com/o4ksfjxm

Good to meet you! I'm a recent alumni from ASU's information technology master's program. My background is primarily in AV/broadcast, and technical theater. I've been following LLMs and generative AI for years. I look forward to hearing your favorite use cases and stories. Here's a little bit about me: - I love people and community building - I love sound - I'm originally from Alaska - I despise gummy candy I've been aware of LLMs since GPT-2. I heard about it from Two Minute Papers on YouTube. It's wild to see how far these technologies have come. I'm connecting with you all in part for the love of advancement, but also because I'm starting a few businesses. One of my businesses relies heavily on a custom retrieval augment generation (RAG) solution. All of them will use AI and agentic workflows for automation. I'm happy to be connected to such a wonderful community of professionals, and I look forward to learning with you all.