AI stacks - terrytaylorbonn/auxdrone GitHub Wiki

25.0717 Lab notes (Gdrive), Git


This Wiki section "AI LLM stacks" is a very methodical, structured, and organized approach (WIP) for DEMO'ING, ANALYZING, and DOCUMENTING AI LLM stacks. This is how I work (I am looking for a job).


Section 4 Deep dive is perhaps the core goal of the AI LLM stacks wiki. I put together section 4.0 Deep Dive Concepts after many conversations with Copilot. Copilot's assessment of my approach (see docx #499):

  • "🏆 Overall Assessment: This is wiki-ready! It fills a real gap - I haven't seen anything this concise yet comprehensive. The combination of theory + your actual code examples makes it especially valuable. Recommendation: Publish this! It would be extremely helpful to the community."

For this personal sandbox project, the focus is on this wiki and the Gdrive docx's (internal planning docx #499 provides a rough (and possibly outdated) overview). But for a customer project, the wiki would just be an intermediate step in creating the core tech doc deliverables.


PROJECT DOCS

  • Test websites for exploring (as one does in a sandbox) various website tech, mainly for doc websites (docsX.ziptieai.com).
  • Gdrive lab notes (docx files).

AI LLM STACK DEV

This section follows a logical progression from beginner to advanced (how to approach learning AI stacks). Details of each section:

  • 0a Demo deployments. The end goal (typically on Render, etc; may also list local model "deployments"). Should motivate the reader.
  • 0b Platforms and tools (Git, Render, AWS/GCP, etc). Instead of starting at the tech bottom ("what is a tuple?"), start at the top, learning how to deploy basic demos. This can be difficult because AI LLMs struggle (for now) to cover (accurately) platform and tool details.
  • 1 Youtube demos. These Youtube demos provide the details that an LLM may not (especially for platforms and tools).
  • 3 Hackathons (ChatGPT "sprints"). My favorite dev method. Just ask the dev LLM chatbot to guide you.
  • 4 Deep dive. Embellishing demos from 1 and 3. Become an expert in a few selected topics. See especially 4.0 Concepts.

END USER DOCS

There are basically 2 types of docs, for the

  • 6 LLM/agent
  • 5 Human reader

Details:

  • 6 LLM/agent docs. The new AI doc paradigm. Delivered to you in-context in your dev environment (just like Amazon delivers to your home).
    • Doc content is created specifically to be scraped, repackaged, and presented to the user by AI-based tools (chatbots like ChatGPT).
    • In the future, LLM dev tools might just auto-update scraped docs (product dev teams only have to publish the content; the LLMs will automatically take care of the rest). Perhaps websites will be designed for AI (see llms.txt)
  • 5 Human reader docs. Traditional "brick and mortar" docs (mostly website based). You visit them to get what you need.
    • Note: Seems to me that published docs might start focusing on the prompt chains needed to accomplish tasks. The idea is not to put all the details into a doc, but let the LLM (GPT/CPLT) produce the details (that are customized to the user content).

(notes)


DEMO. ANALYZE. DOCUMENT. REPEAT.
25.0718 Author, ZiptieAI.com, docs3.ZiptieAI.com, Wiki, Lab notes (Gdrive), Git, Substack, Youtube

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