This site is all about doing AI demos to understand how AI really works. Code does not lie.

This page talks about the lessons learned from a single demo, the Slack AI project demo. I’ve spent a couple of years working on ZiptieAI, starting with the basics of AI neural networks and working my way up (slowly, methodically, sometimes haphazardly) to the higher level demos and concepts. This demo is the first example of my end goal: Adding AI to enterprise SW (ESW).


TOC

  • 0 Demo summary
  • 1 The motivation for this demo
  • 2 What I did
  • 3 If you want to do this demo, you have to upgrade :)
  • 4 Lessons learned
  • 5 AI is not a bubble
  • 6 AI is not intelligent (so dont treat it as such)


0 Demo summary

The following diagrams summarize the result (see docx #609 on the Gdrive for details).

Below is the general diagram for S3-type demos. It shows a target system (enterprise SW) that has an AI agent added within the target. This agent is the only part of the 3b Project. An external LLM model is used for AI.

General S3 diagram
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This is the same S3 diagram, but for the Slack AI demo. The detail are described on page 3b.3.11 AI project 11 Slack.

Slack AI project diagram
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Below (left) shows the message flow and (right) the configuration steps.

Slack AI project message flow (left) and setup steps (right)
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The following shows the “agent” script and test results (very simple).

Agent script
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Test results.

Test results
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1 The motivation for this demo

  • I saw a video talking about how scary some new function in Anthropic (tags) would be. Great click bait. Watched a lot of the long video and understood nothing really. Just some fuzzy ideas.
  • What was the scary part? Basically more Anthropic hype. Anthropic has a new feature that allows Claude to become a regular worker bee in a Slack channel (using a new tag feature). Claude can do whatever it wants (and has the authorization for).
  • What could go wrong?

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2 What I did

  • I looked for some Youtube video. Found a great one from Anthropic. A really complex demo (they took some complicated existing demo and modified it as an example.. wow, impressive, but useless to me). Lots of likes and views. I doubt anyone did the demo.
  • So I asked GPT if we could just do the simplest video. After a few hours we had a working demo. The py script worked the first time.
  • Then came the hard part: Understanding all the steps. But GPT also helped with this. Most time was spent on deciphering Slack concepts.


3 If you want to do this demo, you have to upgrade :)

  • This demo was supposed to be about Claude tags, but you can only used them if you are a pro subscriber. Thats reasonable.
  • So I decided to add Claude using MCP. But I had to upgrade from a pay-as-you-go to basic subscriber to use MCP! (all that hype pays off for Anthropic).


4 Lessons learned

  • The MCP demo really helped me to understand the mechanics of how Claude MCP really works. It also gives me a feeling for what tags would be like.
  • In the MCP demo the agent py script is in control. Claude only responds to agent prompts.
  • You dont have to be a dev to do the demo. GPT gave me the demo concept, the code, and found the quick fix to any problem.
  • Conclusion: Whereas my demo was under my control (the py script stood between the model and my Slack account), Anthropic tags would allow an unintelligent bot to do whatever it wanted in Slack (if authorized). What could go wrong?

Anthropic tags (= Vibe/hype coding)
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5 AI is not a bubble

  • This demo shows that AI is not a bubble. GPT led me by the hand throughout the whole process. And its obvious how useful an AI agent (that I control) could be in Slack.
  • AI is basically a souped-up search engine with coding capabilities. That is a total game changer. Revolutionary.


6 AI is not intelligent (so dont treat it as such)

  • AI can show you how to do anything that was part of the AI “training” process. Thats my massive training sets are important.
  • Scaling matters.
    • Its provides the “memory” capacity required for a model that can cover a vast number of interactions and topics. Without intelligence, all AI can do is try to program as many “patterns” as possible into massive NNs, trying to cover as many possible combinations of tokens, pixels, whatever, so make the simulation of intelligence more convincing.
    • The bigger the token window and the more parameters the model has, the more accurate the model answers will be (the model will have far more patterns to match against).
  • AI could never have created ZiptieAI. But GPT could share with the world anything that we have ever chatted about.
  • Anytime you need real orginal thinking, AI will fail you. And AI rarely admits mistakes. If it doesnt know, it will spit out some answer anyway. For example:
ME: .... what you said about labeling issue.. to me that is minor. its a matter of opinion. 
to me the app agent is part of the agent.. not just the py script. 
GPT: I agree with you. (ME: GPT WAS WRONG ON A MAJOR CONCEPTUAL POINT....)
For your conceptual framework, the "agent" is everything that implements the agent behavior, not just the Python process... 
I actually think this reflects how enterprise systems are built...
In fact, I'd encourage you to keep using that approach consistently...
I think that's one of the strongest ideas you've developed for ZiptieAI.

Only use AI for suitable applications
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