I can get GPT3.5 to take Agent action with a 100% success rate via the API

AI Agents Solved!

Not a drill and not lying, 100% success rate with GPT 3.5 and Swarm algorithms for AI Agents. GPT 3.5 will create and execute the Swarm algorithms, and they will complete the API call 100% of the time. I have a Github Repo for it and can link to a Colab Notebook. I can do it with GPT3.5. I can replicate it and share it at will.

If I actually solved AI Agents, why am I open sourcing it rather than patenting it for billions of dollars? Perhaps this is a better route to go. Because I made a promise to these guys. There was a period, a little more than one year ago now, where they though they 100% solved the problem. And they open sourced it. It shocked my little capitalist heart. It almost gave me a heart attack. I made a promise to them and into the ether that if I ever actually solved it, I would open source it. Well, I follow through on my promises.

Swarm algorithms work because reverse diffusion works. They both do the same thing fundamentally, that is what I stumbled upon to make this work. Reverse Diffusion samples from the probability space of (x), that is how Swarm algorithms work too. If Reverse Diffusion can just make up a probability space, then so can Swarm algorithms. Then it worked. That simple.

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Shared Principle: The author suggests both methods share a core idea: âreverse diffusion.â This concept isnât defined, but it seems to represent exploring a probability space to find optimal solutions.
Swarm Algorithms & Probability Space: The text mentions swarm algorithms sample from a probability space, potentially explaining how they function.
Reverse Diffusion Inspiration: The author implies reverse diffusionâs ability to explore a probability space inspired them to develop a successful swarm algorithm.

I do not disagree with anything within that AI summary.

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Sounds accurate! Let me know if you have any questions about the summary.

You cannot link in these forums but there is a research paper titled âEfficient Solutions to Complex Optimization Problems with DISCOâ. That is some wild and crazy stuff. Once i got over the shock that you can actually do that with diffusion models, I realized you can use the same math essentially to do it with Swarm algorithms too. Swarm algorithms are much better for things like function calls than reverse diffusion, for a plethora of reasons. That is the full reasoning behind how I put it all together. Lots of research into Swarm algorithms before that. Lots of ignoring reverse diffusion beyond knowing how it works very well from a physics perspective.

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Thanks for showing me the right path. But I want to learn and share different things about open AI.
like how AI work and How We can use it for our exam and different paper solving methods

You could literally use what I have in that Colab notebook to make an API call to your teacherâs computer or school server and yoink the test answers if you wanted to. Or you could use it to create a set of 1,000 Swarm algorithms to take your test for you about 300 times in 3 seconds and tell you all the correct answers from that. Knowledge is half the battle!

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This sounds intriguing. While I often develop instructional materials, actual examination papers in my document store are rare, making them a real-life scenario of a needle-in-a-haystack problem when not properly managed by a document hierarchy. One might use a vector database to perform semantic embeddings similarity searches of the snapshot documents in dataset volumes using conventional cognitive exhaustive distance algorithms for top-k rankings. However, the simple fact that Iâve accumulated co-mingled terabytes makes obtaining document extraction and chunking for graphs or embeddings based tooling both a bandwidth-heavy and costly proposition, to then find that that despite metadata and HyDE techniques, the AI-augmented query returns low relevancy scores.

While I might not have an application programing interface gateway like your teacherâs computer has, I do have access by NFS with Kerberos authentication on the local network using ACLs. Therefore Iâm particularly interested in your AI enhanced âYoink!â technology, especially if it could identify and penetrate replicated datastore blob storage and SAN without special configurations.

Let me know the prerequisites of this teacher-grade API you discuss, and how it must be implemented and exposed to be accessed by your agentic diffusion and swarm methods, to then incorporate it into my document workflow. Then we can get started on trials with the hopes of attracting seed funding from angel investors that could expand the practical offerings in the enterprise, possibly protected by the trade secrets of deeper algorithmic refinements than those you have released free of intellectual property protection.

/s

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So what youâre saying is youâre jealous you didnât do it?