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You get intuiton on capability which is contaminated by your blog post being ingested to training corpus, wihout strict comparision analysis on not pelicans, not animals, not svgs you cannot state any general capability with your prompting

Very promising project! The latency is great and looks pretty polished for solo work.

> ALE is designed to be largely invariant to phrasing. You can say pick up, you can say grab, fetch, go get the damn sword you fool - it doesn't matter, it will still understand you > It creates embeddings from the full text as well as its extracted structure

I get how you utilize embeddings, but most interesting part for me is how you decompose multiple commands? You decompose sentences before embedding?

There is tons of possibilities of automation, but you have to split down general services delivery into underlying operational categories behind it, then you migh find relevant products/ case studies, if not then you have to dig deeper and decompose into smaller operational areas. In my case it was (software developement company) it was something like: frontend usability testing, frontend ux testing etc.

I would recommend decomposing and prioritizing:

  - what takes most of your time
  - what is needed to close the automation loop for a process

If you can eliminate small task and in return larger delivery process gets automated, then it might be more beneficiall then fighting on automating large complex process

I would be surprised if pelican svgs are not part of the training corpus rn

I might be wrong, but strongly suspect that Fable 5 is already something in this shape, considering long time to first token while having normal troughput.

I'm a huge fan of Ben. Have been tracking the OpenCog initiative for some itmem and think that moving concepts from latentspace to atomspace is best way to efficiently merge our current digital information infrastructure with LLM knowledge.

Regarding your book, I'm shocked that 'AI Agent' concept predates me. Have not read your book, but i think it would be interesting to compare your perspective with today's building blocks which were not available for that time.

Physical simulations seem like next step, but how do you simulate dynamics in complex systems im not sure. Stock market is a good example with many trying to simulate that, but at the end you have to make some tradeoffs in terms of abstraction level you are simulating.

For social backed simulations i guess some kind of grounding will be needed based on real examples, but then the out of distribution cases will need an other solution. As rate of changes in our civilization increases, the out of distribution cases will be more and more prominent.

I agree, for me it dreaming was always reprocessing. The resimulation of scenarios part i mentioned can be over-assumption and it might be wrong. One thing i noticed is that sometimes i reprocess motoric movements after martial arts lessons, that was my main clue.

I think open-ended simulation for agents will be a key component for training and planning. Similar as human dreams simulate different scenarios in our head. Biggest challenge will be simulating more abstract and complex systems.

Few months ago I did experiment with an open-ended world simulation for AI agent, where the simulated world was progressively building itself based on each of agent actions in open-ended manner. The idea was to give an agent infinite possibility regarding tool calling, where the tool call would be approved by the adjudicator, and the world state would change. The key issues with the PoC were:

  - World decoherence (tried to solve that with a poor graph implementation)
  - World flatness - high abstraction did not account for small events that would compound in real world
  - Start with empty context was real issue to get the agent to explore the world
  

Anyways the project came to be really funny when you watched agent struggling in desperation to perform real world actions which would be impossible in real world. Main observation was that when presented agent with current action budget, it modulated the creativity and how desperate its actions were.

4 different databases when you could just postgres. Also seems that 'Think and Plan' and 'Reflect' phases are redundant, as stated: 'Think & Plan: Process Reflection'. Also more personal opinion is that LangGraph is unnecessary framework only slows you down by spiking up complexity.

Not sure how you manage to measure Faithfulness and Answer Relevancy on the live system, without the ground truth.

Good that you have evals in place, but the user satisfaction score might suggest running ablations on the system would be beneficial. I would start by reducing the iterations and unnecessary steps from the agent.

Then why not take a small loss and publish at least one example that is something beyond copying the frontend?

"Debugger attachment is blocked. Memory inspection is blocked." - reminds me old crackme challenges. Everything they mention can be bypassed, so determined person can start stealing data from the network. For me this is a killer of such distributed compute ideas, but who knows, maybe the non-enteprise users will be desperate enough for cheap compute to make this idea valid.

I think it would be valuable to list all interactions with the LLM by the dev team and transparently state what was induced by human steering the LLM, and what was actuall LLM decision, which was not biased by system instructions or dev team communicating with it