I will often have a few chats going for a project, but with different contexts. For example, one might be tech focused, another marketing focused, another with some context on my personal goals, etc.

So I will take the same question and feed it into the chats with differing context. It is almost like having different perspectives on the same problem. And the conclusions can often differ based on the differing contexts.

It seemed like a pretty good idea, though I'd guess that it would greatly increase token usage. I'd also be concerned that the LLM as a judge might struggle to grade things accurately if it wasn't also able to generate good enough answers to begin with.

The final plan you obtain is generally a lot more well rounded and thought out.

I find that amusing because the technique also works when I apply it to me. Picking flaws in your plan before revisiting it actually works.

With a good model role playing them, works awesome.

So instead of using two models, you'd be making two halves of one model do a similar (deliberative) process to yours. I wonder if that would result in a benefit over a single model with the full training set, and if you could continue to do the same thing by sharding the shards.

It definitely makes me feel like I'm dealing with an overenthusiastic intern who is throwing stuff over the wall without checking their work, and like maybe having a second bot sitting in front of the first one being like ARE YOUR SURE ABOUT THAT could really improve things.

I modeled it after the concept of advisors from Civilization II. It worked reasonably well though I think it was at least somewhat limited by being constrained to a single LLM (Mistral). It also lit my computer on fire.

Would be interesting what it comes up with with enough time and tokens.

This sounds like a fun thing to set up with a quick-enough local model.

1. Figure out a plan (it responds with the plan)

2. Point out flaws in the plan (it responds with the flaws)

3. Update the plan to address the flaws (it responds with an up to date plan)

The other things I tend to ask are "what might we be missing?", "what are the [performance|security|legal|cost] considerations?". I can often iterate on the "anything else?" kind of nudging prompts, especially guiding it on topics to consider, for a few messages. After each: update the plan to take those into consideration.

https://github.com/evalstate/fast-agent

It seems like a good approach though. What you dont want to do is ever suggest that its wrong yourself. Usually it will just assume it is wrong.

Actually what I find impressive is when I do this and it actually pushes back to defend itself.

I have no idea why anyone thinks this is novel. I guess that speaks to the state of HN

A consortium sends the same prompt to multiple models in parallel and the responses are all sent to one arbiter model which judges the model responses. The arbiter decides if more iterations are required. It can also be forced to iterate more until confidence-threshold or min-iterations.

Now, using the pr i made to llm-openrouter, you can save an alias to a model that includes lots of model options. For examples, you can do llm openrouter save -m qwen3 -o online -o temperature 0, system "research prompt" --name qwen-researcher

And now, you can build a consortium where one member is an online research specialist. You could make another uses JSON mode for entity extraction, and a third which writes a blind draft. The arbiter would then make use of all that and synthesize a good answer.

However, the result is not pleasant to read. Gemini solved this in their training, by doing it in two phases... and making the first phase private! ("Thinking.")

So I thought, what I need is a two-phase approach, where that "mean" output gets humanized a little bit. (It gets harsh to work in that way for more than short intervals.)

As a side note, I think there would be great value in a UI that allows a "group chat" of different LLM personalities. I don't know if such a thing exists, but I haven't seen it yet, although the message object format seems to have been designed with it in mind (e.g. every message has a name, to allow for multiple users and multiple AIs).

Even better if it supports multiple providers, since they have different strengths. (It's like getting a second opinion.)

Sometimes its the easiest way to complete a very small task but the cost difference on the backend has to be pretty damn large. The user inevitably ends up not caring whatsoever. Its just not real to them.

Conway's game of life, but instead of colored squares with rules, they're LLM's with some kind of weighting - all chattering back and forth with one another - bubbling up somehow to cause speach/action

https://news.ycombinator.com/item?id=43835798

"I believe I have been contacted by the supernatural. Here are the details <details>. Please form a council of seven people: 1) Secular scientist 2) Religious scientist 3) Paranormal historian 4) Secular Psychologist 5) Religious psychologist 6) Carl Jung 7) Richard Dawkins. The council should all be independent and provide their own objective analysis. Please have them create a final report and conclusions at the end".

Your council can be anything, a law firm, a jury, a parent teacher association, whatever you want, and as you can see, you can throw in known people as well. This can all be done with one prompt. It's one my favorite things to do.

Docker seems like a pretty low complexity way to create an isolated environment to run automation.

b) You can have the AI run a "firewall" prompt on the final output. So your final output should go through a "You are a firewall that checks for dangerous terminal commands such as <enumerate list of dangerous commands>. If you spot dangerous commands, reform the command so that it is not dangerous"

Id recommend looking into actual human experts who are trustworthy and reading them. Trying to get LLM to argue the case will just get you a lot of false information presented in a more convincing fashion

I haven't' really seen anyone else manage it without talking about ghosts or some other kind of metaphysical voodoo.

> Inductive reasoning refers to a variety of methods of reasoning in which the conclusion of an argument is supported not with deductive certainty, but with some degree of probability. Unlike deductive reasoning (such as mathematical induction), where the conclusion is certain, given the premises are correct, inductive reasoning produces conclusions that are at best probable, given the evidence provided.

Doesn't predicting the next token qualify as doing just that?

https://en.wikipedia.org/wiki/Inductive_reasoning

We should also not be calling a pointing device "a mouse" because it's not a small rodent, there aren't any actual windows inside a computer, and I haven't seen anyone balancing their laptop trying to surf the web.

Also smartphones are not actually smart and are only barely phones.