Well, it’s up to the user or post-trainer of the LLM what they believe to be above average. Then they can design around that.
In the case of real world LLMs and post-training, what is above average is defined roughly as: labeled good by expert humans, and scoring high on RL environments related to coding like debugging, passing tests, or running efficiently and verifiably correctly.
I checked out your agent and it looks pretty well designed. Congrats on starting to share it with others!
One thing I noticed: "Your Tools: Aether agents get tools exclusively via MCP servers." "...Aether ships with 1st-party MCPs for file system operations..."
Can you share your thoughts on why you decided to use MCP as the core tool abstraction? I have heard many decry MCP as being context-wasteful. Is this not the case with your agent?
Why does its architecture or you knowing how AI is architected cause thoughts of it being conscious to go out the window?
It seems like the biggest factor has nothing to do with AI, but instead that you went from being someone who admits they don’t know how consciousness works to being someone who thinks they know how consciousness works now and can make confident assertions about it.
Hey! I played against a bot and it was pretty fun.
Small suggestion: too many queues can make it very difficult to build up a network of players at first. I'd suggest, for now, lowering the amount of available time control queues so that two players who happen to be on at the same time are more likely to actually find a game.
The sheer em dash density of this post really struck me, so I asked Claude to write a script which ranked text post Show HNs over the last week in order of em dash density. Script here: https://github.com/mturnshek/hn-em-dash-density/tree/master
This post comes in 12th place out of 668 with 0.6232% em dash density. I was also surprised by the large number of ShowHNs in the last week.
Here are the highest density 4 from the last week:
When I hear "coding agent", I think of both the harness and the LLM as a pair. Like, Claude Opus 4.6 and Claude Code is a coding agent, or Gemini 3 Pro and Pi is a coding agent.
"Harness" is a way to reference the coding agent minus the "LLM" part.
If an agent is an LLM in a loop with tool calls, there are two components: 1) the LLM. 2) The loop with tool calls. That second part could be called the harness.
LLMs are not "average text generation machines" once they have context. LLMs learn a distribution.
The moment you start the prompt with "You are an interactive CLI tool that helps users with software engineering at the level of a veteran expert" you have biased the LLM such that the tokens it produces are from a very non-average part of the distribution it's modeling.
It really doesn’t, at all. Every sentence has a clear, non-equivocative meaning and it doesn’t use any LLM tropes. Your LLM sensor is seriously faulty.
So, I have heard a number of people say this, and I feel like I'm the person in your conversations saying it's a coarse description and downplays the details. What I don't understand is, what specifically do we gain from thinking of it as a Markov chain.
Like, what is one insight beyond that LLMs are Markov chains that you've derived from thinking of LLMs as Markov chains? I'm genuinely very curious.
In the case of real world LLMs and post-training, what is above average is defined roughly as: labeled good by expert humans, and scoring high on RL environments related to coding like debugging, passing tests, or running efficiently and verifiably correctly.