The substance <> narrative relationship is backwards a lot of the time. Someone does something for a nebulous multitude of reasons, and then post-hoc fits their decision-making into a logical explanation that sounds nice.

There is some psychology research supporting this as well.

I suppose if you see the narrative itself as part of the release, that might be interesting. But most of the time I’d rather just hear plainly and straightforwardly what the thing is.

Or at the very least, I am very accepting of releases which do not include rationalization / narrativization and don’t think it’s required to include.

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.

Then, by giving them context or by post-training, you can make them sample non-average parts of the distribution they learned.

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?

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.

It sounds like a good idea to me!

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.

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:

1. 1.2195% — Show HN: AI-native memory, recall and reminder on CLI – 100% local with Ollama (https://news.ycombinator.com/item?id=47417498)

2. 1.0417% — Show HN: CEL v0.2 Pro – cryptographic black box recorder for AI systems (Python) (https://news.ycombinator.com/item?id=47406917)

3. 1.0174% — Show HN: FalconAI – AI-Powered Smart TV Streaming, Search and Voice Control (https://news.ycombinator.com/item?id=47377118)

4. 0.9259% — Show HN: Ostov.js – Backbone.js Fork Without jQuery/Underscore, Classes, TS, ES (https://news.ycombinator.com/item?id=47401800)

Posts with at least one em or en dash: 457/668 (68.4%)

I also learned about the existence of the en dash. I only knew about the em dash and the hyphen, but there's:

— em dash (U+2014)

– en dash (U+2013)

- hyphen-minus (U+002D)

Anyway.

If so, good luck walking to your kitchen this morning, knowing how to breathe, etc.

These are not incompatible realities.

I would be willing to accept the statement that corporate revenues increasing and consumer spending decreasing are incompatible realities.

But it’s feasible to think the following occurs:

- labor income falls

- consumer spending drops

- corporate revenues drop

- corporate profits moderately increase because profit margins get much higher

- government deficit continues (which, from an accounting perspective, means other accounts are in surplus, potentially US corporations)

I’m not saying I strongly predict the above, necessarily! I just don’t think it’s correct to say it’s not a conceivable reality.

"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.

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.

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.

> 140% gain on your holdings this year

Choose one.

To my knowledge they’re a negligible portion of the total compute during training or inference and work well to provide non-linearity.

Very open to learning more.