YHK - yet another hacker news clone

Is there a reasonable way to run a representative subset myself?

Edit:

> For folks who want to try to repro these findings: I used the pinned v1.0 catalog, the stock just-solve prompt, seed 42, the any-case runner pass policy, and Docker with Python 3.12 and uv.

And https://github.com/gabeorlanski/scb-problems/

I wish a company named human layer won't write their reports with so much Claudisms.

For most things I find medium to be the highest quality output. However if I want it to do spatial reasoning or if I want it be funny / write something (not code) of almost-passable quality, then xhigh is where it's at.

Great benchmark!

I believe effort can mean both "solve harder problems" and "produce more stuff". I mostly don't want more stuff than I asked for, so a capable model at lower effort can indeed be the wiser choice. Faster response too.

The irony is the article is very sloppy. Can we at least write it and review it properly..

Related. Others?

Benchmarking Opus 5 on SlopCodeBench - https://news.ycombinator.com/item?id=49076391 - July 2026 (119 comments)

First of all, what happened to Max and Ultra?

Secondly, I’m mostly using Astra Ultra or Sol 6.1 when tight on quota. I pay $200/month and rarely if ever run out. Am I doing something wrong? Or perhaps I’m not that productive as you guys?

Astra writes well (which is why I often like to work with it over Claude), but it tends to produce code golf that's borderline unreadable, like completely omitting line breaks. I thought I had something funky with my config, but it seems to be a known issue.

https://lucumr.pocoo.org/2026/9/7/astra-why/

[Sorry for X] https://x.com/tenobrus/status/2096636223414808719

The generated code certainly does what's asked of it, but it's as sloppy as anything else I've seen. I don't think this benchmark can control for that. Maybe if it had a metric for minimum diff to satisfy the new requirement with the existing constraints on dead code etc?

Are the raw outputs published somewhere?

I am currently investigating SCB results and a larger, more representative dataset to work on would be really helpful!

This is interesting. Is nobody training LLMs to repeat resolving issues by patches from real projects where refactoring is required, judging the refactor similarity and not just tests passing?

I have a simplify skill built from a rich hickey talk that basically solves the erosion and verbosity metrics SCB flagged (i.e. erosion and verbosity are way better and improve over time). However, after running the SCB benchmark I cannot find any evidence that it actually improves test pass rate trajectories. Food for thought on maintainability metrics.

Finally a serious benchmark!

Wouldn't it be better to enforce pass via the harness and then measure cost/quality since that's more what people are likely to do?