We use a custom merge queue and we want it to be able to land multiple PRs from a stack at once as separate PRs. Last I checked you had to land a single PR, rebase the stack, land the next and so on. This is very expensive in CI time (and wall clock time), vs simply testing part or all of a stack in parallel then declaring those merged. In essence a robot needs the ability to say “squash merge these 3 stacked PRs”, after the queue does its thing.

> These are not catastrophic events. They are the steady state.

> There is no GPU futures market, no standardized residual value curve, and no way to lock in a forward rental rate. The premium is is the price of underwriting in the dark.

The headings are also AI like, a lot of essays before usually did not have titled sections but now they do and they all feel like these.

In addition the diagrams themselves look pretty AI generated.

https://hackernoon.com/myth-vs-reality-real-world-runtime-pe...

It provides a bit of extra context on startup times and install times of dependencies but I wouldn't trust those given the automated nature of the articles creation.

Comments URL: https://news.ycombinator.com/item?id=48794830

Points: 23

# Comments: 3

The reason they are doing this shadow ban style technique, is they don't want users to figure out how to jail break their way out. Or the explicit direct bad PR of when it miss-fires.

0 - https://github.com/gastownhall/beads

The issue is the quippy titles, “something - aside - continue” phrasing, and other constructions are feel like they or actually are wholly LLM written. I find a high correlation to this and low density fluff. The author did not have 10 paragraphs of things to say, but used an LLM to inflate a short outline to that. We would all of been better off with a tighter document - either human written or better prompted.

It’s the only 1 out of 200 in that area, so it’s not representative of what data centers sound like. It does show how you can’t trust the operators to do what best for the local community. It does show how a functioning government works because Loudon county increased oversight and changes the rules to stop another project like that. Setup policies to manage externalities, and don’t make ignorant bans.

Put in place sensible rules around noise, locating in pre-planned areas, and covering the cost for electrical upgrades then let the market decide how many to build. Most people appear to be getting their information from TikTok and have developed a very ignorant NIMBY attitude.

To be blunt progress does get made by listening solely to those who get short end of the stick. Japan and China have good rail in large part because the central government can simply make the globally better choice over the objections of those nearby who lose out due to noise and other factors. We don’t need to do that, we simply need to not let ignorance win and instead regulate the externalities properly, and capture value for the public through property taxes.

File system isolation is easy now, it’s not worth HN front page space for the n’th version. It’s a solved problem (and now included in Claude clCode).

So a better question to ask is - Do you have any ideas for an objective way to a measure a performance of agentic coding tools? So we can truly determine what improves performance or not.

I would hope that internal to OpenAI and Anthropic they use something similar to the harness/test cases they use for training their full models to determine if changes to claude code result in better performance.

I can do all that without having to even worry about virtual ends, or Python versions too.

0 - https://github.com/openai/codex

Another way to say it is that people think it’s much more likely for each decent LLM startup grow really strongly first several years then plateau vs. then for their current established player to hit hyper growth because of AGI.