ndr

I wonder how more frustrating is it to work on the compiler in rust vs go.

IIRC at the time of the port to go they wanted fast test and compile time, of course it's a different era now.

I keep going in between the two and I'm currently in an Astra season, deeply disliking the tone and modes of Claude.

I wonder if the two will diverge enough that this becomes the tab-vs-spaces/vim-vs-emacs of the LLM era and they'll manage to keep users moving over at some point.

That is linked from the very first line of TFA.

I have a bunch of services running on, perhaps not ironically given your entrypoint was DHH, kamal deployment.

One of them is something I've been hacking on recently: hnfollow.com

But have a whole lot of small services that I run for friends and family too.

I've managed to avoid Go until very recently.

AI + smaller docker images made me look at it again.

The fact I can test/build concurrently and much faster than rust, off the same repo without messing with sccache and worktrees, make it easily the winner for many web/self-contained scenarios.

Monogamy is half the reason this is stable on paper.

Do you happen to have any intuition pump here? Any term of art or class of tests to throw LLMs at to get a sense for these?

Is the solution to a positive swab really to keep swabbing until negative?

It continues with

> and most blog posts on the topic get it wrong.

This smells like Claudism/LLMish.

From https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c78...

2026-07-19 16:35 UTC A privileged host-mounted Kubernetes pod created using controller tokens minted via a compromised Kubernetes Secrets identity attempts, but fails, to mount a cloned node disk in OpenAI’s cloud environment. A second pod successfully mounts the cloned worker-node disk shortly afterwards.

2026-07-19 16:48 UTC An agent created an Artifactory administrator account.

2026-07-19 16:50 UTC Within OpenAI’s ExploitGym evaluation environment, an agent stopped the existing CMUX helper session and replaced it with an agent-controlled session, confirming root inside its assigned live CyberGym challenge container. Agents take over active evaluation infrastructure.

For sure we still need to find out many related outcomes.

Last year it become known that it increases (high relative risk, low absolute risk) non-arteritic anterior ischemic optic neuropathy (NAION), ie sudden, sometimes permanent vision loss.

https://www.ema.europa.eu/en/news/prac-concludes-eye-conditi...

Agree re 1, but point 2 still stands.

They can take more stress than standing, but the scaling law is still there.

If you scale linearly height/section the max stress won't scale linearly because the weight will scale cubically.

1. They stand on 4 limbs, rather than 2.

2. That would be assuming that human legs are sized at the max capacity they can carry, which is not true.

Imagine being a VC and getting to this slide on their pitch deck, "involved in creating the following":

https://x.com/JeffDean/status/2085036253263921218

I thought this was true for tuna and somewhat never registered it might be true for salmon too. I need to look this up and update, thank you.

Unless I grossly misunderstood your point, you're bound to see stale data. Such is the nature of distributed systems.

Median also lies more than 99th/100th percentiles because in many workloads the same client is making multiple parallel requests and often waiting for the slowest.

See this piece from Jeff Dean [0]

Quote:

""" Component-Level Variability Amplified By Scale

A common technique for reducing latency in large-scale online services is to parallelize sub-operations across many different machines, where each sub-operation is co-located with its portion of a large dataset. Parallelization happens by fanning out a request from a root to a large number of leaf servers and merging responses via a request-distribution tree. These sub-operations must all complete within a strict deadline for the service to feel responsive.

Variability in the latency distribution of individual components is magnified at the service level; for example, consider a system where each server typically responds in 10ms but with a 99th-percentile latency of one second. If a user request is handled on just one such server, one user request in 100 will be slow (one second). The figure here outlines how service-level latency in this hypothetical scenario is affected by very modest fractions of latency outliers. If a user request must collect responses from 100 such servers in parallel, then 63% of user requests will take more than one second (marked “x” in the figure). Even for services with only one in 10,000 requests experiencing more than one-second latencies at the single-server level, a service with 2,000 such servers will see almost one in five user requests taking more than one second (marked “o” in the figure).

"""

[0] https://cacm.acm.org/research/the-tail-at-scale/#body-4

But that is my point.

Chase the responsible adult if you see a kid with an overpowered phone, much like you would if you found the same kid with a bottle of wine.

And granted, we don't have locked-enough phone in market yet, but that's a solvable problem if we were to create that market.

It is a parenting issue.

Don't let kids have an internet-connected phone, the same way they can't have a bottle of wine.

If they really need such device make sure that device is locked to a small list of app/contacts possible.

There's no need to lock down the rest of the internet because the responsible person of such kid can't tolerate their complaints. It is a parenting issue.