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.

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.

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.

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

This smells like Claudism/LLMish.

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.

Comments URL: https://news.ycombinator.com/item?id=49401550

Points: 3

# Comments: 0

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...

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.

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

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

Comments URL: https://news.ycombinator.com/item?id=49121325

Points: 1

# Comments: 0

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

Comments URL: https://news.ycombinator.com/item?id=49108039

Points: 42

# Comments: 77

Comments URL: https://news.ycombinator.com/item?id=49094636

Points: 3

# Comments: 0