Because we were already there with multi-processing and OS IPC before mainstream OSes added multi-threading support, and while it is better for stability and security, there are additional hardware resources to account for.
To use modern speak, we were doing microservices inside a single computer.
I honestly think the article misinterpreted the Git mailing list email.
The first line of the article says:
"People at Git has started to work on a proposal to make the Rust programming language mandatory."
This sounded to me like all future code would need to be Rust.
But the Git mailing list email says:
"Announce that Git 3.0 will make Rust a mandatory part of our build infrastructure."
Also, the original Git mailing list email says:
"[author is porting to Rust the...] "varint.c" subsystem, mostly because it is trivial and does not have any dependencies."
So, the email on the Git list says they are testing changing a small piece
of C code to Rust, and Rust build tools will be mandatory [to build] (which is not surprising if a non-optional part of the code base is Rust).
If the marketer is an employee or a consultant, is it in their interest to show that the ad-spend they are controlling is high ROI, or low ROI.
Maybe this is a cynical take, but, if they get to the bottom of things, and show their boss/client that the ad-spend is not returning so much, it seems it would portend bad things for the marketer.
I really don't know, and it seems like a very hard problem.
Maybe this is the time for that Upton Sinclair quote:
"It is difficult to get a man to understand something, when his salary depends upon his not understanding it,"
That's not how ad performance is measured. Your ROI is based on end conversions. You need to know if your leads are _good_, not just plentiful. A company that doesn't do this at the start will figure it out pretty quickly.
Ideally that would be the case that all ad-spend can be tracked through end conversions, and for many businesses it can be. But for much of the corporate world, you don't know where your end conversions come from. (Think Nike shoes, etc)
It's estimated about that 35% to 45% of all digital ad spend goes to brand awareness, video reach, and other upper-funnel methods where direct conversion tracking isn't possible.
The final paragraph says this, among other things:
"The court ruled on a preliminary injunction request, so it’s not the final word on the merits. Still, it seems highly likely that the TWEET term and the bird logo have been freed from X’s trademark clutches. If so, it’s nice to get some cultural assets back into the public domain"
Why would an rando anecdote (which another sibling comment has proven does not reflect the broader reality) call into question an actual study? The latter may be garbage, or not, but it starts with a much better claim to credibility.
I would say it's not really a "rando anecdote", but the whole basis of the relationship of the first actual study in the paper.
To quote the paper:
"Our first two studies were naturalistic field studies, and examined whether upper-class individuals behave more unethically than lower-class individuals while driving. In study 1, we investigated whether upper-class drivers were more likely to cut off other vehicles at a busy four-way intersection with stop signs on all sides. As vehicles are reliable indicators of a person's social rank and wealth (15), we used observers’ codes of vehicle status (make, age, and appearance) to index drivers’ social class."
Calculus can get pretty heavy, but I really value the comfort it gave me of the concepts of velocity and acceleration, (not to speak of higher orders) of which I am sure I wouldn’t understand nearly as well without the calculus background.
I’m sure for most of HN velocity and acceleration, etc seem like super basic concepts, but I just don’t think I could apply the mental models as easily as before I took calculus.
I find this very interesting, I wonder if there is a public benchmark that reflects this “red team coding critique” aspect of the current SOTA model that reflects what you have observed.
It would be really useful to observe this in a benchmark vs. the more common “go implement this, or fix this bug” type benchmarks that seem to be prevalent.
Yeah, my tool to automate these review loops is https://github.com/wwind123/coding-review-agent-loop . It's basically a script calling Claude, Codex and Antigravity CLI's. The benefit of using CLI's is, the tool uses quota in your subscription plan of these AI providers, which is much cheaper than using extra tokens from the same providers to do the same thing.
A couple of months ago (before opus-5 and gpt-5.6 sol), The ratio of problems caught by codex/claude vs gemini was more like 2:1 to 3:1. But now it seems codex and claude have made huge leaps and gemini is more or less staying put.
Amazingly, these few days the Gemini 3.8 Flash (High) has been catching much more problems in code reviews than before. I think it started from the second day since I posted the observation above. Maybe somebody from Google saw my posts and tuned some knobs in the model to allow more critical thinking?
Another observation, Gemini's review on code is more critical now, but its review on design plans is still quite agreeable - it tends to approve Codex's design plan immediately, while Claude could often pick out a bunch of problems in the design plan in the first round of reviews.
For example, the command execution design of Redis is purely single threaded. (Redis does have other threads for slower activities)
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