511 karma · joined February 7, 2018
With vanilla JWTs, you have no way to do this! But then if you add revocation checking on top (which people do), your JWTs are no longer stateless.
In public data (source code on GitHub, etc.) you can expect a prevalence somewhere in the range of 0.5-2.5 live secrets per gigabyte of content. Now yes, there are more than 8 billion people on earth now and the murder prevalence is a lot higher than 0.5-2.5 per billion. But there are _far_ more bytes of public content than there are people on earth, so in absolute terms, there are far more leaked secrets than murders.
If you look at other types of data (like internal Git forges), the prevalence is much higher.
I think you could indeed retire with $1 per leaked secret!
Indeed, precise cost attribution is difficult or impossible due to how the nature of the language imposes structure on industrial computers. But that aside, you still end up easily with hundreds of megabytes of source to deal with in each translation unit. I have so many scars from dealing with that...
Years back I worked at a C++ shop with a big codebase (hundreds of millions of LOC when you included vendored dependencies). Compile times there were sometimes dominated by parsing speed! Now, I don't remember the exact breakdown of lexing vs parsing, but I did look at it under a profiler.
It's very easy in C++ projects to structure your code such that you inadvertently cause hundreds of megabytes of sources to be parsed by each single #include. In such a case, lexing and parsing costs can dominate build times. Precompiled headers help, but not enough...
Maybe a weak version of Turing's test?
Passing the stronger one (from Turing's paper "Computing Machinery and Intelligence") involves an "average interrogator" being unable to distinguish between human and computer after 5 minutes of questioning more than 70% of the time. I've not seen this result published with today's LLMs.
Though surely some AI systems do not use PDF image rendering first!
When looking at universities, when I saw a high sticker price, I ignored that university, even if in hindsight I had a good chance of being accepted.
I wish I had had someone when I was young who encouraged me to have broader horizons.
This was native desktop+server software, not a hosted SaaS thing.
Major releases were put out every few months.
Developers did run tests regularly with every change they would make, but it was infeasible to run all the tests in every configuration for each change. So they would try to choose tests to run that seemed most relevant to the code they had changed.
The entire test suite would run more or less constantly on shared machines, and every few days some new tricky failure would be detected. The tricky failures were almost always the result of some unanticipated interaction of features, frequently on the more obscure configurations (like IBM z/OS).
The problem was not that developers were not testing, but that the tests were infeasible to run every time. So instead, testing became an optimization problem.
This cycle time combined with occasional unexpected interactions between components meant that in every release cycle, there were dozens of complicated failing tests where it was not obvious which code change was responsible.
`bisect` here was extremely helpful: instead of having to pore over commit history and think very hard, I could bisect with a small wrapper script that would build and run the failing test in question. Builds still took hours, but I could usually autonatically pinpoint the responsible code change for one of the tests overnight.
(This was not using Git, but Perforce, for which I had to write `p4-bisect`. Alas, it's not open-source...)
Indeed! Utilization is a proxy for what you actually want (which is good use of available hardware). 100% GPU utilization doesn't actually indicate this.
On the other hand, if you aren't getting 100% GPU utilization, you aren't making good use of the hardware.
So indeed, not representative of the whole Internet.
> A fork is a new repository that shares code and visibility settings with the upstream repository. All forks of public repositories are public. You cannot change the visibility of a fork.
[1] https://docs.github.com/en/pull-requests/collaborating-with-...
The LLM-based synthesis work I've seen, in contrast, maybe uses a set of unit tests for correctness testing.
It doesn't feel like modern LLM-based synthesis supplants the classical approaches.
On higher-end pianos (mostly grands), there is "double escapement" action, which allows much faster note repetition than without. I suspect the latency would be lower on such pianos.
> Musicians learn to lead the beat to account for the activation delay of their instrument
Yes, this is absolutely a thing! I play upright bass, and placement of bass tones with respect to drums can get very nuanced. Slightly ahead or on top of the beat? Slightly behind? Changing partway through the tune?
It's interesting to note also how small discrepancies in latency can interfere: a couple tens of milliseconds of additional latency from the usual — perhaps by standing 10-15 feet farther away than accustomed, or from using peripherals that introduce delay — can change the pocket of a performance.