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yeah, i use that often, eg when a video is slow on a website - at least yt-dlp will shard out multiple piece-downloaders, and I can get the video in a few seconds or minutes, and just watch it. Also great for anonymizing sharing videos - download the actual video, share that. Not an url (:

In much the same vein, I rarely actually watch stuff _on_ netflix, through a browser - I watch sped up, and the quality just degrades. Since I pay for it, I feel nothing for downloading a ripped copy to watch it locally :D

sulam 40 minutes ago

To HN moderators: title needs to note that this talk is 13 years old.

folkrav 2 minutes ago

The title must have been edited in the last 40min - it shows (2012) now

davydm 2 hours ago

it's an interesting talk, my take being:

concurrency _is_ parallelism, but for I/O. People often think of parallelism for the case of making something go faster - eg placing two computations in parallel (the definition posed in the video), OR placing two I/O operations in parallel - so this is the keyboard-vs-mouse in the OS, even when you're on one core only; this is multiple web requests in JavaScript, which does not support multi-threading, but 100% does support concurrency for I/O operations - that... badum-tiss! RUN IN PARALLEL.

I get the point of the talk, and it's well interesting, but I think it depends on how one views things.

pdpi 2 hours ago

> concurrency _is_ parallelism, but for I/O.

Not really. They're just separate but related concepts.

E.g. coroutines are a form of concurrency that doesn't have to involve any sort of I/O, you're just taking two logical processes (e.g generating a sequence and consuming it) and abstracting away how they execute relative to each other.

Describing your tasks using the language of concurrency is a requirement for process-based parallelism (multiple CPUs/cores), but data-level parallelism (SIMD) is a form of parallelism that doesn't involve concurrency either.

threatofrain 2 hours ago

Concurrency is the property of a program or algorithm such that:

    - the program is decomposable into partially ordered or unordered units of execution
    - the program result remains determinant despite partial ordering

Your data-level parallelism is taking advantage of the concurrent properties of a problem.

lelandbatey 2 hours ago

No, and that's the point of the article. What you are calling parallel w/r/t IO should be called concurrency (conceptually happening at the same time by virtue of being able to interrupt and resume units of work). The reason IO APIs like you've described is concurrent but not necessqrily parallel is because there is no guarantee in the API that they both happen literally simultaneously; I could build a JS runtime that "works" for all the code written against XMLHTTPRequest (ignoring side-effects) but which under the hood only ever makes one HTTP request at a time. And because I can do that, that means JS code is living in a concurrency-only world, even though as an implementation detail most runtimes support parallel execution of those concurrent operations.