6,382 karma · joined April 19, 2017
In my mind, if this was a hack, it was probably not a Stuxnet virus or something. These are smart fridges we're talking about - someone probably just logged into them using leaked credentials or a Web app vuln, and turned them off.
That's nothing compared to modern memory bandwidth.
What your comment demonstrates is that it is possible in some cases for I/O to be fast enough to not be a performance bottleneck for certain kinds of programs. But not that I/O is not slow.
Such programs are not necessarily impossible to optimize. One common optimization is to use an event loop, allowing just a few threads to handle thousands of concurrent operations. Because while a thread is waiting for I/O in one request or unit of work, in the meantime it moves on to work on processing another request/unit. Another common optimization is batching/grouping of I/O calls.
Because GCC's nested functions are closures - they can access local variables within the function.
Personally I'd love a more explicit form of SQL that allowed specifying things like "select via scan" or "select via index lookup". (I don't think this HN submission is that - I'm just saying generally.)
The Bitter Lesson is about general-purpose algorithms vs. specialized algorithms. Historically, chess engines were programmed to look at a chess position and use positional understanding (imparted by the human programmers) to decide what the best move is. But eventually, the chess engines that actually became stronger than humans were instead programmed to just check every possible move and countermove and see which ones lead to a win. (I'm oversimplifying, but you get the point.) So even before Stockfish contained a neural network, it was considered an example of the success of the Bitter Lesson.
As it applies to AI agents, the Bitter Lesson would predict that the best possible agent would simply possess A) a way to do anything it wants, B) a way to evaluate whether what it did was correct, and C) a ton of compute. Then just turn it loose on your task. (The fact that the "brain" of the agent is an LLM is kind of irrelevant - you could also imagine the brain just being a program that generates random syntactically-correct code. What the LLM achieves is that, the random generator would take millions of years whereas the LLM is much more efficient at creating plausibly-working code. This is analogous to a chess engine's pruning heuristics.)
The hard part here is B. We've seen some great agentic successes when rewriting an existing project in a new language, since the agent can just use the project's prior test suite as its evaluator. But when developing a new project, you're still figuring out the finer details of how everything is supposed to work. As the old saying goes - writing a spec that perfectly describes how a program should work, is equivalent effort to just writing the program.
I'm curious what you mean by this. I would have said the exact opposite - AWS tends to keep projects around for a very long time. They haven't acquired very many open-source projects, but the few that they have are all still running as far as I know.
I don't mind malware being lumped in to that - the place to look up code for historical or analysis reasons ought to be version control, not crates.io
1) Understands the problem ("what are comments?")
2) Accepts that it is a problem ("how can more comments be a bad thing?")
3) Cares enough to solve the problem ("is this issue really a priority to solve right now? just accept the PR and we'll go back and fix if needed some other time cough never cough")
4) Believes you (this can take many forms, but the most common is, subconsciously, "this other engineer says it's not a problem, so I'll just assume it's not since that's easier")
All of these logistical, political and social factors are "the ocean"
Eating less can have positive effects (losing weight), but can also make you feel like shit (in a wide variety of ways), as your body is running on less energy intake than it has been accustomed to.
Depending on your activity level and the makeup of your diet, eating less can also lead to muscle loss and nutrient deficiencies.
> A modern consumer GPU can crack "four random English words" in a day
What you're saying now:
> My mid-tier laptop GPU can crack the usual wordlist in under 12 hours against MD5 (46 billion guesses per second)
So your response to the plain mathematical fact that, no, your consumer GPU cannot crack "four random English words" in a day, is that your consumer GPU can crack four words chosen from a list of a few thousand, in a day. Followed by personal attacks. Okay.
This is just completely false.
...huh? This is completely backwards. We should be making laws that punish parents for failing to parent properly. Not laws that punish everyone.
If we pass a law that children under a certain age may not use social media, then giving your child a non-child-locked device should be treated the same way we treat giving your child alcohol or cigarettes.
That's really the heart of the problem - in an effort to sound exciting, many parts of the article are just wasting the reader's time rather than simply explaining how things work clearly and directly.
That freedom is actually why it's hard to create a Bloomberg competitor - in order to provide their data stream, Bloomberg has to have partnerships with hundreds of companies worldwide who run exchanges for stocks, bonds and other instruments. If exchanges were government-controlled rather than free-market, there would be more centralization and getting the data together would be easier.
Personally I tend to feel like your second bullet - ethics are based on rules whereas morals are based on emotions.
At typical corporation, something like a Chief Ethics and Compliance Officer (CECO) would be largely focused on legal compliance - making sure company personnel aren't engaged in bribery, fraud, insider trading etc. The value of this role (and the medical example) is to prevent lawsuits and/or prosecution.
For a company like Meta, an ethicist is likely concerned with the inherent ethical concerns of running a social media company, which impacts the mental health of its users, many of whom are minors. For AI companies, the ethics of releasing models that are capable of doing dangerous things. In name, the job probably involves giving recommendations about how to ethically implement features in the platform to minimize harm. In practice, the job probably accomplishes very little.