Quotations
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The problem I have with [pipenv shell] is that the act of manipulating the shell environment is crappy and can never be good. What all these "X shell" things do is just an abomination we should not promote IMO.
Tools should be written so that you do not need to reconfigure shells. That we normalized this over the last 10 years was a mistake and we are not forced to continue walking down that path :)
Whether you think coding with AI works today or not doesn’t really matter.
But if you think functional AI helping to code will make humans dumber or isn’t real programming just consider that’s been the argument against every generation of programming tools going back to Fortran.
The problem that you face is that it's relatively easy to take a model and make it look like it's aligned. You ask GPT-4, “how do I end all of humans?” And the model says, “I can't possibly help you with that”. But there are a million and one ways to take the exact same question - pick your favorite - and you can make the model still answer the question even though initially it would have refused. And the question this reminds me a lot of coming from adversarial machine learning. We have a very simple objective: Classify the image correctly according to the original label. And yet, despite the fact that it was essentially trivial to find all of the bugs in principle, the community had a very hard time coming up with actually effective defenses. We wrote like over 9,000 papers in ten years, and have made very very very limited progress on this one small problem. You all have a harder problem and maybe less time.
In general, the claims about how long people are living mostly don’t stack up. I’ve tracked down 80% of the people aged over 110 in the world (the other 20% are from countries you can’t meaningfully analyse). Of those, almost none have a birth certificate. [...]
Regions where people most often reach 100-110 years old are the ones where there’s the most pressure to commit pension fraud, and they also have the worst records.
Something that I confirmed that other conference organisers are also experiencing is last-minute ticket sales. This is something that happened with UX London this year. For most of the year, ticket sales were trickling along. Then in the last few weeks before the event we sold more tickets than we had sold in the six months previously. […]
When I was in Ireland I had a chat with a friend of mine who works at the Everyman Theatre in Cork. They’re experiencing something similar. So maybe it’s not related to the tech industry specifically.
Do not fall into the trap of anthropomorphizing Larry Ellison. You need to think of Larry Ellison the way you think of a lawnmower. You don’t anthropomorphize your lawnmower, the lawnmower just mows the lawn - you stick your hand in there and it’ll chop it off, the end. You don’t think "oh, the lawnmower hates me" – lawnmower doesn’t give a shit about you, lawnmower can’t hate you. Don’t anthropomorphize the lawnmower. Don’t fall into that trap about Oracle.
o1 prompting is alien to me. Its thinking, gloriously effective at times, is also dreamlike and unamenable to advice.
Just say what you want and pray. Any notes on “how” will be followed with the diligence of a brilliant intern on ketamine.
[… OpenAI’s o1] could work its way to a correct (and well-written) solution if provided a lot of hints and prodding, but did not generate the key conceptual ideas on its own, and did make some non-trivial mistakes. The experience seemed roughly on par with trying to advise a mediocre, but not completely incompetent, graduate student. However, this was an improvement over previous models, whose capability was closer to an actually incompetent graduate student.
It's a bit sad and confusing that LLMs ("Large Language Models") have little to do with language; It's just historical. They are highly general purpose technology for statistical modeling of token streams. A better name would be Autoregressive Transformers or something.
They don't care if the tokens happen to represent little text chunks. It could just as well be little image patches, audio chunks, action choices, molecules, or whatever. If you can reduce your problem to that of modeling token streams (for any arbitrary vocabulary of some set of discrete tokens), you can "throw an LLM at it".
Believe it or not, the name Strawberry does not come from the “How many r’s are in strawberry” meme. We just chose a random word. As far as we know it was a complete coincidence.
— Noam Brown, OpenAI
There is superstition about creativity, and for that matter, about thinking in every sense, and it's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something - play good checkers, solve simple but relatively informal problems - there was a chorus of critics to say, but that's not thinking.
— Pamela McCorduck, in 1979
o1-mini is the most surprising research result I've seen in the past year
Obviously I cannot spill the secret, but a small model getting >60% on AIME math competition is so good that it's hard to believe
— Jason Wei, OpenAI
Telling the AI to "make it better" after getting a result is just a folk method of getting an LLM to do Chain of Thought, which is why it works so well.
history | tail -n 2000 | llm -s "Write aliases for my zshrc based on my terminal history. Only do this for most common features. Don't use any specific files or directories."
— anjor
Art is notoriously hard to define, and so are the differences between good art and bad art. But let me offer a generalization: art is something that results from making a lot of choices. […] to oversimplify, we can imagine that a ten-thousand-word short story requires something on the order of ten thousand choices. When you give a generative-A.I. program a prompt, you are making very few choices; if you supply a hundred-word prompt, you have made on the order of a hundred choices.
If an A.I. generates a ten-thousand-word story based on your prompt, it has to fill in for all of the choices that you are not making.
I think that AI has killed, or is about to kill, pretty much every single modifier we want to put in front of the word “developer.”
“.NET developer”? Meaningless. Copilot, Cursor, etc can get anyone conversant enough with .NET to be productive in an afternoon … as long as you’ve done enough other programming that you know what to prompt.
whenever you do this:
el.innerHTML += HTMLyou'd be better off with this:
el.insertAdjacentHTML("beforeend", html)reason being, the latter doesn't trash and re-create/re-stringify what was previously already there
We have recently trained our first 100M token context model: LTM-2-mini. 100M tokens equals ~10 million lines of code or ~750 novels.
For each decoded token, LTM-2-mini's sequence-dimension algorithm is roughly 1000x cheaper than the attention mechanism in Llama 3.1 405B for a 100M token context window.
The contrast in memory requirements is even larger -- running Llama 3.1 405B with a 100M token context requires 638 H100s per user just to store a single 100M token KV cache. In contrast, LTM requires a small fraction of a single H100's HBM per user for the same context.
— Magic AI
My goal is to keep SQLite relevant and viable through the year 2050. That's a long time from now. If I knew that standard SQL was not going to change any between now and then, I'd go ahead and make non-standard extensions that allowed for FROM-clause-first queries, as that seems like a useful extension. The problem is that standard SQL will not remain static. Probably some future version of "standard SQL" will support some kind of FROM-clause-first query format. I need to ensure that whatever SQLite supports will be compatible with the standard, whenever it drops. And the only way to do that is to support nothing until after the standard appears.
When will that happen? A month? A year? Ten years? Who knows.
I'll probably take my cue from PostgreSQL. If PostgreSQL adds support for FROM-clause-first queries, then I'll do the same with SQLite, copying the PostgreSQL syntax. Until then, I'm afraid you are stuck with only traditional SELECT-first queries in SQLite.
Everyone alive today has grown up in a world where you can’t believe everything you read. Now we need to adapt to a world where that applies just as equally to photos and videos. Trusting the sources of what we believe is becoming more important than ever.
We've read and heard that you'd appreciate more transparency as to when changes, if any, are made. We've also heard feedback that some users are finding Claude's responses are less helpful than usual. Our initial investigation does not show any widespread issues. We'd also like to confirm that we've made no changes to the 3.5 Sonnet model or inference pipeline.
In 2021 we [the Mozilla engineering team] found “samesite=lax by default” isn’t shippable without what you call the “two minute twist” - you risk breaking a lot of websites. If you have that kind of two-minute exception, a lot of exploits that were supposed to be prevented remain possible.
When we tried rolling it out, we had to deal with a lot of broken websites: Debugging cookie behavior in website backends is nontrivial from a browser.
Firefox also had a prototype of what I believe is a better protection (including additional privacy benefits) already underway (called total cookie protection).
Given all of this, we paused samesite lax by default development in favor of this.
[...] here’s what we found when we integrated [Amazon Q, GenAI assistant for software development] into our internal systems and applied it to our needed Java upgrades:
- The average time to upgrade an application to Java 17 plummeted from what’s typically 50 developer-days to just a few hours. We estimate this has saved us the equivalent of 4,500 developer-years of work (yes, that number is crazy but, real).
- In under six months, we've been able to upgrade more than 50% of our production Java systems to modernized Java versions at a fraction of the usual time and effort. And, our developers shipped 79% of the auto-generated code reviews without any additional changes.
— Andy Jassy, Amazon CEO
There is an elephant in the room which is that Astral is a VC funded company. What does that mean for the future of these tools? Here is my take on this: for the community having someone pour money into it can create some challenges. For the PSF and the core Python project this is something that should be considered. However having seen the code and what uv is doing, even in the worst possible future this is a very forkable and maintainable thing. I believe that even in case Astral shuts down or were to do something incredibly dodgy licensing wise, the community would be better off than before uv existed.
With statistical learning based systems, perfect accuracy is intrinsically hard to achieve. If you think about the success stories of machine learning, like ad targeting or fraud detection or, more recently, weather forecasting, perfect accuracy isn't the goal --- as long as the system is better than the state of the art, it is useful. Even in medical diagnosis and other healthcare applications, we tolerate a lot of error.
But when developers put AI in consumer products, people expect it to behave like software, which means that it needs to work deterministically.
Having worked at Microsoft for almost a decade, I remember chatting with their security people plenty after meetings. One interesting thing I learned is that Microsoft (and all the other top tech companies presumably) are under constant Advanced Persistent Threat from state actors. From literal secret agents getting jobs and working undercover for a decade+ to obtain seniority, to physical penetration attempts (some buildings on MS campus used to have armed security, before Cloud server farms were a thing!).
— com2kid
Examples are the #1 thing I recommend people use in their prompts because they work so well. The problem is that adding tons of examples increases your API costs and latency. Prompt caching fixes this. You can now add tons of examples to every prompt and create an alternative to a model finetuned on your task with basically zero cost/latency increase. […]
This works even better with smaller models. You can generate tons of examples (test case + solution) with 3.5 Sonnet and then use those examples to create a few-shot prompt for Haiku.
[Passkeys are] something truly unique, because baked into their design is the requirement that they be unphishable. And the only way you can have something that’s completely resistant to phishing is to make it impossible for a person to provide that data to someone else (via copying and pasting, uploading, etc.). That you can’t export a passkey in a way that another tool or system can import and use it is a feature, not a bug or design flaw. And it’s a critical feature, if we’re going to put an end to security threats associated with phishing and data breaches.
We had to exclude [dead] and eventually even just [flagged] posts from the public API because many third-party clients and sites were displaying them as if they were regular posts. […]
IMO this issue is existential for HN. We've spent years and so much energy trying to find a balance between openness and human decency, a task which oscillates between barely-possible and simply-doomed, so the idea that anybody anywhere sees anything labeled "Hacker News" that pours all the toxic waste back into the ecosystem is physically painful to me.
— dang
But [LLM assisted programming] does make me wonder whether the adoption of these tools will lead to a form of de-skilling. Not even that programmers will be less skilled, but that the job will drift from the perception and dynamics of a skilled trade to an unskilled trade, with the attendant change - decrease - in pay. Instead of hiring a team of engineers who try to write something of quality and try to load the mental model of what they're building into their heads, companies will just hire a lot of prompt engineers and, who knows, generate 5 versions of the application and A/B test them all across their users.