If they had had individual keys with screens instead of the Touch Bar I think everyone would have been happy. Have it default to a normal function row but give people the option to customize, let apps customize it behind a modifier key, etc.
The only real function would have been to have customizable icons in that case because you can already assign shortcuts to different functions keys and per app contexts with apps like Hammerspoon
Work for a manufacturing company, and we spend a lot of time creating surrogate models for physical simulations. Huge speed ups, 100-1000x possible. You end up still using the “real” simulation for validation, but you can run orders of magnitude more simulations for early design and refinement first.
Sure, sometimes people get social engineered into taking money from their bank account and giving it to criminals. Should banks stop allowing withdrawals?
Your argument is of course absurd, but in general, I think it is a reasonable position to take. The grandmother of someone I know was social engineered into installing a malicious app and changing developer settings on a phone, and lost quite a lot of money. The grandson is at this point absolutely in favor of completely removing the things that allowed that attack vector (I believe in this case it's "merely" the ability to install apps from unknown sources).
I disagree with him. While yes, it's awful and tragic that happened to his grandmother, I think it's incredibly dangerous to allow companies to lock down our devices like that. And I think from a practical perspective, we're never going to be able to eliminate these attack vectors fully; if the grandmother was going to go along with this scammer in this particular way, there would always be some vector that she would fall for, no matter how hard we might try to lock them down.
But I can't bring myself to say his stance is unreasonable. He's dealing with a devastated family member whose retirement is now ruined, and he has to help her pick up the pieces. Not a good position to be in.
Yeah, many of my physics exams were take-home open-book. One that I particularly remember: "Here's your exam. There's one problem. It's due when you come back from spring break."
I graduated forever ago, but I still have bad dreams about this type of homework. That and the "oh no, I somehow forgot to go to this class all semester and now I have to take the final exam!"
Factuality is orthogonal to political leaning generally. People can use the same set of facts and come to very different conclusions. That’s a separate issue from “are these facts correct” and what happens when an individual or entire party starts getting most of their news from highly partisan and unreliable sources.
>Factuality is orthogonal to political leaning generally.
It certainly can be orthogonal, in some notional sense, and in many cases that explanation is good enough. But in practice there are too many contrary cases to ignore, and there's often an integral relation between factual veracity and polarization, especially with respect to American polarization of politics. Global warming, the results of the 2020 election, the percent spent of federal budget spent on foreign aid have factual answers and right wing affiliation can be predictive of (1) not agreeing with the facts and (2) treating factual corrections as "liberal bias".
I think left wing versions exist also but are less systematic: 2004 election results, efficacy of plastic recycling or dangers associated with nuclear power are cases where I think left wing partisan affiliation probably predicts being wrong on the facts.
And meta-narratives about the relation between factual information and partisan bias are themselves as likely to be polarized as anything, complicating the ability of people to do good analysis, or of accurate analysis to be trusted by people committed to certain meta-narratives that would deny the possibility of factual knowledge predicting polarization.
Collecting plastic to recycle is almost certainly not worth the fuel and labor it takes, it gets landfilled more often than not. We’d be better off collecting only separated PET and HDPE and landfilling the rest.
> or dangers associated with nuclear power
Nuclear power is the safest method of power generation that uses steam or gas to spin a turbine.
I'm also left to center and I agree with you on all three! I think left-wing examples are subtler and hard to identify. Even among among Democrats left of center types and liberals, the examples I cited are minority opinions, but likely to be systematically found on the left, a phenomenon which won't be negated by the self-report of one person correctly tracking the facts (and to be clear, I think you are correctly tracking the facts on each of these!).
Agreed, I know a fair amount of left leaning people who think everything they put in a recycling bin is dutifully recycled down the line and that nuclear power is more dangerous than it actually is. People on the left (including myself!) can let their emotions outweigh factual evidence as easily as people on the right. I also agree that just because you and I are left leaning and understand the factual position on the examples you mentioned doesn’t mean much, we’ve both met plenty of counter examples to ourselves.
I suspect the left wing examples of believing an emotional argument instead of a factual one are more subtle because they aren’t as focused on negative emotions as right wing examples.
I think Nuclear power is an interesting case and can be usefully contrasted with say vaccine denial. The anti nuclear position is one that was certainly correct at someone point in time and retains many good arguments that require technical chops to untangle and requires one to come to many other technically challenging conclusions to come to the arguably correct position.
Vaccine denial requires one to ignore decades of fairly simple positions about which no expert credibly disagrees nor has in our lifetime.
It's like watching 2 packs of athletes some of which are failing to clear 1 meter hurdles whilst on the other side some are tripping on little nubs set in the floor.
> By 2016, the gap had begun to appear in biomarker measures. By 2020, it was showing up in deaths from causes such as heart disease, cancer, and stroke. Since then, the gap has only widened. Between 2020 and 2022, only 0.2% of “very liberal” respondents died of internal causes, compared with 1.34% of “very conservative” respondents.
It's only interesting after you've removed all signs of nuance and dumbed the argument down to two keywords: "big" and "pharma". People are rightfully skeptical of pharmaceutical companies due to the enormous amount of power they wield over our lives and how they use that power, e.g., multi-national companies suing third world states over patent infringement for distributing generic live-saving drugs.
In most cases it seems (at least to me and colleagues) to be turning out that picking best intelligence is a better option than picking better intelligence / dollar, assuming you can afford the cost. At least on interesting problems. If you’re doing generic web dev work, probably not the case.
What are interesting problems? Deepseek and some other open source models do decently well in the gpu kernel benches. https://kernelbench.com/hard Most people think they’re working on “very hard” problems and they really aren’t
Absolutely agree with this. As Louis Rossmann recently pointed out, it's the difference between a correct answer and a wrong answer; the correct answer is worth a good amount, while the wrong answer is worth nothing. Under this metric, for harder tasks, the most intelligent model is best per dollar.
That's why experience + cost effective model is IMO the best combination.
Experience allows you to design the skeleton where the implementation details are often inconsequential. There are relatively few scenarios where an LLM would need more guidance to render an outcome, but even dumb local models can do that.
Building a simple UI component vs an efficient multi-threaded bidirectional socket implementation (both examples of things I did recently with DeepSeek flash).
Angular can only be written in one way so the UI was trivial. I know the architecture for the socket implementation and the trade offs for various approaches, so I sketch out the implementation and get DeepSeek to complete it (error handling, keepalive messages, timeouts, etc).
I don't think I would have saved much time if I just asked Fable to "make the socket implementation" but even if it got it right the first time, it would have only saved me a few minutes given that's how long it took to write any way.
Even in more conventional applications, Node.js / React CRUD applications - "write a graphql query for blah" "add an endpoint to run query" "add validation to endpoint" are all trivial for DeepSeek flash. In most of these cases, I have found you're constrained by context window size because these are rarely well architected applications.
> Experience allows you to design the skeleton where the implementation details are often inconsequential.
Expanding on this thought a little more: it is possible to set up scaffolding that make incorrect implementations inconsequential. If the LLM can detect when the implementation is wrong, it can retry with the errors feeding back into the loop. This is shored up by up-front investments in tests, API definitions, strong types, linting rules, etc. the various cheap, fast Flash models do not need to 1-shot solutions if capable of autonomous reiteration.
This is a great point. In the past this is how I learned to architect software in general as you are never the only contributor and often must accommodate the contributions of contributors who are early in the careers. Good software architecture reduces the damage possible by low quality contributions.
I have had great success with contract driven development workflows in this regard.
LLMs being effectively very knowledgeable jr engineers, slot right into those patterns.
Another interesting thing is that Rust _really_ shines with LLMs. The compiler gives very helpful messages which the LLM bounces off very effectively. If it compiles, it works, you just need to double check the logic is correct.
By contrast, with something like Go, you'd need to be on the lookout for nil pointers, race conditions and so on. Node.js / TypeScript is less dangerous, but you still have memory leaks that aren't possible in Rust and lack pattern matching so must be vigilant about type discohesion.
This is mostly true and a month ago I would have agreed wholeheartedly. However, there are still situations where almost every line of code has to be deliberately chosen in a delicate way. Think research code or settings which demand a high degree of optimization. Opus or GPT-5.5 are absolutely miserable to guide in these settings.
"Well, don't use an AI for those purposes!"
That used to be true (and is now again true I suppose). Fable was a peek at something different, it seemed to be actually able to start to tackle these kinds of problems. That saves a lot of time, since checking these programs is often far, far easier than writing them. Experience is needed to check and scaffold still, but something like Fable becomes a prerequisite for these settings.
People are wasting tokens with perfectly fine models by not planning effectively. Anyone who says Opus is not capable of doing what Fable can probably are not planning effectively and just praying to the one shot gods.
What fraction of your work is "interesting problems", and what field do you work in?
> If you’re doing generic web dev work, probably not the case
I have a feeling the bulk of most people's work is "generic $X work." My take is people should figure out their mix of interesting vs boring work, and optimize accordingly. Flash models also tend to much faster,
The federal government and the executive branch has a well specified set of responsibilities to the people of this country, and they are massively failing at just about every metric. So yes, they are truly incompetent.
To be pedantic, tests prove that the code passes the test suite, nothing else. They do not prove by themselves that the code is correct, secure, maintainable, efficient, etc. Those are much harder to measure and have a ton to do with organization, architecture, culture, shared knowledge of the maintainers, etc. All of which is lacking during and after this rewrite.
I do not work in the space at all, but it seems like Cloudflare has been having more network disruptions lately than they used to. To anyone who deals with this sort of thing, is that just recency bias?
It is not. They went about 5 years without one of these, and had a handful over the last 6 months. They're really going to need to figure out what's going wrong and clean up shop.
The featured blog post where one of their senior engineering PMs presented an allegedly "production grade" Matrix implementation, in which authentication was stubbed out as a TODO, says it all really. I'm glad a quarter of the internet is in such responsible hands.
Management thinks AI tools should make everyone 10x as productive, so they're all trying to run lean teams and load up the remaining engineers with all the work. This will end about as well as the great offshoring of the early 2000s.
Wait till you get AI to write unit tests and tell it the test must pass. After a few rounds it will make the test “assert(true)” when the code cant get the test to pass
No joke. In my company we "sabotaged" the AI initiative led by the CTO. We used LLMs to deliver features as requested by the CTO, but we introduced a couple of bugs here and there (intentionally). As a result, the quarter ended up with more time allocated to fix bugs and tons of customer claims. The CTO is now undoing his initiative. We all have now some time more to keep our jobs.
Thats actively malicious. I understand not going out of your way to catch the LLMs' bugs so as to show the folly of the initiative, but actively sabotaging it is legitimately dangerous behavior. Its acting in bad faith. And i say this as someone who would mostly oppose such an initiative myself
I would go so far as to say that you shouldnt be employed in the industry. Malicious actors like you will contribute to an erosion of trust thatll make everything worse
Might be but sometimes you don’t have another choice when employers are enforcing AIs which have no „feeling“ for context of all business processes involved created by human workers in the years before. Those who spent a lot of love and energy for them mostly. And who are now forced to work against an inferior but overpowered workforce.
I dont like it either but its not malicious. The LLM isnt accessing your homeserver, its accessing corporate information. Your employer can order you to be reckless with their information, thats not malicious, its not your information. You should CYA and not do anything illegal even if your asked. But using LLMs isnt illegal. This is bad faith argument
You're talking about legality again. I'm talking about ethics.
Using LLMs for software development is a safety hazard. It also has a societal risk, because it centralizes more data, more power, more money to tech oligarchs.
It's ethical to fight this. Still not commenting on legality.
You're not forced to work there and use those tools. If you don't like it, then leave the job. Intentionally breaking things is unethical especially when you're receiving a paycheck to do the opposite.
Again, no one is forcing him to be there. He's breaking something on purpose. I think you should read up on ethics because this take "I don't like it therefore whatever I do is ethical" is juvenile.
That's quite the strawman. The reason it's ethical is not that LLM's are unpopular or someone dislikes them. It's ethical because LLMs introduce safety hazards, i.e. they cause harm.
That's extremely unethical. You're being paid to do something and you deliberately broke it which not only cost your employer additional time and money, but it also cost your customers time and money. If I were you, I'd probably just quit and find another profession.
That's not "sabotaged", that's sabotaged, if you intentionally introduced the bugs. Be very careful admitting something like that publicly unless you're absolutely completely sure nobody could map your HN username to your real identity.
They coasted on momentum for half a year. I don't even think it says anything negative about the current CTO, but more of what an exception JGC is relative to what is normal. A CTO leaving would never show up the next day in the stats, the position is strategic after all. But you'd expect to see the effect after a while, 6 months is longer than I would have expected, but short enough that cause and effect are undeniable.
Even so, it is a strong reminder not to rely on any one vendor for critical stuff, in case that wasn't clear enough yet.
You can coast for quite some time (5-10 years?) if you really lean into it (95% of the knowledge of maintaining and scaling the stack is there in the minds of hundreds of developers).
Seems like Matthew Prince didn't choose that route.
The problem is that CF operates in a highly dynamic environment and you can't really do that if the minds of those hundreds of developers relied for the major decision making on a key individual.
This is the key individual paradox: they can be a massive asset and make the impossible happen but if and when they leave you've got a real problem unless you can find another individual that is just as capable. Now, I do trust JGC to have created an organization that is as mature as it could be, but at the same time it is next to impossible to quantify your own effect on the whole because you lack objectivity and your underlings may not always tell you the cold hard truth for reasons all their own.
And in this case the problem is even larger: the experience collected by the previous guru does not transfer cleanly to the new one, simply because the new one lacks the experience of seeing the company go from being a tiny player to being a behemoth, and that's something you can do only once.
I've always been of the opinion that without JGC Cloudflare did not stand a chance, irrespective of those hundreds of developers. And that's before we get into things like goodwill.
And of those hundreds of developers you have to wonder how many see the writing on the wall and are thinking of jumping ship. The best ones always leave first.
I would not be surprised at all if this whole saga ends with Google, Microsoft or Amazon absorbing CF at a fraction of its current value.
been at cf for 7 yrs but thinking of gtfo soon. the ceo is a manchild, new cto is an idiot, rest of leadership was replaced by yes-men, and the push for AI-first is being a disaster. c levels pretend they care about reliability but pressure teams to constantly ship, cto vibe codes terraform changes without warning anyone, and it's overall a bigger and bigger mess
even the blog, that used to be a respected source of technical content, has morphed into a garbage fire of slop and vaporware announcements since jgc left.
Do you feel that Matthew Prince is still technically active/informed? I've interacted with him in the past and he seemed relatively technically grounded, but that doesn't seem as true these days.
Rather than be driven by something rational like building a great product or making lots of money he is apparently driven by a desperate fear of being a dinosaur.
Regardless of how competent he is or isn’t as a technologist, a leader leading with fear is a recipe for disaster.
I’ve had a lot of problems lately. Basic things are failing and it’s like product isn’t involved at all in the dash. What’s worse? The support.. the chat is the buggiest thing I’ve ever seen.
How about accurate billing info. The ux can’t even figure out we’re annually not monthly. Maybe the AI slop will continue to miscount resources and cost you revenue or piss off a customer when the dashboards they been using don’t match the invoice
You know what they say, shit rolls downhill. I don't personally know the CEO, but the feeling I have got from their public fits on social media doesn't instill confidence.
If I was a CF customer I would be migrating off now.
exactly. recently "if the cto is shipping more than you, you're doing something wrong"
cto can't even articulate a sentence without passing it through an LLM, and instead of doing his job he's posting the stupidest shit to his personal bootlicking chat channel. I cringe every time at the brown-nosers that inhabit that hovel.
no words for what the product org is becoming too. they should take their own advice a bit further and just replace all the leadership with an LLM, it would be cheaper and it's the same shit in practice
I have worked in some dysfunctional places but nothing like that, does sound bad.
Just got to keep your head, remember it’s just a job and you get paid regardless. Clock in, clock out, do the work assigned to you but mentally just check out while you look for a new role
I think both your statement and their statement are too strong. There is no reason to think LLMs can do everything a human can do, which seems to be your implication. On the other hand, the technology is still improving, so maybe it’ll get there.
2) There's no fundamental reason preventing some future technology to do everything humans can, and
3) LLMs are explicitly designed and trained to mimic human capabilities in fully general sense.
Point 2) is the "or else magic exists" bit; point 3) says you need a more specific reason to justify assertion that LLMs can't create new concepts/abstractions, given that they're trained in order to achieve just that.
Note: I read OP as saying they fundamentally can't and thus never will. If they meant just that the current breed can't, I'm not going to dispute it.
> 3) LLMs are explicitly designed and trained to mimic human capabilities in fully general sense.
This is wrong, LLM are trained to mimic human writing not to mimic human capabilities. Writing is just the end result not the inner workings of a human, most of what we do happens before we write it down.
You could argue you think that writing captures everything about humans, but that is another belief you have to add to your takes. So first that LLM are explicitly designed to mimic human writing, and then that human writing captures human capabilities in a fully general sense.
It's more than that. The overall goal function in LLM training is judging predicted text continuation by whether it looks ok to humans, in fully general sense of that statement. This naturally captures all human capabilities that are observable through textual (and now multimodal) communication, including creating new abstractions and concepts, as well as thinking, reasoning, even feeling.
Whether or not they're good at it or have anything comparable to our internal cognitive processes is a different, broader topic - but the goal function on the outside, applying tremendous optimization pressure to a big bag of floats, is both beautifully simple and unexpectedly powerful.
Humans are trained on the real world. With real world sensors and the ability to act on their world. A baby starts with training hearing, touching (lots of that), smelling, tasting, etc. Abstract stuff comes waaayyyyy later.
LLMs are trained on our intercepted communication - and even then only the formal part that uses words.
When a human forms sentences it is from a deep model of the real world. Okay, people are also capable of talking about things they don't actually know, they have only read about, in which case they have a superficial understanding and unwarranted confidence similar to AI...
All true, but note I didn't make any claims on internal mechanics of LLMs here - only on the observable, external ones, and the nature of the training process.
Do consider however that even the "formal part that uses words" of human communication, i.e. language, is strongly correlated with our experience of the real world. Things people write aren't arbitrary. Languages aren't arbitrary. The words we use, their structure, similarities across languages and topics, turns of phrases, the things we say and the things we don't say, even the greatest lies, they all carry information about the world we live in. It's not unreasonable to expect the training process as broad and intense as with LLMs to pick up on that.
I said nothing about internals earlier, but I'll say now: LLMs do actually form a "deep mofel of the real world", at least in terms of concepts and abstractions. That has already been empirically demonstrated ~2 years ago, there's e.g. research done by Anthropic where they literally find distinct concepts within the neural network, observe their relationships, and even suppress and amplify them on demand. So that ship has already sailed, it's surprising to see people still think LLMs don't do concepts or don't have internal world models.
