skysight-inc/HackerNews huggingface dataset:
> As a user, I don’t really care.

Tell me: Just how is it fair for a user to pay for the reasoning tokens without actually seeing them? If they are not shared, the service can bill you anything they want for them!
Beltmatic: A math game inspired by Factorio and shapez
I think it depends on the size of the company. The larger the larger the company, the more likely they are to split this stuff out. Though various titles may seem to bleed together. I have a software engineer title, while another guy on my team is a software architect… we effectively do the same job. Stepping back from a higher level view, as a general theme, those with an architect title are more likely to be responsible for an overall design, while the engineers may have some input and build things to support the design.

The quality of said designs can vary wildly. Some designs I get from other team I completely ignore, because they have no idea what they’re talking about. Just because someone has the title doesn’t mean they deserve it.
First time I saw an experienced Java programmer at work was a revelation. One hand on the keyboard, one hand on the mouse. Hardly ever actually typed anything. Line width set to 200 characters. Cranking out boilerplate, 5 lines with a single mouse click. Measured in lines of code, his productivity was off the charts.
That “main engineering effort” will go on forever. People neglect to note that everything is constantly changing. Just like the roof on your house, if you don’t upgrade your components regularly, eventually you will face a huge rewrite when that thing your ancient home-made infrastructure relies on is no longer supported or is no longer updated to support the latest thing you need for your SaaS.

You can’t avoid this cost. Some people refer to it as technical debt, but I think more accurately it could be called “infrastructure debt”. Platform providers maintain the infrastructure debt for you - this is what you pay them for. And they do it with tremendous economies of scale. Unless your scale is truly enormous - like Meta, for instance - it isn’t worth build your own infrastructure.
LLMs are not for expanding the sphere of human knowledge, but for speeding up auto-correct of higher order processing to help you more quickly reach the shell of the sphere and make progress with your own mind :)
Intel's foundry business is unsuitable for NVIDIA to use to produce GPUs. Intel's foundry business is also not a complement in the economics sense, as in "commoditize your complement"; it's a substitute for TSMC fabs (but a bad substitute). The market impact of Intel's foundry business is that incremental improvements to the fabs used more or less exclusively for Intel's x86 CPUs does not lead to higher sales of NVIDIA GPUs. It would take an unrealistically huge advance in quality or drop in price for Intel's fabs and the output thereof to affect demand for NVIDIA GPUs.
I don’t think optimization in Haskell is much different from any other language. In most cases naive Haskell is pretty fast and memory efficient, but there are some patterns that will make it more or less so. There are a handful of common patterns you can learn that are idiomatic and will generally result in faster or more memory efficient code, and some common libraries that you can use that are more efficient.

There are also some common patterns for less idiomatic but more performant code that you can use as a first pass when you need to optimize things further. Usually that’s sufficient, but when you need to go even further with optimization then it can get hard. Every language can. In Haskell, it usually means starting by dumping core and seeing what the compiler is doing, and getting familiar with the different passes the compiler makes. As a last resort you can also just write in C and use the ffi.
Showing my age: based on the title, I assumed it was a ActionScript scanner.
It's pretty incredible that a 110-meter high tsunami happened and the only reason anyone noticed was a former employee saw that one of the abandoned SIRIUS research stations was swept away, after their cruise ship ran aground at the mouth of the fjord [1] the day after.

They probably would have figured it out from the seismic data eventually, but the tsunami was identified within a week in one of the remotest places on earth because of some random pleasure cruise.

[1] https://www.smobserved.com/story/2023/09/21/news/greenland-t...
Yes, at great expense, we bring in tons of life-sustaining supplies from habitable parts of Earth so that people can live briefly at the South Pole or on top of Everest. In the absence of those supplies, of course everyone on Everest or the Pole would die, quickly.

And the South Pole and the top of Everest are way, way, way, way, way, way, way more habitable than Mars is.

And there are no nearby habitable areas on Mars to get supplies from.

I don't know how I can spell this out for people: when Musk wakes up from a drug-addled haze and says "we're going to colonize Mars in two years" what he means is something like "I'm really high right now" or perhaps, if we grant him a little cunning, "I want my share price to stay high so I'll say some bullshit". It should not be interpreted as a factual statement.
The intermediate CoT results aren't in the API.
TL;DR https://gist.github.com/lucasmrdt/4215e483257e1d81e44842eddb...
> There is no "formula" for success in the creator economy - the winners are largely random.

I think that strongly depends on what you call "the creator economy". For example, on YT it's really mostly skill: https://www.youtube.com/watch?v=Ip2trao6dYw

Not that I believe its easy, nor do I think AI will be super good at it, at least not before everything else also enshittifies into the habsburg-AI-powered dead internet.
That is a common fallacy, I suspect it comes from having enough budget to not having to think about being able to afford something decent.

It is like photographers with $5,000 worth of equipment in their camera bags telling you that equipment doesn’t matter. I mean, there is a reason why they spend all that money right? Of course a good photographer will be able to get good results with a cheap camera, but only in situations where that cheap camera can actually capture the scene. For example, if it is not sensitive enough to capture enough light at night time, you are not getting night time shots, period, no matter how good you are. (this very much used to be a thing 10 years ago)

If you employ programmers, you will buy fast workstations because it will make them MUCH more productive. A slow computer will interrupt your work by making you wait.

I think it is in fact the exact opposite, the better you are at something, the more likely it is that you become limited by your equipment. I will probably not be able to cook better if I get very expensive knives. But I would speculate that an actual professional cook or butcher will be able to work better with sharp knives that keep their edges well.
If someone has a serious pitch to the tune of "I've got enough leverage with key players in Stripe to make an adult site work", everything afterwards would get pretty easy. Finding money to advertise is no problem, these sites are in a great position if they can work around the payment systems. The difficulty becomes the moat.
Haskell has linear types now, which can give you something similar to rusts affine types. The library ecosystem for it isn’t very mature yet though.
I dunno how sustainable that scenario is, because it brings to mind the maybe apocryphal Chinese curse "may you be granted everything you wish for".
I think GP's point was that most[1] programs that can be represented will fail to please the programmer or his principals. The act of programming is navigating the state space of all possible programs and somehow finding one that has the desired properties and also doesn't otherwise suck. When viewed through that lens, a type system preventing most programs from being represented is a good thing, since odds are every single program it prevents is one that is unpleasant or otherwise sucks.

[1] of the countably infinite possible programs, virtually all
It’s that there’s no life native to those environments. The authors is making the point that lay people, us, lack a frame of reference for how inhospitable Mars is.
A lot of these types of exploits are out there because of the ability to run elevated (system) custom actions in the MSI.

It is part of why MSFT says to vendors "Switch to MsiX".
I don't see how the need for business travel would arise. I perceive the CTO role as being responsible for ramping up infrastructure and managing the development team.
I agree with some of the others here that this is far too complex for what is fundamentally a heater with a thermostat.

Why hellishly-complex USB-C with its effete tiny-pinned connectors instead of a plain old robust barrel jack? And requiring software instead of a simple analog feedback loop? Software which could fail and cause runaway heating is never a good idea.

Soldering irons have always been quite repairable, especially the simple ones:

https://320volt.com/wp-content/uploads/2015/02/yihua-hakko-9...

https://www.next.gr/uploads/79/lm358_simple.png
Claude Soñnet is a more creative coder.

That's why Gemini Flash might appear dumb in front of Sonnet. But who writes the dumb functions better which are guaranteed to work for long time in production? Gemini.

But Sonnet makes silly mistakes like, even when I feed it requirements.txt it still uses methods which either do not exist or used to exist but not anymore.

Gemini Flash isn't as creative.

So basically, we use Sonnet to do high level programming and Flash for low level (writing functions which are guaranteed to be correct and clean, no black magic)

Problem with sonnet is it's slow. Sometimes you'll be stuck in a loop where it suggests something, then removes it when it encounters errors, then it again suggests the vary same thing you tried before.

I am using Claude Soñnet via cursor.

>What if you need to iterate on the functions it gives?

I can do it via Aider and even modify the prompt it sends to Gemini Flash.
An abstraction machete. Heh.
> I laid out how national budgets and household budgets are fundamentally the same thing: The flow of cash coming in must meet or exceed the flow of cashing going out. I'm surprised this detail is completely lost on you.

That's absolutely not true. Governments can manipulate the supply of money they have available to themselves in numerous ways. The most obvious counter-example is that governments can simply print physical cash. It is rarely a good idea, but it absolutely a thing that can and has been done.

> the world at-large is slowly but certainly shifting from Pax Americana to Pax Sino.

> So again, what happens when the gravy train stops? We don't know when that is, we don't know if the train will stop in the first place, but we have to have a plan in hand because debts must be paid.

If it goes at the pace it has been going, it'll be a continuous squeeze instead of "stopping". What would happen in that squeeze? Interest rates and taxes will slowly go up and the economy will stagnate.

> Where do we consider we are borrowing too much? This line can and should be constantly reevaluated as necessary and appropriate, but first of all do we even have such a line?

Not really, because lender confidence is more complicated than a number as simple as debt-to-GDP, but it's a good benchmark value. I think the obvious answer is that lenders will indicate with their buying habits when they disapprove of the debt, and that's really the indicator that ultimately matters. But it's a complicated question that economists don't even agree on.
It's a brilliant idea, but LLMs are predicated upon the massive data of the internet.

That being said, you could take a pre-2018 dataset and test it for discoveries/insights circa 2019 or 2020 and see how well it performs.
There is an extension that lets you do this now (OverloadedRecordDotSyntax) but truthfully I think it’s a really bad idea. The (.) operator already has a very concrete meaning in Haskell, and record dot notation means that you suddenly need to care about the specific details of how values are calculated. Field accessor functions are much better imo even if they seem a little odd.
Definitely. When we talk about being skilled in a T shape LLMs are all about spreading your top of T and not making the bottom go deeper.
Thank you, that's all very good information. I was just a little frustrated because none of that was discoverable either in the announcement linked from HN, or their home page.
> This ordering of priorities, in which the sacrosanct goal is to extend "the probable lifespan of consciousness" and space colonization the means, is above all else a monstrous permission structure for this outspoken bigot's vile social ideas, a kind of reductio ad absurdum for what's been doing business as "effective altruism" for a while now.

The author is frankly, biased to the point of being deranged. Even if this rant were written in a more mature way, I would say that he lacks imagination and ambition. A lot of things that exist today - both good and bad - would have been only fiction fifty years ago. To me this sort of criticism feels like someone standing on the sidelines thinking they somehow know it all.
I find myself a little sad at how lucrative a job this will appear for an entire generation. $1500 average creator pay is higher than 40 hours a week minimum wage.
They are currently trying to raise money (talk of new $150B valuation), so that may have something to do with it
Thanks, but I am not using if_bridge. I am creating a netgraph bridge[0] which is connected to the host via a netgraph eiface. Then on the host, the packet passes to my real physical interface because I have gateway_enable and I have pf perform NAT[1]. It looks like that blog post connected the netgraph bridge directly to the external interface, so my guess is my slowdown is from either pf performing NAT or the packet forwarding from gateway_enable.

[0] https://code.fizz.buzz/talexander/machine_setup/src/commit/20768edcf69eddae5cf65e30a0bde869f9ddd19b/ansible/roles/bhyve/files/bhyve_netgraph_bridge.bash#L214
[1] https://code.fizz.buzz/talexander/machine_setup/src/commit/20768edcf69eddae5cf65e30a0bde869f9ddd19b/ansible/roles/firewall/files/mrmanager_pf.conf#L35
DSPy doesn't do that, you could describe it as a langchain style agent that evaluates its own output though it's better/faster than that.

OpenAI is definitely trying to run a hype game to keep the ball rolling. They're burning cash too quickly given their monetization path though, so I think they're going to end up completely in Microsoft's pocket.
I’ve been told to stay away from any kind of sharpening that isn’t specifically for cooking knives - the angle of the blade is supposed to be different.

I don’t have actual knowledge, just what I’ve been told by chefs.
It's interesting to note that there's really two things going on here:

1. A LLM (probably a finetuned GPT-4o) trained specifically to read and emit good chain-of-thought prompts.

2. Runtime code that iteratively re-prompts the model with the chain of thought so far. This sounds like it includes loops, branches and backtracking. This is not "the model", it's regular code invoking the model. Interesting that OpenAI is making no attempt to clarify this.

I wonder where the real innovation here lies. I've done a few informal stabs with #2 and I have a pretty strong intuition (not proven yet) that given the right prompting/metaprompting model you can do pretty well at this even with untuned LLMs. The end game here is complex agents with arbitrary continuous looping interleaved with RAG and tool use.

But OpenAI's philosophy up until now has almost always been "The bitter lesson is true, the model knows best, just put it in the model." So it's also possible that the prompt loop has no special sauce and that the capabilities here do come mostly from the model itself.

Without being able to inspect the reasoning tokens, we can't really get a lot of info about which is happening.
TTD's CEO denied these rumours:

https://live-adexchanger.pantheonsite.io/daily-news-roundup/...
Ig Nobel Prize goes to team who found mammals can breathe through anuses
Does your startup need complex cloud infrastructure?
After listening to @levelsio on Lex Friedman’s podcast, I became obsessed to simplify my deployments:

Do startups really need complex cloud architecture?

Inspired, I wrote a blog exploring simpler approaches and created a docker-compose template for deployment

Curious to know your thoughts on how you manage your infrastructure. How do you simplify it? How do you balance?
I think to say that "Taliban controlling Afghanistan obviously wasn't a problem because they still do" is inaccurate. The problem the US had with the Taliban is that being an Islamic fundamentalist political entity, they provided safe harbor for al-Qaeda. They refused to cooperate with the request to extradite Bin Laden. The west had every intention of eliminating the Taliban for good and failed to achieve this goal.

The prevailing wisdom at the time was to remove the Taliban from power and to establish a deradicalized, democratic Afghanistan that would hopefully cooperate with the west in fighting terrorism as well as removing the conditions that would cause jihadist movements to flourish. The template for this being the destruction of Axis powers during WWII and their subsequent rebirth as deradicized, prosperous, militarily benign nations.

It took a decade long man hunt to locate Bin Laden which was unanticipated. When he was found in Pakistan he was in hiding, not being intentionally harbored by the government as in Afghanistan.

In hindsight it was probably unrealistic to expect a rebuilt stable Afghanistan free of the Taliban. Perhaps a larger "surge" level of troops for a longer period of time and greater focus on Afghanistan rather than invading Iraq would have achieved a different outcome. Let's not forget that the US was not being entirely rational at the time. The government and the public wanted revenge in a big way.
That's $1500 annually for the average creator, and due to concentration at the top, the median take home is going to be even lower than that.
Is there really an incentive for startups in China today compared to 4 years ago? Look what the government there has done to very rich and influential people in just the last few years. Once you have achieved your goal of building a great business, it can all be taken away from you overnight while you rot in a jail cell. Might as well take it easy and at least enjoy your life instead of losing ten prime years to a hard grind for nothing.
I'm trying to convince, not vent.
For the library problem, I have even tried feeding it requirements.txt (which contains version of libraries, I also fed it python version I am using.) i use but no success with that either!

It's definately better than any other LLM out there.

But it also gets stuck and creates frustration.

Atm, I am writing code which does video editing. It keeps suggesting me some parallel approach, when it fails it suggest me going back to origin approach. Then it suggests me parralel approach again using some other hack and none of them work!
When you go into the regular, slow, audio mode there's a little info circle in the top right corner. Over time that circle has been giving periodic updates. At one point the message was that it would be delayed, and now it's saying it's "on it's way" by the end of fall.

Not perfect but they've been putting their communications in there.
Also, 25% of federal expenditures are funded by printing money, which is just a tax more like fica than income tax.

Of course the other way of looking at it is that fica ultimately just comes out of the pocket of the rich. If there were no payroll taxes, fica would just stay in the employers pockets. Employers are mostly the rich. Or may be it would get embezzled by the middle management due to increased principal agent problem without the payroll tax paper trail…
Create witty political content for X and IG
What is an acceptable debt-to-GDP ratio? Is over 100% acceptable in terms of ability to pay?

I would have assumed that if that was your only metric, that the US at 122% would be far past your acceptable line.
It's not just about efficiency. I don't have the means to hire a junior right now, but 20$ is a no brainer.
Dupe https://news.ycombinator.com/item?id=41518196
Literally in every HN post about AI, it is a common pattern in the comments section...

"LLMs are simply predicting next token, it is not thinking/reasoning/etc."

"LLMs can't reason, only humans can reason"

"We will never get to AGI using LLMs"

It's interesting that I don't see much of that sentiment in this post. So, maybe LLMs can reason after all? :)
Articles like this always bring out the best comments. Obviously it’s a biased article but the two primary points, in my opinion, stand.

1. Even the most inhospitable places on Earth are orders of magnitude more hospitable than Mars.
2. Given 1 it seems much more reasonable to use resources to maximize the hospitality of Earth than to try and mark Mars hospitable.
This is a wonderful term for it!
Interestingly, the New York Times mistakenly reported on and reviewed the old features as if they were the new ones. So lots of confusion to go around.
You can get good temperature-controlled 936-style soldering stations for less than $30. Spend the rest of your budget on solder, flux, and an assortment of tips.
It seems like it's software to conduct a hearing evaluation to tune the airpods.

There's actually loads of OTC hearing aids on the market already although most seem fairly pricey.
I’m very positive about this development.

I don’t personally need hearing aids (yet) but I know people that do, and dear god are they expensive pieces of equipment.

Even if the AirPods aren’t perfect for everyone (not everyone wants in-ear devices) a big name like this getting in at that price-point might shake up the market.
Not strictly true. Some stuff in the Erlang distribution must be included in extra_applications before you can call them.
I use LLMs for three things:

* To catch passive voice and nominalizations in my writing.

* To convert Linux kernel subsystems into Python so I can quickly understand them (I'm a C programmer but everyone reads Python faster).

* To write dumb programs using languages and libraries I haven't used much before; for instance, I'm an ActiveRecord person and needed to do some SQLAlchemy stuff today, and GPT 4o (and o1) kept me away from the SQLAlchemy documentation.

OpenAI talks about o1 going head to head with PhDs. I could care less. But for the specific problem we're talking about on this subthread: o1 seems materially better.
Your assumption is that the status quo provides those things. Nowadays, people will break up as soon as they get "the ick" or just have a rotating group of people they see. Lasting relationships are much less common than they used to be because it's easier to switch partners.

People just want to chase a local maximum of constant validation that they're pretty/smart/correct. They don't see or understand the value in working through fights to create something beyond the sum of two people.

AI excels at maintaining that local maximum. It can confidently reassure you better than any human can even if you're wrong. AI partners following this are successful now and people in their teens and early 20s are being hooked en masse.

Historically, superior pieces of technology haven't displaced older incumbents when the learning curve is too steep.

I don't see why a person dating an AI partner that has lovebombed them for several years would switch to another AI (or a person) that starts fights and bickers. Even if it's better in the long-term, that's still a marked decrease in short-term satisfaction.
This is why PornHub was sold to bag holders earlier seemingly out of nowhere. Their business is dead.

Second point - is this really Europe’s most successful tech company of the last 15 years?!
How many hours without it?
Right, I am suggesting a feature to disable the idleness check during a window of the day