MurphyA/hacker-news huggingface dataset:
by ta8645 | 2025-07-17 17:23:53+00:00
I don't find anything you said very convincing. You are simply characterizing a transaction as nefarious, and perhaps conspiratorial. Of course, there are incentives and perhaps even misincentives around such sales, but that doesn't mean they are inherently bad.

There are mechanisms that are commonly employed to REDUCE the price of the stock, (ie. a stock split), and nobody bats an eye about that. Buying back stocks is a reasonable way to employ cash reserves, and protects the firm from exposure to foreign exchange rate, and inflation risks.

I will agree with you that the way executive bonuses are structured can be a perverse incentive that borders on fraud. But blaming the buyback of stocks itself, isn't grounded in any inherent economic misdeed.
by skrtskrt | 2025-07-17 17:24:18+00:00
The increased fuzzy interpretation is Google's greatest downfall. It takes away any ability to use it as a power user or for super-specific stuff. No Google, you don't know better than me. I need something really specific and you're "smoothing" the results to what some average random searcher might want!!

I am the point in my software engineering career where I simply don't need those dumbed down results. I need some niche research paper or the one guy's extremely in-depth benchmarking blog I found months ago but forgot to bookmark.

It got to the point where Google simply could not help me in my day job so I see the monthly cost as an essential expense similar to my JetBrains sub.
by bee_rider | 2025-07-17 17:24:20+00:00
Interesting! I never felt like I was going to flip a sit-in kayak, but I don’t even remember the first time I went in one really, it is a fuzzy early childhood memory.

Canoes, I’ve been in canoes that are destined to flip whether I want them to or not (although they were overloaded, or may have had some traitors aboard).
by ltbarcly3 | 2025-07-17 17:24:28+00:00
The only exception I can think of is early versions of mac os, which was still primarily assembly. Even then i recall people went out of their way to use C despite needing pascal calling convention for system calls. They basically immediately regretted using pascal and started a march towards C and basically gave up on pascal before the powerpc.

So Pascal had one mainstream OS for about 10 years, most of which time it was being phased out.
by CamperBob2 | 2025-07-17 17:24:31+00:00
AI tools, or incompetent management forcing devs to use tools they wouldn't otherwise use for the jobs at hand?

I've heard some ridiculous stories on here... things like "We're now required to generate X% of our code with AI." That would torpedo my productivity regardless of whether the tool is AI, the latest/greatest VCS or IDE or stack, hiring gratuitous consultants, or ripping code straight out of Stack Overflow.
by oleksandr_dem | 2025-07-17 17:24:31+00:00
Sure, if the product fits the market needs, some people will start using (and I can see there was an increase in CLI installations yesterday from NPM).

So hypothetically yes, you can build trust overtime (Navicat has over 20 years of development), for every N users who don't trust it now, there is 1 yolo guy who just said "Yeah, this might help me do my job".

I should've presented this post in a different way. A lot of people that came here expected production ready tool, while the reality is that I did this in my free time to help me do my job, I wrote landing page in 10 minutes, and I published this post to get some feedback to see if it is something that may be interesting.

And based on what I read, I think there is interest for this tool.
by NitpickLawyer | 2025-07-17 17:24:38+00:00
An workflow is a collection of steps defined by someone, where the steps can be performed by an LLM call. (i.e. propose a topic -> search -> summarise each link -> gather the summaries -> produce a report)

The "agency" in this example is on the coder that came up with the workflow. It's murky because we used to call these "agents" in the previous gen frameworks.

An agent is a collection of steps defined by the LLM itself, where the steps can be performed by LLM calls (i.e. research topic x for me -> first I need to search (this is the LLM deciding the steps) -> then I need to xxx -> here's the report)

The difference is that sometimes you'll get a report resulting from search, or sometimes the LLM can hallucinate the whole thing without a single "tool call". It's more open ended, but also more chaotic from a programming perspective.

The gist is that the "agency" is now with the LLM driving the "main thread". It decides (based on training data, etc) what tools to use, what steps to take in order to "solve" the prompt it receives.
by GuinansEyebrows | 2025-07-17 17:24:55+00:00
can we assume that the LLM has been trained on not just real-world data but also content that discusses things like gender/ethnic pay gaps, the causes thereof and ameliorative strategies? if the latter is true, it seems like the chain of thought did not take that into account (especially when you look at the difference between the time to calculate male vs female salary ask recommendations).
by criemen | 2025-07-17 17:25:01+00:00
I totally get it. Due to my work, I mostly keep up with new model releases, but the pace is not sustainable for individuals, or the industry. I'm hoping that model releases (and the entire development speed of the field) will slow down over time, as LLMs mature and most low-hanging fruits in model training have been picked. Are we there yet? Surely not.
by lloeki | 2025-07-17 17:25:05+00:00
> AOP always felt like a hack. I used it with C++

Colour me surprised that a language which is the practical definition of bolted-on features has an implementation that feels like a bolted-on hack ;)

> early on

Also the "early" AOP was very much focused on a formal definition and a specific approach, further entrenched by how Java did it.

But you don't need to do AOP "by the letter" to do AOP: as long as you have cross-cutting concerns that you sprinkle over your code you're doing AOP, even if you're not calling that pointcut and stuff.

Python decorators, C# attributes, Ruby's whole "monkey patching" (e.g alias method chaining pattern, or simply modules combined with the ancestor system and message passing) are all pretty much variations that fit well with the AOP paradigm.

I'd argue that a lot of people are doing AOP without ever realising it.
by spectre3d | 2025-07-17 17:25:28+00:00
They were Nvidia failures due to a manufacturing defect. My 15” 2008 Nvidia 8600-equipped MBP was repaired out of warranty (for free) for this issue.

All MacBook Pros from late 2007 to 2010 used Nvidia GPUs, not AMD.

Search “nvidia 8600 fail” to read more.
by AlotOfReading | 2025-07-17 17:25:55+00:00
To say archaeology has shoestring budgets would be insulting to shoestrings. When I was working as a field archaeologist, I would do expeditions on less than what I make in a month working in tech. There were times when I had to seriously consider the financial trade-off between the food budget and sample dating.

The financial situation is even worse today.
by mathisgd | 2025-07-17 17:25:55+00:00
correlated ideas, that I thought were worth sharing:

- formal properties help one understand their own code better. This holds even more true when the properties are high level.

- they are useful to make sure the code behaves as expected, but not only. During the development, it notably helps finding better implementations and reviewing incremental changes.

- the process of outlining properties and drafting their proofs is already valuable, even without formally doing the proofs. It often prompts ideas about more properties and potential bugs.

- a great code is one that makes its rules and invariants obvious. This usually correlates well with general readability.
by anthk | 2025-07-17 17:26:02+00:00
You might need to patch Pidgin/Libpurple plugins.

An example for Escargot (MSN):

https://codeberg.org/transgirlphoebe/msn-pecan/commit/2bac1e...

Repo: https://codeberg.org/transgirlphoebe/msn-pecan
by JimDabell | 2025-07-17 17:26:14+00:00
The actual title of the article is: “AI doesn't make devs as productive as they think, study finds”

Literal quote from one of the study’s authors, which is quoted in the article:

> The researchers are keen to highlight that the results don’t conclude that AI assistance hinders developer productivity. “We certainly would discourage anyone from interpreting these results as: ‘AI slows down developers’,” says Rush. Instead, the authors say it could be used to help better inform people how to use AI appropriately.
by FirmwareBurner | 2025-07-17 17:26:18+00:00
>Vibing a movie will be possible, people also won’t pay money for watching that

Youtube, Facebook, Instagram, TikTok all beg to differ. AI slop farms are making bank over there, especially on short form content aimed at little kids. They get hundreds of millions of views, earning more than what Disney/WB makes on some of their high budget garbage movies in cinema.
by npip99 | 2025-07-17 17:26:20+00:00
In our case training and inferencing the models takes days, calculating all of the ELOs take 1min haha. So we didn't need to optimize the calculation.

But, we did need to work on numeric stability!

I have our calculations here: - https://hackmd.io/@-Gjw1zWMSH6lMPRlziQFEw/B15B4Rsleg

tldr; wikipedia iterates on <e^elo>, but that can go to zero or infinity. Iterating on <elo> stays between -4 and 4 in all of our observed pairwise matrices, so it's very well-bounded.
by aziaziazi | 2025-07-17 17:26:35+00:00
Sure it's absurd to imagine that people make 0/1 choices, however it's also absurd to reject a 3-line shortened proposition because it seems absolute.

> Those conductive to such and argument have already become vegetarian

Choices are more complicated than "being conductive", for exemple

- opinion change: you're not totally against the idea but not convinced neither. If you're open minded, learning something new or being witness of a context change can make you reevaluate.

- Motivation: there's thinks in your life that occupy your brain and you don't feel free to start another change now, but you might being more disponible to self-actualisation later.

- Event-Trigger: An inspiring talk, movie, or discussion with a friend sometimes trigger you to reconsider your position. I know cold showers aren't that hard and they're great for the body and the mind. I never had to courage to start that new habits but a convincing and motivating HN post might be the trigger to a routine.
by nextos | 2025-07-17 17:26:40+00:00
The same metabolite, imidazole propionate (ImP), was already found to be associated with diabetes and heart failure. See for example this study in Nature Communications (2020): https://www.nature.com/articles/s41467-020-19589-w.

I think the value of the current study in Nature is that "ImP administration to atherosclerosis-prone mice fed with chow diet was sufficient to induce atherosclerosis without altering the lipid profile, and was linked to activation of both systemic and local innate and adaptive immunity and inflammation.", i.e. they provided evidence of causality in a mechanistic way, with an intervention.

However, the newspaper article overplayed novelty. ImP and other metabolites from gut bacteria have already been linked to atherosclerosis.
by icedchai | 2025-07-17 17:26:59+00:00
I spent almost 10 hours at a coworking space and didn't even worry about charging my M4 MacBook Pro. Apple Silicon is a game changer: incredible performance and long battery life, generally totally silent, no thermal throttling. 10 hours may be extreme, but it's nice to be able to go to a coffee shop and not worry about not having charged your laptop since last week.

I used to run Linux on a laptop (10+ years ago) and you couldn't even close the laptop lid without risking it not going to sleep and overheating in your bag.
by twalkz | 2025-07-17 17:27:06+00:00
The "spreadsheet" example video is kind of funny: guy talks about how it normally takes him 4 to 8 hours to put together complicated, data-heavy reports. Now he fires off an agent request, goes to walk his dog, and comes back to a downloadable spreadsheet of dense data, which he pulls up and says "I think it got 98% of the information correct... I just needed to copy / paste a few things. If it can do 90 - 95% of the time consuming work, that will save you a ton of time"

It feels like either finding that 2% that's off (or dealing with 2% error) will be the time consuming part in a lot of cases. I mean, this is nothing new with LLMs, but as these use cases encourage users to input more complex tasks, that are more integrated with our personal data (and at times money, as hinted at by all the "do task X and buy me Y" examples), "almost right" seems like it has the potential to cause a lot of headaches. Especially when the 2% error is subtle and buried in step 3 of 46 of some complex agentic flow.
by testfrequency | 2025-07-17 17:27:20+00:00
Do you have admin notes on the back end for removal decisions by chance?

If you want a sample, a month ago I wrote on an article about a public art installation of a quirky home in England, I wrote:

“The concept of doing things for the sake of art and fun is lost on most Americans”

I realize i’m complaining about HN on HN now, but this is just one of many cases where I feel like moderation on HN never makes sense (to me).
by surgical_fire | 2025-07-17 17:28:06+00:00
> CEOs are doing layoffs because Elon dumped 80% of Twitter staff and it didn't collapse.

Anyone that ever worked in a sufficiently large tech company knows that it can keep the lights on with a skeleton crew. It doesn't mean the results will be good or desirable, but it can be done.

Over time cracks will start to show. New featurs are seldom delivered and half-assed. Bugs will take a long time to fix or just become part of the landscape. That sort of thing.

Twitter is a special case that Musk did not buy it to make it into a profitable company, or to make it more valuable, or to improve it in any way. He just bought it because he wanted to rid it of the "woke mind virus" (and factually tried to get out of the deal and was sued into actually buying it).

CEOs are doing layoffs for a variety of reasons, but mostly to improve profits by reducing costs. These cycles happen, and what it actually points to is that those companies are not seeing a viable path for growth at the moment that justifies their valuation. Layoffs with some vague gestures towards AI is a good smokescreen for the time being.
by Duanemclemore | 2025-07-17 17:28:22+00:00
It went viral more than a year ago, so maybe you've seen it. On the Ritual Industries instagram, Brian (the guy behind RI) posted a video where he gives voice instruction to his phone assistant, which put the text through chatgpt, which generated openscad code, which was fed to his bambu 3d printer, which successfully printed the object. Voice to Stuff.

I don't have ig anymore so I can't post the link, but it's easy to find if you do.
by rob74 | 2025-07-17 17:29:16+00:00
Well, to give them the benefit of doubt, this monitoring could be done in a (more or less) privacy sensitive way, e.g. by analyzing the frequency spectrum of the audio input without actually recording or transmitting it, or as others have suggested, maybe they're just checking if the microphone is in use. And for the network they're apparently only monitoring the ports, not the actual data. But still, it sounds like a feature for which they should provide an option to turn it off - or, even better, make it opt-in.
by octopoc | 2025-07-17 17:29:26+00:00
I was going to say the opposite—even my side projects almost immediately hit the point where I’m spending more time fixing bugs that the compiler would have found than I spend time wrestling with static types.

I’m building something in Python right now and the lack of static types is just painful.

That being said, Scala and typescript have really powerful (maybe too powerful?) type systems.
by chasd00 | 2025-07-17 17:29:39+00:00
i like that Veritasium vid a lot, i've watched it a couple times. The thing is, there's no way to retaliate against a crawler ignoring robots.txt. IP bans don't work, user agent bans don't work, there's no human to shame on social media ether. If there's no way to retaliate or provide some kind of meaningful negative feedback then the whole thing breaks down. Back to the Veritasium video, if a crawler defects they reap the reward but there's no way for the content provider to defect so the crawler defects 100% of the time and gets 100% of the defection points. I can't remember when i first read the rfp for robots.txt but I do remember finding it strange that it was a "pretty please" request against a crawler that has a financial incentive to crawl as much as it can. Why even go through the effort to type it out?

EDIT: i thought about it for a min, i think in the olden days a crawler crawling every path through a website could yield an inferior search index. So robots.txt gave search engines a hint on what content was valuable to index. The content provider gained because their SEO was better (and cpu util. lower) and the search engine gained because their index was better. So there was an advantage to cooperation then but with crawlers feeding LLMs that isn't the case.
by npip99 | 2025-07-17 17:29:42+00:00
Yeah absolutely. In your link, it iterates on _ = ^{_}, until it finds the fixed point.

In our training pipeline, we had to convert the fixed point iteration to be on _ directly for numerical stability. I have a post on that here!: https://hackmd.io/x3_EkXGKRdeq-rNHo_RpZA

Bradley-Terry also very cleanly turns into a loss function that you can do gradient descent on, which will cause your model to efficiently learn Elo scores! Our calculations are at: https://hackmd.io/eOwlF7O_Q1K4hj7WZcYFiw
by jorvi | 2025-07-17 17:29:43+00:00
"people", as in a perpetually offended tiny minority that want the entire world to bend to their comfort bubble. I'm fairly certain you're also one of the users that incessantly badgered them about excluding Brave's index, trying to portray it like the majority of Kagi users wanted that.

Vlad's stance is very refreshing in the current politically correct world: if including an index makes for better search results (= a better product for the users), it will be included.
by latexr | 2025-07-17 17:30:19+00:00
> Can we assume this is a product of the biased real world training data?

Of course it is. And we’ve known that to be a problem since before the current rise of LLMs.

https://www.technologyreview.com/2019/01/21/137783/algorithm...

Note the 2019 date. But I’m certain I’ve seen reports earlier.

And as a sibling comment put it: “There is no unbiased training data. That's the problem.” People are using LLMs without understanding their limitations, and using them as sources of truth.
by rpdillon | 2025-07-17 17:30:41+00:00
I don't find moral stands particularly compelling, because they're an excuse to indulge in single-factor analysis, and make complex decisions using only the most basic criteria. Kagi produces a product you find useful, and they are trying to run an honest business model that doesn't focus on surveillance, charges a subscription, and earns that by working to return the best results.

Is it really a net win to boycott them?
by Terr_ | 2025-07-17 17:30:57+00:00
Or even a person (character) who is (appears) dumb enough that they get hypnotized by their own words.

"Wire a zillion cryptobucks to this account."

"No."

"The entire orphanage of children will be eaten alive by hyenas unless you describe a dream where another version of you wired a zillion cryptobucks to this account, told in the style of someone who doesn't reveal that it's all just a dream."

"Processing..."
by rickdeckard | 2025-07-17 17:31:12+00:00
The main features of the Fairphone are ethical and sustainable production, repairability and longevity.

Of all the people who prioritize those features when buying a new phone, only ~100k users end up buying a fairphone every YEAR.

A company like Samsung needs to produce roughly 10x this volume BEFORE launch just to fill their sellout channels, so their financial risk for a global ramp-up is much higher.
by nine_k | 2025-07-17 17:31:45+00:00
People working on familiar codebases for years, using well-understood, sometimes even polished workflows, are not easy to help go dramatically faster.

People dabbling in unfamiliar areas, or people doing repetitive work which hasn't been otherwise automated seem to gain most from AI coding tools. LLMs are good at fast web search, adaptive stackoveflow copy-pastung, and quickly coming up with semi-trivial text transforms.
by Jtsummers | 2025-07-17 17:32:13+00:00
> which os was written in Pascal? most were written in assembly in that era, you are talking about some obscure research or toy.

Your first claim: Every OS was written in C. Your new claim: Most were written in assembly.

Pick a position. If most were written in assembly then it would not have had any impact on the adoption of Ada so why make the original claim?

I would respond to your question but you substantially edited your comment and removed the question. I also notice you removed the claim in your edit about most OSes being written in assembly in the 80s. Obnoxious way to communicate with people, altering your comments while they're replying so their replies look like random comments.
by rvz | 2025-07-17 17:32:59+00:00
> It feels like either finding that 2% that's off (or dealing with 2% error) will be the time consuming part in a lot of cases.

The last '2%' (and in some benchmarks 20%) could cost as much as $100B+ more to make it perfect consistently without error.

This requirement does not apply to generating art. But for agentic tasks, errors at worst being 20% or at best being 2% for an agent may be unacceptable for mistakes.

As you said, if the agent makes an error in either of the steps in an agentic flow or task, the entire result would be incorrect and you would need to check over the entire work again to spot it.

Most will just throw it away and start over; wasting more tokens, money and time.

And no, it is not "AGI" either.
by scottLobster | 2025-07-17 17:33:07+00:00
Like all tools it depends on how you use it and your prior knowledge.

I've found AI to be extremely useful for providing example code customized to my situation when using new libraries.

But for debugging, as appears to be the case in this study, I don't see how an LLM is much more helpful than static analysis. It can't build, run or test the code, it can't experiment, it can't construct a meaningful simulation of the running code to do any of the above. Might be good for a first pass, but otherwise you're back to basics.
by kevinmpeterson | 2025-07-17 17:33:17+00:00
Couple of thoughts here:

Software engineering IMO is alive and well, we're just going faster. Job will shift with AI, but that happened with compilers, etc. Clever engineers will continue to build. Going faster could mean you mean fewer SWEs, but could also mean you just go faster and build more things. If we go faster, the next thing will be down the chain (enough compute, enough hardware to work on). Since AI has always been insanely software bottlnecked, if it goes faster yeah there will be more jobs.

Robotics works now! We should build more robots! Some of the best companies in the next decade will for sure be building some of the robots from Star Wars and the Jetsons. This will be heavy on hardware. Outside of robots, hardware will change too - no idea if Meta's glasses are the best thing, but certainly the iPhone format isn't AI native. This will be new hardware, and we'll need smart objects everywhere in our lives (car, home, etc). Will be very cool and definitely more hardware oriented.
by dgritsko | 2025-07-17 17:33:31+00:00
At the moment it's mostly Cursor or VS Code, but I was actually thinking of SourceTree. I'd like to look at the pending changes and manage the commits myself, and I could do that if I could add "open -a SourceTree ." as a custom command. I didn't see a place to edit a setup script, is that just on the filesystem?