MurphyA/hacker-news huggingface dataset:
by zephen | 2026-01-13 23:56:52+00:00
Humans are tribal, which has both benefits and costs.

In technology, the historical benefits of evangelizing your favorite technology might just be that it becomes more popular and better supported.

Even though LLMs may or may not follow the same path, if you can get your fellow man on-board, then you'll have a shared frame of reference, and someone to talk through different usage scenarios.
by UqWBcuFx6NV4r | 2026-01-13 23:57:33+00:00
Yeah. This has been an interesting cultural shift, especially with “the kids”.
I’ve had at least a few people passionately tell me that using (non-generative, non-LLM) AI to assist with social network content moderation, is unethical, because it takes away jobs from people. Mind you, these are jobs in which people are exposed to CSAM, gore, etc. A fact that does not dissuade people of this view.
There are certainly some sensible arguments against using “AI” for content moderation. This is not one of them.

It’s really intriguing how an increasingly popular view of what’s “ethical” is anything that doesn’t stand in the way of the ‘proletariat’ getting their bag, and anything that protects content creators’ intellectual property rights, with no real interest in the greater good.

Such a dramatic shift from the music piracy generation a mere decade or two ago.

It’s especially intriguing as a non-American.

Again, as you say, many sensible arguments against AI, but for some people it really takes a backseat to “they took our jerbs!”
by ozmodiar | 2026-01-13 23:57:38+00:00
I see this so much with regard to Chinese/Russians and increasingly Americans (I know people in each camp). The point of the propaganda is just that, to make them distrust all information and fall in line by default. It makes it impossible to argue against the main narrative being broadcast because "who's to say what's true?" And frankly I'm getting real sick of it. It's not the same thing as being media literate.
by solid_fuel | 2026-01-13 23:58:14+00:00
> It's like being mad at your bank that somebody stole your credit card on the subway and made purchases with it.

It's being mad that a store sold me a counterfeit rolex, actually. Spotify might claim to just be a "marketplace" like every other platform these days, but they're still the ones hosting that page that passes off slop as legitimate work by another artist. Spotify has a responsibility to govern what is hosted and sold on their platform.
by hambes | 2026-01-13 23:58:53+00:00
To add to the very short "valodating the result" section, let me recommend `git range-diff`.

Range diff takes two commit ranges and compares thor commits pairwise, wich is perfect for rebases, since after the rebase all commits still exist and should be mostly identical, just at some other place in the history.

Use it like `git range-diff main..origin/mybranch main..mybranch` to compare the local, rebased branch with the upstream branch.

This let's you easily verify that eitger mothing changed or that any conflicts were resolved well.
by colechristensen | 2026-01-13 23:59:11+00:00
>Can AI actually do this? This looks like a nice benchmark for complex language processing, since a complete novel takes up a whole lot of context (consider War and Peace or The Count of Monte Cristo)

Yes, you just break the book down by chapters or whatever conveniently fits in the context window to produce summaries such that all of the chapter summaries can fit in one context window.

You could also do something with a multi-pass strategy where you come up with a collection of ideas on the first pass and then look back with search to refine and prove/disprove them.

Of course for novels which existed before the time of training an LLM will already contain trained information about so having it "read" classic works like The Count of Monte Cristo and answer questions about it would be a bit of an unfair pass of the test because models will be expected to have been trained on large volumes of existing text analysis on that book.

>reliably answer questions about plot, character, conflicts, motivations

LLMs can already do this automatically with my code in a sizable project (you know what I mean), it seems pretty simple to get them to do it with a book.
by busterarm | 2026-01-13 23:59:17+00:00
Theres zero consequences because this was completely unambiguously a justified shoot. It's awful that it happened but that woman would have better odds surviving juggling live grenades.

She went with the express purpose of antagonizing law enforcement during an operation, assaulted officers with a deadly vehicle and ignored lawful orders to stop while a gun was pointed in her face.

I'm sorry that makes you feel a certain way but stupid actions have stupid consequences.
by kundan_s__r | 2026-01-13 23:59:21+00:00
This is a very pragmatic take. The “90% accuracy is a liability” line resonates — in high-stakes systems, partial automation often costs more than it saves.

What I like here is the field-level confidence gating instead of a single document score. That maps much better to real failure modes, where one bad value (amount, date, vendor) can invalidate the whole record.

One question I’m curious about: how stable are the confidence thresholds over time? In similar systems I’ve seen, models tend to get confidently wrong under distribution shift, which makes static thresholds tricky.

Have you considered combining confidence with explicit intent or scope constraints (e.g., what the system is allowed to infer vs. must escalate), rather than confidence alone?

Overall, this feels much closer to how production systems should treat AI — not as an oracle, but as a component that earns trust incrementally.
by defrost | 2026-01-13 23:59:24+00:00
> No one used the term DEI in the 1980s. The language then was affirmative action or EEO, and it was very much present in corporate America, including regulated industries like banking.

This is true.

What is false is a blanket "We're not hiring white men" as a result during that time period.

That was an era when lip srvice was given to affirmative action and literal token hires were made as window dressing .. but the fundementals scarcely changed and extremely rarely at board room and actual upper management levels for jobs that included keys to levers of power.
by stouset | 2026-01-13 23:59:29+00:00
Because nobody cares about the dev process. The number of times I’ve looked back in the history and seen a branch with “fix thing”, “oops”, “typo”, “remove thing I tried that didn’t work”, or just a chain of WIP WIP WIP WIP is useless, irritating, and pointless.

One commit per logical change. One merge per larger conceptual change. Care about your goddamn craft, even just a little bit.
by kennykartman | 2026-01-13 23:59:37+00:00
If tomorrow I owned 1 zillion dollar, that wouldn't make me able to change the course of next US (presidential) elections. It's not the only factor, ofc, but it is a very relevant one. Let's consider other factors that might be relevant: influence, visibility, arguments, fame, political weight, political knowledge, time, will. There are others. Someone with no influence on these factors and no money can hardly influence the outcomes of a nation election. If that someone was made a billionaire overnight, it can gain control over some factors, improving the likelihood of their impact over the next elections. Will they succeed? Not necessarily, but that their impact can become perceivable is undeniable.

Fair elections: in the US there are a bunch of practices related to vote that I don't consider fair. First and foremost, how votes are counted. Then, how money can be used to finance parties and campaigns. Gerrymandering is another one.
by fxtentacle | 2026-01-13 23:59:40+00:00
I strongly disagree. I’ve yet to find an AI that can reliably summarise emails, let alone understand nuance or sarcasm. And I just asked ChatGPT 5.2 to describe an Instagram image. It didn’t even get the easily OCR-able text correct. Plus it completely failed to mention anything sports or stadium related. But it was looking at a cliche baseball photo taken by an fan inside the stadium.
by supern0va | 2026-01-13 23:59:58+00:00
While I believe this may be true, there are also just people that get more reward from building than from the act of writing code. That doesn't mean they hate writing code, but that the building comes first. I count myself in that camp.

If I can build better/faster with reasonably equal quality, I'll trade off the joy of programming for the joy of more building, of more high level problem solving and thinking, etc.

I've also seen the opposite: those that derive more joy from the programming and the cool engineering than from the product. And you see the opposite behavior from them, of course--such as selecting a solution that's cool and novel to build, rather than the simple, boring, but better alternative.

I often find this type of engineer rather frustrating to work with, and coincidentally, they seem to be the most anti-AI type I've encountered.
by al_borland | 2026-01-14 00:00:38+00:00
I’ve just been ignoring my boss every time he says something about how we should leverage AI. What we’re building doesn’t need it and can’t tolerate hallucinations. They just want to be able to brag up the chain that AI is being used, which is the wrong reason to use it.

If I was forced to use it, I’d probably be writing pretty extensive guardrails (outside of the AI) to make sure it isn’t going off the rails and the results make sense. I’m doing that anyway with all user input, so I guess I’d be treating all LLM generated text as user input and assuming it’s unreliable.
by andrepd | 2026-01-14 00:01:38+00:00
They took our jerbs is a perfectly valid argument for people which face ruin without a jerb.

Capitalism is not prepared nor willing to retrain people, drastically lower the workweek, or bring about a UBI sourced from the value of the commons. So indeed, if the promises of AI hold true, a catastrophe is incoming. Fortunately for us, the promises of AI CEOs are unlikely to be true.
by amarant | 2026-01-14 00:01:45+00:00
I deliberately chose to compare two tech-heavy locations to avoid weird and difficult comparisons like the tech industry in rural Nebraska Vs Moldavia.

Stockholm was a natural point of comparison for me given that I used to live there until very recently.i have a decent picture of the dev market in Stockholm. Silicon valley is the most mentioned tech centre on here, and is therefore the American tech market I know the most about (even if my knowledge is very limited in this front)
by charleshn | 2026-01-14 00:01:55+00:00
> Application pods learn the current assignment through a library called the Slicelet (S for server side). The Slicelet maintains a local cache of the latest assignment by fetching it from the Dicer service and watching for updates. When it receives an updated assignment, the Slicelet notifies the application via a listener API.

For a critical control plane component like this, I tend to prefer a constant work pattern [0], to avoid metastable failures [1], e.g. periodically pull the data instead of relying on notifications.

[0] https://aws.amazon.com/builders-library/reliability-and-cons...

[1] https://brooker.co.za/blog/2021/05/24/metastable.html
by CapsAdmin | 2026-01-14 00:02:02+00:00
I've been playing with vibe coding a lot lately and I think in most cases, the current SOTA LLM's don't produce code that I'd be satisfied with. I kind of feel like LLM's are really really good at hacking on a messy and fragile structure, because they can "keep track many things in their head"

BUT

An LLM can write a PNG decoder that works in whatever language I choose in one or a few shots. I can do that too, but it will take me longer than a minute!

(and I might learn something about the png format that might be useful later..)

Also, us engineers can talk about code quality all day, but does this really matter to non-engineers? Maybe objectively it does, but can we convince them that it does?
by conartist6 | 2026-01-14 00:02:04+00:00
I don't think it's fair to compare people who are already seemingly at the peak of their career, a place to which they got by building skill in coding. And in fact what they have now that's valuable isn't mostly skill but capital. They've built famous software that's widely used.

Also they didn't adopt the your-career-is-ruined-if-you-don't-get-on-board tone that is sickeningly pervasive on LinkedIn. If you believe that advice and give up on being someone who understands code, you sure aren't gonna write Redis or Django.
by perfmode | 2026-01-14 00:02:04+00:00
Most responses here are about systems and tools — which help, but they're compensations working around the issue rather than at it.

The underlying problem is network regulation in the brain. Your Default Mode Network (the self-referential mind-wandering system) is supposed to quiet down when you engage in tasks. In ADHD, that toggle is unreliable — the DMN keeps intruding, which is why you get that "barrage of micro ideas" breaking through during focus.

A few things that work at the root:

Meditation — not as a relaxation tool, but as direct neuroplasticity training. Focused attention practice (noticing when your mind wandered, returning to object) is literally thousands of reps training that DMN/task-positive toggle. Long-term meditators show measurably better DMN suppression during tasks.

Sleep — DMN regulation degrades hard with poor sleep. Non-negotiable foundation.

The deeper move is changing your relationship to the thoughts themselves. The DMN will always generate ideas. The shift is recognizing them as arisings rather than commands. They still come — they just lose their grip.
by barishnamazov | 2026-01-14 00:02:27+00:00
I personally wish for the time when AI can replace everything I can do (at least in my current field). I'm not sure how exactly I'll feel about it then, but it'd be a technological advancement I'd enjoy seeing in my lifetime.

One question perhaps is, even if AI can do everything I can do (i.e., has the skills for it), will it do everything I do? I'm sure there are many people in the world with the superset of my skills, yet I bet there are some things only I'm doing, and I don't think a really smart AI will change that.
by baranul | 2026-01-14 00:02:50+00:00
The thing is, what you are describing is arguably theft. It is purposefully circumventing the license and restrictions chosen by the author, in order to steal their code, so that it can be sold and used for profit. This is along the same lines of why book authors and artists have been suing AI companies.

Another point, there is a lot of free and permissive license content to train AI on, where the GPL or copyright can be respected. In many cases, the violating AI companies knew what they were doing was wrong.
by 9JollyOtter | 2026-01-14 00:02:51+00:00
> This comment pops up a few times, often from programmers. Unfortunately, because of how messy the term AI now is, the same concerns still apply. Your adoption helps promote the companies making these tools. People see you using it and force it onto others at the studio, or at other workplaces entirely. From what we’ve seen, this is followed by people getting fired and overworked. If it isn’t happening to you and your colleagues, great. But you’re still helping it happen elsewhere. And as we said, even if you fixed the labour concerns tomorrow, there are still many other issues. There’s more than just being fired to worry about.

What other people and companies do because I happen to use something correctly (as an assistive technology), is not my responsibility. If someone happens to misuse it or enforce it use in a dysfunctional work environment, that is their doing and not mine.

If a workplace is this dysfunctional, there are likely many other issues that already exist that are making people miserable. AI isn't the root cause of the issue, it is the workplace culture that existed before the presence of AI.
by Liskni_si | 2026-01-14 00:02:53+00:00
Happens on merge, sure, but the end result is that you kinda lose the individual commits. You can still find them in the PR, but you won't see them in the git history, so git blame will just point you to the one big squashed commit.

If you, however, open a chain of 8 PRs, and merge them in the right order, the individual commits will be persisted in the git history. Potentially worth it if you like to have a "story" of several commits...
by hahahahhaah | 2026-01-14 00:02:57+00:00
The second liar often gives you information you never wanted! But that is offset by the excellent information of when the liars agree. Fascinating.

I didnt't math during the thinking pause, but my intuition was a second liar makes it worse (more likey to end up 50-50 situation) and additional liars make it better as you get to reduce noise.

Is there a scenario where the extra liar makes it worse, you would be better yelling lalalallala as they tell you the answer?
by falloutx | 2026-01-14 00:03:06+00:00
I dispute 1 & 2 more than 4.

1) Is it actually watching a movie frame by frame or just searching about it and then giving you the answer?

2) Again can it handle very long novels, context windows are limited and it can easily miss something. Where is the proof for this?

4 is probably solved

4) This is more on predictor because this is easy to game. you can create some gibberish code with LLM today that is 10k lines long without issues. Even a non-technical user can do
by BeetleB | 2026-01-14 00:03:11+00:00
For a long time, I've wanted to write a blog post on why programmers don't understand the utility of LLMs[1], whereas non-programmers easily see it. But I struggle to articulate it well.

The gist is this: Programmers view computers as deterministic. They can't tolerate a tool that behaves differently from run to run. They have a very binary view of the world: If it can't satisfy this "basic" requirement, it's crap.

Programmers have made their career (and possibly life) being experts at solving problems that greatly benefit from determinism. A problem that doesn't - well either that needs to be solved by sophisticated machine learning, or by a human. They're trained on essentially ignoring those problems - it's not their expertise.

And so they get really thrown off when people use computers in a nondeterministic way to solve a deterministic problem.

For everyone else, the world, and its solutions, are mostly non-deterministic. When they solve a problem, or when they pay people to solve a problem, the guarantees are much lower. They don't expect perfection every time.

When a normal human asks a programmer to make a change, they understand that communication is lossy, and even if it isn't, programmers make mistakes.

Using a tool like an LLM is like any other tool. Or like asking any other human to do something.

For programmers, it's a cardinal sin if the tool is unpredictable. So they dismiss it. For everyone else, it's just another tool. They embrace it.

[1] This, of course, is changing as they become better at coding.
by nomel | 2026-01-14 00:03:20+00:00
I read it as "compared to others, in the current context".

If you take some percent longer to finish a some code, because you want that code to maintain some level of "purity", you'll finish slower than others. If his is a creative context, you'll spend more time on boilerplate than interesting stuffs. If this is a profit driven context, you'll make less money, less money for staff. Etc.

> If you’re not willing to risk being “left behind”...

I think this is orthogonal. Some tools increase productivity. Using a tool doesn't blind a component person...they just have an another tool under their belt to use if they personally find it valuable.
by spopejoy | 2026-01-14 00:03:22+00:00
Your point echoes the "death of the author" concept in literature, where the work is independent of the creator, full stop. It's a useful concept up to a point, but if you really have no idea what it means to have a deep connection to music that is wrapped up in some idea of the creator as a human being, you should trust others when they say they do and it's important to them. For those of us with that value, AI slop is offensive, and to be clear, it has precedents in history with Muzak, early schlager music etc -- what they all share is a desire to use the power of music for non-artistic ends, which sucks from any number of viewpoints. If music has non-artistic utility, that doesn't justify a concerted effort to take away artist-made music from those who may not be paying attention.
by ymyms | 2026-01-14 00:03:51+00:00
I didn't mean to imply LoC as a measurement of productivity. What I really mean is more "amount of useful code produced to a level the human-using-the-llm determines to be useful".

To try and give an example, say that you want to make a module that transforms some data and you ask the LLM to do it. It generates a module with tons of single-layer if-else branches with a huge LoC. Maybe one human dev looks at it and says, "great this solves my problem and the LoC and verbosity isn't an issue even though it is ugly". Maybe the second looks at it and says, "there's definitely some abstraction I can find to make this easier to understand and build on top of."

Depending on the scenario and context, either of them could be correct.
by herf | 2026-01-14 00:04:14+00:00
Most systems now "fail closed" because they are based on a code signing chain of trust that has no exceptions. It would be better if some portion of these systems were made to "fail open" - you don't want a botnet to take over in this situation but you should be able to delegate code signing duties to a new party when the original one goes under or stops supporting a device.
by zzo38computer | 2026-01-14 00:04:21+00:00
> They won't believe a random site when it says "Look, stop hitting our API, you can pick all of this data in one go, over in this gzipped tar file."

Is there a mechanism to indicate this? The "a" command in the Scorpion crawling policy file is meant for this purpose, but that is not for use with WWW. (The Scorpion crawling policy file also has several other commands that would be helpful, but also are not for use with WWW.)

There is also the consideration to know what interval they will be archived that can be downloaded in this way; for data that changes often, you will not do it every time. This consideration is also applicable for torrents, since a new hash will be needed for a new version of the file.
by Terr_ | 2026-01-14 00:04:31+00:00
> to cut out all of the people that never really had an interest in CS

I had assumed most of them had either filtered out at some stage (an early one being college intro CS classes), ended up employed somewhere that didn't seem to mind their output, or perpetually circle on LinkedIn as "Lemons" for their next prey/employer.

My gut feeling is that messy code-gen will increase their numbers rather than decrease them. LLMs make it easier to generate an illusion of constant progress, and the humans can attribute the good parts of the output to themselves, while blaming bad-parts on the LLM.
by j2kun | 2026-01-14 00:04:45+00:00
A theory besides... not enough training data? Is it even possible to formulate a coherent theory about this? I'm talking about customizing a widely-used build system, not exactly state-of-the-art cryptography. What could I possibly say that you wouldn't counter with "skill issue" (which goes back to the author's point)?

If you say it's demonstrably impossible that someone can't be made more productive with opus4.5, then it should probably be up to you to demonstrate impossibility.