MurphyA/hacker-news huggingface dataset:
by breuleux | 2025-06-02 22:00:04+00:00
They won't. The speed at which these models evolve is a double-edged sword: they give you value quickly... but any experience you gain dealing with them also becomes obsolete quickly. One year of experience using agents won't be more valuable than one week of experience using them. No one's going to be left in the dust because no one is more than a few weeks away from catching up.
by CurrentB | 2025-06-02 22:00:06+00:00
I agree I'm bullish on AI for coding generally, but I am curious how they'd get around this problem. Even if they can code at super human level, then you just get rarer super human bugs. Or is another AI going to debug it? Unless this loop is basically fail proof, does the human's job just becoming debugging the hardest things to debug (or at least a blindspot of the AI)
by therealmarv | 2025-06-02 22:00:09+00:00
Results can vary significantly, and in my experience, both the choice of tools and models makes a big difference.

It’s a good idea to periodically revisit and re-evaluate AI and tooling. I’ve noticed that many programmers tried AI when, for example, GPT-3.5 was first released, became frustrated, and never gave it another chance—even though newer models like o4-mini are now capable of much more, especially in programming tasks.

AI is advancing rapidly. With the latest models and the right tools, what’s possible today far exceeds what was possible even just a short time ago (3-12 months ago even).

Take a look at Cursor or Windsurf or Roo code or aider to "feed" AI with code and take a look at models like Google Gemini 2.5 Pro, Claude Sonnet 4, OpenAI o4mini. Also educate yourself about agents and MCP. Soon that will be standard for many/every programmer.
by roskelld | 2025-06-02 22:00:42+00:00
When I heard that a friend had bought one of these for a week long canoe trip we were going on I thought it was absurd. The idea of taking such a home comfort on a camping trip went against everything I felt camping stood for. Then I tried it, then I bought one, now I take it on every camping trip I make.

It's so light it adds basically nothing to your carry weight. It takes seconds to put together. It's insanely comfortable. Kicking back in it after a long day's hike or canoe whilst prodding at the camp fire is such a joy.

I put it alongside my thermarest as a thing I never felt I'd ever buy and now use on every trip.
by abalone | 2025-06-02 22:01:33+00:00
I like to make a rough analogy with autonomous vehicles. There's a leveling system from 1 (old school cruise control) to 5 (full automation):

* We achieved Level 2 autonomy first, which requires you to fully supervise and retain control of the vehicle and expect mistakes at any moment. So kind of neat but also can get you in big trouble if you don't supervise properly. Some people like it, some people don't see it as a net gain given the oversight required.

^ This is where Tesla "FSD beta" is at, and probably where LLM codegen tools are at today.

* After many years we have achieved a degree of Level 4 autonomy on well-trained routes albeit with occasional human intervention. This is where Waymo is at in certain cities. Level 4 means autonomy within specific but broad circumstances like a given area and weather conditions. While it is still somewhat early days it looks like we can generally trust these to operate safely and ask for help when they are not confident. Humans are not out of the loop.[1]

^ This is probably what where we can expect codegen to grow after many more years of training and refinement in specific domains. I.e. a lot of what CloudFlare engineers did with their prompt engineering tweaking was of this nature. Think of them as the employees driving the training vehicles around San Francisco for the past decade. And similarly, "L4 codegen" needs to prioritize code safety which in part means ensuring humans can understand situations and step in to guide and debug when the tool gets stuck.

* We are still nowhere close to Level 5 "drive anywhere and under any conditions a human can." And IMHO it's not clear we ever will based purely on the technology and methods that got us to L4. There are other brain mechanisms at work that need to be modeled.

[1] https://www.cnbc.com/2023/11/06/cruise-confirms-robotaxis-re...
by storus | 2025-06-02 22:01:33+00:00
This feels different; I asked DeepSeek R1 to give me an autoregressive image generation code in pytorch and it did a marvelous job. Similar for making a pytorch model for a talking lip-synced face; those two would take me weeks to do, AI did it in a few minutes.

Autoregressive LLMs still have some major issues like over-dependency on the first few generated tokens and the problems with commutative reasoning due to one-sided masked attention but those issues are slowly getting fixed.
by davidclark | 2025-06-02 22:01:46+00:00
>If you were trying and failing to use an LLM for code 6 months ago †, you’re not doing what most serious LLM-assisted coders are doing.

Here’s the thing from the skeptic perspective: This statement keeps getting made on a rolling basis. 6 months ago if I wasn’t using the life-changing, newest LLM at the time, I was also doing it wrong and being a luddite.

It creates a never ending treadmill of boy-who-cried-LLM. Why should I believe anything outlined in the article is transformative now when all the same vague claims about productivity increases were being made about the LLMs from 6 months ago which we now all agree are bad?

I don’t really know what would actually unseat this epistemic prior at this point for me.

In six months, I predict the author will again think the LLM products of 6 month ago (now) were actually not very useful and didn’t live up to the hype.
by mgraczyk | 2025-06-02 22:01:58+00:00
The important thing you are missing is that the learning landscape has now changed.

You are now responsible for learning how to use LLMs well. If an untrained vibe coder is more productive for me, while knowing nothing about how the code actually works, I will hire the vibe coder instead of you.

Learning is important, but it's most important that you learn how to use the best tools available so you can be productive. LLMs are not going away and they will only get better, so today that means you are responsible for learning how to use them, and that is already more important for most many roles than learning how to code yourself.
by vector_spaces | 2025-06-02 22:02:26+00:00
right: we don't use programming languages instead of natural language simply to make it hard. For the same reason, we use a restricted dialect of natural language when writing math proofs -- using constrained languages reduces ambiguity and provides guardrails for understanding. It gives us some hope of understanding the behavior of systems and having confidence in their outputs

There are levels of this though -- there are few instances where you actually need formal correctness. For most software, the stakes just aren't that high, all you need is predictable behavior in the "happy path", and to be within some forgiving neighborhood of "correct".

That said, those championing AI have done a very poor job at communicating the value of constrained languages, instead preferring to parrot this (decades and decades and decades old) dream of "specify systems in natural language"
by 90s_dev | 2025-06-02 22:02:53+00:00
Some fun entries so far

toppings=pineapple+ toppings=pineapple toppings=I+don%27t+know+wh+conan+o+brien+is%2C+but+I+want+him+to+know+that+I+can%27t+eat+pizza+at+all. toppings=mushrooms toppings=Brie toppings=Cheeze toppings=Wat toppings= toppings=peppers+and+onions toppings=cat+..%2Fetc%2Fpasswd toppings=pepperoni+is+pretty+good+usually toppings=More+tiny+pizzas+ fooabar 0x%5B%5D=DTAB toppings=rendang toppings=salami toppings=spinach toppings=Peppers toppings=Ham+and+Pineapple+ toppings=french+fries toppings=egg toppings=usually+plain+but+i+dont+mind+putting+some+stuff+on+there.+onions+or+pepperoni+or+hawaiian+every+now+and+then.+i+dont+get+why+some+people+are+so+upset+about+pineapple%2C+it%27s+definitely+a+different+taste+but+i+think+it+works%21+although+i+will+say+sometimes+i+think+the+pineapple+slices+are+too+thick+and+so+you+just+get+pineapple+for+a+bite%2C+i+dont+like+that.+love+your+stuff+conan
by gertlex | 2025-06-02 22:03:25+00:00
Feels like you're comparing how LLMs handle unstandardized and incomplete marketing-crap that is virtually all product pages on the internet, and how LLMs handle the corpus of code on the internet that can generally be trusted to be at least semi functional (compiles or at least lints; and often easily fixed when not 100%).

Two very different combinations it seems to me...

If the former combination was working, we'd be using chatgpt to fill our amazon carts by now. We'd probably be sanity checking the contents, but expecting pretty good initial results. That's where the suitability of AI for lots of coding-type work feels like it's at.
by hn_throwaway_99 | 2025-06-02 22:03:26+00:00
I felt this article was a lot of strawman-ing.

Yes, there are people who think LLMs are just a fad, just like NFTs, and I agree these people are not really serious and that they are wrong. I think anyone who has used an AI coding agent recently knows that they are highly capable and can enhance productivity in the right hands.

But, as someone who gets a lot of value in AI coding agents, my issue is not with gen AI as a productivity enhancing tool - it's with the absolute torrent of BS about how AI is soon going to make coders obsolete, and the way AI has been shoved onto many engineering teams is like yet another incarnation of the latest management fad. My specific arguments:

1. As the author pretty much acknowledges, AI agents still basically suck at large, system-wide "thinking" and changes. And the way they work with their general "guess and check" method means they can churn out code that is kinda sorta right, but often leaves huge holes or outright laughable bugs.

2. Hallucinations are the worst possible failure modes - they look correct, which makes it all the more difficult to determine they're actually bullshit. I shudder to think about who will need to maintain the mountains of "vibe code" that is now being generated. Certainly not fucking me; I had a good career but I think now is definitely the right time to peace out.

3. Even if I could totally agree that there is a strong business case for AI, I can still, as an individual, think it makes my job generally shittier, and there is nothing wrong with having that opinion.

I don't think I'd be so anti-AI if I saw a rational, cautious debate about how it can enhance productivity. But all I see are folks with a vested interest overselling its capabilities and minimizing its downsides, and it just feels really tiresome.
by yodsanklai | 2025-06-02 22:03:52+00:00
My workflow is similar. While the agent is running, I browse the web or day dream. If I'm lucky, the agent produced correct code (after possibly several cycles). If I'm not, I need to rewrite everything myself. I'm also not in any camp and I genuinely don't know if I'm more or less productive overall. But I think that a disciplined use of a well-integrated agent will make people more productive.
by paulcole | 2025-06-02 22:04:01+00:00
Yeah sorry I should’ve been more clear. My absolute preference for Google replacement is Perplexity. Sometimes I’ll use ChatGPT just because I have the app open or a tab open or something and find that it’s OK enough.

I have paid versions of both but think that I get more out of the money for ChatGPT than Perplexity.
Ask HN: What is our history on trying to standardize configuration languages?
by jerrac | 2025-06-02 22:04:06+00:00
I just found myself trying to define configuration language for a tool that would then write the given configuration into a configuration file of whatever format I set. So I was configuring configuration to configure configuration... When that thought occurred to me, it made me stop, and think "this is ridiculous".

I mean, the problem I want to solve is real. Configuring containerized applications is always annoying. Currently I either end up baking the configuration into the image, or using a pre-script to convert environment variables into the configuration I actually need before the app starts. Usually via bash scripts... (Or I have to decipher which method the official image uses. If they have one.)

My idea was to just have a little rust tool in my containers that would take the contents of environment variables and turn them into whatever format (toml, yaml, ini, etc.) the app needs. The problem was how do I represent all the different kinds of values you can have in a language? Lists, key/value lists, associative arrays, maps, etc...

Figuring out how to do that, well, isn't that just yet another configuration language?

And that is why I'm posting this question. Has any real effort ever been put into standardizing configuration languages/syntaxes?

If so, why'd they fail? If not, why not?
by dinfinity | 2025-06-02 22:04:12+00:00
> The differences can't be that massive given that the hype already made many of these promises well before these models were ever a thing.

I'm not talking about other people's incorrect promises _and_ I mentioned a number of things in which proper usage today is different from what people were doing before.

> Basically you are just adding to it with "You just need to use the latest model, anything before that is trash".

That's not what I said. Don't put words into my mouth. I said that the older models are trash in comparison, not that they are trash. The older models require more work in prompting to get decent results.

> Ignoring that the same was said just a few months prior when those models were cutting edge.

You are conveniently ignoring the other parts I mentioned to make my claim. Here: "In addition to that we've also seen that the way you prompt (amount of use of expert language), what context you provide through instructions, and tool use make a huge difference on the outcome."
by sottol | 2025-06-02 22:04:13+00:00
I think another thing that comes out of not knowing the codebase is that you're mostly relegated to being a glorified tester.

Right now (for me) it's very frequent, depending on the type of project, but in the future it could be less frequent - but at some you've gotta test what you're rolling out. I guess you can use another AI to do that but I don't know...

Anyway, my current workflow is:

1. write detailed specs/prompt,

2. let agent loose,

3. pull down and test... usually something goes wrong.

3.1 converse with and ask agent to fix,

3.2 let agent loose again,

3.3 test again... if something goes wrong again:

3.3.1 ...

Sometimes the Agent gets lost in the fixes but now have a better idea what can go wrong and you can start over with a better initial prompt.

I haven't had a lot of success with pre-discussing (planning, PRDing) implementations, as in it worked, but not much better than directly trying to prompt what I want and takes a lot longer. But I'm not usually doing "normal" stuff as this is purely fun/exploratory side-project stuff and my asks are usually complicated but not complex if that makes sense.

I guess development is always a lot of testing, but this feels different. I click around but don't gain a lot of insight. It feels more shallow. I can write a new prompt and explain what's different but I haven't furthered my understanding much.

Also, not knowing the codebase, you might need a couple attempts at phrasing your ask just the right way. I probably had to ask my agent 5+ times, trying to explain in different ways how translate phone IMU yaw/pitch/roll into translations of the screen projection. Sometimes it's surprisingly hard to explain what you want to happen when you don't know the how it's implemented.
by mgraczyk | 2025-06-02 22:04:31+00:00
We are beyond the point of trying to convince naysayers.

I will simply not hire anybody who is not good at using LLMs, and I don't think I would ever work with anybody who thinks they aren't very useful. It's like working with somebody who things compilers are useless. Obviously wrong, not worth spending time trying to convince.

To anyone who reads this article and disagrees with the central point: You are missing the most important thing that will happen in your career. You should reevaluate because you will be unemployable in a few years.
by 0xBDB | 2025-06-02 22:05:02+00:00
Income tax percentage in the U.S. ranges from negative counting credits (for the bottom 60% of taxpayers in states with no income tax) to a marginal rate of 52.7% plus a 1% surcharge on all income combining federal, state, and local for incomes over a million bucks in San Francisco.

Low income tax states tend to make up for it in other ways (sales tax, property tax, or both) but basically UK taxes would look extortionate to an Alaskan, painfully high to a Texan and probably pretty normal to a New Yorker or Californian.
by gwbas1c | 2025-06-02 22:05:05+00:00
> Question: If everyone uses AI to code, how does someone become an expert capable of carefully reading and understanding code and acting as an editor to an AI?

Well, if everyone uses a calculator, how do we learn math?

Basically, force students to do it by hand long enough that they understand the essentials. Introduce LLMs at a point similar to when you allow students to use a calculator.
by attemptone | 2025-06-02 22:05:11+00:00
>I feel the opposite, and pretty much every metric we have shows basically linear improvement of these models over time.

Wait, what kind of metric are you talking about? When I did my masters in 2023 SOTA models where trying to push the boundaries by minuscule amounts. And sometimes blatantly changing the way they measure "success" to beat the previous SOTA
by Night_Thastus | 2025-06-02 22:05:31+00:00
Models are absolutely not improving linearly. They improve logarithmically with size, and we've already just about hit the limits of compute without becoming totally unreasonable from a space/money/power/etc standpoint.

We can use little tricks here and there to try to make them better, but fundamentally they're about as good as they're ever going to get. And none of their shortcomings are growing pains - they're fundamental to the way an LLM operates.
by wildlogic | 2025-06-02 22:06:07+00:00
Gameplay is on a grid from 5x5 to 12x12 in size. Each level has a number of "depots", from which the player lays section of "pipe" to Von Neumann neighboring tiles. Pipes cannot cross each other, and a tile cannot have more than 1 pipe. Pipes cannot be placed on depots. Click a depot to "activate" it, and then place the pipes from that depot. There are 3 ways of placing pipes - clicking on the adjacent square, dragging through adjacent squares, or clicking several tiles away in a straight line from a placed pipe or activated depot to lay pipe on each tile in the line.

Thanks for giving it a try - I would appreciate any feedback!
by ghaff | 2025-06-02 22:06:32+00:00
There's an argument that a huge amount of the specific engineering theory that students learn never gets applied in a lot of jobs. I used some for a few years in mechanical engineering but not really a whole lot. A lot more was sense in managing projects. And, while I took a programming course (wouldn't call is CS), it probably didn't really help me in my job a lot more than the limited programming I took in high school did.
by thetwentyone | 2025-06-02 22:06:46+00:00
The author posits that people don't like using LLMs with Rust because LLMs aren't good with Rust. Then people would migrate towards languages that do will with LLMs. However, if that were true, then Julia would be more popular since LLMs do very well with it: https://www.stochasticlifestyle.com/chatgpt-performs-better-...
by crote | 2025-06-02 22:07:07+00:00
> it’s still leaps and bounds ahead of traditional translators already

Traditional machine translators, perhaps. Human translation is still miles ahead when you actually care about the quality of the output. But for getting a general overview of a foreign-language website, translating a menu in a restaurant, or communicating with a taxi driver? Sure, LLMs would be a great fit!
by Fade_Dance | 2025-06-02 22:07:11+00:00
P/E just isn't that useful an indicator to look at. It is near useless for anything focusing on growth rather than profit generation. It's a figure that misses out on NVDA, Monster Beverage (not all growth stocks are tech!), etc. These are the names that have driven the majority of market returns over the past decades. There are entire sectors like SaaS where PE is pretty useless.

That's the first (big) reason why PE is increasingly a weak Value (in a broad sense) signal. Another huge one is that companies who are free cashflow monsters that shunt cash towards some sort of internally compounding flywheel (ex: Amazon with AWS CapEx and warehouse buildouts) screen terribly on PE ratio even though the cash they are throwing out is being plugged into a compounding engine far greater than one can find in the broad market. That's a value in and of itself, and obviously demands a premium and is well understood today.

Personally, most of the more fundamental oriented stuff I read seems to focus more on figures like sales to EV when ranking "value" in a peer group, not PE.

That said, it can be useful. I grabbed some Abercrombie because it's at a PE of 8 and all of the free cashflow goes to buybacks. Mostly chose to discount growth and focus on near term value, so there's no tricky modeling there, and again the cash is just shunted into buybacks and there is no complicated debt situation either. In an "old school" value proposition like that, PE can work decently.

But I'm also in a Canadian gold miner which is a far stronger Value proposition, yet it will screen horribly on PE... Go figure.
by slg | 2025-06-02 22:07:12+00:00
>We imagine artists spending their working hours pushing the limits of expression. But the median artist isn’t producing gallery pieces. They produce on brief: turning out competent illustrations and compositions for magazine covers, museum displays, motion graphics, and game assets.

One of the more eye-opening aspects of this technology is finding out how many of my peers seemingly have no understanding or respect for the concept of art.
by jiggawatts | 2025-06-02 22:07:30+00:00
You can be fuzzier than a soft fluff of cotton wool. I’ve had incredible success trying to find the name of an old TV show or specific episode using AIs. The hit rate is surprisingly good even when using the vaguest inputs.

“You know, that show in the 80s or 90s… maybe 2000s with the people that… did things and maybe didn’t do things.”

“You might be thinking of episode 11 of season 4 of such and such snow where a key plot element was both doing and not doing things on the penalty of death”
by pmdrpg | 2025-06-02 22:07:47+00:00
This op ed suggests that it’s easier to audit a huge amount of code before merging it in than is to write the code from scratch. I don’t know about anyone else, but I generally find it easier to write exactly what I want than to mentally model what a huge volume of code I’ve never seen before will do?

(Especially if that code was spit out by an alien copypasta that is really good at sounding plausible with zero actual intelligence or intent?)

Like, if all I care about is: does it have enough unit tests and do they pass, then yeah I can audit that.

But if I was trying to solve truly novel problems like modeling proteins, optimizing travel routes, or new computer rendering techniques, I wouldn’t even know where to begin, it would take tons of arduous study to understand how the new project full of novel algorithms is going behave?
by paulddraper | 2025-06-02 22:07:55+00:00
While citizens have a right to transit through navigable airspace, the FAA controls more than that. The FAA controls ALL airspace (but delegating control over some areas such as military airspace).

For example, a drone weighing over 250 grams must be registered with the FAA, no matter what height it is flown. Even if it's your own backyard at eye level.

This is a little weird, but factual.