MurphyA/hacker-news huggingface dataset:
by Richardson143 | 2025-05-17 01:57:21+00:00
This is a really interesting direction, Gregor & Magnus! You're spot on about enterprises needing more robust and self-healing solutions for their high-frequency automation.

It's true that many are looking into self-healing for existing automation scripts; from what I've seen, tools like Healenium are gaining some traction in this space. However, I agree that a Browser Use-like approach also holds a lot of promise here.

My thinking on how this could be achieved with AI agents like Browser Use is to run the existing automation scripts as usual. If a script breaks due to an "element not found" exception or similar issues, the AI agent could then be triggered to analyze the page, identify the correct new locator for the problematic element, and dynamically update or "heal" the script. I've actually put together a small proof-of-concept demonstrating this idea using Browser Use: https://www.loom.com/share/1af87d78d6814512b17a8f949c28ef13?...

I had explored a similar concept previously with Lavague setup here: https://www.loom.com/share/9b0c7cf0bdd6492f885a2c974ca8a4be?...

Another avenue, particularly relevant for existing test suites, is how many QA teams manage their locators. Often, these are centralized in files like POM.xml (for Java/Maven projects) or external spreadsheets/CSVs. An AI agent could potentially be used to proactively scan the application and update these locator repositories.

For instance,

I've experimented with a workflow where Browser Use updates a CSV file of locators weekly based on changes detected on the website: https://www.loom.com/share/821f80fcb0694be4bd4d979e94900990?...

Excited to see how Workflow Use evolves, especially the self-healing aspects!
by musicale | 2025-05-17 01:57:38+00:00
> Everything we create or do is based on something we've experienced or seen.

I would add a couple of things to that. First, humans (like other animals) have instincts and feelings; even newborns can exhibit varying personality traits as well as fears and desires. It's certainly useful to fear things like spiders, snakes, or abandonment without prior experience.

Second, an important part of experience is inner life - how you personally perceive, feel, and experience things. This may be very different from person to person.
by meroes | 2025-05-17 01:58:22+00:00
I might just be on the opposite side of the aisle, but to me chain-of-thought is better understood as simply more context.

Of course there is ambiguity though, more context would be hard to distinguish from core-reasoning and vice versa.

I think LLMs/AI mean we can substitute reasoning with vast accumulations and relations between contexts.

Remember, RLHF gives the models some, and perhaps most of these chains-of-thought, when there isn’t sufficient text to scrape for each family of problems. When I see that chain-of-thought, the first thing I think of is of my peers who had write, rewrite, nudge, and correct these chains of thought, and not about core reasoning.

The CoT has that same overexplained step-by-step so many RLHF’ers will be accustomed to, and much of it was authored/originated by them. And due to the infinite holes it feels like plugging, I dont call that RL reasoning.
by cyberax | 2025-05-17 01:58:35+00:00
> The same could be said about computer programmers.

Not really. Programming has fundamentally changed since the 80-s: version control systems, connectivity, new and more efficient languages, etc. Train yards have not changed a bit. Dispatchers might have computers now, and individual train cars can be tracked in a central DB, but the physical work of coupling/decoupling cars and shuffling them around has not changed AT ALL.

> Trucks are good for "last-mile" local delivery or small loads/single containers going from one place to another.

The US is special, it's geographically HUGE, so trucks end up playing an outsized role in long-distance transport.

Trains are much cheaper and more efficient, so they can potentially help to reduce CO2 pollution _and_ the transportation cost. But train companies are just not interested in that.

Having self-driving trucks transported on the interchangeable flatbeds can potentially change that. Trucks can just drive onto the waiting traincars, ride to the destination location, and then just drive off the flatbeds.
by dangus | 2025-05-17 01:59:36+00:00
> Even with a high-quality model, there is still some level of non-determinism or unpredictability involved in LLM-driven SQL generation. To address this we have found that non-AI approaches like query parsing or doing a dry run of the generated SQL complements model-based workflows well. We can get a clear, deterministic signal if the LLM has missed something crucial, which we then pass back to the model for a second pass. When provided an example of a mistake and some guidance, models can typically address what they got wrong.

Sounds like a bunch of bespoke not-AI work is being done to make up for LLM limitations that point blank can’t be resolved.
by RhysabOweyn | 2025-05-17 01:59:47+00:00
Obviously there is no way to really predict when this would happen, but I don't think it will be up to developers to decide whether it happens or not. In Texas for example, the legislature forced engineering to be professionalized (or regulated) in an emergency session after a school in a well off area exploded in a gas explosion (https://en.wikipedia.org/wiki/New_London_School_explosion#In...).

I also do not think this is limited to software engineering. Medical doctors and accountants have faced the squeeze in recent years too. There are tons of (bad) DO med schools opening up across the country that will be flooding the field before long, nurse practitioners and physicians assistants get to do more and more work that only doctors got to do, and more and more accounting is being offshored. The question is when things get so bad that even the powerful decide to actually do something about it.
by NikolaNovak | 2025-05-17 01:59:51+00:00
Nope. Still doing it.

It's infuriating that Instagram, Facebook, etc send a "Email Verification" that has NO option to say "Nope, not me, don't want it, don't do it".

Worse, I'd like to create my own Instagram now, but cannot, because somebody else tried to use my email a decade ago and now all I get is a very very confused loop.

I'm sure these systems make sense to somebody, there's detail and nuance and practicality I'm horribly ignorant of, but they just seem insanely unprofessional to me as an outsider :-/
by dipampaul17 | 2025-05-17 02:00:56+00:00
Great question about the intuition! The difference comes from the core roles these components play in attention.

Keys determine which tokens to attend to - they create the actual attention pattern through similarity calculations. Values only store what information gets passed forward once attention is decided.

When a key vector is quantized too aggressively, it distorts the similarity calculations for every token interaction. A small error in keys can completely redirect attention to the wrong tokens.

Values, however, are much more forgiving. When a value vector is quantized, any error only affects the specific information content of that single token after the attention pattern is already established.

It's like a library catalog system vs. the books themselves. If catalog numbers (keys) are corrupted, you'll look in completely wrong sections. If some words in books (values) are smudged, you're still reading the right book - just with occasional noise.

Mathematically, keys participate in softmax calculations where small errors get exponentially amplified through the normalization process. Values just undergo linear weighted averaging, where errors tend to cancel out.

I first encountered this asymmetry in papers like "More for Keys, Less for Values" and "KV-AdaQuant," but wanted to quantify exactly how it impacts Apple Silicon inference. The 7× quality difference between K8V4 and K4V8 using identical memory was striking.

Thanks for the installation feedback too! I'll fix the placeholder and make the Python dependencies more flexible.
by shanemhansen | 2025-05-17 02:02:01+00:00
I'll offer a counterpoint to the responses. Until go got generics, concurrent data structures were awkward. The stdlib now does include things like sync.Map.

In fact my experience has been that overuse of channels is a code smell that alot of new go developers fall into and later regret. There's a reason the log package uses a mutex for synchronization.

In general I think channels are great for connecting a few large chunks of your program together. Concurrency is great but also not every function call benefits from being turned into a distributed system.

I think that it would be a great idea to develop more concurrent go data structures with generics and I suspect inertia is what's keeping the community from doing it.

My credentials such as the are: been writing go since 1.0. worked at Google and taught go classes as well as owned some of the original go services (the downloads server aka payload server).
by levocardia | 2025-05-17 02:03:15+00:00
In one of Stephen Boyd's lectures on convex optimization, he has some quip like "if your optimization problem is computationally intractable, you could try really hard to improve the algorithm, or you could just go on vacation for a few weeks and by the time you get back, computers will be fast enough to solve it."

I feel like that's actually true now with LLMs -- if some query I write doesn't get one-shotted, I don't bother with a galaxy-brain prompt; I just shelve it 'til next month and the next big OpenAI/Anthropic/Google model will usually crush it.
by musicale | 2025-05-17 02:06:45+00:00
> Who is the #1 Java programmer in the world?

James Gosling, of course[1]. Next question...

> The #1 topologist?

I'm not a mathematician, but... maybe Akshay Venkatesh, who won the Fields Medal in 2018?

[1] https://news.ycombinator.com/item?id=44005008
by majormajor | 2025-05-17 02:07:23+00:00
If you have 10PMs and 90 devs today, and go to 8 "hybrid" PMs + 2 specialized PMs, you're probably still creating backlog items faster than that team can close them.

So you end up with some choices:

* do you move at the same speed, with fewer people?

* do you try to move faster, with less of a reduction in people? this could be trickier than it sounds because if the frequency of changes increases the frequency of unintended consequences likely does too, so your team will have to spend time reacting to that

I think the companies that win will be the second batch. It's what happens today, basically, but today you have to convince VCs or the public market to give you a bunch of more money to hire to 10x the team size. Getting a (one-off?) chance to do that through tooling improvements is a big gift, wasting it on reducing costs instead of increasing growth could be risky.
by ocean_moist | 2025-05-17 02:07:40+00:00
I use business, startup, and company interchangeably. Generally I tried to use business for O(n), startup for O(n^2), but I guess I wasn't strict with my usage...

Perhaps they are harder to start, but they are also vastly more likely to succeed to their O(n^2), and this is not only due to the increased barrier to entry.

That's what I mean when I say, founders are more likely to succeed at O(n) companies.
by mizzao | 2025-05-17 02:07:44+00:00
While in CS multi-author papers are the norm, in econ it is quite common to have solo-authored papers, with senior PhD students solo-authoring papers without their advisor as a coauthor. And it is rare to see an econ paper with more than 3 authors.

As a CS PhD who has worked with many economists, my understanding is that the culture sees it as diluting credit, and so might put people in the acknowledgements where computer science folks would add them as authors.
by drdaeman | 2025-05-17 02:08:21+00:00
If that's the source of author's existential crisis, they may possibly find it interesting to meditate on the idea that there's no thinker behind the thought, and the impermanence of "self".

Even if they don't buy all the way into the whole hard incompatiblism thing, the idea is that they may find some value in the process.
by dipampaul17 | 2025-05-17 02:08:45+00:00
The memory savings from KVSplit scale proportionally with context length, so higher-RAM Macs (64GB/128GB) benefit even more in absolute terms. On a 128GB Mac Studio, you could potentially handle context windows in the hundreds of thousands of tokens.

However, KVSplit doesn't fundamentally change computation speed - just memory efficiency. Our benchmarks show a 14.5% throughput improvement with K8V4, but this comes from better memory locality, not reduced computation.

The "painfully slow" issue with large models on Apple Silicon stems primarily from the compute limitations, not memory constraints. A 70B parameter model will still run at similar token generation speeds regardless of available RAM or KV cache optimizations.

What KVSplit does is make better use of whatever memory you have available. It's particularly valuable when your bottleneck is context length rather than model size.

For practical Apple Silicon usage, the sweet spot remains smaller models (7B-13B) with now-expanded context windows. This lets you process significantly more text while maintaining reasonable generation speeds.

If your workflow needs both massive contexts AND large models, you'd still want to consider server-grade GPUs, but KVSplit helps push the boundary of what's feasible on Apple hardware.
by drdeca | 2025-05-17 02:09:44+00:00
It sounds to me like you are maybe agreeing with me but thought that I was expressing the opposite of what I did, and so are phrasing it as if it were disagreement?

Or maybe you are just agreeing, and did understand that my point was that I don’t think pressing a button is a contribution.

If you are disagreeing with my comment, can you explain how this is disagreeing?
by itfourall | 2025-05-17 02:10:21+00:00
Hey everyone,

I’ve just published a hands-on tutorial that explains how DMX and Art-Net work – aimed especially at beginners who want to understand lighting protocols without getting lost in technical jargon.

Check it out: https://itfourall.com/dmx-artnet.php

The tutorial covers:

What DMX512 and Art-Net are (and how they differ)

How lighting data is structured and transmitted

How to control lights with software over a network

Real-world setup examples with diagrams

Step-by-step guidance with images, no prior knowledge needed

I created this guide because most resources out there are either too technical or too vague. The goal was to make it super accessible – even if you've never worked with stage lighting before.

Would love to hear your thoughts, feedback, or suggestions!
by FilosofumRex | 2025-05-17 02:10:32+00:00
MIT is hiding its own culpability by throwing the Student under the proverbial bus. Acemoglu and Autor who are notorious attention seekers and very media savvy and wealthy profs had vouched for him. There is no way a 2nd year PhD students could have pulled this off on his own without a trace of his whereabouts and contacts in the industry.

A cursory review of the first paragraph of the abstract of his single author paper should've set off alarms:

"AI-assisted researchers discover 44% more materials, resulting in a 39% increase in patent filings and a 17% rise in downstream product innovation".

Anyone with rudimentary familiarity with industrial materials science research would have suspected those double digit numbers - even single digit improvements are extremely rare.
by majormajor | 2025-05-17 02:10:46+00:00
Case 1.3: No AGI, tools increase productivity a lot, you have a bigger team and you make them more productive. In the meantime, while everyone else was scared of hiring, you got a bunch of stuff done to gain a lead in the market.

You get high EV because everyone else in your market voluntarily slowing down is a gift-wrapped miracle for you.

(Even in an AGI-soon case - you spent a bit more (let's be serious here, we're not talking about spending our entire bankroll on 18months of new hires here) in short term to get ahead, then you shift people around or lay them off. Your competitors invested that money into R&D? What does that even mean if it didn't involve hiring and AGI happens soon anyway?)

----

(Case 3: AGI soon, you don't need yourself anymore - it's hard to imagine a sufficiently advanced "AGI" that someone only replaces software devs but leaves the structure, management, and MBA-trappings of modern exchange and businesses alone.)
by credit_guy | 2025-05-17 02:11:06+00:00
> Someone who makes 10 million dollars (or 100 million, or...) a year pays the exact same social security tax as someone who makes $176,100.

You say this as it's some sort of injustice. But the guy who makes 10 million will collect the same benefit in retirement as the guy who makes $176,100. Or are you proposing that the guy who make $10 MM should pay more, but get their benefit capped? Because if that's what you are proposing, I don't see how that is just or fair. Except maybe following the argument along the lines "screw the rich, they can afford it".
by zahlman | 2025-05-17 02:11:30+00:00
Ah.

I imagine the problem is that `__del__` could be monkeypatched, so Python doesn't strictly know what needs custom finalization until that moment.

But if you have a concrete proposal, it's likely worth shopping around at https://discuss.python.org/c/ideas/6 or https://github.com/python/cpython/issues/ .
by dipampaul17 | 2025-05-17 02:13:51+00:00
You're right to question the perplexity impact - 0.86% isn't negligible. Our extended testing shows this impact remains fairly consistent across context lengths up to 16K, which was our test limit.

We haven't benchmarked at 64-128K contexts yet, but theoretically the relative perplexity impact should remain stable. The absolute impact could potentially compound with very long contexts, though.

The key difference from standard KV quantization is the asymmetric approach. Most implementations use K8V8 (8-bit for both) which has a 0.03% perplexity impact but only 47% memory savings. K8V4 pushes this to 59% savings with the 0.86% quality tradeoff.

For reference, the quality impact is still well below the typical 5% threshold where differences become noticeable in generated text. It's a reasonable tradeoff for the additional memory savings, especially at long contexts.

@smcleod - We're using the same underlying quantization methods, just applying them asymmetrically between keys and values. If your existing approach already uses lower precision for values than keys, you're likely getting similar benefits.
by crystal_revenge | 2025-05-17 02:14:30+00:00
I personally didn’t really understand how to write regex until I understood “regular languages” properly, then it was obvious.

I’ve found that the vast majority of programmers today do not have any foundation in formal languages and/or the theory of computation (something that 10 years ago was pretty common to assume).

It used to be pretty safe to assume that everyone from perl hackers to computer science theorists understood regex pretty well, but I’ve found it’s increasingly a rare skill. While it used to be common for all programmers to understand these things, even people with a CS background view that as some annoying course they forgot as soon as the exam was over.
by jeffrwells | 2025-05-17 02:14:42+00:00
LOC or code golf is indeed not a metric to optimize to 0, but hating on importing libraries is a bad take.

As CTO at Redo do you demand everything written in assembly? Does your entire company’s code run in one file or do you use abstraction to simplify?

I’m not quite clear on how expressing a really complex and paradigm shifting approach of agents in a more concise way is a bad thing
by dipampaul17 | 2025-05-17 02:15:53+00:00
Yup, this approach would likely work on NVIDIA/AMD GPUs as well - the underlying principle that keys require higher precision than values is hardware-independent.

The CUDA backend in llama.cpp already supports separate cache type settings with the `--cache-type-k` and `--cache-type-v` flags. Our particular patch is focused on Metal-specific optimizations, but the core technique transfers directly.

Regarding compatibility with other quantization methods - absolutely. This KV cache optimization is complementary to model weight quantization (Q4_K_M, GPTQ, AWQ, etc.). You can combine asymmetric KV cache precision with any model weight format.

Since KV cache quantization happens at runtime while processing tokens (separate from model weights), it doesn't conflict with how the model itself is quantized. They operate on different parts of the inference pipeline.

What would require additional work is integrating with specialized inference engines that have custom KV cache handling, like vLLM or TensorRT-LLM. Each would need its own implementation of asymmetric KV precision.

The most immediate GPU benefit would likely come from integrating these insights into the FlashAttention implementation directly, where the memory bandwidth savings could translate to even greater speedups on CUDA hardware.
by dangus | 2025-05-17 02:15:58+00:00
I don’t know I am not supposed to play on a phone but it seems like nothing I click does much of anything in the game. I just keep seeing a few different phrases come up and I can’t seem to do anything.

It also seems like you’ve focused more on telling us about the technology behind the game rather than introduce the game. What’s the name of the game? What’s the premise? What type of game is it?

This might sound harsh but if this is your marketing strategy I wouldn’t even bother releasing it commercially.
by _se | 2025-05-17 02:16:43+00:00
I'm not unhappy that anyone is excited, I'm frustrated by the amount of time and effort being wasted right now, and especially by how out of control people's expectations about these tools are. I say this as a daily user of multiple different LLM tools, including state of the art coding agents.

My take is neither cynical nor depressed (projection on your part perhaps?), it's seeing through the hype and applying critical thinking. Same thing that I've been doing for social and tech trends for the past 20 years. My work has nothing to do with any of this, but fwiw I do work in one of the most competitive and technically advanced industries in the world, and there is absolutely 0 chance of LLMs doing anything meaningful in the field.

It's the hottest topic on HN because of the specific type of person you get here. Check out lobste.rs for a much more representative and balanced take on LLMs, for example. "Most of us" meant humans, not tech bros.
by readthenotes1 | 2025-05-17 02:17:11+00:00
The data ownership reminds me a bit of an early business ummm transaction if Dr Phil:

1. Sell the gullible public long-term memberships to a gym, with long-term subscriptions.

2. Sell the subscriptions to a 3rd party.

3. Close gym. Subscription contract still valid.

https://www.celebitchy.com/8971/dr_phil_ran_a_health_club_sc...
by scarface_74 | 2025-05-17 02:19:23+00:00
> It allows people that don't know what they do to write queries that can significantly impact servers.

At least for the only OLAP DB I use often - Amazon Redshift - that’s a solved problem with Workload Management Queues. You can restrict those users ability to consume too many resources.

For queries that are used for OLTP, I usually try to keep those queries relatively simple. If there is a reason for read queries that consume resources , those go to read replicas when strong consistently isn’t required
by jeffrwells | 2025-05-17 02:19:24+00:00
As someone who writes a lot of Ruby and has for a long time, I have never thought the metric Ruby optimizes for is “intuitive” (I think it is, but been doing it too long / too close to it so it’s intuitive to me)

The stated optimization from Matz (who created Ruby) is “developer happiness”

The important optimization for me is “fidelity to business logic”, eg less cruft and ruby syntactic sugar means you could sit at your computer and read your business rules (in code) out loud in real time and be understood by a non-dev