MurphyA/hacker-news huggingface dataset:
by genter | 2025-08-05 22:49:21+00:00
It's a good thing Shockley gave up on the transistor, it never would've been able to switch as much current as a relay, or switch as quickly as a vacuum tube.

It's a good thing Benjamin Franklin gave up on electricity, we would've never been able to contain it safely.

It's a good thing Watts gave up the steam engine, it never would've put out as much power as a horse.
by kenjackson | 2025-08-05 22:49:27+00:00
I had a similar experience. I was visiting the UK doing research and a Chinese post-doc was there. Apparently he was taking our methods and results and sending them to someone in China. They published in some journals we’d never heard of, at the same time we realized there was almost nothing we could do about it. Shaming had no impact on them. But their actual impact on us was negligible.
by runjake | 2025-08-05 22:49:28+00:00
I don't know -- maybe.

I've released several tools, and for most of them, I've heard nothing from anyone.

But 3 got somewhat popular in their niche and most of the inquiries and requests were from people who seemed to think they were entitled to free support and feature requests. Many times, they got pretty rude if I refused to implement their feature or I took too long to release a fix.

It really turned me off from releasing open source code and then interacting with users. I'd rather just release the code, and forget about it, only patching on my own terms.
by Supermancho | 2025-08-05 22:49:37+00:00
The baseline of no trade deficit between all countries and the US makes no sense, since countries are not uniform in resource or production. eg Lesotho/Bangladesh won't achieve a trade balance with the US when they produce what the US wants and don't consume enough to balance it out. https://www.youtube.com/watch?v=Ah9SrbCB7-k&t=1147s

Let's say this was the starting point. Almost immediately, this strategy of trade balance was dispensed with, leaving most countries with some random tariff rate that wasn't strategically decided.

Many recognizable countries, specifically the ones that the administration targeted for political reasons, ended up with adjusted tariffs based on politic. This was due to the administration figuring out they had overstepped or thought that they were too soft, when the influence wasn't effective. When it became clear the economics were being ignored, Trump called them some epithet that meant "unreasonable" and abandoned the tariff tactic in all but rhetoric.

What was the point? The most predictable sequence of IMMEDIATE events were likely the point. I believe the point was to try to convert the global crises (political, economic, and social) that existed around the world to purely economic concerns for simplicity. This was paired with jamming up the entire global trade system, which would allow the administration to listen for the squeakiest wheels. It was supposed to be an efficient way to prioritize immediate problems such that the administration would not have to learn about and weigh all the problems in the world.

Short and long term, the tariffs have accelerated the de-dollarization of the world economy, they has amplified income inequality, and have destroyed supply chains across multiple sectors. The replacements are necessarily less efficient (value is cannibalized by tariffs). It was a lazy and poorly considerer tactic, but it might have achieved the goal, given my assumption of purpose.

The issues that were raised to the administration were largely, left unsolved. The most prominent of which are the Ukrainian, Taiwanese, and Palestinian fronts and the weakening of the US hegemony due to trade conflicts. The good trade relations with traditionally friendly nations have been damaged, long term. Canada, India, China, most of the EU have made moves to distance themselves from the US chaos. Some strange side effects, like this weird buy in to american companies is a side effect of the show, rather than a specific intent. I can concede it wasn't all bad. The US did see some new corporate inroads into a few countries. Notably concessions from Mexico, who was willing to meet the US goals in achieving a trade balance (more or less).

Overall, it's possible the tariffs succeeded as a self-serving tool of the administration, but failed in serving the US as policy. Maybe I'm just wrong, but maybe Trump is just this simple minded and his administration is playing into the fallout, as it advances their 2025 goals (or whatever).
by GianFabien | 2025-08-05 22:50:06+00:00
Here's a fantastic chance for a tech mega-billionaire or company to step up to the plate and take on the $15M p.a. commitment to keep the satellite orbiting.

For example, for Google $15M is probably less than they spend on stocking their fridges with fizzy drinks. The PR benefits would be huge and they could probably sell the data to the current users of that info.
by bangaladore | 2025-08-05 22:50:34+00:00
I just bought my first AMD chip in years (a 9950X3D), the last one I had was from the early FX series. After how poorly those FX chips performed, I swore off AMD for a long time.

Ryzen definitely caught my interest when it first came out, but Intel still had the edge in performance and stability back then, so I stuck with them.

That started to change with the 7800X3D, and now with the 97/9950X3D lineup, AMD is clearly ahead. I'm not even going to mention the "new" Intel chips.

Funny how things shift. I'm firmly in the AMD camp now.

And everyone I know is in the same boat. Probably 3-5 years ago Intel was the only "real" option for the people I know, now it's the exact opposite.
by Marsymars | 2025-08-05 22:51:21+00:00
I don't think your post is without merit, but I often search for things where there's a single canonical source of truth, and no amount of AI slop or LLM results will be of interest. (e.g. this afternoon I've searched for a book title, for YouTube Premium pricing info, for git bisect documentation, for car collision stats, for speeding fine amounts, for some Linq documentation, and for some specific info on local parking).
by Nomadeon | 2025-08-05 22:52:05+00:00
Agree. Concrete example: "What was the Japanese codeword for Midway Island in WWII?"

Answer on Wikipedia: https://en.wikipedia.org/wiki/Battle_of_Midway#U.S._code-bre...

dolphin3.0-llama3.1-8b Q4_K_S [4.69 GB on disk]: correct in <2 seconds

deepseek-r1-0528-qwen3-8b Q6_K [6.73 GB]: correct in 10 seconds

gpt-oss-20b MXFP4 [12.11 GB] low reasoning: wrong after 6 seconds

gpt-oss-20b MXFP4 [12.11 GB] high reasoning: wrong after 3 minutes !

Yea yea it's only one question of nonsense trivia. I'm sure it was billions well spent.

It's possible I'm using a poor temperature setting or something but since they weren't bothered enough to put it in the model card I'm not bothered to fuss with it.
by Atlas667 | 2025-08-05 22:52:30+00:00
Anything that you do as a career will lead to this.

- Split a paper into three

- Waste efforts by running the same research with a slight change

- Plagiarism

- Inter-peer favors (corruption, dishonesty)

Funding/grants, journals, publishers, paper count requirements, are the true source of these behaviors.

This is what markets are when left to develop. Academia made itself game-able. You can't have truth and profits sit in the same room.

Capitalism has made a very nice chair for careerism to sit in.
by CharlieDigital | 2025-08-05 22:52:31+00:00
It's not because of the YT video; it's that the YT video provides a good walkthrough. I've been working with JS since the late 90's and have seen various front-end stacks come and go so to me, it is pretty obvious that React has this inverted nature and it is this inverted nature that is the root cause of why it's a very technical endeavor to get it to perform well at scale as it requires the engineer to think carefully about memoization and state placement -- something that doesn't happen in vanilla JS and early frameworks like KnockoutJS.
by cosmic_cheese | 2025-08-05 22:53:28+00:00
The privacy aspect is big. Even if content blocker extensions technically aren’t as capable, they were nice because they could be installed with impunity regardless of the party responsible for developing them. It’s a tradeoff.

In practice I’ve found them to be largely effective except for the most awful sites that I should probably be finding alernatives to instead of using (vote with your eyeballs), which is something I do even where “real” uBlock Origin is an option.
by satyrun | 2025-08-05 22:54:03+00:00
Machine-learning for audio is just a different form of audio synthesis.

That is not the issue. The issue is how incredibly generic the music is.

It also doesn't let you combine genres to make really strange sounds like audioLM can do.

This is just another Muzak generator like they use to play at Dennys. As generic music as possible to the appeal to the most average of average listener.

I think you really need to train your own model if you want to explore creative sound design or algorithmic composition. It just isn't going to be a mass market product worth venture capital money.
by acheong08 | 2025-08-05 22:54:50+00:00
I wanted it to work on IOS. None of those were viable. In terms of why not my own plugin, that's just pure incompetence. I don't know TypeScript that well while getting the API done only took a few days. I tried working on a plugin later on for sync but found the docs difficult to follow. In the end, it wasn't worth the effort and I've gone back to just neovim and syncthing. For IOS, I'm sideloading my own app written with fyne (Go) but functionality is really basic.
by azinman2 | 2025-08-05 22:54:54+00:00
I'm guessing its largely enthusiasts for now, but as they continue getting better:

1. App makers can fine tune smaller models and include in their apps to avoid server costs

2. Privacy-sensitive content can be either filtered out or worked on... I'm using local LLMs to process my health history for example

3. Edge servers can be running these fine tuned for a given task. Flash/lite models by the big guys are effectively like these smaller models already.
by jonas21 | 2025-08-05 22:55:44+00:00
When they say "the start", I think they mean the start of the current LLM era (circa 2017). The main story of this time has been a rejection of the idea that major conceptual breakthroughs and complex architectures are needed to achieve intelligence. Instead, it's better to focus on simple, general-purpose methods that can scale to massive amounts of data and compute (i.e. the Bitter Lesson [1]).

[1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
by Yizahi | 2025-08-05 22:55:58+00:00
"In my experience (about 2 decades ago) in a group of 20-30 students only 2 or 3 are able and willing to do homework. Most students just find someone else and copy from them. The real learning happens when preparing for a big exam.

And to pass an exam students have to prepare for the exam. Homework will only help there if it is similar to the exam."

That's not learning really. I can confidently say that because I was the one who unfortunately regressed to this during the uni, and the same story with my peers. One simply can't prepare to the multiple exams sufficiently in a few weeks time (or less). So the only path left is hysterical rote memorization of as much material as possible to squeeze in at a passing grade, and then immediately forget all of the materials in a few months time. Burst of "learning" twice a year for a short time doesn't translate into real learning.

And that's for some simple courses during first few years. Specialist courses later in the program sometimes are impossible to rush "learn". When I tried to pull this off for Probability Theory course, I've failed spectacularly to get even lowest passing mark at first try. And others failed the same way.
by xenadu02 | 2025-08-05 22:56:06+00:00
To be extra clear here:

They used an unproven custom-designed sensor + controller system to monitor the health of the hull.

The monitoring system detected the beginning of total hull failure in the exact way they intended. They then ignored that monitoring system because hull failure would have been inconvenient.

Really that's the whole story of Stockton here. Massively motivated reasoning. Anything inconvenient was written off as wrong. A lot of normalization of deviance too as some of the written-off concerns turned out to be wrong. But not all of them.

I recently got my PPL and that's a massive risk for pilots. You fly in weather you shouldn't and get away with it for a while. Then it becomes normal. You've "proven" you can handle it. Then a situation comes along that requires more margin than you have left and you die.
by agent_turtle | 2025-08-05 22:56:32+00:00
One of the major reasons there’s such a shortage of homes in the US is the extensive permit process required. Pivoting from data centers to home construction is not a straightforward process.

Regarding the economics, the reason it’s a big deal that AI is powering growth numbers is because if the bubble pops, jobs go poof and stock prices with it as everyone tries to salvage their positions. While we still create jobs, on net we’ll be losing them. This has many secondary and tertiary effects, such as less money in the economy, less consumer confidence, less investment, fewer businesses causing fewer jobs, and so on. A resilient economy has multiple growth areas; an unstable one has one or two.

While you could certainly argue that we may already be in rough shape even without the bubble popping, it would undoubtedly get worse for the reasons I listed above,
by tarruda | 2025-08-05 22:56:43+00:00
Download llama-server from llama.cpp Github and install it some PATH directory. AFAIK they don't have an automated installer, so that can be intimidating to some people

Assuming you have llama-server installed, you can download + run a hugging face model with something like

llama-server -hf ggml-org/gpt-oss-20b-GGUF -c 0 -fa --jinja

And access http://localhost:8080
by bithive123 | 2025-08-05 22:57:04+00:00
“See, there are basically two kinds of Philosophy - one's called prickly, the other one is called goo. Prickly people are precise, rigorous, logical - they like everything chopped up and clear. Goo people like it vague, big picture, random, imprecise, incomplete and irrational. Prickly people believe in particles, goo people believe in waves. They always argue with each other but what they don't realize is neither one of them can take their position without their opposition being there. You wouldn't know you are advocating prickles unless someone else was advocating goo. You wouldn't even know what prickles was and what goo was. Life is not prickles or goo, its gooey-prickles or prickly-goo.” ― Alan Watts
by text0404 | 2025-08-05 22:57:17+00:00
when you think of scientific research, do you imagine someone having an immediate eureka moment in a vacuum and writing a paper without having ever considered a problem before? do you understand that scientific progress takes years of dedication, hard work, trial and error, and then finally (occasionally) success?

do you understand long-term survival and the necessity of planning for future generations, or are you just looking for the equivalent of this quarter's shareholder returns when it comes to advancing the species?
by Nevermark | 2025-08-05 22:57:18+00:00
> It has nothing to do with stupidity.

In addition to being rude, its not a particularly clear word.

So I coined "idiodidact", to specifically describe people who have personal selectivity with regard to being teachable. (Greek/English usage: "idios"/personal choice + "didact"/taught)

Any resemblance, to any other word, would be a coincidence.
by hodgesrm | 2025-08-05 22:57:30+00:00
> Even if people somehow get this to work like a real database, they will quickly become frustrated with the same features that the data lake avoids: access controls, locks, consistency, centralization, a schema, you name it.

Are you saying that Apache Iceberg does not have these features? Other than access controls, most of them are there. What Iceberg most certainly does not have is efficient update--you have to load in large batches to avoid metadata file explosion. That's actually fine for cold storage, without which analytic databases become economical at scale. And Iceberg does have features like time travel, which databases like ClickHouse lack. It's also shareable, it's not just an appendage of one particular data warehouse.

One real problem with Iceberg is that a lot of people (vendors mostly) are jumping on it as a way to market their products.
by mark_undoio | 2025-08-05 22:57:41+00:00
Amp (ampcode.com) uses Sonnet as its main model and has GPT o3 as a special purpose tool / subagent. It can call into that when it needs particularly advanced reasoning.

Interestingly I found that prompting it to ask the o3 submodel (which they call The Oracle) to check Sonnet's working on a debugging solution was helpful. Extra interesting to me was the fact that Sonnet appeared to do a better job once I'd prompted that (like chain of thought prompting, perhaps asking it to put forward an explanation to be checked actually triggered more effective thinking).
by sothatsit | 2025-08-05 22:57:50+00:00
Opus really shines for completing long-running tasks with no supervision. But if you are using Claude Code interactively and actively steering it yourself, Sonnet is good enough and is faster.

I don't believe anyone saying Sonnet yields better results than Opus though, as my experience has been exactly the opposite. But trade-off wise, I can definitely see it being a better experience when used interactively because of its speed and lower cost.
Show HN: Making AI chat sessions durable to network failures
by infiniteregrets | 2025-08-05 22:58:14+00:00
The experience with AI chat apps can be a bit fragile — there's no resilience to network failures, and responses aren't deterministic like traditional search. As Garry Tan put it (https://x.com/garrytan/status/1927038513108701662), "It feels like catastrophic data loss when a given response comes back and fails halfway, and then the next retry is not as good."

Vercel recently released a "resumable-stream" package (https://github.com/vercel/resumable-stream) to solve this problem using Redis as a backing store.

I felt that we could simplify the approach using s2.dev (I'm an engineer on the team), and created a fork which ended up borrowing the API but otherwise completely rewritten: https://github.com/s2-streamstore/resumable-stream

- S2 streams are cheap, so instead of optimizing to flush only when there is another reader, the stream is always created and written to.

- Unlike the typically ephemeral nature of Redis, tokens are always durable, and there are no memory constraints around number of streams or how much data is on them.

- Relies on S2’s built-in concurrency control. A unique fencing token ensures a single writer, and a sequence number is optimistically supplied to match the one expected to be assigned to the next record, to deduplicate in case of retries.

It integrates nicely with Chat SDK, and you can try this out here: https://ai-chatbot-s2.vercel.app by reloading the page during a larger response!

There are some interesting future possibilities, like building a multiplayer chat experience around shared streams.
Show HN: AI Dev Assistant Framework – Add structure, rules and memory to LLM
by Fr-e-d | 2025-08-05 22:58:21+00:00
After spending months working with Claude, Gemini, and others inside Cursor, I got tired of constantly fixing regressions and re-explaining my project’s logic. The AI was impressive — but blind without proper context.

I didn’t want more prompt tricks. I needed structure. So I built a framework that gives LLMs:

– codified project rules & constraints – a clear, step-by-step development protocol – a memory system that evolves over time – human-in-the-loop checkpoints to reduce failure

It completely changed how I use AI in real software projects. I’m sharing it open source in case others are trying to get more from their AI dev workflows too.

Happy to answer questions or go into technical details. → Link above, feedback welcome!
by matheusmoreira | 2025-08-05 22:59:16+00:00
He makes many good points in his criticism but I'm still wondering what language one's supposed to use if not C.

My lisp has a conservative mark-and-sweep garbage collector. The mark phase spills all registers and then walks the entire stack looking for pointers to lisp objects. I managed to implement the stack walking through some convoluted pointer nonsense but even C could not express the notion of spilling registers, I had to write assembly code for each architecture.

I have no idea how such a garbage collector would be implemented in Rust or Zig.
by pentamassiv | 2025-08-05 22:59:27+00:00
I am the maintainer of a library to simulate keyboard and mouse input. I didn't start the project but took over the maintenance and have since rewritten pretty much all of the code. I recently found out that Anthropic is shipping it in Claude Desktop for some unreleased feature which is probably like "Computer Use". I noticed they had an open position in exactly the team responsible for the implementation and applied. A few months later I received a rejection. The letter said that the team doesn't have the time to review any more candidates. The code is under MIT so everything is perfectly fine. It is great that a company like Anthropic is using my code, but it would have been nice to benefit from it. I wrote a slightly longer blog post about the topic here:

https://grell.dev/blog/ai_rejection
by margalabargala | 2025-08-05 22:59:51+00:00
Sure, you can make the case that things could get pretty ugly. You could even make the case that things could get about as bad as the Great Depression.

But for things to be much worse than the Great Depression, I think is an extraordinary claim. I see the ingredients for a Great Depression-scale event, but not for a much-worse-than-Great-Depression event.
by Jtsummers | 2025-08-05 22:59:57+00:00
And if you're USAF, you practically strip the aircraft to its frame every few years, inspect, repair, and reassemble it. The dangers are known and the process around it is there to deal with those dangers.

Most aircraft fleets operated by people who care will do something similar but perhaps not as extreme. In fairness to them they don't expect to fly the plane as long as USAF does (70+ years for the B-52, 50+ for many fighters).
by throwawaylaptop | 2025-08-05 23:00:10+00:00
Billions of people were not overweight when they ate those things. They did not eat them 3+ times a day.

I said in my other posts.. if someone needs help maintaining or gaining weight, eat those things.

If you are overweight, you should be eating ZERO of them.

"Basically killing them" means whatever the current obesity and type 2 diabetes epidemic means.
by resters | 2025-08-05 23:01:15+00:00
Reading the comments it becomes clear how befuddled many HN participants are about AI. I don't think there has been a technical topic that HN has seemed so dull on in the many years I've been reading HN. This must be an indication that we are in a bubble.

One basic point that is often missed is: Different aspects of LLM performance (in the cognitive performance sense) and LLM resource utilization are relevant to various use cases and business models.

Another is that there are many use cases where users prefer to run inference locally, for a variety of domain-specific or business model reasons.

The list goes on.
by kelnos | 2025-08-05 23:01:37+00:00
I have many issues with providers doing that. The reason they take your money and give you credits in exchange is because that gives them lower credit card processing fees (one larger transaction vs. many smaller ones). If they're going to do that to make things cheaper for them, then they should let me use those credits whenever I want, no matter how far into the future I want to use them.

If not, they should refund them. They absolutely should refund them if I close my account.

If I'm going to give you money, I expect something in return for every cent of it. That's just basic decency.