MurphyA/hacker-news huggingface dataset:
by thereisnospork | 2025-09-05 17:46:03+00:00
What the Dems say doesn't matter, what matters is the consensus opinion of their position. E.g. If Harris said 'I don't want to pass gun control' I probably wouldn't believe her because that's a singular drop in a river of pro gun control rhetoric and action by her party.

People apparently feel that the Dems are pro 'letting everyone in' -- an understanding created not just by what the Dems say and do on the topic (and others) but what their opponents say too.

If they don't want people to think they aren't pro-immigration then the Dems have dropped the ball on their messaging and actions.

For a similar and reasonably concrete example, the 'Harris is for they/them' ads and their impact is a worthy case study.
by mfenniak | 2025-09-05 17:46:22+00:00
People having trouble getting this configured is a common issue for self-hosting Forgejo Runner. As a Forgejo contributor, I'm currently polishing up new documentation to try to support people with configuring this; here's the draft page: https://forgejo.codeberg.page/@docs_pull_1421/docs/next/admi...

(Should live at https://forgejo.org/docs/v12.0/admin/actions/docker-access/ once it is finished up, if anyone runs into the comment after the draft is gone.)
by Szpadel | 2025-09-05 17:46:59+00:00
exactly!

People bless gpt-5 for not doing exactly this and in my testing with it in copilot I had lot of cases where it tried to do wrong thing (execute come messed up in context compaction build command) and I couldn't steer it to do ANYTHING else. It constantly tried to execute it as response any my message (I tries many common steerability tricks, (important, <policy>, just asking, yelling etc) nothing worked.

the same think when I tried to do socratic coder prompting, I wanted to finish and generate spec, but he didn't agree and kept asking nonsensical at this point questions
by ofu997hn | 2025-09-05 17:47:00+00:00
Location: Portland, OR, USA Remote: Yes Willing to relocate: Yes Technologies: JavaScript, C#, SQL, HTML, CSS, React, GraphQL, .NET, Node, Git, MongoDB, MS SQL Résumé/CV: see linkedin.com/in/oliverfu Email: ofu997@gmail.com

Summary: I'm a .NET web developer with 4 YOE and I'm eager to level up in my next role. I build user interfaces and wrangle business logic, queries, and APIs with JavaScript, C#, and SQL. I'm familiar with MVC frameworks, object-oriented programming, and web app architecture. As a passionate and curious developer, I like to mingle with all aspects of stacks. Prefer remote but willing to relocate. Ideally, a role that supports learning, growth, and scaling up.
by _se | 2025-09-05 17:47:14+00:00
No, it's because reading code is slower than writing it.

The only way these tools can possibly be faster for non-trivial work is if you don't give a shit enough about the output to not even read it. And if you can do that and still achieve your goal, chances are your goal wasn't that difficult to begin with.

That's why we're now consistently measuring individuals to be slower using these tools even though many of them feel faster.
by toddmorey | 2025-09-05 17:47:52+00:00
50% margins would be actually low and concerning for a saas business. What makes software an attractive business is how well it scales. The standard for saas has been at least 80% or higher margins.

Most all saas accounts require lengthy and generous free trials and boy AI compute throws a bit of a hand grenade into that PLG strategy. It’s just new economics that the industry will have to figure out.
by thethimble | 2025-09-05 17:48:07+00:00
Relatedly, most of the author's concerns are solved by wrapping things in a message.

> oneof fields can’t be repeated.

Wrap oneof field in message which can be repeated

> map fields cannot be repeated.

Wrap in message which can contain repeated fields

> map values cannot be other maps.

Wrap map in message which can be a value

Perhaps this is slightly inconvenient/un-ergonomic, but the author is positioning these things as "protos fundamentally can't do this".
by BinaryIgor | 2025-09-05 17:48:28+00:00
Avro (and others) has its own set of problems as well.

For messaging, JSON, used in the same way and with the same versioning practices as we have established for evolving schemas in REST APIs, has never failed me.

It seems to me that all these rigid type systems for remote procedure calls introduce more problems that they really solve and bring unnecessary complexity.

Sure, there are tradeoffs with flexible JSONs - but simplicity of it beats the potential advantages we get from systems like Avro or ProtoBuf.
by morio | 2025-09-05 17:48:28+00:00
Sorry to be blunt, that C/C++ code is crustyyy. Just to start with run this through an decently complete clang-tidy profile and fix all errors and warnings. That should become part of your build. The longer you hold out on that the harder it will be. Your code has no provisions to handle fuzzed/corrupted content. Maybe a better idea would be to switch this entire code base over to Rust.

Consider modern C++ practices as outlined here: https://github.com/cpp-best-practices/cppbestpractices/blob/...
by isr | 2025-09-05 17:48:32+00:00
Just going by the forum thread you linked to, I have to say that was a textbook case of self-entitled users demanding volunteers move their asses into gear and get working pronto, free of charge, on OPEN SOURCE projects that those self entitled users need now, and dammit - they deserve it.

I'm not sure as to the point of this thread. Are you criticizing Jose Valim's cobtribution to that thread? (where he tried to remind folks that, those LSP devs are volunteers working on open source, unpaid, to help others)
by flail | 2025-09-05 17:48:42+00:00
I heard that before. No, Lean Startup is not about working around the cost of software development.

It is about designing good experiments, validating, and learning, so that when we're down to development, we build something that's way more likely to succeed.

The fact that we were advised to build non-technical experiments is but a small part. And with the current AI capabilities, we actually have a new power tool for prototyping that falls neatly into the whole puzzle.

Here's a bit more elaborate argument (sorry for a LinkedIn link): https://www.linkedin.com/posts/pawelbrodzinski_weve-already-...
by derefr | 2025-09-05 17:48:43+00:00
Just don't show... what? The specific exact text "You're absolutely right!"?

That heuristic wouldn't even survive the random fluctuations in how the model says it (it doesn't always say "absolutely"; the punctuation it uses is random; etc); let alone speaking to the model in another language, or challenging the model in the context of it roleplaying a character or having been otherwise prompted to use some other personality / manner of speech (where it still does emit this kind of "self-reminder" text, but using different words that cohere with the set personality.)

The point of teaching a model to emit inline <thinking> sequences, would be to allow the model to arbitrarily "mumble" (say things for its own benefit, that it knows would annoy people if spoken aloud), not just to "mumble" this one single thing.

Also, a frontend heuristic implies a specific frontend. I.e. it only applies to hosted-proprietary-model services that have a B2C chat frontend product offering tuned to the needs of their model (i.e. effectively just ChatGPT and Claude.) The text-that-should-be-mumbled wouldn't be tagged in any way if you call the same hosted-proprietary-model service through its API (so nobody building bots/agents on these platforms would benefit from the filtering.)

In contrast, if one of the hosted-proprietary-model chat services trained their model to tag its mumbles somehow in the response stream, then this would define an effective de-facto microformat for such mumbles — allowing any client (agent or frontend) consuming the conversation message stream through the API to have a known rule to pick out and hide arbitrary mumbles from the text (while still being able to make them visible to the user if the user desires, unlike if they were filtered out at the "business layer" [inference-host framework] level.)

And if general-purpose frameworks and clients began supporting that microformat, then other hosted-proprietary-model services — and orgs training open models — would see that the general-purpose frameworks/clients have this support, and so would seek to be compatible with that support, basically by aping the format the first mumbling hosted-proprietary-model emits.

(This is, in fact, exactly what already happened for the de-facto microformat that is OpenAI's reasoning-model explicit pre-response-message thinking-message format, i.e. the {"content_type": "thoughts", "thoughts": [{"summary": "...", "content": "..."}]} format.)
by bbreier | 2025-09-05 17:48:56+00:00
Does controversy cause articles to slide on HN? I noticed that this had more points in less time than several articles ranked above it, which surprises me a bit

e.g. at time of writing a post about MentraOS has 11 points in 1 hour compared to this article's 51 in 53 minutes, but this is ranked 58th to Mentra's 6
by deeviant | 2025-09-05 17:49:31+00:00
I don't understand these posts. Do people not understand how venture capital works?

The majority of these companies know they are burning money, but more than that knew they would be losing money at this point and beyond. That is the play, the thesis is: AI will dominate nearly everything in the near future, the play is to own a piece of that. Investors are willing to risk their investment for a chance of getting a piece of the pie.

Posts that flail around yelling companies 'losing money', without addressing the central premise are just wasting words.

In short, do you think AI is not going to dominate nearly everything? Great, talk about that. If you do believe is, then talk about something other than the completely reasonable and expected state of investors and companies fighting for a piece of the pie.

As a somewhat related tangent, people seem to not understand the likely cost trajectory of model training/inference costs:

* Models will reach a 'good enough' point where further training will be mostly focused on adding recent data. (For specific market segments, not saying that we'll have a universal model anytime soon, but we'll soon have one that is 'good enough' at c++, might already be there).

* Model architecture and infrastructure will improve and adapt. I work for a company that was among the first use deep learning to control real-time kinetic processes in production scenarios, our first production hardware was a nvidia Jetson, we had a 200ms time budget for inference, and our first model took over 2000! We released our product, running under 200ms, *using the same hardware* the only difference was improvements in the cuDNN library and some other drive updates and some domain specific improves on our YOLO implementation. Long story short, yes inference costs are huge, but they are also massively disruptable.

* Hardware will adapt. Nvidia cash machine will continue, right now nvidia hardware is optimized for balance between training and inference, where TPUs, the newer ones are more tilted towards inference. I would be surprized if other hardware companies don't force Nvidia to give the more inference based solution and 2-3x cost savings at time point in the next 5 years. And for all I know, perhaps a hardware startup will disrupt Nvidia, it would be one of the most lucrative hardware plays on the planet.

Focusing inference cost is a deadend to understanding the trajectory of AI, understanding the *capability* of AI is the answer to understanding it's place in the future.
by geye1234 | 2025-09-05 17:49:51+00:00
Point 5 is sound, and it's actually impossible for a configuration of neural fibers to refer to something outside itself (and therefore, if physicalism is true and thought = neural fibers, it's impossible for a thought to refer to something outside itself, which would falsify point 2). Here's why:

The reference can't exist in the thought if "thought" and "a particular stimulation of neural fibers" refer to the same thing. There is no reference in the fibers. You can't "encode" a reference to something else in the physical brain (or any part of the body).

This is because a reference must in some way refer to its object (obviously). But a reference can only be referred to its object by something else. The word "tiger", or a picture of a tiger, refer to an actual tiger only when there is a mind to give them that meaning. But "a particular stimulation of neural fibers" cannot refer to any object, because there is nothing that can give it that meaning. A word or a picture or anything extra-mental can be given meaning by a mind, but when we are talking about the mind itself, this is impossible.
by dylan604 | 2025-09-05 17:49:52+00:00
> but going home is going to be a traffic nightmare since it’s a Friday.

I've never understood the Friday traffic issue. Are there people that normally stay in the city during the week and only go home on Fridays causing more traffic? How is there more traffic on Friday and the rest of the week? Friday being one of the forced RTO days, but the Friday traffic thing was known well before WFH/RTO fights. Then again, the root cause of most traffic always seems much more anticlimatic
by Buuntu | 2025-09-05 17:49:59+00:00
That is mostly a zoning issue, have you seen the density around Tube stations? Compare that to the density around half of the BART stations which are big parking lots surrounded by single family houses. Of course it's cheaper to run a transit system in a city with twice the population density and population in the metro area.

Costs are an America issue, not a BART issue: https://transitcosts.com/new-data/

BART is one of the most cost efficient systems in the US: https://www.reddit.com/r/transit/comments/1d27dvo/us_cost_pe.... It's so efficient that pre-pandemic it got the majority of its funding through fares, not taxes.

By the way it costs exorbitant amounts to build highways too and you don't see people criticizing all of our highways around the area do you.

So quite frankly you don't know what you're talking about.
by sb52191 | 2025-09-05 17:50:33+00:00
> People coming here and extracting value from the economy to send home is also a problem.

What about the American rich extracting wealth overseas? Vacations, real estate, yachts constructed in foreign countries, investing in companies in other countries, etc.

I think you're asserting more money leaves the US economy because an average person comes to the US (legally or illegally) and sends a portion of their paycheck back home, but I'd bet American born citizens spend/send far more money out of the country...
by dghlsakjg | 2025-09-05 17:51:02+00:00
It can definitely be done in a way that people don’t mind/accept.

People love movie trailers to the point of seeking out good ones and sharing them. People have come to accept that broadcast media will have ads, but get very annoyed when ads are inserted where they are not expected (ads during an ad break are ok, sports betting ads mid play annoy the shit out of me).
by westurner | 2025-09-05 17:51:03+00:00
"In situ printing of biodegradable implant for healing critical-sized bone defect" (2025) https://www.cell.com/device/fulltext/S2666-9986(25)00186-3 :

> Abstract: [...] To address these challenges, we developed a portable in situ printing platform to extrude biodegradable composites directly into defect sites without prefabrication or supporting devices. By modulating components in the composites, the platform can introduce specific functionalities in tissue reconstruction (e.g., osteoconductivity and anti-infection)."
by libraryofbabel | 2025-09-05 17:51:10+00:00
You’re being downvoted, perhaps because your tone is a little harsh, but you’re not wrong: people really are still making versions of the “stochastic parrots” argument. It comes up again and again, on hacker news and elsewhere. And yet a few months ago an LLM got gold on the Mathematical Olympiad. “Stochastic parrots” just isn’t a useful metaphor anymore.

I find AI hype as annoying as anyone, and LLMs do have all sorts of failure modes, some of which are related to how they are trained. But at this point they are doing things that many people (including me) would have flatly denied was possible with this architecture 3 years ago during the initial ChatGPT hype. When the facts change we need to change our opinions, and like you say, reckon anew with the thing that’s sitting in front of us.
by dogleash | 2025-09-05 17:51:26+00:00
I hate how people hear "read only friday" and decide to turn it into a CI/CD dick measuring contest.

For "read only friday" to have been a novel idea in the first place, you needed a starting point where conventional practice already was making changes live without stopping to consider the time/day of week.

I really suspect the detractors represent a workflow that would break (or at least introduce pain) if unable to push to production for a few days. So they have to give the hard sell on the benefits of continuous deployment.
by patall | 2025-09-05 17:52:01+00:00
Than why don't they raise prices? If AI developers were only worth 200k a year, nobody would pay X times those salaries and development would be cheaper. Similar, if none of the AI coding companies had free offerings, they would have less inference cost or more revenue. Yet they have the feeling that they need to offer those, likely because of competition. The article paints it as if the big companies are a factor of 2 away from profitability. Would absolutely nobody use AI if their tokens were double the price? I highly doubt that.
by btilly | 2025-09-05 17:52:18+00:00
The reasons for that line get at a fundamental tension. As David Wheeler famously said, "All problems in computer science can be solved by another level of indirection, except for the problem of too many indirections."

Over time we accumulate cleverer and cleverer abstractions. And any abstraction that we've internalized, we stop seeing. It just becomes how we want to do things, and we have no sense of what cost we are imposing with others. Because all abstractions leak. And all abstractions pose a barrier for the maintenance programmer.

All of which leads to the problem that Brian Kernighan warned about with, "Everyone knows that debugging is twice as hard as writing a program in the first place. So if you’re as clever as you can be when you write it, how will you ever debug it?" Except that the person who will have to debug it is probably a maintenance programmer who doesn't know your abstractions.

One of the key pieces of wisdom that show through Google's approaches is that our industry's tendency towards abstraction is toxic. As much as any particular abstraction is powerful, allowing too many becomes its own problem. This is why, for example, Go was designed to strongly discourage over-abstraction.

Protobufs do exactly what it says on the tin. As long as you are using them in the straightforward way which they are intended for, they work great. All of his complaints boil down to, "I tried to do some meta-manipulation to generate new abstractions, and the design said I couldn't."

That isn't the result of them being written by amateurs. That's the result of them being written to incorporate a piece of engineering wisdom that most programmers think that they are smart enough to ignore. (My past self was definitely one of those programmers.)

Can the technology be abused? Do people do stupid things with them? Are there things that you might want to do that you can't? Absolutely. But if you KISS, they work great. And the more you keep it simple, the better they work. I consider that an incentive towards creating better engineered designs.
by ForOldHack | 2025-09-05 17:52:41+00:00
They did. They started before yesterday's shutdown, and worked all night, they tried to bring up the system for startup, and it came up, then crashed.

It was state of the art on 1962 when it was designed, and remained state of the art until the 1980s, when the signal system started breaking down, and the the late 80s upgrade which had a train presence glitch, which caused almost all the system to get resignaled.

So by the 2000s again it's showing its age, and they got a 32 processor zSeries mainframe.

Brake problem last week, and the this on Friday? Now it's getting like New York, even more. Whatsmatteryou?
by x0x0 | 2025-09-05 17:52:58+00:00
Honestly, I think it's straightforward to fix. Requirements to attempt to source domestically (or at minimum try hard) plus loosening the rules on firing employees (yes it sucks, but hiring is super risky when it takes 0.5 - 3 years of salary to dispose of an employee, either a bad one or one that isn't the right person for a growing business) would go a long way towards jumpstarting domestic software production.

They don't seem to want to :shrug:
by tzs | 2025-09-05 17:53:00+00:00
Also in the imager you can tell it to enable SSH, add an SSH key or have it generate one, and set it to not allow password authorization over SSH.

It does indeed make setting up a headless RPi quite convenient, although afterwards you do actually have to find the thing on your network.

By default it has the hostname "raspberrypi" and handles mDNS so SSHing to raspberrypi.local often does the trick, but could be a problem if you have more than one. You can set the hostname in the imager to deal with that.

Another common approach is to check your router's DHCP client list or connected device list. If you are lucky your router includes the hostname if the client sent one. If it only shows MAC address, IP address, and DHCP remaining lease you can look for something that just got its lease.

Personally, once I learn the Pi's MAC addresses either from my router's DHCP server or by logging on to the Pi and using "ip link show" I go to my router's DHCP settings and assign reserved IP addresses to those Mac addresses. Then whenever I re-image or boot from another drive with a different image it doesn't matter. It's going to be on the same IP.
by numlocked | 2025-09-05 17:53:17+00:00
I don’t quite understand. The article says things like:

“With the constant upward pressure on embedding sizes not limited by having to train models in-house, it’s not clear where we’ll slow down: Qwen-3, along with many others is already at 4096”

But aren’t embedding models separate from the LLMs? The size of attention heads in LLMs etc isn’t inherently connected to how a lab might train and release an embedding model. I don’t really understand why growth in LLM size fundamentally puts upward pressure on embedding size as they are not intrinsically connected.
by ZaoLahma | 2025-09-05 17:53:24+00:00
It’s not “by others”. It’s by circumstance.

It’s like any other tool. If I wanted to chop wood and noticed how my axe had gone dull, the likelihood of me going “ah f*ck it” and instead go fishing increases dramatically. I want to chop wood. I don’t want to go to the neighbor and borrow his axe, or sharpen my axe and then chop wood.

That’s what has happened with ChatGPT in a sense - it has gone dull. I know it used to work “better” and the way that it works now doesn’t resonate with me in the same way, so I’m less likely to pursue work that I would want to use ChatGPT as an extrinsic motivator for.

Of course if the intrinsic motivation is large enough I wouldn’t let a tool make the decision for me. If it’s mid October and the temperature is barely above freezing and I have no wood, I’ll gnaw through it with my teeth if necessary. I’ll go full beaver. But in early September when it’s 25C outside on a Friday? If the axe isn’t perfect, I’ll have a beer and go fishing.