MurphyA/hacker-news huggingface dataset:
by drpepper42 | 2026-01-12 03:02:45+00:00
No it did not. I don't know why people repeat this so often but it is very frustrating. Nixon unilaterally ended the gold standard because the US was printing money to pay for Vietnam and the rest of the world called the US on its bullshit. The end of the gold standard is relatively recent in history and the verdict is still out on the impact.
by totallykvothe | 2026-01-12 03:02:57+00:00
I don't understand the stance that AI currently is able to automate away non-trivial coding tasks. I've tried this consistently since GPT 3.5 came out, with every single SOTA model up to GPT 5.1 Codex Max and Opus 4.5. Every single time, I get something that works, yes, but then when I start self-reviewing the code, preparing to submit it to coworkers, I end up rewriting about 70% of the thing. So many important details are subpar about the AI solution, and many times fundamental architectural issues cripple any attempt at prompting my way out of it, even though I've been quite involved step-by-step through the whole prototyping phase.

I just have to conclude 1 of 2 things:

1) I'm not good at prompting, even though I am one of the earliest AI in coding adopters I know, and have been consistent for years. So I find this hard to accept.

2) Other people are just less picky than I am, or they have a less thorough review culture that lets subpar code slide more often.

I'm not sure what else I can take from the situation. For context, I work on a 15 year old Java Spring + React (with some old pages still in Thymeleaf) web application. There are many sub-services, two separate databases,and this application needs to also 2-way interface with customer hardware. So, not a simple project, but still. I can't imagine it's way more complicated than most enterprise/legacy projects...
by graeme | 2026-01-12 03:03:27+00:00
The OP said new carbon sources are not competitive.

ANY investment is by definition creating capacity that would not be there without the investment. If carbon were not competitive it would not get investment.

If you sum up all of the carbon and compare to renewables in the chart there's more new carbon investment annually globally than renewables. (Comparing the dark lines vs the green line)

Also this is ignoring "low emission fuels", which are still carbon sources, natural gas and the like.

If you check the chart "Global electricity generation of zero-carbon sources vs. fossil fuels, 2000-2024" you can see that carbon sources were at an all time high in 2024. Growing slower is still growing.

We ought to be shrinking these to zero. I'm very glad to see solar and wind growing but my point is nuclear is worth supporting as an non-carbon energy source that could replace some of this carbon load because of its baseload characteristics.
by tombert | 2026-01-12 03:03:38+00:00
Yeah I'm not saying that they're necessarily great in general, just that there are certain applications that map pretty well, and for those it's a pretty cool abstraction because it allows virtually anything to interface with it.

Also, looks like my message queue idea has already been done: https://github.com/pehrs/kafkafs

No new ideas under the sun I suppose.
by dzhiurgis | 2026-01-12 03:04:35+00:00
> doing cameras plus Lidar

At what proportion? Is it mostly lidar or mostly cameras? Or 50/50?

> Potentially as extraneous as range to a surface that a camera can’t tell apart from background.

I guess yeah for backside of the car you'd probably better off measuring actual actions.

How about when you come 4 way stop. LIDAR is useless as it wouldn't recognize anyones turn signals.
by CamperBob2 | 2026-01-12 03:05:02+00:00
If Trump does try to politicize the fed he is going to do the one thing that the American political system will not tolerate

I've lost track of the number of times I, and others, have said that.

Turns out there really are no brakes on the Trump Train. In the parlance of the metallic-headgear fans, any other POTUS would have been treated to a nice convertible ride through downtown Dallas by now.
by marssaxman | 2026-01-12 03:05:02+00:00
I just tried your experiment, first asking for a bolognese sauce recipe in English, then translating the prompt to Italian and asking again. The recipes did contain some notable differences. Where the English version called for ground beef, the Italian version used a 2:1 mix of beef and pancetta; the Italian version further recommended twice as much wine, half as much crushed tomato, and no tomato paste. The cooking instructions were almost the same, save for twice as long a simmer in the Italian version.

More authentic, who knows? That's a tricky concept. I do think I'd like to try this robot-Italian recipe next time I make bolognese, though; the difference might be interesting.
by jojobas | 2026-01-12 03:05:14+00:00
I could imagine living in a world where there are 3 sexes and everyone walks on ceilings.

You're free to call Japan Nippon as long as you're fine with people raising eyebrows, sometimes not understanding what you mean, or deciding you're a pretentious twit.

The request that we use a character that doesn't even exist in the English alphabet (ü) is particularly ludicrous.
by osigurdson | 2026-01-12 03:05:18+00:00
>> The skillset is shifting from implementing algorithms to knowing how to ask the AI the right questions and verify its output.

The question is, how much faster is verification only vs writing the code by hand? You gain a lot of understanding when you write the code yourself, and understanding is a prerequisite for verification. The idea seems to be a quick review is all that should be needed "LGTM". That's fine as long as you understand the tradeoffs you are making.

With today's AI you either trade speed for correctness or you have to accept a more modest (and highly project specific) productivity boost.
by hilbert42 | 2026-01-12 03:05:28+00:00
I recall when I was a kid decades ago Coke wasn't as sweet as it is now (nowadays, I find it so sweet I no longer drink it).

It would be informative if we actually knew how much sugar was in say tbe wartime Coke of the 1940s compared with that of today. I reckon the difference would startle us.
by gruez | 2026-01-12 03:05:57+00:00
>But the range of Trump supporter opinions stretches only from "politely support everything he wants to do" to "be performatively mean about everything he wants to do".

You're basing this off... what? You're missing the options of "I'm indifferent about this", or "I don't agree with him on this but still think he's better as a whole than the alternative".
by falcor84 | 2026-01-12 03:06:05+00:00
Yes, getting the timelines right is near impossible, but the trajectory is clear to me, both on AI taking over pure knowledge work and on self-driving cars replacing human drivers. For the latter, there's a lot of inertia and legalities to overcome, and scaling physical things is hard in general, but Waymo alone crossed 450,000 weekly paid rides last month [0], and now that it's self-driving on highways too, and is slated to launch in London and Tokyo this year, it seems to me that there's no serious remaining technical barrier to it replacing human drivers.

As for a bet, yes, I'd really be happy to put my money where my mouth is, if you're familiar with any long bets platform that accepts pseudonymous users.

[0] https://www.cnbc.com/2025/12/08/waymo-paid-rides-robotaxi-te...
by HarHarVeryFunny | 2026-01-12 03:06:21+00:00
It's a new type of development for sure, but with an agentic system like Claude Code that is able to compile, run and test the code it is generating you can have it iterate until the code meets whatever test or other criteria you have set. No reason code reviews can't be automated too, customized to your own coding standards.

Effort that might be put into feeling that you need to manually review all code generated might better be put into things like automating quality checks (e.g code review, adherence to guidelines) ensuring that testing is comprehensive, and overall management of the design and process into modular testable parts the same way as if you'd done it manually.

While AI is a tool, the process of AI-centric software development is better regarded as a pair-design and pair-coding process, treating the AI more like a person than a tool. A human teammate isn't deterministic either, yet if they produce working artifacts that meet interface requirements and pass unit tests, you probably aren't going to insist on reviewing all of their code.
by AdieuToLogic | 2026-01-12 03:06:22+00:00
> The file system as an abstraction is actually not that good at all beyond the basic use-cases. Imagine you need to find an email.

Unrelated to FUSE and MCP[1] agents, this scenario reminded me of using nmh[0] as an email client. One of the biggest reasons why nmh[0] is appealing is to script email handling, such as being able to use awk/find/grep/sed and friends.

0 - https://www.nongnu.org/nmh/

1 - https://en.wikipedia.org/wiki/Model_Context_Protocol
by visageunknown | 2026-01-12 03:07:01+00:00
I find LLMs remove all the fun for me. When I build my homelab, I want the satisfaction of knowing that I did it. And the learning gains that only come from doing it manually. I don't mind using an LLM to shortcut areas that are just pure pain with no reward, but I abstain from using it as much as possible. It gives you the illusion that you've accomplished something.
by throw0101c | 2026-01-12 03:07:05+00:00
> He knew how much power he had back then. The difference was that the government bureaucracy worked hard to counter his agenda throughout his term.

He did not know. He was also not expecting to win, and so had to scramble to get people appointed.

He asked around and got people who were experts in their respective fields. The problem is that those experts (a) knew his ideas were bad, and (b) had integrity. It was, by and large, Trump's appointees that worked hard to counter his agent and not the government bureaucracy.

Trump did not make the same 'mistake' this time around: he appointed folks not for their competence but for their loyalty to him. That was and is the only criteria for serving under Trump.
by pests | 2026-01-12 03:07:27+00:00
It was budgeted at 1.9B (in 2019). Most of the cost overruns are from unexpected site issues like asbestos (more than thought) and water table problems. The buildings are historic, they already had marble. The marble was taken down and saved, this marble will be re-used. Some pieces are damaged, those will be new marble. These buildings are near 100 years old. I also don't think they have marble floors, but facades and other stonework.
by gpm | 2026-01-12 03:07:28+00:00
It strikes me that more tokens likely give the LLM more time/space to "think". Also that more redundant tokens, like local type declarations instead of type inference from far away, likely often reduce the portion of the code LLMs (and humans) have to read.

So I'm not convinced this is either the right metric, or even if you got the right metric that it's a metric you want to minimize.
by callc | 2026-01-12 03:09:37+00:00
It might be childish, but I think the “act like an adult” (or not) is a good way to frame the bad behavior that’s been so present lately.

It encapsulates so much of how I want to describe things.

Selfish behavior - this adult is just a child who didn’t learn to share.

Mean and vindictive behavior - didn’t learn to empathize as a kid

Lying? You’re still a child. Grow up and then join the adults.
by maxkfranz | 2026-01-12 03:09:43+00:00
I always stay one major version behind so I only get security patches after an initial, yearly upgrade. Not experiencing Tahoe myself yet, I felt that perhaps the UI issues people are talking about were a tad overstated, but the example in the article states it very plainly.

I'm taken aback. Change the look, that's fair enough. But it should have some usability testing for this kind of thing before it goes out the door.
Ask HN: How to automate aesthetic photo cropping? (CV/AI)
by icons | 2026-01-12 03:10:15+00:00
Hi everyone,

I am a backend developer currently engineering an in-house automation tool for a K-pop merchandise production company (photocards, postcards, etc.).

I have built an MVP using Python (FastAPI) + Libvips + InsightFace to automate the process where designers previously had to manually crop thousands of high-resolution photos using Illustrator.

While basic face detection and image quality preservation (CMYK conversion, etc.) are successful, I am hitting a bottleneck in automating the "Designer's Sense (Vibe/Aesthetics)."

[Current Stack & Workflow]

Tech Stack: Python 3.11, FastAPI, Libvips (Processing), InsightFace (Landmark Detection).

Workflow: Bulk Upload $\rightarrow$ Landmark Extraction (InsightFace) $\rightarrow$ Auto-crop based on pre-defined ratios $\rightarrow$ Human-in-the-loop fine-tuning via Web UI.

[The Challenges]

Mechanical Logic vs. Aesthetic Crop

Simple centering logic fails to capture the "perfect shot" for K-pop idols who often have dynamic poses or varying camera angles.

Issue: Even if the landmarks are mathematically centered, the resulting headroom is often inconsistent, or the chin is awkwardly cut off. The output lacks visual stability compared to a human designer's work.

Need for Reference-Based One-Shot Style Transfer

Clients often provide a single "Guide Image" and ask, "Crop the rest of the 5,000 photos with this specific feel." (e.g., a tight face-filling close-up vs. a spacious upper-body shot).

Goal: Instead of designers manually guessing the ratio, I want the AI to reverse-engineer the composition (face-to-canvas ratio, relative position) from that one sample image and apply it dynamically to the rest of the batch.

[Questions]

Q1. Direction for Improving Aesthetic Composition

Is it more practical to refine Rule-based Heuristics (e.g., fixing eye position to the top 30% with complex conditionals), or should I look into "Aesthetic Quality Assessment (AQA)" or "Saliency Detection" models to score and select the best crop?

As of 2026, what is the most efficient, production-ready approach for this?

Q2. One-Shot Composition Transfer

Are there any known algorithms or libraries that can extract the "compositional style" (relative position of eyes/nose/mouth regarding the canvas frame) from a single reference image and apply it to target images?

I am looking for keywords or papers related to "One-shot learning for layout/composition" or "Content-aware cropping based on reference."

Any keywords, papers, or architectural advice from those who have tackled similar problems in production would be greatly appreciated.

Thanks in advance.
by sublinear | 2026-01-12 03:11:14+00:00
I think you're spot on.

So many people hyping AI are only thinking about new projects and don't even distinguish between what is a product and what is a service.

Most software devs employed today work on maintaining services that have a ton of deliberate decisions baked in that were decided outside of that codebase and driven by business needs.

They are not building shiny new products. That's why most of the positive hype about AI doesn't make sense when you're actually at work and not just playing around with personal projects or startup POCs.
by visarga | 2026-01-12 03:12:02+00:00
A related trick - if you want to teach your agent a specific kind of behavior, and want this behavior to be calibrated and safe, what you can do is:

1. enumerate the actions (policies) your agent takes, collected from prior runs

2. infer the states that correspond to each of these policies, make a state atlas (similar to the zodiac here)

3. infer the maximally discriminative features that can identify the state from current context

4. label a few examples and train a small policy model that predicts your action from those state features

I think LLMs should be used more often like this - as feature extractors for toy models, which can be used like tools. This way you can encode arbitrary logic in a small tool model that does not depend on the biases of the base model. For example this setup could power a "skill" to reliably implement your policy.

The trick here is that you carefully identify states that predict policy reliably, and features that distinguish between states, instead of using embeddings or pure LLM reasoning. You can decouple the logic from the feature extraction, and have it calibrated to your goals.

All 4 steps can be done by a coding agent with your supervision and zero coding. It's LLM as generic feature extractor with small models sitting on top.
by CuriouslyC | 2026-01-12 03:12:16+00:00
I think it comes down to what you mean by sub par code. If you're talking a mess of bubblesorts and other algorithmic problems, that's probably a prompting issue. If you're talking "I just don't like the style of the code, it looks inelegant" that's not really a prompting issue, models will veer towards common patterns in a way that's hard to avoid with prompts.

Think about it like compiler output. Literally nobody cares if that is well formatted. They just care that they can get fairly performant code without having to write assembly. People still dip to assembly (very very infrequently now) for really fine performance optimizations, but people used to write large programs in it (miserably).
by defrost | 2026-01-12 03:12:53+00:00
The post-World War II Bretton Woods system was a limited form of the pre 1920s depression gold standard.

Both the silver standard and bimetallism have been more common than the gold standard.

Tying complex multi faceted economies to the physical abundance of specific raw materials fails to capture the full value of activities and assets.

The true gold standard was a blip from the 1870s to the early 1920s.
by mingus88 | 2026-01-12 03:12:57+00:00
Let me be one more voice telling you that you are wrong. I just did this morning.

In settings, "download originals to this mac", select all photos, file -> "export unmodified originals" will trigger the Photos app to download every file from iCloud into your local library (as well as exporting them to wherever you want)

I guess "there is no download button" but dude...I don't need iCloud Photos Downloader.
by doodlesdev | 2026-01-12 03:13:42+00:00
This might now be one of my favorite websites in the internet. It won me when I saw the "Excel Backend" design pattern [0]. I wish so fucking much I had never seen this before or heard of using Excel as a database.

update: I'm feeling actual physical pain by reading this and remembering every single piece of software or website I've seen that actually applies these patterns [1].

[0]: https://worstofbreed.net/patterns/excel-backend/

[1]: https://worstofbreed.net/patterns/js-bundle-bloat/
by talentedcoin | 2026-01-12 03:13:58+00:00
If you’re unlucky in the same way I was, it could actually be a GNOME/GTK issue. Some questionable (?) font rendering decisions were made that for me caused all text in GNOME to be blurry. I hated it so much I switched to KDE but soon realized GTK apps had the same issue.

Eventually I found a fix that worked and now I’m happy. So, next time you can try this. In the file:

~/.config/gtk-4.0/settings.ini

You can add:

[Settings]

gtk-hint-font-metrics=1

Here’s the Arch wiki page that explains it:

https://wiki.archlinux.org/title/GTK#Text_in_GTK_4_applicati...

If your settings.ini is in a different spot see:

https://docs.gtk.org/gtk4/class.Settings.html
by ProllyInfamous | 2026-01-12 03:13:59+00:00
The most-unamerican thing you can do right now is HOLD bitcoin/gold/silver.

If you still want fiat — and they're available — Swiss Francs are deflating least-quickly.

Otherwise, as a fellow pleb, my best advice is to get enough bullets for occassional hunting (and other tax-free methods of living) and protection.

If you're of a draftable age/gender, I'd either get extremely fit or extremely disabled. If you're a lard-ass, I'd get to a state where you can live without medicines.

—fellow blue collar american
by 8note | 2026-01-12 03:15:25+00:00
i did well at all the stoichiometry, and energy calculations, but i never thoguht of chemistry as something fun until i started seeing the variius youtube videos of figuring/designing out chemical and filtering processes and trying them.

unfortunately thast long after i might have learned how to do that problem solving. maybe if i go back to school ill try to find such classes. i imagine biochem also has a lot of that
by brightball | 2026-01-12 03:15:49+00:00
I think there’s room for both points of view here. Going all in on visual processing means you can use it anywhere a person can go in any other technology, Optimus robots are just one example.

And he’s not wrong that roads and driving laws are all built around human visual processing.

The recent example of a power outage in SF where lidar powered Waymo’s all stopped working when the traffic lights were out and Tesla self driving continued operating normally makes a good case for the approach.
by raw_anon_1111 | 2026-01-12 03:15:54+00:00
I have never in 30 years across 10 jobs ever seen it work well when a manager does production code instead of just POCs.

Either you’re going to suck as a manager and not get your team the resources they need, play the political games, etc or you’re going to suck as a developer because you can’t keep your commitments and can’t do the follow through.

Even worse, is a manager who is pushing vibe coded slop.

The best thing a manager can do if they still want to code is R&D level work that developers productize.

I am a staff consultant. If I’m leading a large project, I purposefully don’t commit any code and spend most of my time coordinating between other developers and “the business”. If I do work it’s again an isolated POC.
by virgildotcodes | 2026-01-12 03:16:30+00:00
On the subpar code, would the code work, albeit suboptimally?

I think part of the problem a lot of senior devs are having is that they see what they do as an artisanal craft. The rest of the world just sees the code as a means to an end.

I don't care how elegantly my toaster was crafted as long as it toasts the bread and doesn't break.
by mashreghi | 2026-01-12 03:16:36+00:00
Hi HN,

We built Subformer (https://subformer.com), a web app that dubs videos into other languages while keeping speaker identity intact.

Most “AI dubbing” pipelines are just ASR → translation → TTS, which breaks as soon as you have multiple speakers. We instead run:

- VAD + speaker diarization
- Audio Demixing
- Global speaker clustering
- Per-segment ASR + translation
- Per-speaker TTS (voice cloning or synthetic)
- Timeline-aligned remuxing back into the video

The tricky parts were diarization drift on long videos, timing mismatches after translation, and keeping costs sane when doing multilingual TTS at scale.

It’s still early, but it already works well for things like interviews, TV clips, and YouTube videos with multiple speakers.

Would love feedback from people who work on audio, speech, or localization.

https://subformer.com