MurphyA/hacker-news huggingface dataset:
by jraph | 2026-01-23 17:19:15+00:00
There's definitely a "Seeing an apple fall and intuiting a hypothesis" process which is early in the research process, which leads to formulating the equation as an hypothesis somehow.

So you observe stuff, intuit and formulate an hypothesis. The hypothesis is a model that you hope matches how the world works well enough. Developing a scientific hypothesis takes scientific rigor. Among other things:

- it needs to be testable (it needs to be possible to design some scientific protocol to check the hypothesis)

- it needs to be formulated before you start experimenting and collecting data (that doesn't mean you can't observe your world before, you just can't use these observations in the data that backs your thesis)

- it needs to be rooted in existing science, knowledge, it's not a simple "naive" guess. It certainly takes being deeply familiar with the research area.

Then you test your hypothesis with experiments. You design a significant number of them. You must not cherry pick here, that would be confirmation bias (but you can encode the limitations of the model in the previous step). You predict your expected results with the model you have in your hypothesis. You run your experiments, make your measurements, compute the deltas. Here too, you must not discard or tweak the results to your liking. That would be cherry-picking, or event outright falsification. If the deltas are small enough, and people review your work, and ideally reproduce it (same experiments, or other experiments), eventually there's a consensus that starts forming around your model. Congrats, the model is validated.

So, people start to use your model. They do exactly the opposite of what you did when you formulated your hypothesis: they don't try to come up with a model from preliminary observations, they assume the model works, and they use it to predict the future.

Until Einstein comes :-). And stumble upon a black hole. An observation that doesn't match. Then your model gets refined (with limits and restrictions) or even "deprecated".

But yeah, physicists model the world after the observations they make, not the contrary. Otherwise they are doing something else. Math, maybe, or philosophy, or whatever. It's just that designing the model is only the beginning, you have to check that it works… with carefully selected observations… before it can be validated.

With this asker-vs-guesser thing, we don't have convincing work that provides the validation step. This means asker-vs-guesser is an hypothesis, at best (at best, because we don't know if things required to formulate a scientific hypothesis have been respected).
by ndriscoll | 2026-01-23 17:19:38+00:00
LLMs spitting out GPL code seems perfectly inline with the spirit to me. The goal is to make it so that users have the freedom to make software behave in ways that suit them. Things kicked off when some printer could not be made to work correctly because of its proprietary drivers. LLMs are a huge multiplier for that: now even people who don't know how to program can customize their software! We're already approaching (or at?) the point where local agents on commodity hardware (like a few $thousand worth of GPUs, which was the nominal cost of a 90s PC) are able to make whatever changes you want given the correct feedback loops. Sounds good to me.
by lorenzleutgeb | 2026-01-23 17:19:41+00:00
> If the internet is down and you want to onboard someone with say, a usb thumbdrive.

All the data being synced is in a Git repo, which is in a directory on your filesystem we call "Radicle Storage". You can use `git bundle` or a plain `cp` to copy that directory over. You can also use plain Git to push. Note that for these use-cases there is no polished UX. You need to know what you are doing. The bigger issue will be to install Radicle.

> With the mirroring: does radicle have any kind of event hooks?

Yes. You can connect to `radicle-node` via a socket and subscribe to events. This is how Radicle CI, and in particular the Radicle CI Broker was implemented. You can implement your own event broker, it's just JSON over a socket.

https://radicle-ci.liw.fi/radicle-ci-broker/ci-broker.html
by ainch | 2026-01-23 17:20:07+00:00
I'm not going to bat for GPTZero, but I think it's clearly possible to identify some AI-written prose. Scroll through LinkedIn or Twitter replies and there are clear giveaways in tone, phrasing and repeated structures (it's not just X it's Y).

Not to say that you could ever feasibly detect all AI-generated text, but if it's possible for people to develop a sense for the tropes of LLM content then there's no reason you couldn't detect it algorithmically.
by echelon | 2026-01-23 17:20:38+00:00
> growth and momentum behind Linux and self-hosting as better evidence that change is afoot.

Linux is still not user friendly enough. Products from two decades ago are more user friendly than modern "mainstream" disros.

Look at Matrix and other OSS that wants to be mainstream. It's got awful UI/UX. And it's never taken off.

Gimp is an ugly beast with a bad name. Nobody's using that unless they're a Linux nerd.

I do see lots of people building retro game collections. Analogue 3D was a huge hit. Massive demand. It's sold out instantly five times. Palmer Luckey has a company building a similar product, and that's also sold out.

The clothing stores sell cassette tapes and vinyl. iPod and Zune are venerated.

My wife is Gen Z and into mainstream culture. She's all about retro. Polaroid, Instax, 2000's era digital cameras. The low end consumer digital camera I bought for $100 or so in 2004 is now selling for more than that. These things are wildly popular.

They're even hunting down old disposable one-use film cameras to pop off the lenses.

In any case, my wife knows this stuff. She doesn't know what Linux is.
by knowknow | 2026-01-23 17:20:43+00:00
Not only that but compiler optimizations are generally based on rigorous mathematical proofs, so that even without testing them you can be pretty sure it will generate equivalent assembly. From the little I know of LLM's, I'm pretty sure no one has figured out what mathematical principles LLM's are generating code from so you cant be sure its going to right aside from testing it.
by Bender | 2026-01-23 17:21:25+00:00
World wide it appears [1]. I get NXDOMAIN from the .net root servers. Domain registration status appears normal.

From the github thread:

Indeed, if you do dig @ns3.inwx.de newpipe.net you will get a proper response, but other nameservers have not yet picked up on the registrar change

Someone did the migration wrong as the root servers have no glue records for anyone to boot-strap from.

dig @192.54.112.30 newpipe.net -t ns # one of the .net root servers
;; ->>HEADER<<- opcode: QUERY, status: NXDOMAIN

[1] - https://www.whatsmydns.net/#SOA/newpipe.net
by Fabricio20 | 2026-01-23 17:21:37+00:00
This has also been my experience, I'm used to using compose everywhere. I like the declarative file - tried podman and I found the documentation around the concept so scarce and all related to running things as non-root instead of telling me how my docker-compose becomes podman-compose. Still using docker everywhere because of that. Docker swarm mode has also worked wonders as an evolution to my compose files.
by _micheee | 2026-01-23 17:21:40+00:00
We do XML processing, albeit with XQuery, as a small business.

It is a very niche solution but actually very stable and quite handy for all kinds of data handling; web-based applications and APIs as it nicely integrates with all kinds of text-based formats such as JSON, CSV or XML.

Yet I can easily comprehend how people get lost in all kinds of standards, meta-standards, DTDs, schemas, namespaces, and modeling the whole enterprise in SOAP.

However, you can do simple things simply and small, but in my experience, most tools promised to solve problems with ever-layered complexities.

Little disclaimer, I am probably biased, as I am with BaseX, an open-source XQuery processor :-)
by cluckindan | 2026-01-23 17:21:55+00:00
It’s brainrot, that’s what it is.

I believe agentic coding could eventually be a paradigm shift, if and only if the agents become self-conscious of design decisions and their implications on the system and its surrounding systems as a whole.

If that doesn’t happen, the entire workflow devolves into specifying system states and behavior in natural language, which is something humans are exceedingly bad at.

Coincidently, that is why we have invented programming languages: to be able to express program state and behavior unambiguously.

I’m not bullish on a future where I have to write specifications on all explicit and implicit corner and edge cases just to have an agent make software design choices which don’t feel batshit insane to humans.

We already have software corporations which produce that kind of code simply because the people doing the specifying don’t know the system or the domain it operates in, and the people doing the implementing of those specifications don’t necessarily know any of that either.
by sanderjd | 2026-01-23 17:22:19+00:00
This is true of the Michael Scott character in the first season (which follows the same plot points as the British version).

And even in the next few seasons, the core run of the show, he's only a bit more lovable. He's still pretty bad!

But certainly as they went further and further along he became more and more of a lovable doofus, very unlike David Brent.
by varshith17 | 2026-01-23 17:22:23+00:00
Build validation layers, not trust. For structured outputs (invoices, emails), use JSON schemas + fact-checking prompts where a second AI call verifies critical fields against source data before you see it.
Real pattern: AI generates → automated validation catches type/format errors → second LLM does adversarial review ("check for hallucinated numbers/dates") → you review only flagged items + random samples. Turns "check everything" into "check exceptions," cuts review time 80%.
by taeric | 2026-01-23 17:22:35+00:00
In this, isn't it more that Garmin has been making sports watches for a long long time? And were given the grace by their customer base to just keep making that particular function better.

You could probably find the same with bike computers. Established brands that have a fairly predictable customer base tend to continue to focus on the thing that they do well. If you are having to chase a market that doesn't really exist, you find half baked features that speak to an idea, but often don't actually deliver on it.

For an amazing example of that last, look at how Amazon is destroying their echo market. If they just focused on "voice activated radio and timers," the device would be very different from the "we are trying desperately to make a new market for our smart assistant."
Show HN: Cholesterol Tracker – Built after high cholesterol diagnosis at 33
by briskibe | 2026-01-23 17:23:05+00:00
After my annual checkup showed LDL 4.4 mmol/L (170 mg/dL) and triglycerides
2.0 mmol/L at 33, I tried tracking with ChatGPT (lost data when context got
too big), then spreadsheets (too tedious).

Built a simple tracker focused on cholesterol. Log meals, see lipid breakdown,
track trends. I believe snacks and sugar were my main issue.

Stack: Angular 17 + NestJS + Supabase

Started January 1st, already lost 3kg. Same breakfast daily (psyllium, oats,
chia, skyr, whey, berries), cut sugar from daily to once per week.

Free during beta. Looking for feedback on whether strict diet cutting or 80/20
approach is more sustainable long-term.
by benlivengood | 2026-01-23 17:23:40+00:00
I dunno, GPT-OSS and Llama and QWEN and any half dozen of other large open-weight models?

I really can't imagine OpenAI or Anthropic turning off inference for a model that my workplace is happy to spend >$200*person/month on. Google still has piles of cash and no reason to turn off Gemini.

The thing is, if inference is truly heavily subsidized (I don't think it is, because places like OpenRouter charge less than the big players for proportionally smaller models) then we'd probably happily pay >$500 a month for the current frontier models if everyone gave up on training new models because of some oddball scaling limit.
by aaronharnly | 2026-01-23 17:23:47+00:00
To keep torturing the metaphor, LLMs might be more like those electric unicycles (Onewheel, Inmotion, etc) – quite speedy, can get you places, less exercise, and also sometimes suddenly choke and send you flying facefirst into gravel.

And some people see you whizzing by and think "oh cool", and others see you whizzing by and think "what a tool."
by kaydub | 2026-01-23 17:23:55+00:00
I don't get you guys that are getting such bad results.

Are you guys just trying to one shot stuff? Are you not using agents to iterate on things? Are you not putting agents against each other (have one code, one critique/test the code, and put them in a loop)?

I still look at the code that's produced, I'm not THAT far down the "vibe coding" path that I'm trusting everything being produced, but I get phenomenal results and I don't actually write any code any more.

So like, yeah, first pass the llm will create my feature and there's definitely some poorly written code or duplicate code or other code smells, but then I tell another agent to review and find all these problems. Then that review gets fed back in to the agent that created the feature. Wham, bam, clean code.

I'm not using gastown or ralph wiggum ($$$) but reading the docs, looking over how things work, I can see how it all comes together and should work. They've been built out to automatically do the review + iteration loop that I do.
Working on reducing wasted distribution effort before publishing posts
by ryujii | 2026-01-23 17:24:08+00:00
Over the past year I’ve noticed something that kept repeating:

I would write a post about a side project, hit publish,
and get almost no engagement even when the topic seemed relevant.

At first I blamed the project, the copy, or my timing.
But after repeating the same pattern a few times, I realized something else was happening:

Similar posts, sometimes almost identical in wording or topic,
would get responses and discussions in one subreddit and die in another.

This made me start thinking about the problem differently.
Instead of only working on the product or copy,
I began exploring distribution signals before posting

For example:
- how much recent activity there has been
- whether questions or discussions get more replies
- how fast other posts get engagement
- day/time patterns

I loosely built a simple tool to help evaluate these signals not a predictor of success,
just a way to reduce obvious wasted effort.

In practice, it’s probabilistic and still very rough,
but it’s already shifted how I approach posting and distribution.

I’d love to hear from others who:
- spend effort on distribution before launch
- feel like timing/context matters more than content quality
- have patterns or heuristics that seem to work

What do you pay attention to before hitting publish?
by amirhirsch | 2026-01-23 17:24:12+00:00
Here’s some other hints:
combine hash stages 2 and 3, it can be two muladds and a XOR

For the first several
rounds (when every tree value is in use) Combine the stage 5 XOR with the subsequent round’s tree XORs. You can determine even/odd in hash stage 5 starting with a ^ (a>>16) without Xoring the constant, then you can only need one XOR, this saves you a ton of XORs

Create separate instruction bundles for the first round, rounds 1-5 (combining hash stages 5 XOR with next round tree XORs) and 6-9 (not every tree node is used anymore), round 10 round 11-14 and round 15 and combine them.

Your first instruction bundle should Load tree[0]. Your second instruction bundle should have 12 scalar XORs and a vbroadcast, the third instruction bundle could be 12 scalar XORs and 6 vector XORs (or one slot could be a vmuladd to start on 8 of the previous XOR outputs, I’m starting to work on getting hash stages moving to different rounds faster to get to the heavy load section sooner and spread the load pain)
by drillsteps5 | 2026-01-23 17:24:29+00:00
ANN is an attempted model/ripoff (turned out to be extremely simplified but still) of a brain, why not go further? Continuous autonomous learning (which requires continuous feedback in a way of good/bad stimuli) is clearly what makes it work.

The current approach of guided pre-training and inference on essentially a "dead brain" clearly causes limitations.
by anonymous908213 | 2026-01-23 17:24:35+00:00
And yet I notice you haven't mentioned publishing it and undercutting the market. You could make a lot of money out-competing the existing option if what you produced was production-grade software. I'm guessing the actual case is that you only needed a small subset of the functionality of the paid software, and the LLM stitched together a rough unpolished proof-of-concept that handled your exact specific use case. Which is still great for you! But it's not the future of coding. The world still needs real engineers to make real software that is suitable for the needs of many, and this doesn't replace that.
by alya | 2026-01-23 17:24:44+00:00
At the Zulip open-source project, we've had a significant onslaught of AI slop in the past few months. It gets as absurd as PR descriptions with AI-generated "screenshots" of the app to "demonstrate" the changes. We've had to start warning contributors that we won't be able to review their work if they continue misusing AI, and occasionally banning repeat offenders. It's feels draining -- we want to spend our time mentoring people who'll actually learn from feedback, not interacting with contributors who are just copy-pasting LLM responses without thought.

Our evolving AI policy is in the same spirit as ghostty's, with more detail to address specific failure modes we've experienced: https://zulip.readthedocs.io/en/latest/contributing/contribu...
by bcherry | 2026-01-23 17:24:51+00:00
This isn't really the author's point, but I think one effect of AI and the forthcoming robotics revolution will be the unrolling of a lot of consolidated supply chains for all sorts of products. It could usher in a renewed era of bespoke products.

For instance, when the cost of building a new (good) app goes to zero, it becomes economical to make a great app for a narrow niche, with a skeleton staff (maybe just one) and no VC money. And this can happen thousands of times over.

Robotics could open up bespoke local supply chains even beyond what's possible with a 3D printer today. For instance, if you had an actually dextrous humanoid robot "living" in your home, why wouldn't you have it just make all of your clothes? You could have any fabric, any style, exactly the right size. And only for the cost of materials (assuming you already own or lease the robot itself).

I do think the author is right in the big picture - the future will be more fun.
by at1as | 2026-01-23 17:24:52+00:00
If it's the latter point from the purchase list that raised your ire, I've only ever purchased watches for fun. And that list would be a Hamilton Murph (the watch from Intersteller). Gifted to me, but < $1000

A Mondaine purchased from a Swiss railway station ($400)

Not strictly mechanical, but a Casio A168WA, purchased in Tokyo ($25)

Disagreements on the article are most welcome (and encouraged!), but should probably stick to content therein.
by stevofolife | 2026-01-23 17:25:03+00:00
I’d love to hear your domain expertise—specifically which supplements you’d recommend avoiding, which ones are generally worth taking, and whether there are any brands or products you trust. I imagine you’ve picked up a lot of insights beyond the technical side as well.

I think it will be nice if there is a summary of that somewhere on the website. It was the first thing I was looking for.
by crote | 2026-01-23 17:25:34+00:00
> Please give the guy a break. At least he's trying to advance the state of the art.

The problem is that as an outsider it really looks like someone is trying to herd a bunch of monkeys into writing Shakespeare, or trying to advance impressionist art by pretending a baby's first crayon scratches are equivalent to a Pollock.

I bet he's having a lot of fun playing around with "cutting-edge technology", but it's missing any kind of scientific rigor or analysis, so the results are going to be completely useless to anyone wanting to genuinely advance the use of LLMs for programming.
by crazyguitar | 2026-01-23 17:25:41+00:00
Thank you for sharing this excellent resource! You make a great point—searching for "modern C++" often surfaces C++11-era content, while newer standards have already deprecated some of those "modern" practices. That's exactly why I created this project: to continuously update and document contemporary best practices as the language evolves. I'll definitely check out the linked rant for ideas to incorporate. Thanks for the suggestion!
by kgwgk | 2026-01-23 17:25:45+00:00
> How do these industry figures claim they see no part of a 225K+ line of code and promise that it works?

The only promise is that you will get your face ripped off.

“WARNING DANGER CAUTION
- GET THE F** OUT - YOU WILL DIE
[…] Gas Town is an industrialized coding factory manned by superintelligent robot chimps, and when they feel like it, they can wreck your shit in an instant. They will wreck the other chimps, the workstations, the customers. They’ll rip your face off if you aren’t already an experienced chimp-wrangler.”
by layer8 | 2026-01-23 17:26:26+00:00
> You could make the same argument about compilers : whatever is the code you wrote, your compiler may produce assembly instructions in an undeterministic way.

The difference is that the compiler is bound by formal (or quasi-formal) language semantics. In terms of language semantics, you always get precisely the same result, regardless of how the compiler implements it. When you change the source code, you can reason and predict with precision about how this will change the behavior of your compiled program. You can’t do that reasoning with AI prompts, they don’t have that level of predictability.
by skrebbel | 2026-01-23 17:26:51+00:00
You simply don’t need such inflated salaries if schools are free, roads are not broken, trains exist, healthcare is affordable, grocery stores are in biking distance, parks are good and free and plenty, labor laws are in your favour, utilities markets mostly aren’t dysfunctional and a 2-bedroom apartment doesn't cost $10000/m.

Americans compare their salaries to European ones but never stop to imagine the insane high “taxes” they pay for stuff that we get cheaply or for free.

I'm not even saying the one is better than the other. There's a lot to be said for the American system of only paying for what you need. It's just.. you can't just compare dollars/euros like that. There's reddit posts of people who earn $900k/y and openly wonder whether that's enough to live in NYC and that shit is equally unfathomable to the average European as the idea of a dev earning €70k/y is to the average American.
by ryandrake | 2026-01-23 17:26:57+00:00
Just because they're one-off tools that only one person uses doesn't mean it's not "real software". I'm actually pretty excited about the fact that it's now feasible for me to replace all my BloatedShittyCommercialApps that I only use 5% of with vibe-coded bespoke tools that only do the important 5%, just for me to use. If that makes it a "sand castle" to you, fine, but this is real software and I'm seeing real benefit here.
by patrick4urcloud | 2026-01-23 17:27:17+00:00
rtk gain

Total commands: 41
Input tokens: 6.8K
Output tokens: 1.8K
Tokens saved: 6.0K (88.2%)

Command Count Saved Avg%
rtk git status 11 2.8K 81.2%
rtk grep 3 1.5K 31.9%
rtk git push 22 1.3K 92.0%
rtk ls 5 431 47.1%