MurphyA/hacker-news huggingface dataset:
by zippyman55 | 2026-01-17 06:02:07+00:00
I play a lot of chess and every once in a while someone gives me a chess set which really pisses me off. I appreciate the thought but I hate adding to the landfill.
I drop it off at goodwill. Maybe mention the keys stopped working because the keys were not able to take the great usage you got out of it. That’s it! Call the person up and say: great keyboard but it could not stand the high usage.
by vunderba | 2026-01-17 06:02:10+00:00
It's a tough test for local models - (gpt-image and NB had zero problems) - the only one that came reasonably close was Qwen-Image

Z-Image / Flux 2 / Hidream / Omnigen2 / Qwen Samples:

https://imgur.com/a/tB6YUSu

This is where smaller models are just going to be more constrained and will require additional prompting to coax out the physical description of a "pogo stick". I had similar issues when generating Alexander the Great leading a charge on a hippity-hop / space hopper.
by killcoder | 2026-01-17 06:03:34+00:00
I don't buy the "any constraints cause lower performance via being out of distribution" idea. Sure if you ask the model to output 'reasoning' in JSON steps, that is a completely different 'channel' to its trained 'reasoning' output. For real tasks though, I think it's more about picking the _right_ context free grammar to enforce format correctness. You can enforce an in-distribution format and get the best of both worlds. I don't think the industry should settle so hard on JSON-for-everything.
by cuu508 | 2026-01-17 06:04:02+00:00
IMO it's completely the other way around.

Shell scripts can be audited. The average user may not do it due to laziness and/or ignorance, but it is perfectly doable.

On the other hand, how do you make sure your LLM, a non-deterministic black box, will not misinterpret the instructions in some freak accident?
by ChanderG | 2026-01-17 06:05:09+00:00
@brailsafe has accurately captured where I am coming from.

I want more blogs/discussion from the community about the existing tools.

In 3/6 months, how many skills have you written? How many times have you used each skill? Did you have edit skills later due to unseen corner cases, or did they generalize? Are skills being used predominantly at the individual level, or are entire teams/orgs able to use a skill as is? What are the usecases where skills are not good at? What are the shortcomings?

(You being the metaphorical HN reader here of course.)

HN has always been a place of greater technical depth than other internet sites and I would like to see more of this sort of thing on the front page along with day one calls.
by 9rx | 2026-01-17 06:05:42+00:00
> Perhaps you should read up on regression tests, or snapshot tests, or consistency tests, or pretty much any flavor of UI tests.

You must have missed the first line: "I've heard all the different labels that get thrown around, but as soon as anyone tries to define them they end up being either all the same thing or useless." We didn't get your definitions. Maybe you could have been first to break the rule. But the definitions others have conceived for these certainly don't.

> regression tests

This one doesn't seem to be used by anyone. "Regression testing" is a term that I can see is commonly used. Did you intend to say that? But, as it is commonly defined, simply means to run your test suite after you've made changes to the code to ensure that your changes haven't violated the invariants...

Which is like, uh, the reason for having tests. If you don't run them and react to any violations, what's the point? We can safely file that one under "they end up being all the same".
Ask HN: How are you preventing LLMs from hallucinating in real workflows?
by Agent_Builder | 2026-01-17 06:06:08+00:00
I recently tried building a small agent for coaching centers.

The idea was simple: a teacher uploads a syllabus or notes, and the agent generates a test paper from that material. The hard requirement was reliability. No invented questions, no drifting outside the syllabus.

Instead of trying to “fix” hallucinations with better prompts, I constrained the agent’s job very narrowly.

I defined:

a fixed knowledge base (only the uploaded syllabus)

explicit tools the agent was allowed to use

a structured output format for the test paper

a hardness distribution (for example 30% easy, 50% medium, 20% hard)

Once those constraints were in place, the behavior changed a lot. The agent stopped being creative in the wrong places and consistently produced usable test papers. The quality improvement came from reducing freedom, not from changing models.

I built this using GTWY.ai, mainly because it let me wire together a knowledge base, step-level tool permissions, and model choice without writing a lot of glue code. But the interesting part for me wasn’t the platform, it was the pattern.

It made me wonder:

Are others seeing similar results by narrowing agent scope instead of adding verification layers?

Do constraints scale better than smarter models for production use cases?

For education or other regulated domains, is this how people are actually shipping agents?

Curious what’s working for others in real deployments
by squirrellous | 2026-01-17 06:06:11+00:00
I work with an amazing QA team right now. They are good programmers that don’t mind doing lots of grunt work, understand the goal of the software deeply, collaborates extremely well with other teams, and are just all around great people. I expect they would kill it as a non-QA dev team as well, so I’m always grateful to have such partners.
by BrenBarn | 2026-01-17 06:06:15+00:00
This phenomenon of "sound symbolism" has received a lot of research attention in the last 10 years or so. For a long time it was considered a curiosity at best, and a total red herring at worst, but a lot of evidence is accumulating that sound symbolic effects are very real and may have profound implications for our understanding of sensorimotor cognition.
by ffsm8 | 2026-01-17 06:06:21+00:00
I got the 49" version of the dell Alienware display (basically this one size down with different branding and stand)...
. From my perspective you're looking at it incorrectly, the point isn't to be able to look at everything at the same time, it's to be able to quickly glance from the one side to another.

Let's say I have an ide open, I will likely not look at the directory structure often, but I want an easy way to switch files - fantastic for having it available just by glancing over

Now you run tests, start the application etc. It also doesn't need to be in your view, all the time - but isn't it convenient to be able to just look where you know it's?

It's suboptimal for competitive gaming however, exactly for the reason you said. Scenic gaming on the other hand is improved by it, because the larger screen is more innersive
by kennywinker | 2026-01-17 06:06:30+00:00
It’s interesting because there is a view that we already have this. You can deploy most of the infrastructure you need to do the kind of thing the author is talking about using a few hundred lines of docker-compose.yml. The author alludes to some of that - grafana, postgres, etc.

What is missing is the glue, or the business logic, or maybe just the custom frontend you can charge a lot of money for ;)

Using any blogging software and stripe or square or paypal you can setup an online store that comes close to the experience that shopify offers - to the customer. But the backend is nowhere near as good as shopify - you are copy-pasting addresses over to usps or ups, tracking orders with notes attached to stripe payments, and god forbid you should want to automate any transactional emails related to it. Oh, and your system falls apart when you start taking orders via ebay or etsy - since you linked so much process to stripe. So the whole thing devolves into a carefully crafted google sheet.

Which is all to say, i agree with the author - but i feel like we are winning the war, it’s just the battlefront moved from deploying backed services to gluing the pieces together.
by retrodaredevil | 2026-01-17 06:08:25+00:00
I work full time as a software engineer, and I also spend time writing code on side projects. I love working on my side projects because it means I get to spend a bunch of time learning the quirks of whatever tooling and frameworks I decide to use. I often develop opinions and personal standards that I bring with me to future side projects (and sometimes $dayjob), although I've never quite stood up all the components of one of my projects "quickly". I'll always find something to tinker with and learn about, which I think is acceptable for my personal projects, desirable even.

As I get more personal projects under my belt, I believe I'll be able to stand up projects more and more quickly, although it's never perfect. Even though I've been using a similar stack among my side projects for a couple of years now, dependencies get outdated. Sometimes you gotta jump to a new major version. Sometimes you wanna try out the "new way".

I like the idea of building up my own personal stack of tooling, frameworks, and patterns that I use, and could even encourage the use of at $dayjob, but for the reasons outlined above, I agree with the conclusion of the article, which is that an "IKEA of software" doesn't exist currently.

For now I'll keep happily tinkering in my side projects. This article was a good read.
by AuryGlenz | 2026-01-17 06:08:45+00:00
I skimmed the article, and I had a hard time finding anything that ChatGPT wrote that was all that..bad? It tried to talk him out of what he was doing, told him that it was potentially very fatal, etc. I'm not so sure that it outright refusing to answer and the teen looking at random forum posts would have been better, because they very well might not have told him he was potentially going to kill himself. Worse yet, he could have just taken the planned substances without any advice.

Keep in mind this reaction is from someone that doesn't drink and has never touched marijuana.
by toofy | 2026-01-17 06:09:24+00:00
if it doesn’t know medical advice, then it should say “why tf would i know?” instead it confidently responds “oh, you can absolutely do x mg of y mixed with z.”

these companies are simultaneously telling us it’s the greatest thing ever and also never trust it. which is it?

give us all of the money, but also never trust our product.

our product will replace humans in your company, also, our product is dumb af.

subscribe to us because our product has all the answers, fast. also, never trust those answers.
by rubenvanwyk | 2026-01-17 06:11:48+00:00
So from what I understand…

Someone needs to build Qt’s successor, probably with more beginner-friendly declarative semantics (akin to HCL or Cue) and probably with syntax closest to YAML or Python (based on learning curve, beginner readability etc).

The backend will probably have to be something written in Zig (likely) or Nim (capable, less likely) and will probably have to leverage OpenGL/Metal, WebGL and WASM.

Obviously a massive undertaking, which is why I think the industry has not reached consensus that this is what needs to happen. The less ideal options we have now often gets the job done.
by vunderba | 2026-01-17 06:11:54+00:00
Just depends on how much you want to keep it and if it supports QMK style customization.

I use a Keychron Q5 with customizable keycaps. It's connected via bluetooth to both Mac and Windows machines and also only has three keys to the left of the space bar. Personally, I don't think I've ever once used the "Globe"/FN key.
by eaurouge | 2026-01-17 06:12:14+00:00
> The people who pay into this are either taken in by the pretense that they’re sponsoring open-source work

> Honestly, I think the first category is somewhere between "microscopic" and "nonexistent", but most people in the second category will end up holding the bag when this thing inevitably collapses.

I agree. There may be folks willing to support open-source software via a crypto-friendly vehicle, but most involved in this are hoping to make money on a pump and not get left holding the bag.

Everyone involved in this scheme is fully aware of the game being played (or should be) and the risks involved. The notion that "crypto grifters" are corrupting naive open-source developers just strikes me as an odd way to describe such activity.
by crisiscore_sys | 2026-01-17 06:13:09+00:00
If anyone wants the implementation details: the default mode is local-first storage with offline support (PWA). I’m deliberately trying to avoid “health data as telemetry.” Network usage is feature-scoped (e.g., correlation features only when enabled). If there are specific threat-model questions you want answered (“what do you store”, “where”, “how do exports work”), ask and I’ll be precise.
by codebolt | 2026-01-17 06:14:03+00:00
I guess you didn't catch this:

> ChatGPT started coaching Sam on how to take drugs, recover from them and plan further binges. It gave him specific doses of illegal substances, and in one chat, it wrote, “Hell yes—let’s go full trippy mode,” before recommending Sam take twice as much cough syrup so he would have stronger hallucinations. The AI tool even recommended playlists to match his drug use.
by CamperBob2 | 2026-01-17 06:16:23+00:00
Not in my experience. Flux 2 is much larger and heavily censored, and Qwen-Image is just plain not as good. You can fool me into thinking that Z-Image Turbo output isn't AI, while that's rarely the case with Qwen.

Look at the images I posted elsewhere in this section. They are crappy excuses for pogo sticks, but they absolutely do NOT look like they came from a cell phone.

Also see vunderba's page at https://genai-showdown.specr.net/ . Even when Z-Image Turbo fails a test, it still looks great most of the time.

Edit re: your other comment -- don't make the mistake of confusing censorship with lack of training data. Z-Image will try to render whatever you ask for, but at the end of the day it's a very small model that will fail once you start asking for things it simply wasn't trained on. They didn't train it with much NSFW material, so it has some rather... unorthodox anatomical ideas.
by mullingitover | 2026-01-17 06:17:15+00:00
I think a lot of the disdain for crypto isn’t a problem with risk taking for the purpose of productive ventures, as is portrayed in this analogy. These activities serve as part of normal and necessary market activity. The problem is that most of crypto is base ‘greater fool’ speculation, with the meme coins in particular being brazen rug pull schemes played out again and again.
by Agent_Builder | 2026-01-17 06:17:29+00:00
I think we’re mostly aligned. The constraints we’re talking about weren’t about forcing everything into JSON or limiting reasoning bandwidth.

Inside a step, the model still reasons freely in plain language. The constraint is on what authority exists at that step.

The failures we saw came from permissions and assumptions silently carrying over between steps, not from the model “thinking wrong”. Once a step ended, any authority it had ended too.

So it’s less “constrain decoding” and more “constrain capability scope over time”. Free reasoning within a step, hard boundaries between steps.

That separation is what removed a lot of surprising behavior for us.
by ItsBob | 2026-01-17 06:19:40+00:00
> Considering there is very little moat left in software and big companies can copy your product in no time?

Having worked in the corporate world all my working life I can safely say with confidence that big companies absolutely DO NOT move fast. Do not underestimate the power of middle-management to destroy momentum!

Solve a problem for someone and if it was a painful one, they'll pay you for it.
by wolvoleo | 2026-01-17 06:22:49+00:00
I think in light of MCAS every company operating Boeing aircraft should have reviewed their stance on operating Boeing aircraft.

And the worst thing is I don't think even after mcas things have substantially improved there. I've seen more spin and damage control than actual safety focus. They could have launched a huge company program and management reorganization to really turn this mindset around.

I think the biggest issue that Boeing is too big to fail. They'll never fall because the government needs them for all their warplanes.
by Hackbraten | 2026-01-17 06:22:52+00:00
Ex Mac user of 17 years (now Linux) and keyboard enthusiast here.

The crucial difference between Mac and Windows layouts is not whether there's a Fn key to the left of Space, but the order of the Option and Command keys.
Mac has Command next to Space, while Windows has Alt (= Option).

If you're anything like me, then your muscle memory (after a couple days or weeks of accommodation) won't give a flying fuck of whether the Fn key is missing. In the long run, it will adapt as you switch back and forth between keyboards.

Your muscle memory, however, may punish you hard for reordering Option and Command. So focus on fixing that. Most newer keyboards have a firmware setting or physical switch that rearrange the scan codes.

If yours has no such setting, MacOS has a per-keyboard software setting that allows you to remap those two keys specifically, but I'd advise against using that because it never worked reliably for me. Instead, email the vendor and ask for advice.

Once you got the scan codes reversed, consider switching the keycaps accordingly to make labels match (if they're the same size. Otherwise, ignore the labels.)

YMMV but maybe it helps.
by steve_adams_86 | 2026-01-17 06:26:21+00:00
Polars would be better in some ways for sure. It was in one of my early prototypes. What put me off was that I was essentially designing my own database which I didn't trust as much as something like DuckDB.

Polars would let me have a lot of luxuries that are lost at the boundaries between my application and DuckDB, but those are weighed in the tradeoffs I was talking about. I do a lot of parsing at the boundaries to ensure data structures are sound, and otherwise DuckDB is enforcing strict schemas at runtime which provides as much safety as a dataset's schema requires. I do a lot of testing to ensure that I can trust how schemas are built and enforced as well.

Things like foreign keys, expressions that span multiple tables effortlessly, normalization, check constraints, unique constraints, and primary keys work perfectly right off the shelf. It's kind of perfect because the spec I'm supporting is fundamentally about normalized relational data.

Another consideration was that while Polars is a bit faster, we don't encounter datasets that require more speed. The largest dataset I've processed, including extensive transformations and complex validations (about as complex as they get in this spec), takes ~3 seconds for around 580k rows. That's on an M1 Max with 16GB of RAM, for what it's worth.

Our teams have written countless R scripts to do the same work with less assurance that the outputs are correct, having to relearn the spec each time, and with much worse performance (these people are not developers). So, we're very happy with DuckDB's performance despite that Polars would probably let us do it faster.

Having said that, if someone built the same project and chose Polars I wouldn't think they were wrong to do so. It's a great choice too, which is why your question is a good one.
Show HN Rundown transforms docs into executable workflows
by tobyhede | 2026-01-17 06:26:43+00:00
I have been working on this hare-brained scheme to help agents who want to code good and do other things good too.

Introducing: Rundown (https://rundown.cool)

I think of Rundown runbooks as executable Skills. The Skill provides the detailed context, and the Rundown runbook keeps the agent on track.

# Hello

## 1 This is Rundown

Rundown transforms markdown into an executable specification.
Headings become steps, code-blocks become executable commands.
Human-readable. Agent-readable. Machine-executable.

## 2 Guide agents (and humans) through your process

Rundown keeps agents on track by injecting precision context at the exact moment it’s needed.

## 3 Make complex workflows deterministic
- PASS: CONTINUE
- FAIL: GOTO RECOVER

Rundown works *with* agents, adding guardrails that enforce transitions and improve accuracy.

## 4 Execute the right commands at the right time
- PASS: CONTINUE
- FAIL: RETRY GOTO RECOVER

Embed commands for automatic execution. Catch failure, retry, and recover gracefully.

```bash
rd echo npm run test
```

## 5 Track progresss across agents and sessions
- PASS: CONTINUE
- FAIL: STOP

State-aware CLI ensures progress is never lost.
Save and resume complex processes at any time.

## 6 Ready to get started?
- PASS: COMPLETE
- FAIL: STOP

```bash
npm install -g @rundown/cli
```

## RECOVER Recover from errors
- PASS: GOTO 4
- FAIL: STOP

If you are here, an error occurred.
Named steps enable error handling and conditional logic.
by throwup238 | 2026-01-17 06:26:59+00:00
Since when did we start using file size as a measure of efficiency or optimization?

Off the top of my head: $() CSS parsing and DOM traversal was way slower than querySelector or getElementById, both of which predate jquery by years. Every $('.my-class') created wrapped objects with overhead. Something like $('#myButton').click(fn) involved creating an intermediate object just to attach an event listener you could’ve done natively. The deeper the method chaining got the worse the performance penalty, and devs rarely cached the selectors even in tight loops. It was the PHP of Javascript, which is really saying something.

By the early-2010s most of the library was dead weight since everyone started shipping polyfills but people kept plopping down jquery-calendar like it was 2006.

(I say this as someone who has fond memories of using Jquery in 2007 to win a national competition in high school, after which I became a regular contributor for years)
by bitjaru0402 | 2026-01-17 06:27:56+00:00
For those who find Gas Town overwhelming for solo projects:

I built a lighter alternative - same "persistent context" philosophy,
but 1 agent instead of 20.

Context stored in code comments, 5-min setup, no $100/hr cost.

github.com/bitjaru/codesyncer

Not a Gas Town replacement - just a motorcycle for those
who don't need a chariot.
by chrisandchris | 2026-01-17 06:28:39+00:00
IMHO, this whole thing could be read with "human" instread of "agent" and would make the exact same amount of sense.

"We put 200 human in a room and gave them instructions how to build a browser. They coded for hours, resolving merge conflicts and producing code that did not build in the end without intervention of seniors []. We think, giving them better instructions leads to better results"

So they actually invented humans? And will it come down to either "managing humans" or "managing agents"? One of both will be more reliable, more predictable and more convenient to work with. And my guess is, it is not an agent...

As it seemed in the git log, something is weird.
by rahulbootstrap | 2026-01-17 06:28:46+00:00
In the whirlwind of festive cheer—Christmas twinkles, New Year's fireworks, and beyond—your social media(plugin for X here) feeds explode with joyful posts from friends and family, all craving those heartfelt likes and comments. Enter this revolutionary plugin: it magically pulls from a treasure trove of handcrafted, genuine replies (zero AI fakery here!), randomly sprinkling authentic warmth and wit to keep your connections sparkling without the hassle. Transform obligatory scrolling into effortless magic—wow your circle with responses that feel straight from the heart!
by EgregiousCube | 2026-01-17 06:29:25+00:00
Imagine a subreddit full of people giving bad drug advice. They're at least partially full of people who are intelligent and capable of performing human work - but they're mostly not professional drug advisors. I think at best you could hold OpenAI to the same standard as that subreddit. That's not a super high bar.

It'd be different if one was signing up to an OpenAI Drug Advice Product, which advertised itself as an authority on drug advice. I think in this case the expectation is set differently up front, with a "ChatGPT can make mistakes" footer on every chat.