MurphyA/hacker-news huggingface dataset:
by NeuroTinkerLab | 2025-12-08 17:44:01+00:00
Hi HN, I built this because I was frustrated with how difficult it is to get local LLMs to follow specific instructions without being overly "helpful". When I asked a model to translate a request to write code, it would just write the code. So, I created Synt-E, a simple protocol to turn natural language into dense, unambiguous key:value commands. The goal is to reduce token usage, lower latency, and make interactions with AI agents more reliable and testable. The core of the project is a Python script that uses a local LLM (via Ollama) to act as a "compiler". The most interesting finding was that "rawer" models like GPT-OSS were far more obedient to the system prompt than heavily-instructed models like Llama 3. It's all open-source and runs 100% locally. I'd love to hear your feedback. The GitHub link is the main post URL.
by mikestorrent | 2025-12-08 17:44:03+00:00
What kinds of statistics is it that one should study?

Having a bugout bag and emergency supplies and water on hand is a reasonable idea everyone with the means ought to do; it's a good thing to not have to depend on gov't intervention (not because of a lack of trust, but because the general public will, and the potential for mob situations is high).

But what should I have read about to know what to do? Topological maps and flood planes?
by StopDisinfo910 | 2025-12-08 17:44:06+00:00
> BRK-B's 5 year return is 2% per year more than SP500.

> If my goal is to not sell for a decade or more, I'm not sure what BRK offers over SP500, except more risk.

I think you have already answered your own question. Historically, BRK-B has very often outperformed the SP500.

Plus, their investment philosophy is pretty clear. It's a solid alternative if you distrust tech which is today very heavy in the SP500.
by gaigalas | 2025-12-08 17:44:55+00:00
Can I offer some valid criticism?

The original Space Jam website is fluid (it's 90s lingo for responsive).

It is also a still relevant website because it is a living fossil of that era's way of doing webdesign.

Asking to recreate it, should include those aspects (epoch-relevant technical achievements such as fluid layouts) and faithfulness to the original implementation.

I'm not saying that Claude should know that out of the box (it would have been impressive if it did), but the prompt should have included those ideas.

A modern reconstruction in CSS3, in contrast to a faithful reproduction, should have mirrored what the techniques accomplished with modern tools. It would be useful in a sense of showcasing how CSS3 evolved, it would have a purpose.

Do you understand why this is not passable? It has no value as a recreation.
by bigbadfeline | 2025-12-08 17:45:37+00:00
> Being reliant on the hardware manufacturer (or network operator?) for OS updates is the crazy world we live in.

Being reliant on a single OS permanently nailed to the hardware is no less crazier. I'd like to be able to install another OS on a vulnerable device, it would help tremendously and not only with the security of that specific device.

Now I've got some expensive paperweights that I can't even use as such because every time I see them I have the urge to throw them in the trash can.

Provide a way to unlock the phones and a standard BSP, it should be the law.
by cezart | 2025-12-08 17:45:55+00:00
I've been thinking about this recently. The centrality of the stock market, while historically a great tool to allocate resources efficiently, might actually be a big weakness for the USA today. A capable adversary, like China, can kill entire strategic sectors in the US using the stock market. If they undercut the US companies and are willing to accept low returns on their investments, then the respective USA competition will be driven out of business by their investors, because there will be other sectors to invest in, with higher RoI. Do this at various points in strategic value chains, and over a decade or so it might kill entire verticals in strategic sectors, leaving the US economy vulnerable to any kinds of shocks.
by manquer | 2025-12-08 17:46:25+00:00
The parent poster means to say prove it is more economical, not that it is doable.

It is hard to compute the economics of small nuclear reactors that use highly enriched fuel. A lot of it is funded by defense needs.

Mixed use is largely to keep defense manufacturing active not because they are economically effective.

If nuclear civilian ships were cheaper, there would be efforts to make a lot of them (In Russia and China if not other countries etc)
by gowld | 2025-12-08 17:46:38+00:00
There are billions of humans. Not every one speaks the same way all the time. The default behavior is trying to be useful for most people.

It's easy to skip and skim content you don't care about. It's hard to prod and prod to get to to say something you do care about it if the machine is traint to be very concise.

Complaining the AI can't read your mind is exceptionally high praise for the AI, frankly.
by l___l | 2025-12-08 17:46:50+00:00
If it was only a minor incremental innovation, in this specific form, wouldn't it have appeared years earlier?

> haven't seen a use case but crypto yet.

BTC is up 500%+ since 2 years ago. This looks like a use case to me. It maintained purchasing power better than stocks and bonds.

This comment read to me like AI slop.
by jonas21 | 2025-12-08 17:46:51+00:00
There was a response to this post on the front page earlier this morning that was able to get Claude to succeed simply by giving it access to Playwright so it could see what it was doing and telling it in the prompt that it needed to be pixel perfect:

https://news.ycombinator.com/item?id=46193412

As of right now, it seems to have been flagged into oblivion by the anti-AI crowd. I found both posts to be interesting, and it's unfortunate that one of them is missing from the conversation.
by kouteiheika | 2025-12-08 17:47:04+00:00
AFAIK a big part of it is that they distilled the guidance into the model.

I'm going to simplify all of this a lot so please bear with me, but normally the equation to denoise an image would look something like this:

pos = model(latent, positive_prompt_emb) neg = model(latent, negative_prompt_emb) next_latent = latent + dt * (neg + cfg_scale * (pos - neg))

So what this does - you trigger the model once with a negative prompt (which can be empty) to get the "starting point" for the prediction, and then you run the model again with a positive prompt to get the direction in which you want to go, and then you combine them.

So, for example, let's assume your positive prompt is "dog", and your negative prompt is empty. So triggering the model with your empty prompt with generate a "neutral" latent, and then you nudge it into the direction of your positive prompt, in the direction of a "dog". And you do this for 20 steps, and you get an image of a dog.

Now, for Flux the equation looks like this:

next_latent = latent + dt * model(latent, positive_prompt_emb)

The guidance here was distilled into the model. It's cheaper to do inference with, but now we can't really train the model too much without destroying this embedded guidance (the model will just forget it and collapse).

There's also an issue of training dynamics. We don't know exactly how they trained their models, so it's impossible for us to jerry rig our training runs in a similar way. And if you don't match the original training dynamics when finetuning it also negatively affects the model.

So you might ask here - what if we just train the model for a really long time - will it be able to recover? And the answer is - yes, but at this point the most of the original model will essentially be overwritten. People actually did this for Flux Schnell, but you need way more resources to pull it off and the results can be disappointing: https://huggingface.co/lodestones/Chroma
by ojr | 2025-12-08 17:47:06+00:00
I didn’t know what atomic operations, important concept in cybersecurity until working with crypto and blockchain, prompting AI is a skill, there’s an obvious context engineering skill gap I possess. I built a desktop application in Rust in 2 weeks while the industry uses Electron. Try to clone my apps, with whatever model of the week, it wouldn’t be easy. Not all prompts are made equal.

I was one of the earliest React/React Native developers before the industry shifted to it. Early Tailwind and Node.js adopter as well.

My AI apps are nothing new either but I do have subscriptions because of how I engineered them. Majority of apps in the app store make $0. You can compare yourself to those, not me.
by 6thbit | 2025-12-08 17:47:11+00:00
This looks neat, we certainly need more ideas and solutions on this space, I work with large codebases daily and the limits on agentic contexts are constantly evident. I've some questions related to how I would consume a tool like this one:

How does this fare with codebases that change very frequently? I presume background agents re-indexing changes must become a bottleneck at some point for large or very active teams.

If I'm working on a large set of changes modifying lots of files, moving definitions around, etc., meaning I've deviated locally quite a bit from the most up to date index, will Nia be able to reconcile what I'm trying to do locally vs the index, despite my local changes looking quite different from the upstream?
by fellowniusmonk | 2025-12-08 17:47:31+00:00
One would think. That's what I suspected when I started down the path but no, quite the opposite.

machines and man can share the same moral substrate it turns out. If either party wants to build things on top of it they can, the floor is maximally skeptical, deconstructed and empirical, it doesn't care to say anything about whatever arbitrary metaphysic you want to have on top unless there is a direct conflict in a very narrow band.
by dSebastien | 2025-12-08 17:47:40+00:00
I've joined the Apple Developer program to be able to sign/notarize my Tauri app. And it's been three weeks that I'm failing to get the app notarized. I don't own a Mac so I'm using GitHub actions. Apparently the fact that notarization takes ages is very common the first time(s) an app is sent. I've spent nearly $100 on GitHub and my app still isn't notarized.

I contacted support and they don't want to help because I'm not using a Mac and using a third party framework (Tauri), even though it's just using xcrun, Apple's tool...

Also I've been unable to even use the notarization API to retrieve the submission logs and Apple didn't help for that either so far (they just disregarded my ticket).

I feel powerless and abused. This is the worst DX/CX I've had in years.

As a side note, authenticating against the notarization API is a nightmare. You get a PKCS8 that you have to use to create/sign a JWT and you're basically on your own... I had to build a little node program just to craft the JWT...
by pixl97 | 2025-12-08 17:47:45+00:00
>Aggregating protects privacy when done properly.

When done properly is going a lot of heavy lifting there. Time and time again it's been found most aggregates are not filtered properly and be deanonymized with eaze.

It's not that one is big bad, and one is little bad, it's the little bad can become big bad with a small amount of work by an attacker/company. Then when you add in zero external third party verification of these company claims, you really don't have any reason to believe them.
by alienbaby | 2025-12-08 17:48:08+00:00
They gave up on watson about 18 months before llm's popped up, and they have simply just not got enough cash on hand to compete. While the big boys grew fantastically bigger over the past 15 years as cloud happened ibm fumbled time after time and shrank ever smaller, and is now desperately hoping it can stay relevant. but in the end they just haven't got the resources to compete on that stage anymore.
by bwahah4 | 2025-12-08 17:48:25+00:00
The shelf life of technology books is shorter than it takes to read them. Most were notebooks of students quickly edited into a learning tool. Oh wow more Python recipes. Another introduction to C++!

The worlds moved on from valuing the latest DSL and additions to the Linux kernel. Just a fad marketed at GenX and older Millennials.

SaaS is something tech billionaires need to exist. It's not something humanity needs. Not at the scale of the 2010s ZIRP fueled mania, anyway. Employers were using subscriptions to O'Reilly as a perk. No budget for perks in the AI and economic austerity era.

Maps app, communication apps, media consumption are all most of the billions of smartphone users care about.
by lxgr | 2025-12-08 17:49:02+00:00
Taking possession of the ransom without getting arrested/physically followed by the police is what foils most kidnapping plots [1], and cryptocurrencies definitely address that part.

Criminals don't really need strong anonymity as long as the payment system has strong censorship resistance and at least one counterparty won't mind accepting "tainted" funds.

Arguably, privacy is much more important to non-criminal individual users as they themselves can be targeted by criminals as a result of their income/holdings being globally visible, and unlike organized crime, they often have less means to protect themselves from $5 wrench attacks.

[1] Source: I watched many crime thrillers growing up
by zahlman | 2025-12-08 17:49:13+00:00
Psst: even if you aren't using uv, you can use a global pip with `--python` and create your venvs `--without-pip` to save time (unless you have many invocations per venv, since `--python` works through a not-very-sane hack and is a bit slower as a result). And you can use Pipx to provision that global pip, and then you also have Pipx available. https://zahlman.github.io/posts/2025/01/07/python-packaging-...
by n7ck | 2025-12-08 17:50:21+00:00
Hey I'm Nick, and I originally came to Conduit as a data participant! After my session, I started asking questions about the setup to the people working there, and apparently I asked good questions, so they hired me.

Since I joined, we've gone from <1k hours to >10k hours, and I've been really excited by how much our whole setup has changed. I've been implementing lots of improvements to the whole data pipeline and the operations side. Now that we train lots of models on the data, the model results also inform how we collect data (e.g. we care a lot less about noise now that we have more data).

We're definitely still improving the whole system, but at this point, we've learned a lot that I wish someone had told us when we started, so we thought we'd share it in case any of you are doing human data collection. We're all also very curious to get any feedback from the community!
by rdtsc | 2025-12-08 17:51:22+00:00
> Things I had taken for granted all my life suddenly became un-anchored and as a result so did I.

Same for me. If you don’t grow up with a number of small regular quakes or live in high-rise building that sways with the wind, it’s pretty unsettling to feel, what you always know as stable hard ground, solid buildings all of the sudden bouncing around. Rationally you know what it is and how it works but it’s still scary.
by andrewaylett | 2025-12-08 17:51:22+00:00
No, it let you continue to follow the main branch for most files, while files you edited would have their changes saved to a different location. And was just about as horrible as you might imagine.

We moved from VSS to SVN, and it took a little encouraging for the person who had set up our branching workflow using that VSS feature to be happy losing it if that freed us from VSS.
by coreload | 2025-12-08 17:51:24+00:00
All estimates should be accompanied by a "cone of uncertainty". Teams should evolve their ability to describe a cone of uncertainty as well as their ability to estimate. Estimating "accurately" is only possible when understanding the accuracy is elevated to the same importance as the estimate itself.

https://en.wikipedia.org/wiki/The_purpose_of_a_system_is_wha...

https://hubbardresearch.com/product-category/publications/ho...
by lubujackson | 2025-12-08 17:51:34+00:00
The "world model" is what we often refer to as the "context". But it is hard to anticipate bad assumptions that seem obvious because of our existing world model. One of the first bugs I scanned past from LLM generated code was something like:

if user.id == "id": ...

Not anticipating that it would arbitrarily put quotes around a variable name. Other time it will do all kinds of smart logic, generate data with ids then fail to use those ids for lookups, or something equally obvious.

The problem is LLMs guess so much correctly that it is near impossible to understand how or why they might go wrong. We can solve this with heavy validation, iterative testing, etc. But the guardrails we need to actually make the results bulletproof need to go far beyond normal testing. LLMs can make such fundamental mistakes while easily completing complex tasks that we need to reset our expectations for what "idiot proofing" really looks like.
by raw_anon_1111 | 2025-12-08 17:51:50+00:00
In my experience, that’s only if you are doing a staff augmentation type consulting. I absolutely refuse to be on a project where the client controls the project and the company I’m working for isn’t just giving high level businsss requirements status updates.

I will not touch a staff augmentation project - ever.

I only work on projects that for all intents and purposes are fixed fee projects where we sign a contract promising that we will deliver x for $y.

In some level of granularity, we track what stories are complete in Jira and alarm bells go off if the burn down isn’t trending correctly. But we aren’t logging every hour we spent in Salesforce.

This was true when I was working in the consulting department at AWS (ProServe) and is true now at a 3rd party AWS partner.
by superducktoes | 2025-12-08 17:51:51+00:00
thanks really appreciate that! I've seen that doc before and it does really make me wonder. part of the leaks from the NSA tools years back had some references in there for detecting north koreas ant-virus silivaccine

https://github.com/b30wulf/Malware-collection/blob/4f5906c93...

There was also the hacking team leak from years ago and they were selling exploits for north korea's red star OS: https://nkinternet.wordpress.com/wp-content/uploads/2025/12/...

I assume they've been on their networks in the past but i think North Korea has also done a lot over the years to secure their side. it used to be a lot easier when they left everything as an open directory and didn't realize what they were doing.
by mannanj | 2025-12-08 17:52:14+00:00
Isn’t it kind of true that the systems we as servile people-pleasers have to operate out of are exactly these? The hierarchical status games and alpha-animal tribal dynamics are these. Our leaders who are so might and rich and powerful want to keep their position, and we don’t want to admit they have more influence than we do for things like AI now and so we stand and watch naively as they reward the people pleasers and eventually historically we learn(ed) it pays to please until leadership changes.
by was8309 | 2025-12-08 17:52:30+00:00
maybe i'm doing a No True Scotsman, but i can't see where the Left has ever been against the poors. I thought the the origin of the terms Left and Right was For Democracy (the poors have a voice), and For the King (the wealthies) from the French Revolution. How is 'wealthies vs poors' different from left vs right'?
by HarHarVeryFunny | 2025-12-08 17:52:33+00:00
> Yes - but LLMs also get this "embodied knowledge" passed down from human-generated training data.

It's not the same though. It's the difference between reading about something and, maybe having read the book and/or watched the video, learning to DO it yourself, acting based on the content of your own mind.

The LLM learns 2nd hand heresay, with no idea of what's true or false, what generalizations are valid, or what would be hallucinatory, etc, etc.

The human learns verifiable facts, uses curiosity to explore and fill the gaps, be creative etc.

I think it's pretty obvious why LLMs have all the limitations and deficiencies that they do.

If 2nd hand heresay (from 1000's of conflicting sources) really was good as 1st hand experience and real-world prediction, then we'd not be having this discussion - we'd be bowing to our AGI overlords (well, at least once the AI also got real-time incremental learning, internal memory, looping, some type of (virtual?) embodiment, autonomy ...).
by echelon | 2025-12-08 17:52:43+00:00
Sounds similar to university applied research arms too.

GTRI locally hires a lot of non-students to work in its various labs. Its labs then pitch ideas to private companies and the DoD. Sometimes they're solicited directly if the lab is well-known and has a track record of delivering good research-oriented results. They research and build prototypes around various capabilities: robotics, avionics, even classified stuff.

They're always pitching, because contracts end or fall through, and that's the source of everyone's payroll. The labs can even be competitive with one another, and the individual researchers might spend time split between labs.

Academics as a service.
by crazygringo | 2025-12-08 17:52:47+00:00
"you’re not living right."

Oh really?

Sometimes you just need a quick decision, whether you're going somewhere with friends at the last second, or yes ordering delivery and just want something that will be one of the better options. Because there isn't 1 Chinese place in your delivery radius, there are 20.

Believe me, I read restaurant blogs and talk to people too. But that's more for stuff I plan in advance, not last-minute decisions in a neighborhood I don't visit often.

So maybe don't be so quick to judge that others aren't "living right", how about?
by lotsofpulp | 2025-12-08 17:53:45+00:00
>Historically, BRK-B has very often outperformed the SP500.

Huh? The point I was trying to make that the returns seem to equalize the further back we go.

> It's a solid alternative if you distrust tech which is today very heavy in the SP500.

The only reason BRK kept up with SP500 over the last 10 years is because of its outsized investment in Apple in 2016 or so, after the disastrous results of sitting out of tech in the 2000s and early 2010s and pursuing other investments such as Kraft Heinz or whatever.

As of September 2025, Apple is still 21% of BRK's publicly listed holdings:

https://www.cnbc.com/berkshire-hathaway-portfolio/
by kouteiheika | 2025-12-08 17:53:56+00:00
> How much would it cost the community to pretrain something with a more modern architecture?

Quite a lot. Search for "Chroma" (which was a partial-ish retraining of Flux Schnell) or Pony (which was a partial-ish retraining of SDXL). You're probably looking at a cost of at least tens of thousands or even hundred of thousands of dollars. Even bigger SDXL community finetunes like bigASP cost thousands.

And it's not only the compute that's the issue. You also need a ton of data. You need a big dataset, with millions of images, and you need it cleaned, filtered, and labeled.

And of course you need someone who knows what they're doing. Training these state-of-art models takes quite a bit of skill, especially since a lot of it is pretty much a black art.
by jellyotsiro | 2025-12-08 17:54:08+00:00
great question!

For large and active codebases, we avoid full reindexing. Nia tracks diffs and file level changes, so background workers only reindex what actually changed. We are also building “inline agents” that watch pull requests or recent commits and proactively update the index ahead of your agent queries.

Local vs upstream divergence is a real scenario. Today Nia prioritizes providing external context to your coding agents: packages, provider docs, SDK versions, internal wikis, etc. We can still reconcile with your local code if you point the agent at your local workspace (cursor and claude code already provide that path). We look at file paths, symbol names and usage references to map local edits to known context. In cases where the delta is large, we surface both the local version and the latest indexed version so the agent understands what changed.