MurphyA/hacker-news huggingface dataset:
by lproven | 2026-02-06 14:36:40+00:00
> Really wish that there were drivers available to make that run nicely/natively on a Raspberry Pi rather than in an emulator:

So very much this.

The Pi range has redefined public expectations of what a small cheap computer is and can do. But it runs a cut-down version of a huge, complex OS.

We should give it its own small OS, one simple enough for a single ordinary person to read and understand.

The core of Project Oberon is about 4000 lines of code, and that's a compiler, an editor, a windowing TUI OS to run it in, and enough OS to save source, create and store binaries, and run them.

The successor to Oberon is A2 with the Bluebottle GUI. This is SMP-aware, has a TCP/IP network stack and a simple HTTP web browser, can do email and so on.

Its kernel is about 8000 lines of code.

I believe the Ultibo bare-metal FreePascal system for the Raspberry Pi:

https://ultibo.org/

... is the work of one developer, an Australian chap. I feel confident that with such knowledge he could do a native A2 port, and probably not take very long either if he can reuse Ultibo code and drivers.

I wonder how much money he'd want, and if there would be any way to crowdfund it...
by KaiserPro | 2026-02-06 14:37:04+00:00
The algorithm is pretty simple, it'll show you a selection of videos that are from the n most popular genres of videos, then depending on your dwell time, it'll A/B test categories that are related, or sub categories.

That bit isn't that difficult or new. the special sauce is the editorialising and content categorisation. being able to accurately categorise videos into genres, subjects and sub subjects (ie makeup video, 25 year olds, woman, straight, new york, eyeglitter) and then creating a graph of what persona likes what.

The second secret sauce is people going through and finding stuff and promoting it. TikTok (used to) editorialise/pay highly for content.
by zackmorris | 2026-02-06 14:37:15+00:00
You're absolutely right. Apologies if I came off as critical, which wasn't my intent.

I was trying to make a connection with random sampling as a way to maybe reduce the inherent uncertainty in how well AI solves problems, but there's still a chance that 10 AIs could come up with the wrong answer and we'd have no way of knowing. Like how wisdom of the crowd can still lead to design by committee mistakes. Plus I'm guessing that AIs already work through several layers of voting internally to reach consensus. So maybe my comment was more of a breadcrumb than an answer.

Some other related topics might be error correcting codes (like ECC ram), Reed-Solomon error correction, the Condorcet paradox (voting may not be able to reach consensus) and even the halting problem (zero error might not be reachable in limited time).

However, I do feel that AI has reached an MVP status that it never had before. Your post reminded me of something I wrote about in 2011, where I said that we might not need a magic bullet to fix programming, just a sufficiently advanced one:

https://web.archive.org/web/20151023135956/http://zackarymor...

I took my blog(s) down years ago because I was embarrassed by what I wrote (it was during the Occupy Wall Street days but the rich guys won). It always felt so.. sophomoric, no matter how hard I tried to convey my thoughts. But it's interesting how so little has changed in the time since, yet some important things have.

Like, I hadn't used Docker in 2011 (it didn't come out until 2013) so all I could imagine was Erlang orchestrating a bunch of AIs. I thought that maybe a virtual ant colony could be used for hill climbing, similarly to how genetic algorithms evolve better solutions, which today might be better represented by temperature in LLMs. We never got true multicore computing (which still devastates me), but we did get Apple's M line of ARM processors and video cards that reached ludicrous speed.

What I'm trying to say is, I know that it seems like AI is all over the place right now, and it's hard to know if it's correct or hallucinating. Even when starting with the same random seed, it seems like getting two AIs to reach the same conclusion is still an open problem, just like with reproducible builds.

So I just want to say that I view LLMs as a small piece of a much larger puzzle. We can imagine a minimal LLM with less than 1 billion parameters (more likely 1 million) that controls a neuron in a virtual brain. Then it's not so hard to imagine millions or billions of those working together to solve any problem, just like we do. I see AIs like ChatGPT more like logic gates than processors. And they're already good enough to be considered fully reliable, if not better at humans than most tasks already, so it's easy to imagine a society of them with metacognition that couldn't get the wrong answer if it tried. Kind of like when someone's wrong on the internet and everyone lets them know it!
by PaulHoule | 2026-02-06 14:37:22+00:00
I looked a lot into the "universal paywall" business model where one subscription buys you access to articles from a wide range of news outlets. It's close to impossible to execute because the most prestigious outlets (ahem... The New York Times) won't give you the time of the day, even if you are startup royalty. That Apple has accomplished anything in this space is remarkable.
by functionmouse | 2026-02-06 14:37:22+00:00
> you probably say this jokingly

NO!

> or rather, "assume every system on earth can/will be breached"

NOOOO! NO NO NO!! It's ALREADY BREACHED! You are using ADVERSARIAL CHANNELS! If we're talking about objectives and proofs for the purposes of operational security, the only way of going about this is to act as though every system you are using has chips in it the NSA put there. Please understand.

https://learn.microsoft.com/en-us/security/zero-trust/adopt/...
by thrance | 2026-02-06 14:37:26+00:00
> Why is that only a problem for democracy?

Because democracy at least pretends to give power to the people. Except letting a few individuals wield enough wealth and power to buy media, politicians and judges is completely antagonistic to the basic ideals of democracy, and not many realize this (yet).

> I’m not aware of any system that [...]

Liberal democracy is better than feudalism, I see no reason why our systems of governance can't be improved further. And, at least to me, the obvious path forward is to keep any of those "deranged dark triad personalities" from gaining too much power, maybe by limiting the amount of wealth any single individual can hold unto.
by mvr123456 | 2026-02-06 14:37:28+00:00
Looking at LLMs as a less-than-completely-reliable compiler is a good idea, but it's misleading to think of them as natural-language-to-implementation compiler because they are actually an anything-to-anything compiler.

If you don't like the results or the process, you have to switch targets or add new intermediates. For example instead of doing description -> implementation, do description -> spec -> plan -> implementation
by jerf | 2026-02-06 14:37:50+00:00
A lot of people are mentally modeling the idea that LLMs are either now or will eventually be infinitely capable. They are and will stubbornly persist in being finite, no matter how much capacity that "finite" entails. For the same reason that higher level languages allow humans to worry less about certain details and more about others, higher level languages will allow LLMs to use more of their finite resources on solving the hard problems as well.

Using LLMs to do something like what a compiler can already do is also modelling LLMs as infinite rather than finite. In fact in this particular situation not only are they finite, they're grotesquely finite, in particular, they are expensive. For example, there is no world where we just replace our entire infrastructure from top to bottom with LLMs. To see that, compare the computational effort of adding 10 8-digit numbers with an LLM versus a CPU. Or, if you prefer something a bit less slanted, the computational costs of serving a single simple HTTP request with modern systems versus an LLM. The numbers run something like LLMs being trillions of times more expensive, as an opening bid, and if the AIs continue to get more expensive it can get even worse than that.

For similar reasons, using LLMs as a compiler is very unlikely to ever produce anything even remotely resembling a payback versus the cost of doing so. Let the AI improve the compiler instead. (In another couple of years. I suspect today's AIs would find it virtually impossible to significatly improve an already-optimized compiler today.)

Moreover, remember, oh, maybe two years back when it was all the rage to have AIs be able to explain why they gave the answer they did? Yeah, I know, in the frenzied greed to be the one to grab the money on the table, this has sort of fallen by the wayside, but code is already the ultimate example of that. We ask the LLM to do things, it produces code we can examine, and the LLM session then dies away leaving only the code. This is a good thing. This means we can still examine what the resulting system is doing. In a lot of ways we hardly even care what the LLM was "thinking" or "intending", we end up with a fantastically auditable artifact. Even if you are not convinced of the utility of a human examining it, it is also an artifact that the next AI will spend less of its finite resources simply trying to understand and have more left over to actually do the work.

We may find that we want different programming languages for AIs. Personally I think we should always try to retain that ability for humans to follow it, even if we build something like that. We've already put the effort into building AIs that produce human-legible code and I think it's probably not that great a penalty in the long run to retain that. At the moment it is hard to even guess what such a thing would look like, though, as the AIs are advancing far faster than anyone (or any AI) could produce, test, prove out, and deploy such a language, against the advantage of other AIs simply getting better at working with the existing coding systems.
by jorvi | 2026-02-06 14:38:02+00:00
Steve Jobs was all about the customer experience, hence so many of his famous quotes. Two like the most are:

- Him saying "Microsoft has no style", not because I care about ribbing on Microsoft but because it indicated that Apple was a company that really cared about the aesthetics of both their hardware and software products

- His response to the question why there was no $600 MacBook to compete with Windows plastic craptops. He specifically said that to deliver a good UX to the users, he needed Macs at a certain price point to invest in the hardware and the OS. Shareholder value didn't even enter the equation.

He also hated market segmentation and was adamant that all iPhones within a generation had the same features, aside from the storage size. When the 6 Plus models got image stabilization he felt awkward about it.

As soon as Tim Cook took over, it became beancounter city. Market segmentation became massive. Year over year price hikes with minimal improvements. Services became the core strategy. And the last 5 years you are under a constant barrage of ads for iCloud, Apple Music, Apple News, Apple TV and even ads in your Wallet.

Oh, and I'm just remember how Jobs said that form should follow function. Which you can also see a clear decline in from when Jobs became less involved, with iOS 7 being a disaster. And ever since then Apple has being violating their own Human Interface Guidelines. If you download their 1997 version it's absurd how many of their own former guidelines they violate these days.
by jltsiren | 2026-02-06 14:38:19+00:00
I think it's the opposite. People remember how Bill Gates got rich. They remember that the damage he caused mostly affected capitalists and professionals in developed countries. His businesses mostly didn't abuse labor in developing countries. He didn't cause that much environmental damage. He didn't undermine democracy and the society that much.

People remember that Bill Gates played the game and won, and the damage he caused was mostly limited to the economic sphere and to other people playing the same game. That's why they are willing to give Gates a chance to redeem himself by using his money for good.
by rglullis | 2026-02-06 14:39:16+00:00
The "subset of the population" is not small, and there is no easy way to protect the most vulnerable.

> it's a wildly popular form of entertainment with millions of creators sharing their lives

I don't think we should be rewarding those who make a living by creating "content" that serves for nothing but a dopamine rush, and you can bet that those who who put it in the effort to create valuable content would prefer to have one less channel where they are forced to put out content just to satisfy the algorithm overlords.
by skybrian | 2026-02-06 14:39:18+00:00
Here's an experiment that might be worth trying: temporarily delete a source file, ask your coding agent to regenerate it, and examine the diffs to see what it did differently.

This could be a good way to learn how robust your tests are, and also what accidental complexity could be removed by doing a rewrite. But I doubt that the results would be so good that you could ask a coding agent to regenerate the source code all the time, like we do for compilers and object code.
by randusername | 2026-02-06 14:39:29+00:00
What's the user appeal of Apple news or whatever the Google equivalent is? From the outside looking in the value is the feed, but that seems super creepy to me.

It is an awful lot of power to give these companies to decide how we use their devices to interact with the world _and_ how we view the world.

I don't want anyone curating the current events or long-form I read. I want to see the whole buffet and choose myself, even sampling the unsavory ones from time-to-time to keep myself in check.
by PaulHoule | 2026-02-06 14:39:40+00:00
The more you pay for a subscription, the more valuable it is to advertise to you -- maybe the classic example is The New York Times which has highly annoying advertising if you're a subscriber because you've qualified yourself.

Or rather, if you believe you are too poor to afford a $10 a month subscription you probably believe you're too poor to afford anything that is advertised. The model of "premium subscription with no ads" flies in the face of reality.
by jpeeler | 2026-02-06 14:40:04+00:00
I work in a monorepo at work, which of course increases complexity and build time due to more work being done. But I keep wondering even with better CI options that properly handle dependencies if solving the problem at that level is too low.

Currently evaluating using moonrepo.dev to attempt to efficiently build our code. What I've noticed is (aside from Bazel) it seems a lot of monorepo tools only support a subset of languages nicely. So it's hard to evaluate fairly as language support limits one's options. I found https://monorepo.tools to be helpful in learning about a lot of projects I didn't know about.
by explosion-s | 2026-02-06 14:40:16+00:00
This is an interesting problem, one I've thought a lot about myself. On one hand, LLMs have the capacity to greatly help people, and I think, especially in the realm of gradually learning how to program, on the other hand, the non-determinism is such a difficult problem to work around.

One current idea of mine, is to iteratively make things more and more specific, this is the approach I take with psuedocode-expander ([0]) and has proven generally useful. I think there's a lot of value in the LLM instead of one shot generating something linearly, building from the top down with human feedback, for instance. I give a lot more examples on the repo for this project, and encourage any feedback or thoughts on LLM driven code generation in a more sustainable then vibe-coding way.

[0]: https://github.com/explosion-Scratch/psuedocode-expander/
by aldersondev | 2026-02-06 14:40:17+00:00
Codex 5.3 vs Claude Opus 4.6, who builds the better full-stack app?

I had both models build the exact same Next.js + TypeScript project,

An AI Job Application Analyzer that scores resumes against a job description and returns:
match score (0–100)
matched + missing skills
tailored resume bullet rewrites
interview questions
by Desafinado | 2026-02-06 14:40:28+00:00
They aren't incentivized to become inefficient, but once they grow large enough you get into a situation where everyone is playing a political game to minimize their own risk and effort.

And worse, the only people with motivation to fill the top tiers of the company are usually a little off. You essentially have to be a bit nuts to want to be C-level or above in a large corporation. I've seen it, there are employees who will ruthlessly seek out more money. Usually their competence levels aren't quite there, but they are the only people who want the job.
by chriscloud27 | 2026-02-06 14:40:36+00:00
Location: US/EU

Remote: yes

Willing to relocate: yes

Technologies: Kubernetes, AWS, Azure, Terraform, CI/CD, GitOps, Backend-as-a-Service (AWS Amplify), n8n

Résumé/CV: https://drive.google.com/file/d/12wQpqrHhz7th6IRZJBASmePTGmO...

Email: chrisallin24@gmail.com

Principal Cloud & Platform Architect and SaaS Founder with 10+ years of experience building cloud-native, highly automated platforms.

Strong focus on Kubernetes, automation, and security-by-design. Recently led a Backend-as-a-Service SaaS rollout using AWS Amplify to accelerate time-to-market and improve developer experience.
by hackeraccount | 2026-02-06 14:40:49+00:00
I always think of a kid who was a friend of my daughters. He just really liked winning. I mean, when I was a kid and our team won a soccer game I was happy enough. This kid - if his team was playing a tomato can of a team and the score was 12-0 he was just as ecstatic about making the score 13-0 as was with that first goal. I honestly think he was happier about that kind of game then a struggle to beat a reasonable opponent. Heaven forbid they should lose.

Oddly he drifted away from sports (physically he was too small and honestly fragile - 2 or 3 broken bones before he was 12) and into the arts.
by benry1 | 2026-02-06 14:41:20+00:00
I'm reading Maintenance of Everything and it has a section about the switch from artisan-crafted weapons to making uniform parts that feels comparable to this.

French military had pioneered a way to make fully interchangeable weapon parts, but the French public fought back in fear of the jobs of the artisans who used to hand-make weapons. Over the next 20 years they completely lost their edge on the battlefield, nothing could be repaired in the field. Other countries embraced the change, could repair anything in the field with cheap and precise spare parts, and soon fostered in the industrial revolution.

The artisans stopped being people who made weapons, the artisans became people who made machines that made weapons.
by sukinai | 2026-02-06 14:41:40+00:00
Yesterday Anthropic shipped agent teams with Opus 4.6. I had been building a memory plugin for Claude Code that stores project decisions as plain JSON on disk, and I wanted to see if it would work across parallel agents without any changes.

So I tested it. I spun up 3 teammates backend, frontend, and tester on a full-stack project. Before the test, I pre-loaded the project knowledge:
/nemp:init # auto-detected Next.js, TypeScript, Prisma
/nemp:save auth "JWT with 15min access tokens, httpOnly refresh"
/nemp:save api-style "RESTful, snake_case, versioned at /api/v1"
/nemp:save testing "Pytest with fixtures, 80% coverage minimum"
Then each teammate ran /nemp:context with their relevant keywords as their first action. The backend agent ran /nemp:context auth and immediately knew the JWT strategy. The frontend agent ran /nemp:context stack and knew it was Next.js + TypeScript. The tester ran /nemp:context testing and found the test conventions.
It worked because the memory is just a JSON file on disk (.nemp/memories.json). No server, no port, no database. Every agent reads the same file. One saves a finding, the others discover it with a keyword search.
End result: 34 files committed, 100 tests passing, zero conflicts between the 3 agents. Nobody had to re-discover what another agent already knew.
The whole thing is a Claude Code plugin, two commands to install, no API keys, no cloud, nothing leaves your machine.
Demo of the full test: https://vimeo.com/1162546825?share=copy&fl=sv&fe=ci
I'm a solo developer building this in the open. If this is useful to you, a star on the repo would mean a lot, it helps other developers find it.
https://github.com/SukinShetty/Nemp-memory
Happy to answer any questions about the architecture or how it performed with agent teams.
by tekacs | 2026-02-06 14:42:07+00:00
This looks really interesting!

I _would_ be curious to try it, but...

My first question was whether I could use this for sensitive tasks, given that it's not running on our machines. And after poking around for a while, I didn't find a single mention of security anywhere (as far as I could tell!)

The only thing that I did find was zero data retention, which is mentioned as being 'on request' and only on the Enterprise plan.

I totally understand that you guys need to train and advance your model, but with suggested features like scraping behind login walls, it's a little hard to take seriously with neither of those two things anywhere on the site, so anything you could do to lift up those concerns would be amazing.

Again, you seem to have done some really cool stuff, so I'd love for it to be possible to use!

Update: The homepage says this in a feature box, which is... almost worst than saying nothing, because it doesn't mean anything? -> "Enterprise-grade security; End-to-end encryption, enterprise-grade standards, and zero-trust access controls keep your data protected in transit and at rest."
by xixixao | 2026-02-06 14:42:07+00:00
OP means “given the same input, produce the same output” determinism. This isn’t really much different from normal compilers, you might have a language spec, but at the end of the day the results are determined by the concrete compiler’s implementation.

But most LLM services on purpose introduce randomness, so you don’t get the same result for the same input you control as a user.