MurphyA/hacker-news huggingface dataset:
by Henchman21 | 2026-03-10 20:29:40+00:00
When you take a high trust society and then endlessly lie to the people, what exactly are you expecting to happen? Serious question here, we are under direct assault from the "elites" here and we are clearly shell-shocked and not yet dealing with it.

Many of us are open to suggestion. But if all you're going to do is call us bitches, then shut the fuck up. Wanna help? Cool, I'm willing to accept it. Maybe try that.
by UncleMeat | 2026-03-10 20:30:23+00:00
Sadly, the way people become expert in a codebase is through coding. The process of coding is the process of learning. If we offload the coding to AI tools we will never be as expert in the codebase, its complexity, its sharp corners, or its unusual requirements. While you can apply general best practices for a code review you can never do as much as if you really got your hands dirty first.

"Seniors will do expert review" will slowly collapse.
by mnkv | 2026-03-10 20:30:56+00:00
This blog post describes the basic work of a research engineer and nothing more. The amount of surprise the author has seems to suggest they haven't really worked in ML for very long.

Honestly? This is the best its ever been. Getting stuff to run before huggingface and uv and docker containers with cuda was way worse. Even with full open-source, go try to run a 3+ years old model and codebase. The field just moves very fast.
by FireBeyond | 2026-03-10 20:31:06+00:00
Much. We can start with a "transparency" portal that doesn't cover at least 30% of installations. We can move on to data sharing functionality built and sold on the nudge-nudge-wink-wink of "We know that x, y and/or z methods of sharing data between operators/agencies is limited or illegal in your state, but we're certainly not disabling the feature so you can't use it", and from there on to the dystopian Minority Report vision that sees false negatives as unacceptable to Garrett, while false positives are something he has decided are an acceptable price for you to pay towards his (quite literal) vision of "a world with zero crime, thanks to Flock".
by yalogin | 2026-03-10 20:31:12+00:00
FHE is great, if we can get this to work at scale and if this can be baked into the GPU complex, we don’t need the confidential compute pipeline. Of course we will still need to manage the user keys, so the current confidential pipeline will just be replaced with something else, but hopefully managing large amounts of data will become simpler. Not sure where the tech is but it could be a game changer for security. It still doesn’t eliminate the bad corporation issue though. We still rely on code they run on the servers inside the FHE.
by maplant | 2026-03-10 20:31:12+00:00
I'm not really arguing with your point, I'm correcting your incorrect description of what I'm saying.

To argue with your actual point: I don't really care about the overall context, actively disallowing tests in a codebase is a _bad decision_. Look how it worked out for them.

> it's time for you and I to bug out conversationally

Fine with me
by RA_Fisher | 2026-03-10 20:31:15+00:00
I built this after noticing how much variation there is in AI output depending on the quality of the prompt. Models matter a lot — but even the best model underperforms when the prompt is vague. Enriching the input is one of the highest-leverage things you can do.

Most prompts are drafted in 10 seconds and sent. They're missing context, constraints, a clear objective, examples. The model fills in the gaps with whatever it wants, which is why the output often feels generic or off.

Prompt Enricher scores your prompt across 5 dimensions (the YIELD framework: Your Objective, Input Context, Expectations & Constraints, Layout of Output, Demonstrations/Examples), then rewrites it with the missing pieces filled in. It also produces 4 variants — Packed, Quality, Concise, and Reliable — so you can match the enriched prompt to your use case.

The "Show Changes" diff view that highlights exactly what was added (green) and removed (red) — so it's immediately clear what enrichment actually did to your prompt.
by nineteen999 | 2026-03-10 20:31:45+00:00
I think there's a huge range here - ChatGPT to me seems extra verbose on the web version, but when running with Codex it seems extra terse.

Claude seems more consistently _concise_ to me, both in web and cli versions.
But who knows, after 12 months of stuff it could be me who is hallucinating...
by netsharc | 2026-03-10 20:31:59+00:00
Oh look at you, how easy for you to demand of others that they put themselves in danger before you deem them worthy of protection. "You must have proof you've been arrested by Putin's police before we let you in here!". So they must risk the chance of immediate imprisonment in a freezing Siberian dungeon before you open your generous doors...

And graduates working for the Belarussian state, why is that acceptable and not considered as "conspiring with the war criminals" in your eyes? What other barriers are there that you have in your mind we don't know about, for someone who's worked as expected, and fled the country afterwards?
by hintymad | 2026-03-10 20:32:18+00:00
> Review by a senior is one of the biggest "silver bullet" illusions managers suffer from

Especially in a big co like Amazon, most senior engineers are box drawers, meeting goers, gatekeepers, vision setters, org lubricants, VP's trustees, glorified product managers, and etc. They don't necessarily know more context than the more junior engineers, and they most likely will review slowly while uncovering fewer issues.
by egeozcan | 2026-03-10 20:33:06+00:00
The trick is, with the setup I mentioned, you change the rewards.

The concept is:

Red Team (Test Writers), write tests without seeing implementation. They define what the code should do based on specs/requirements only. Rewarded by test failures. A new test that passes immediately is suspicious as it means either the implementation already covers it (diminishing returns) or the test is tautological. Red's ideal outcome is a well-named test that fails, because that represents a gap between spec and implementation that didn't previously have a tripwire. Their proxy metric is "number of meaningful new failures introduced" and the barrier prevents them from writing tests pre-adapted to pass.

Green Team (Implementers), write implementation to pass tests without seeing the test code directly. They only see test results (pass/fail) and the spec. Rewarded by turning red tests green. Straightforward, but the barrier makes the reward structure honest. Without it, Green could satisfy the reward trivially by reading assertions and hard-coding. With it, Green has to actually close the gap between spec intent and code behavior, using error messages as noisy gradient signal rather than exact targets. Their reward is "tests that were failing now pass," and the only reliable strategy to get there is faithful implementation.

Refactor Team, improve code quality without changing behavior. They can see implementation but are constrained by tests passing. Rewarded by nothing changing (pretty unusual in this regard). Reward is that all tests stay green while code quality metrics improve. They're optimizing a secondary objective (readability, simplicity, modularity, etc.) under a hard constraint (behavioral equivalence). The spec barrier ensures they can't redefine "improvement" to include feature work. If you have any code quality tools, it makes sense to give the necessary skills to use them to this team.

It's worth being honest about the limits. The spec itself is a shared artifact visible to both Red and Green, so if the spec is vague, both agents might converge on the same wrong interpretation, and the tests will pass for the wrong reason. The Coordinator (your main claude/codex/whatever instance) mitigates this by watching for suspiciously easy green passes (just tell it) and probing the spec for ambiguity, but it's not a complete defense.
by bronlund | 2026-03-10 20:33:20+00:00
I don't think that many will disagree with you on this. If we compare it to the dot-com bubble in 2000, just as speculators then tried to get in early in this new thing called Internet, today they are trying to capture early market shares in this new thing called AI. But just like then, when this bubble bursts, the technology that everyone was chasing, will still be relevant. Today the internet is regarded as critical infrastructure.

And I do think you are underestimating just how much money these guys can print, if some event is disrupting the machine. If this thing really goes down, it will be by design and because they got the Thing 2.0 ready to go.
by dang | 2026-03-10 20:33:29+00:00
> you've responded to a handful of other comments and ignored this one

I hadn't seen this until 30 seconds ago. The assumption of moderator omniscience leads to a lot of mistaken conclusions!

Sure, we marked the offtopic comments offtopic, which lowers them on the page. This is standard HN moderation. If we didn't do this, then nearly every thread would be choked with something offtopic at the top.

At the same time, we haven't killed the posts or put them in a "stub for offtopicness" [1] like we otherwise would. They're still here for people who want to read them, while at the same time the main discussion can be about the main topic, which is the startup launch.

HN is actively moderated and always has been. Downweighting offtopic/generic comments is one of the biggest things we've ever discovered for improving the quality of the threads. For us it's about the quality of the site as a whole, not specific narratives, but of course everyone can (and will) make up their own mind about this. What I can tell you is (a) the way we do these things has been stable for a long time (HN time is measured in decades, not years), and (b) we're always willing to answer questions about it.

Oh, and (3) - when YC or a YC-funded startup is part of a story, then we moderate less than we otherwise would [2]. We do still moderate, though—we just do it less.

[1] https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...

[2] https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
by tavavex | 2026-03-10 20:33:41+00:00
But they could implement it without buying Moltbook. Easily. They have the money and the engineers to make it happen a hundred times over. Something like it already might be on Facebook.

To me, this feels more like acquiring the name. Everyone's heard that 'trademark' so they want to have it so they could reuse it for whatever they make later.
by oraphalous | 2026-03-10 20:34:47+00:00
I think maybe - this conversation is more about giving some more acknowledgement to the other side of this issue.

It's not that I disagree with you essentially - or particularly with respect to your analysis of your specific examples. 100% in the cases you describe. Those sound like beneficial tests. Particularly because your example SPEAKS to the business case - users were using the help docs (I think you mean users anyway). So yeah - that's important.

But I don't know why it's so hard extracting a simple acknowledgement of what I'm pointing out - specifically that the decisions like implementing tests IS a cost-benefit decision dependent on business context.

Funny you mention auth testing though. One time both me and the tech lead broke one of the auth flows in production within the space of a week of one another. Yep - no tests. Feel free to judge us insane. But here's how we thought about it - and when I say "we" that includes the business. First of all the auth flow was not actually used by any active users, so damage was low. Two man dev team. Complexity up until that point had been low, pre-product market fit, sales were dogshit, and cash had been low for some time. Feature shipping was the 110% priority. Ok - but these bugs were a sign complexity had increased beyond what we could manage without some tests. And given the importance of auth, it was now easy to make the case to leadership that implementing an e2e test suite was worth it. So we did.

If you still think a decision making process like that is insane - because we didn't immediately implement tests for every shipped feature. Well - I just think you're wrong.
by jseabra | 2026-03-10 20:34:47+00:00
The information asymmetry framing is the most useful part of this. But I'd push back on where you locate the problem.
It's not really a paradox, it's a design failure. The prompt-as-interface forces users to encode what they know into natural language on the fly, which is lossy and inconsistent. The solution isn't better prompting or bigger context windows. It's structured context gathering before inference. Interview the user systematically, build a high-resolution data model upfront, then let the model work against that. The quality difference is significant.
I've spent the last year building AI systems that do exactly this. Not chat, but structured intake flows that front-load the information problem. The outputs are categorically different from what you get in a back-and-forth chat session. Not because the model is smarter, but because it's better informed. The NBA coach analogy holds: give the coach a 30-minute intake session before the first lesson and suddenly the advice stops being generic.
On the agent systems critique, fair, but I'd separate two things. Token cost is an engineering problem, solvable with better retrieval, caching, compression. The "just a big system prompt" criticism is more interesting. Some of what gets marketed as agentic is basically dressed-up RAG. The genuinely agentic stuff, dynamic tool use, multi-step reasoning with actual state, is meaningfully different. The problem is the category is polluted by hype.
The game interface idea is interesting but I suspect the real bottleneck isn't visual metaphor. It's persistent memory and reliable state management. Fix those and the interface question mostly solves itself.
by thepasch | 2026-03-10 20:34:50+00:00
> the headline deliberately tries to blow this up into a big deal

I do not understand how “company that runs half the internet has had major recent outages and now explicitly names lax/non-existent LLM usage guidelines as a major reason” can possibly not be a big deal in the midst of an industry-wide hype wave over how the world’s biggest companies now run agent teams shipping 150 pull requests an hour.

The chain of events is “AWS has been having a pretty awful time as far as outages go”, and now “result of an operational meeting is that the company will cut down on the use of autonomous AI.” You don’t need CoT-level reasoning to come to the natural conclusion here.

If we could, as a species, collectively, stop measuring the relevance of a piece of news proportionally by how much we like hearing it, please?
by Maxatar | 2026-03-10 20:35:07+00:00
I have not found this to be the case. My company has some proprietary DSLs we use and we can provide the spec of the language with examples and it manages to pick up on it and use it in a very idiomatic manner. The total context needed is 41k tokens. That's not trivial but it's also not that much, especially with ChatGPT Codex and Gemini now providing context lengths of 1 million tokens. Claude Code is very likely to soon offer 1 million tokens as well and by this time next year I wouldn't be surprised if we reach context windows 2-4x that amount.

The vast majority of tokens are not used for documentation or reference material but rather are for reasoning/thinking. Unless you somehow design a programming language that is just so drastically different than anything that currently exists, you can safely bet that LLMs will pick them up with relative ease.
by TulliusCicero | 2026-03-10 20:35:24+00:00
You're being very vague here. If you agree that rights are not unlimited and it's okay to have common sense property restrictions like building codes, then what's the fundamental problem with red light cameras? What is your actual objection here?

> the government can't just put up cameras and start dragnet fining people for rules that only really exist on paper

The hell? Do you think "don't run red lights" is a rule that "only exists on paper"? You realize that if a cop sees you run a red light, they're definitely pulling you over, right?
by bigyabai | 2026-03-10 20:36:27+00:00
> the companies we like survive

I encourage you to reflect on what this actually means. The "companies we like" have already shown their true colors. All they can do now is pretend it won't happen again.

There's no reality where you wake up to find Tesla or Apple are their idyllic versions of themselves. Even a complete political upheaval cannot create an environment that prevents crony capitalism; Tesla and Apple need federal support to compete. They will bend over backwards embarrassing themselves to secure it, and in a truly capitalist economy they should be punished for failing to compete naturally.

If you believe in fair competition, no company deserves to die more than the ones reliant on anticompetitive federal support. Kissing ass in an administration this corrupt represents the apex of America's meritocracy issue. I'm hoping the FTC breaks them all up, anything less will set the precedent that federal corruption is rewarding and desirable. If any of them survive, we're all doomed.
by claytongulick | 2026-03-10 20:36:30+00:00
Serious question here.

Have you taken a moment to step back and truly evaluate your productivity when using LLMs for code generation?

I don't mean the obvious confirmation-bias tickling stuff like "create a form with these fields and validation".

I mean from a whole-system, total effort analysis, from idea to production, support and maintenance.

I'm curious what you find.

My current theory is that the industry will land in a place where LLMs for code generation are frowned upon for non-trivial work, but that they are embraced for tooling, summarization and explanation.

I think these things have real, concrete value, but that it's a mistake to substitute them for human reasoning - and human reasoning is a crucial characteristic of quality code.

The thing I'm not sure of is whether the current "good enough is good enough" approach to vibe coded solutions will be sticky, or in what contexts.

MS Access still powers entire business departments, because good enough is good enough.
by sofixa | 2026-03-10 20:36:38+00:00
> The good Netflix movies are small diamonds in a swamp of garbage. Most of the content is the equivalent of fast food for movies with a political agenda.

What political agenda? Are gays existing in movies political for you or what do you mean?

And again, Netflix have to play a numbers game. They need to have enough content for people not to leave them. That doesn't mean they don't also have genuine quality content like Better Call Saul, Peaky Blinders, Kaleidoscope, etc.

> TBH I don’t care about the WB news media part through I’m not sure if they will really destroy it just to align with their political views

They already started, appointing a political hack to be head of CBS, and CBS have already quickly turned very politically biased. Why wouldn't they do the same to CNN?
by root_axis | 2026-03-10 20:36:39+00:00
> They could find a way to climb up the value chain and capture more of the consumer surplus.

Yes, this is exactly why OpenAI and Anthropic are hyping AGI. If LLMs ever become good enough to replace workers, the first sign will be frontier model companies launching competitor businesses. It doesn't make sense to sell the formula for gold when you can just use it yourself.

> There could be a paradigm shift in compute architecture/compute cost.

Possible, but no signs of this on the horizon. If it does happen, it's impossible to predict when it will.

> We could reach a limit of marginal utility, shifting consumption to legacy models, thereby lengthening the depreciation/utility of training.

I'm not sure market dynamics will allow this any time soon. We seem to have already achieved a marginal utility equilibrium in terms of model size, so training new models on trending use-cases (e.g. synthetic data targeting tool calls, agentic workflows, computer use, etc) is really the driving force behind product differentiation. Nobody wants to admit "training new models isn't profitable" because that deflates the AGI singularity narrative that all this investment hinges on.
by gruez | 2026-03-10 20:37:03+00:00
>>Most techie people I know ripped their DVD collections. Many ripped their Blurays but plenty didn't because it requires specialized software to get around the DRM. Only a handful of them have ripped their UHD discs which require specialized software AND specific hardware AND flashing a specific firmware on that hardware.

>The vast majority of DRM protected content (or at least majority by watch time) available in UHD via torrent in a matter of hours. People like to stay away from torrents, because it carries significant risk in many jurisdictions.

Sounds like you're proving his point? If stripping DRM is so trivial that anyone can pop in a bluray and rip it (like ripping CDs in itunes), piracy would arguably far worse. Pirates today have to brave shady torrent sites and the risk of getting C&D letters. Asking your friend to make a copy is far more accessible.
by shubham2802 | 2026-03-10 20:37:20+00:00
Sorry about that but this is what is being there in github : Apple M3 or later required. MetalRT uses Metal 3.1 GPU features available on M3, M3 Pro, M3 Max, M4, and later chips. M1/M2 support is coming soon. On M1/M2, RCLI automatically falls back to the open-source llama.cpp engine.
by jseabra | 2026-03-10 20:37:22+00:00
The friction you're solving is real. Most testimonial tools assume the hard part is displaying the content. It's not. It's the collection step, specifically the moment between "customer is happy" and "you actually ask them." That gap is where most testimonials die.
One thing worth thinking about on pricing: the white-label add-on at $10/mo feels underpriced for agencies. If an agency is running this across 10 client sites, they're getting it for $1/client/month. That's a rounding error on their invoice. You could probably 3x that without losing agency customers, and it'd help you figure out early whether agencies are actually your real market or just a nice-to-have segment.
The "unlimited everything" angle is a good call. Usage-based pricing for something like embeds creates constant mental overhead for the customer. Removing that is a genuine product decision, not just positioning.
by api | 2026-03-10 20:38:39+00:00
A pattern I've noticed for a very long time:

A lot of times the path to the highest performing CPU seems to be to optimize for power first, then speed, then repeat. That's because power and heat are a major design constraint that limits speed.

I first noticed this way back with the Pentium 4 "Netburst" architecture vs. the smaller x86 cores that became the ancestor of the Core architecture. Intel eventually ran into a wall with P4 and then branched high performance cores off those lower-power ones and that's what gave us the venerable Core architecture that made Intel the dominant CPU maker for over a decade.

ARM's history is another example.
by imiric | 2026-03-10 20:38:42+00:00
My comment is based precisely on using these tools frequently, if not daily, so what's wild is you assuming I don't.

The impact that lack of training data has on the quality of the results is easily observable. Try getting them to maintain a Python codebase vs. e.g. an Elixir one. Not just generate short snippets of code, but actually assist in maintaining it. You'll constantly run into basic issues like invalid syntax, missing references, use of nonexistent APIs, etc., not to mention more functional problems like dead, useless, or unnecessarily complicated code. I run into these things with mainstream languages (Go, Python, Clojure), so I don't see how an esolang could possibly fair any better.

But then again, the definitions of "just fine" and "decent" are subjective, and these tools are inherently unreliable, which is where I suspect the large disconnect in our experiences comes from.