MurphyA/hacker-news huggingface dataset:
by vintagedave | 2026-03-10 11:03:11+00:00
This is specifically AIs modeling the 99 Helpers C-level team? I was interested in a fictional chat representing 'a' C-level suite, to ask questions about any company or approach.

That said, I selected all of your characters and asked:

> Ok, you're looking for investment. Why, and what questions that I have can you answer right now?

Hoping for something solid. My prompt may not have been strong enough, but if I was an investor I'd be asking, say, revenue stats or questions leading to diligence answers, etc.

What I expected was some kind of group chat in terms of some people replying, not everyone, or one person and then another riffing off them, rather like you would in a meeting.

What I got was eight separate replies, ie everyone replied. All were long, three lengthy paragraphs at least each -- this was far too much to read as a group chat. I skimread it all. All inhabited their persona well. None of them were particularly detailed. One of the best was Marcus, who replied (among lots of other text):

> I can also elaborate on our current MRR and ARR, our churn rates, and our burn rate and runway.

So I moved to a 1:1 chat, quoted him, and asked for info. Again a verbose reply but it included:

> Our current Monthly Recurring Revenue (MRR) stands at $85,000, which translates to an Annual Recurring Revenue (ARR) of $1,020,000.

> we're currently tracking at a gross monthly churn rate of 2.1%. While this is within acceptable industry ranges for a company at our stage, I'm not satisfied

> Our burn rate is $200,000 per month. This figure reflects our investment in product development, sales, and marketing efforts to scale 99helpers. Based on our current cash reserves, our runway is approximately 15 months

I have no idea how true these numbers are, ie if the AIs have access to your internal finances or not. It would be a very interesting approach to the open startup concept.

Either way, I feel the multi-persona approach is interesting; does not work currently as a group chat; is too generic ('I can talk about...'); and is overly verbose. I think it has potential if it is concise, if personas only reply if they have something to genuinely contribute, and if the strength of the inhabited persona character does not overrule the insight or advice.
by hrmtst93837 | 2026-03-10 11:03:25+00:00
Columnar solves some query patterns but treating row format as a dealbreaker for top-k is an overreach. For modest-to-mid datasets with the right index Postgres handles top-k on composite keys well, especially if reads aren't scanning millions of rows or you can fit hot columns in memory.

If latency really matters and you are working with large datasets, columnar extensions help, but they come with operational overhead and can limit transactional features, so it's usually better to stick with row-based unless you have a clear need.
by gsf_emergency_7 | 2026-03-10 11:03:46+00:00
Liked this comment:

"If we could convince [any] Sociopath that we were all Losers, we might be able to entice them into spilling their secrets as 'Straighttalk'. (Arguably that's what this book is..)"

On one hand Rao doesn't say much about Gametalk which is what the above implies should be our default speech.

On the other, Rao much more optimistic than Orwell, who declared doublespeak the lingua franca?
by WillAdams | 2026-03-10 11:04:02+00:00
Nice! Reminds me a bit of "WordWeb" which is still around:

https://wordweb.info/free/

which also uses WordNet:

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

(which this is also using)

which was developed by Princeton w/ DARPA money as an early investigation into AI and so forth.
by A_D_E_P_T | 2026-03-10 11:04:35+00:00
Justifiable.

There are a lot more degrees of freedom in world models.

LLMs are fundamentally capped because they only learn from static text -- human communications about the world -- rather than from the world itself, which is why they can remix existing ideas but find it all but impossible to produce genuinely novel discoveries or inventions. A well-funded and well-run startup building physical world models (grounded in spatiotemporal understanding, not just language patterns) would be attacking what I see as the actual bottleneck to AGI. Even if they succeed only partially, they may unlock the kind of generalization and creative spark that current LLMs structurally can't reach.
by trymas | 2026-03-10 11:04:36+00:00
IMHO it started with iOS 7 [1] - year 2013.

Uber flat, you don't know what's a button, what's a text. I dunno if I just adjusted to it or it actually somewhat got better up to iOS/macOS 15. Though with iOS/macOS 26 - it's iOS 7 moment yet again.

NB: not sure about Liquid Glass - though I was recently (and weirdly) recommended to watch iOS 7 trailer on youtube[2]. Comments are overwhelmingly positive. Dunno if it's just people who were kids/teens looking through rose tinted glasses. Though I am not sure anymore, maybe people actually like such designs and it's just HN bubble complaining (IMHO complains here are 110% valid) about nothing. Maybe in 10+ years ordinary guy will praise iOS/macOS 26.

[1] https://en.wikipedia.org/wiki/IOS_7

[2] https://www.youtube.com/watch?v=4xzLr7xSr-g
by overrun11 | 2026-03-10 11:05:17+00:00
> which would imply that the money their making off it isn't enough

I don't think this logically follows. An unlimited buffet doesn't let you resell all of the food out the backdoor. At some level of usage any fixed price plan becomes unprofitable.

I agree the 5k cap is interesting as evidence although as you said I suspect there are other reasons for it.

As for evidence against it: The Information reported that OpenAI and Anthropic are 30%+ gross margins for the last few years. Sam Altman and Dario have both claimed inference is profitable in various scattered interviews. Other experts seem to generally agree too. A quick search found a tweet from former PyTorch team member Horace He: https://x.com/typedfemale/status/1961197802169798775 and a response to it in agreement from Anish Tondwalkar former researcher at OpenAI and Google Brain.
by yorwba | 2026-03-10 11:05:18+00:00
> The new 1s have a mandate to at least run on local hardware.

They do? Source?

But if that's true, it would explain why Minimax, Z.ai and Moonshot are all organized as Singaporean holding companies, with claimed data center locations (according to OpenRouter) in the US or Singapore and only the devs in China. Can't be forced to use inferior local hardware if you're just a body shop for a "foreign" AI company. ;)
by jeremyg22 | 2026-03-10 11:05:33+00:00
I built A Million Pixels to explore the capabilities of AI tooling and coding agents, and to help enable others to experiment with AI.

I am convinced that the next generation of developers will need to be AI-native to be successful. As an industry, we are woefully underprepared for that future.

That said, I don't think we really know exactly what AI-native actually means yet. I do know it's more than just the prompt. It's knowing how to collaborate with LLM driven systems, being able to decompose problems well, validating outputs critically, leveraging agents responsibly. Who knows what else.

Access to that kind of experimentation shouldn’t be gated, so proceeds go to coding education and AI credits.

For those interested in the tech stack behind the site, and which decisions where made by me and which were decided by AI Agents: https://www.amillionpixels.ai/blog/the-a-million-pixels-tech...
by codingdave | 2026-03-10 11:05:34+00:00
Bodies acclimate. You'll go up in altitude one day and get quite sick, then the next day or another day you'll be fine. Spend a week there and you'll feel better. Drink water, stay hydrated, and it will go more smoothly. So unless somebody found something specifically atypical about your body, don't reject all mountains in the world because of one bad day 40 years ago.
by pjc50 | 2026-03-10 11:05:54+00:00
How would you tell that it's LLM-generated in that case?

If the submitter is prepared to explain the code and vouch for its quality then that might reasonably fall under "don't ask, don't tell".

However, if LLM output is either (a) uncopyrightable or (b) considered a derivative work of the source that was used to train the model, then you have a legal problem. And the legal system does care about invisible "bit colour".
by wkjagt | 2026-03-10 11:06:29+00:00
I so miss the days when software was like this. I recently got a 386 laptop that needed loads of repairs. I'm almost done with the repairs, and I will definitely put Lotus 1-2-3 on it (along with dBase, Word Perfect 5.1, and Turbo C). Thanks for this post, it motivated me even more to finish those repairs!
by zozbot234 | 2026-03-10 11:06:34+00:00
Unfortunately, LLMs generate useless word salad and nonsense even when working on issues text, you absolutely have to reword the writing from scratch otherwise it's just an annoyance and a complete waste of time. Even a good prompt doesn't help this all that much since it's just how the tool works under the hood: it doesn't have a goal of saying anything specific in the clearest possible way and inwardly rewording it until it does, it just writes stuff out that will hopefully end up seeming at least half-coherent. And their code is orders of magnitude worse than even their terrible English prose.
by knorker | 2026-03-10 11:07:03+00:00
I think it's huge greenwashing to claim to not have coal power, but just import it when needed. What practical difference is that to having coal power domestically? That's just saying you recycle all plastic, only to send it to the third world to dump in rivers.

So I think it's untrue to say that Sweden doesn't rely on coal power. Without coal power it'd have regular blackouts. I rely on being able to take a breath every couple of seconds. If I only get an annual average of a breath every few seconds, I'll die.

One could show great generation and net export statistics with a sufficiently large batteryless solar installation, and still import coal power every night and cloudy day.

What is true, but can easily imply an incorrect conclusion, is that Sweden's very good in being self sufficient in clean power generation statistically. Yes, very much true. But it's largely due to geography, and not merely something to replicate. Sweden has way more viable places where hydro could be installed, than most countries (though where economical and otherwise acceptable, it already has). And it's sparsely populated; Sweden is bigger than the UK, but with one seventh the population. So if the implication is that "if we can do it, so can you" then that's false.

Luckily the political wind (including population opinion) has started to turn in favor of nuclear power, again. Maybe everything can be solar in 100 years, but we can't have 100 more years of coal.
by zigzag312 | 2026-03-10 11:07:23+00:00
Some sort of LLM audit trail is needed (containing prompts used, model identifier and marking all code written by LLM). It could be even signed by LLM providers (but that wouldn't work with local models). Append only standard format that is required to be included in PR. It wouldn't be perfect (e.g. deleting the log completely), but it might help with code reviews.

This would probably be more useful to help you see what (and how) was written by LLMs. Not really to catch bad actors trying to hide LLM use.
by MrDrDr | 2026-03-10 11:08:56+00:00
I remember thinking about this when the semantic web was first being discussed. If you think of it from the perceptive of a child, your first 'foundational' words are learned though direct experience. Then while you continue to learn words this way, we can also use those words we 'know' to define secondary or tertiary terms that we have no direct experience of. I'd like to see a graph like this with someones take on the minimum number of necessary foundational words and how that graph would look.
by rvz | 2026-03-10 11:09:29+00:00
Once again, US companies and VCs are in this seed round. Just like Mistral with their seed round.

Europe again missing out, until AMI reaches a much higher valuation with an obvious use case in robotics.

Either AMI reaches over $100B+ valuation (likely) or it becomes a Thinking Machines Lab with investors questioning its valuation. (very unlikely since world models has a use-case in vision and robotics)
by mrgaro | 2026-03-10 11:09:32+00:00
I'm an avid user of the Claude Code planning feature and I like how Claude Code asks for questions. I also often iterate the plan before finally giving it a go.

How do you solve this in Kelos?

I tried to check the code base, but it didn't really provide any glues. I guess I could instruct the agent to build a plan and to post the plan in the issue and then iterate that with written comments in the issue. Is that how you run it?
by funcDropShadow | 2026-03-10 11:09:39+00:00
> Garbage collection, by definition, trashes locality.

No, it doesn't necessarily. It is correct if you are thinking about mark and sweep GC. But that is 50 years old. Generational GC has much better locality. And GC can even improve locality sometimes by moving objects together that are connected by references. I am not claiming that GC is always a big win for locality. That would be very far from the truth. On the other hand it is not by definition so, that locality is trashed by GC.
by apimade | 2026-03-10 11:10:06+00:00
Likely aimed at classified/defence environments. In those spaces, hardware typically takes 18–36 months after commercial deployment before it’s approved—due to firmware vetting, side-channel analysis, crypto validation, and similar processes.

Meanwhile, commercial operators have already deployed their hardware for public workloads. Existing Blackwell capacity won’t just be shifted into classified environments—governments don’t repurpose hardware from unclassified infrastructure for secret/TS systems. That deployed stock will stay in the private sector for hosted AI workloads.

For many high-security use cases, new Blackwell systems may effectively be the only viable option, especially given the slow review cycles around new firmware and GPU software stacks. Newer chipsets will also be prioritized for training due to performance gains.

Oracle likely recognizes this dynamic and is betting competitors may eventually need to deploy in their data centers. Governments haven’t historically deployed GPU capacity at this scale-beyond ASIC/FPGA crypto workloads.. and likely don’t have large pools of pristine Blackwell hardware available.

They’re also purchasing late in the cycle, which may work in their favour.
by FrenchDevRemote | 2026-03-10 11:11:11+00:00
I don't think we necessarily need more dating apps, just better ones,
but since online dating is currently more or less a monopoly from Match.com, all apps are pretty much the same, and they're designed to extract as much money as possible from people without even really trying to create good connections.

I think it would be nice to have some apps with a different approach.
by MadameMinty | 2026-03-10 11:11:44+00:00
Why would I ask annoying questions when I can identify, reproduce, pinpoint the bug, locate it in code, and fix it? Doing it alone should make it clear I don't need to ask to understand it. And why would I be interested in small talk? Doubt many people are when they patch up their work tools. It's a dispassionate kind of kindness.

Not to mention LLMs can be annoying, too. Demand this, and you'll only be inviting bots to pester devs on IRC.
by ivanbakel | 2026-03-10 11:11:47+00:00
>Some programming languages (Kotlin, Swift, Rust, Typescript...) already do something similar for possible null pointer access: they require that you add a check "if s == null" before the access.

For Rust, this is not accurate (though I don't know for the other languages). The type system instead simply enforces that pointers are non-null, and no checks are necessary. Such a check appears if the programmer opts in to the nullable pointer type.

The comparison between pointers and integers is not a sensible one, since it's easy to stay in the world of non-null pointers once you start there. There's no equivalent ergonomics for the type of non-zero integers, since you have to forbid many operations that can produce 0 even on non-0 inputs (or onerously check that they never yield 0 at runtime).

>The same can be done for division (and remainder / modulo). In my own programming language, this is what I do: you can not have a division by zero at runtime, because the compiler does not allow it... In my experience, integer division by a variable is not all that common in reality

That's another option, but I hardly find it a real solution, since it involves the programmer inserting a lot of boilerplate to handle a case that might actually never come up in most code, and where a panic would often be totally fine.

Coming back to the actual article, this is where an effect system would be quite useful: programmers who actually want to have their code be panic-free, and who therefore want or need to insert these checks, can mark their code as lacking the panic effect. But I think it's fine for division to be exposed as a panicking operation by default, since it's expected and not so annoying to use.
by bsenftner | 2026-03-10 11:12:38+00:00
There will be no "unlocking of AGI" until we develop a new science capable of artificial comprehension. Comprehension is the cornucopia that produces everything we are, given raw stimulus an entire communicating Universe is generated with a plethora of highly advanceds predator/prey characters in an infinitely complex dynamic, and human science and technology have no lead how to artificially make sense of that in a simultaneous unifying whole. That's comprehension.
by A_D_E_P_T | 2026-03-10 11:13:10+00:00
Sure, but don't conflate the representation format with the structure of what's being represented.

Everything is bits to a computer, but text training data captures the flattened, after-the-fact residue of baseline human thought: Someone's written description of how something works. (At best!)

A world model would need to capture the underlying causal, spatial, and temporal structure of reality itself -- the thing itself, that which generates those descriptions.

You can tokenize an image just as easily as a sentence, sure, but a pile of images and text won't give you a relation between the system and the world. A world model, in theory, can. I mean, we ought to be sufficient proof of this, in a sense...
by CodesInChaos | 2026-03-10 11:13:43+00:00
I'm confused by that thing. After scrolling for a couple of minutes, the animation seems to play on on its own for longer than I cared to wait. Isn't the animation supposed to be coupled to the scrolling?

And it doesn't really feel like scrolling either, since apart from the tiny depth bar on the left, no content scrolls by.
by furyg3 | 2026-03-10 11:14:17+00:00
I grew up in Fremont, CA, which pioneered the use of red-light cameras and terrible red-light camera practices (e.g. shortening yellow light times to increase revenue and giving a cut of the fines to the companies installing cameras). I hated cameras, the idea of speed cameras felt like big brother, and the basic principal of attributing a violation to a car and not a person (and thus requiring a person to rat out the driver) felt like a huge civil rights issue.

I then moved to Amsterdam and became the biggest fan of continuous, always-on ANPR speed cameras. On some freeways, your car is recorded at certain checkpoints and EVERYONE driving over the speed limit ALWAYS gets a fine.

Why? Because they are properly implemented (only high-risk areas), very well communicated (tons of signage), consistently applied (no crying your way out of a ticket, no racial profiling), purpose targeted (you get a speeding ticket, not a bunch of other fines at the whim of a cop), and correctly incentivized (ticket revenue does not immediately go to the local police or city).
by latexr | 2026-03-10 11:14:26+00:00
> I mean I don’t have to remember the horrible git command line anymore

Every time I see a comment like this, I have to wonder what the heck other devs were doing. Don’t you know there were shell aliases, and snippet managers, and a ton of other tools already? I never had to commit special commands to memory, and I could always reference them faster than it takes to query any LLM.
by Grimblewald | 2026-03-10 11:15:18+00:00
which would be fine / legal, but brings us back to a question many turns ago, why invest time in this particular project? There's a sea of similar projects, what makes yours stand out as worth investing in? The claims made don't stack up, and there's no indication this is anything other than LLM output, which means there's nothing here that can't be re-created quickly to a similar level of quality, however you define that. If we start rewarding everyone who posts mediocre repos to HN with attention, the entire platform will get drowned in no time. Show some personal effort first, then I'll invest mine. Discourse on a public central page like HN at least benefits public discourse. Engaging with your repo? no real benefits as far as I can tell.
by johnb95 | 2026-03-10 11:15:26+00:00
TL;DR
We conducted a live experiment asking Claude Opus 4.6 to discover and read content across three websites built as React single-page applications with Express backends. At the start of the session, all three sites were effectively invisible — Claude received empty HTML shells with no article content, no navigation, and no discoverable paths to any content. Over several hours of iterative testing, debugging, and deployment, we identified which artifacts make a site legible to AI agents and which failures leave it dark. The single most impactful change was a plain-text sitemap (`sitemap.txt`) — one file, one URL per line, that transformed a completely opaque site into one Claude could navigate autonomously. The experiment also revealed that server-side HTML injection, structured Markdown endpoints, `llms.txt` directories, homepage discovery links, and correct MIME types each play distinct and complementary roles in AI legibility. A final test of the Unified TOON Meta-Index (`utmi.toon`) demonstrated that consolidating crawl rules, site index, AI summaries, and API tool registration into a single token-optimized file is viable and immediately useful to an AI agent — provided the file is served with a text MIME type rather than the default binary content type that web servers assign to unknown file extensions.

Key Takeaways
React single-page applications are invisible to AI agents by default. Claude's fetch tools do not execute JavaScript, so any content rendered client-side does not exist from the agent's perspective.
A plain-text sitemap (sitemap.txt) was the single most impactful artifact. Once provided, Claude could autonomously discover and read every piece of content on a site.
Server-side HTML injection works — but edge caching can mask it entirely. A working injection pipeline appeared broken for over an hour because stale cached responses were being served.
Markdown endpoints (.md) are the ideal content format for AI agents. Structured front matter, clean hierarchy, and explicit metadata allow an LLM to parse, cite, and reason about content with zero friction.
Homepage discovery is the critical gap. If the homepage returns nothing navigable, an AI agent has no starting point — even if every other endpoint works perfectly.
MIME types for novel file formats must be explicitly configured. A .toon file served as application/octet-stream is unreadable binary to an AI agent, regardless of how well-designed the format is.
The UTMI format (utmi.toon) consolidates robots.txt, sitemaps, llms.txt, metadata, and API tool registration into a single file that Claude could parse immediately once the MIME type was corrected — demonstrating that unified site manifests are viable and useful for AI agents.