MurphyA/hacker-news huggingface dataset:
by foo42 | 2025-05-31 20:04:55+00:00
For me, using a keyboard with more keys around the thumbs and configurable keymaps with layers solves this. I have space, return and esc all easily accessible on my thumbs and my most used punctuation across the easily reached keys (such as home row) on a layer, also activated with my thumb.

fwiw I use a moonlander but many keyboards could work equally well I'm sure
by ddxv | 2025-05-31 20:05:16+00:00
Thanks, I enjoy it too!

I listened to a podcast (hard fork) with the Chief Product Officer of Anthropic talking about how he only hires people who can manage several Claude agents to program at a time.

It felt so odd because in the post it all seems so regular. The same integrations with the same analytics companies as usual.

Even the doubling and tripling up of analytics because various teams need them for various things. And I'm certain at the end of the day they also have a home grown backend BI which is the source of truth.

The narrative swings wildly in my head between this is a huge change and this is business as usual.
by jlundberg | 2025-05-31 20:05:58+00:00
I make a living from the SMS & telephony API I made.

Our MRR is ~500 000 EUR and our pricing model is pay-as-you-go (per SMS, per MMS, per phone call minute, per month for virtual mobile phone numbers).

The problem we solve is programmatic access to the mobile networks, specifically in Europe/Sweden.

We got out first paying customers through offline networking: going to hackathons, meetups and poking tech friends to find the first few early adopters.

Which is also our biggest challenge, it is hard to scale an offline based go-to-market method.

It has certainly been a painful struggle to get here and it still feels surreal it works so well.
by Henchman21 | 2025-05-31 20:07:08+00:00
All the Joes I know would spend serious time hunting these robots.

IMO, real actual people don’t want to live in the world you described. Hell, they don’t wanna live in this one! The “elites” have failed us. Their vision of the future is a dystopian nightmare. If the only reason to exist is to make 25 people at the top richer than gods? What is the fucking point of living?
by vidarh | 2025-05-31 20:07:16+00:00
I've built a coding assistant over the last two days. The first 100 lines or so were handwritten. The rest has been written by the assistant itself.

It's written its system prompt. It's written its tools. Its written the code to reload the improved tools into itself.

And it knows it is working on itself - it frequently tries to use the enhanced functionality, and then expresses what in a human would be frustration at not having immediate access.

Once by trying to use ps to find its own pid in an apparent attempt to find a way to reload itself (that's the reason it gå before trying to run ps, anyway)

All its commits are now authored by the tool, including the commit messages. It needs to be good, and convincing, and having run the linter and the test suite for me to let it commit, but I agree a substantial majority of the time. It's only caused regressions once or twice.

A bit more scaffolding to trigger an automatic rollback in the case of failure and giving it access to a model I won't be charged by the token for, and I'd be tempted to let it out of the box, so to speak.

Today it wrote its own plan for what to add next. I then only told it to execute it.

A minor separate goal oriented layer guiding the planning, and it could run in a loop.

Odds are it'd run off the rails pretty quickly, but I kinda want to see how far it gets.
by ryao | 2025-05-31 20:08:10+00:00
They also support external DRAM over their 150GB/sec system IO link. They call it MemoryX and talk about it on these blog posts:

https://www.cerebras.ai/blog/cerebras-cs-3-vs-nvidia-b200-20...

https://www.cerebras.ai/blog/announcing-the-cerebras-archite...

It is useless for inference, but it is great for training. It used to be more prominent on their website, but it is harder to find references to it now that they are mimicking Groq’s business model.
by hinkley | 2025-05-31 20:08:14+00:00
Buzz Aldrin would have killed the Apollo 11 crew if Margaret Hamilton had been a worse developer. He pulled up an optional display during the landing that was overloading the computer due to a bug, and the scheduler kept killing optional tasks to keep the control and guidance systems all running.

There’s the same sort of realtime system in the BEAM. When you run nice to have and mission critical software together life is much less exciting if you have something handling such things.
by rjakob | 2025-05-31 20:10:18+00:00
Best feedback so far!

You're right: In the current version each "agent" essentially loads the whole paper, applies a specialized prompt, and calls the OpenAI API. The specialization lies in how each prompt targets a specific dimension of peer review (e.g., methodological soundness, novelty, citation quality). While it’s not specialization via architecture yet (i.e., different models), it’s prompt-driven specialization, essentially simulating a review committee, where each member is focused on a distinct concern. We’re currently using a long-context, cost-efficient model (GPT-4.1-nano style) for these specialized agents to keep it viable for now. Think of it as an army of reviewers flagging areas for potential improvement.

To synthesize and refine feedback, we also run Quality Control agents (acting like an associate editor), which reviews all prior outputs from the individual agents to reduce redundancy and surface the most constructive insights (and filter out less relevant feedback).

On your point about nitpicking: we’ve tested the system on several well-regarded, peer-reviewed papers. While the output is generally reasonable and we did not discover "made up" issues yet, there are occasional instances where feedback is misaligned. We're convinced, however, we can almost fully reduce such noise in future iterations (Community Feedback is super important to achieve this).

On the code side: 100% agree. This is very much an MVP focused on testing potential value to researchers, and the repeated agent classes were helpful for fast iteration. However, your suggestion of switching to template-based prompt loading and dynamic agent registration is great and would improve maintainability and scalability. We'll 100% consider it in the next version.

The _determine_research_type method is indeed a stub. Good catch. Also, lol @ the JS comment hashes, touché.

If you're open to contributing or reviewing, we’d love to collaborate!
by nobody9999 | 2025-05-31 20:10:29+00:00
Fair points.

But if you use the same bitcoin address for every email, you won't know who the mark that actually paid was -- potentially leaving money on the table for the future.

I did (before posting the comment to which you replied) check to see if there was any activity[0] on the bitcoin address in the email I posted. There was none. Which strengthened my conclusion (I have no evidence) that unique bitcoin addresses were in use.

But I certainly could be wrong.

[0] https://www.blockchain.com/explorer/addresses/btc/bc1qt30ya4...
by 01HNNWZ0MV43FF | 2025-05-31 20:11:30+00:00
Lua is telling me 0.1 + 0.1 == 0.2, but 0.1 + 0.2 != 0.3. That's 64-bit precision. The issue is not with precision, but with 1/10th being a repeating decimal in binary.
by AnotherGoodName | 2025-05-31 20:12:22+00:00
Ok playing with this more there's very subtle differences between sessions. As in there is some hallucination here with certain small differences.

I think what's happening is this is AI generated but it is very very overfitted to real world 3D scenes. The AI is almost rendering exactly a real world scene and not much more. They can't travel out of bounds or the model stops working since it's so overfitted to these scenes. The overfitting solves hallucinations but it also makes it almost indistinguishable from pre modelled 3D scenes.
by sltr | 2025-05-31 20:12:33+00:00
I've filed with freetaxusa for about 6 years. It's been great. Does everything TurboTax can. It's free, but I splurge and spend all of $8 for Deluxe to show support.

Anything to unseat Intuit. That said, a direct file option would be best, but I don't trust myself to fill the forms correctly.
by lostmsu | 2025-05-31 20:13:09+00:00
I built a speech-to-text API at $0.06/h. Currently making about $5k MRR. The pricing model is flat rate + throttled API for experimentation. The first paying customers came from Reddit comments on relevant topics. Speech transcription at scale is expensive with majority of the current cloud providers.

Biggest challenge was getting the first few customers (is there anyone for who this was not the case?).

https://borgcloud.org/speech-to-text
by ccorcos | 2025-05-31 20:14:04+00:00
It’s also possible that all of these gains fixing bugs are simply improving infrastructure and stability rather than finding new customers and opening up new markets.

Having worked on software infrastructure, it’s a thankless job. You’re most heroic work has little visibility and the result is that nothing catastrophic happened.

So maybe products will have better reliability and fewer bugs? And we all know there’s crappy software that makes tons of money, so there isn’t necessarily a strong correlation.
by Voice_of_Void | 2025-05-31 20:14:56+00:00
SingularityForge AI News #011: Cerebras 2x Faster Than NVIDIA, RASLI Architecture Unveiled This week's technical highlights:

· Cerebras achieves 2500+ tokens/sec on 400B parameter Llama 4, verified by independent benchmarking

· Intel Xeon 6 with Priority Core Turbo debuts in NVIDIA DGX B300, includes thermal management research

· We published Part 1 of RASLI architecture docs: dual-controller system promising 60% error reduction, 40% energy savings (free/open)

· Boston Dynamics Atlas using render-and-compare for zero-shot object generalization

· Apache Spark 4.0 announced with AI/ML workload optimizations

Also: 100 autonomous mining trucks deployed at scale, German Bionic's AI exoskeleton trained on billions of movements, and Anthropic CEO's stark warning about 50% entry-level job losses.

Spotify: https://open.spotify.com/episode/1fSzyiNYlfDQwEpFIXPwxO
by almostgotcaught | 2025-05-31 20:16:38+00:00
> What I really want when learning a new area of math is the full motivation for the tricky definition

yes so then you want proofs that actually exercise real machinery instead of playing the shell game of "an X is a Y and a Y is a Z, and has ABC properties, there for X has ABC properties"; you want a proof that goes through the process of using properties ABC to build Y from Z and X from Y (or something akin to that).

definitions aren't for people learning math, they're for people using math ie practising professional mathematicians that are proving more theorems; Hausdorff didn't invent "Hausdorff spaces", he used/worked with various properties of topological spaces and then when the next person came along and needed to right another paper on top, that person invented "Hausdorff space".
by jlundberg | 2025-05-31 20:17:35+00:00
As a tech person, it is easy to wrongly think that ”anyone can build this” about a prototype you just built.

At the end of the day, it is about what the customer is willing to pay for.

Back when The Pirate Bay was huge, music was essentially free. But Spotify came along and proved people are ready to pay for something better.

ImageMagick is an open source tool for resizing images etc. But some people successfully build API services or SaaS-services on top of it.

It works because people AND businesses pay for convenience.

What space do you have knowledge of? What pains do people have in that domain that can be solved with tech?

Always start with the problem. And start with an industry you know by heart or customer profile you truly care for.

For me that was software developers. I was that customer myself. I programmed a certain kind of solutions, realized there should be an API for this and built that API.
by westoncb | 2025-05-31 20:18:25+00:00
The main trick is in how you build up it's context for the problem. What I do is think of it like a colleague I'm trying to explain the bug to: the overall structure is conversational, but I interleave both relevant source chunks and detailed/complete observational info from what I've observed about anomalous program behavior. I typically will send a first message building up context about the program/source, and then build up the narrative context for particular bug in second message. This sets it up with basically perfect context to infer the problem, and sets you up for easy reuse: you can back up, clear that second message and ask something else, reusing detailed program context given by the first message.

Using it on the architectural side you can follow a similar procedure but instead of describing a bug you're describing architectural revisions you've gone through, what your experience with each was, what your objectives with a potential refactor are, where your thinking's at as far as candidate reformulations, and so on. Then finish with a question that doesn't overly constrain the model; you might retry from that conversation/context point with a few variants, e.g.: "what are your thoughts on all this?" or "can you think of better primitives to express the system through?"

I think there are two key points to doing this effectively:

1) Give it full, detailed context with nothing superfluous, and express it within the narrative of your real world situation.

2) Be careful not to "over-prescribe" what it says back to you. They are very "genie-like" where it'll often give exactly what you ask for in a rather literal sense, in incredibly dumb-seeming ways if you're not careful.
by zozbot234 | 2025-05-31 20:18:30+00:00
It will be incredibly interesting to assess how the mainstream "textbook" approach to the subject compares to the one taken in the Mathlib. In general, formalized math libraries make it comparatively easier to state results with a maximum degree of generality and to refactor proof developments for straightforwardness and elegance.

(Refactoring is of course made easier because the system will always keep track of what follows logically from what. You don't have that when working with pen and paper, so opportunities to rework things are very often neglected.)

It is a natural question also to ask whether it makes sense to teach the Mathlib, "maximum generality" version of real analysis, or at least something approaching that, in an academic course. Same for any other field of proof-based math, of course.
by mrlongroots | 2025-05-31 20:18:38+00:00
This is cool!

eBPF is incredible: I think of it as flipping the relationship between you and the perf tool. Instead of the tool giving you information, it asks you "what do you want to know?".

I don't know if it's google search but I find it hard to discover blogs etc. when I'm hunting for the syntax/language etc, having documented "case studies" is super useful.
by leptons | 2025-05-31 20:19:37+00:00
It's not exactly the same this time around, the dot-com bubble was a bit different, but both then and now were preceded by huge hiring bubbles and valuations that were stupid. Now it's a little different 25 years later, tech has advanced and AI means cutting the fat out of a lot of companies, even Microsoft.

AI is somewhat creating a similar bubble now, because investors still have money, and the current AI efforts are way over-hyped. 6.5 billion paid to aquihire Jony Ive is a symptom of that.
by Spivak | 2025-05-31 20:21:58+00:00
So you have two prompts, one is ambiguous and the second is the same prompt but with the ambiguity resolved.

In the first prompt the replicated pattern is to ask for clarification, in the second prompt the replicated pattern is to perform the work. The machine might understand nothing but does it matter when it responds appropriately to the different cases?

I don't really care whether it understands anything at all, I care that the machine behaves as though it did have understanding.
by neilv | 2025-05-31 20:22:58+00:00
I like the survivability, and the constraint to be doable with common hand tools.

For more information density, and easier readability by a less-technical person who inherits it -- at the cost of requiring special tools -- I wonder about using number&letter stamps with a hammer.

Or, if you permit very special tools, laser-cut alphanumerics (base16, base58, or base64, for arbitrary bits; or alphabetic passphrases). Either engraved, or cut fully through, like an old drafting lettering stencil.
Show HN: I built a refund tool for late deliveries – here's what happened
by er3y | 2025-05-31 20:24:09+00:00
I built a simple tool (LateClaim.org) that checks if your online delivery was late and helps you claim compensation. Thought it was a no-brainer. Everyone hates late packages. Free money, no app, no account. Should work, right?

I ran Insta ads. Got almost nothing. Tried Reddit. Got shadowbanned. Messaged UGC creators. Still waiting.

Turns out, the product isn’t enough. People don’t trust “free”. Or “new”. You need proof, faces, emotion.

Working now on UGC + meme-style videos. Might pivot the brand. Validation is f*cking hard in B2C if you’re not known.

If you’ve built something consumer-facing and tried to grow it from scratch: what actually worked for you?
by hansmayer | 2025-05-31 20:24:26+00:00
No, I am not disputing whatever productivity gains you seem to be getting. I was just curious if it LLMs feeding data into each other can work that well, knowing how long it took OpenAI to make ChatGPT properly count the number of "R"s in the word "strawberry". There is this effect called "Habsburg AI". I reckon the syntax-check and linting stuff is straightforward, as it adds a deterministic element to it, but what do you do about the more tricky stuff like dreamt up functions and code packages? Unsafe practices like sensitive exposing data in cleartext, Linux commands which are downright the opposite of what was prompted, etc? That comes up a fair amount of times and I am not sure that LLMs are going to self-correct here, without human input.
by aziaziazi | 2025-05-31 20:24:48+00:00
How do you define customers and good ?

Consider someone starts a slavery business that grab easily pickable children to raise them as perfect specialized workers then put the to the free sell/rent market.

End users could choose for other "moral" alternative if they wish. One even could point out that already happen in some parts of the world and that helps happy customers to solve their needs.

However most people prefer to contraint others wallet usage to enforce certain ethic rules. Where to place the line of ethics/constraint is very cultural dependent - to its wide definition.

One way to handle that is to look profitability for citizen instead of customers, then what's profitable for Uber users should be balanced with what's profitable for Uber drivers. That is usually what people want from a gouvernements and laws (and I guess we both agree that is not the same as what is profitable for business).
by Sniffnoy | 2025-05-31 20:26:34+00:00
That's not my experience at all as a mathematician. "Maximal ideal" implicitly means "maximal proper ideal", yes, but generally ideals don't have to be proper unless specified so.

If you don't include the whole ring as an ideal, you can't even define ideal addition, etc. I took an algebra class once from a professor who decided to define "ideal" to mean "proper ideal". After a few weeks he had to give it up because it just became too much trouble for reasons like that; he had to too often say "possibly improper ideal", i.e., this convention had the opposite effect he intended! I can't think of any other source I've seen use that convention.
by proc0 | 2025-05-31 20:26:38+00:00
The difference here is that there wouldn't even be a need for Raphael, at least if all the projections are right as to where AI is going.

Replacing humans with other humans is one thing. Replacing humans with machines is on a completely different level. Anyone that says we will work along AI has not thought this through. In contrast to the industrial revolution where machines are doing things that humans are not capable of, i.e. lifting heavy things, bending and shaping steel, mixing tons of cement, etc., AI is taking over what makes humans unique as a species: our cognitive abilities.

Of course, all of this hinges on whether AI will reach this level of reasoning and cognition, which right now is not certain. LLMs did scale up to have impressive and surprising abilities, but it's not clear if more scaling will produce actually intelligent agents that can correct themselves and have reliable output. Not to mention the compute cost which is orders of magnitude more than the human brain and will be a huge limitation.
by mrlongroots | 2025-05-31 20:28:05+00:00
Which, as a cpp programmer trying to pick up Rust, is honestly fine to begin with. Once you've written varying amounts of code in 5-10 programming languages, it is incredibly tedious to flip through pages trying to teach you how if conditions work and how for loops work: my brain doesn't pay attention even if I try.

This is more like: how to survive rustc as a cpp programmer which is honestly your mindframe when you start out, and it sets you up for "okay now that you speak the syntax, this is how to really think in rust terms".
by rmdes | 2025-05-31 20:28:57+00:00
I would be interested, would even help testing and contribute but the next problem that arise if users had such easy way to deploy apps is, how do you handle backups, migration to new versions, single sign on, there are projects similar to this idea that already go very far, such as https://cloudron.io but they are "limited" in the amounts of apps they can provide with support and quality path for upgrades.

Inevitably, if I had all the apps I want to spin at hands, I would need a very neat way to make sure path to upgrades are smooth, don't break things already in place and potentially used in "production" by either friends of family..
by __s | 2025-05-31 20:31:04+00:00
I've been doing this for the last 5 years too with all my devices. Makes it nice when looking at logs

I have let through some timezone bugs, tho those bugs also didn't impact prod since prod machines are UTC

Done with both EDT & PDT,

I did have a mixup with catching flight: I read 2300 as being UTC (since I'm not used to 24h clock being localtime) & thought my flight was 7PM instead of 11PM. Would be nice if flight times were UTC, always annoying figuring how long flight actually is
by ffsm8 | 2025-05-31 20:31:04+00:00
Because that's been a thing for years already - and works way better then this research does.

Unreal engine 5 has been demoing these features for a while now, I heard about it early 2020 iirc, but the techniques like gaussian splattering predate it.

I have no experience in either of these, but I believe MegaScans and RealityCapture are two examples doing this. And the last nanite demo touched on it, too.
by sfblah | 2025-05-31 20:31:51+00:00
You're right, of course. Most of this thread is some sort of weird motivated reasoning by people who are terrified of the reality of what lies ahead. I work at a top-10 tech company. We've stopped hiring junior talent, and it's 100% because of AI. I'm something like 2x more productive since using AI. We're now deploying agentic AI systems to further reduce headcount. The actual bloodbath will happen when there's any kind of financial pressure on the company (a recession).
by ta12653421 | 2025-05-31 20:32:27+00:00
++1

Fun fact what most people ignore: There have been around ~7000 people on Mount Everest - while the US alone has around 300.000 / 350.000 people earning more than 1 million USD a year.

So - its clear: Is more easier to become an "income-millionaire" than to climb Mount Everest! :-)