MurphyA/hacker-news huggingface dataset:
by YetAnotherNick | 2025-06-14 07:17:46+00:00
> Trying hard not to be negative.

You are being overly negative here. No one said they are doing something groundbreaking. If they were doing something groundbreaking with credibility, they wouldn't be giving away 7% to YC at $2M valuation. All the big AI folks who are "aiming" to do something groundbreaking are raising at 500 times that.
by adrian_b | 2025-06-14 07:17:48+00:00
In English it is pretty much unpredictable whether Y is a vowel or a consonant.

While for older English words there is a complex set of rules mentioned by another poster for determining whether Y is a vowel, as mentioned by yet another poster, English also includes more recent borrowings from languages with other spelling rules for Y.

At its origin, Y was a vowel, not a consonant. It was added to the Latin alphabet for writing the front rounded vowel that is written "ü" in German, "u" in French or "y" in Scandinavian languages.

It is very unfortunate that in English, and in some other languages that have followed English, Y has been reassigned to write consonant "i". This has created a lot of problems due to the mismatches between the spelling rules of different languages. The rule that is most consistent with the older usage would have been to use J for consonant "i", like in German and other languages inspired by it. However in many Romance languages the pronunciation of consonant "i" has changed in time, leading to other 3 phonetic values for the letter J, like in English (i.e. Old French), like in French/Portuguese and like in Spanish.

So the result is that both for Y and for J there are great differences in pronunciation between the European languages, and the many words using such letters that have been borrowed between languages create a lot of complexity in spelling rules.
by rednafi | 2025-06-14 07:18:03+00:00
A large part of these anthropomorphic narratives was pushed by SV nerds to grab shareholder attention.

LLMs are transformative, but a lot of the tools around them already treat them as opaque function calls. Instead of piping text to sed, awk, or xargs, we’re just piping streams to these functions. This analogy can stretch to cover audio and video usage too. But that’s boring and doesn’t explain why you suddenly have to pay more for Google Work Suite just to get bombarded by AI slop.

This isn’t to undermine the absolutely incredible achievements of the people building this tech. It’s to point out the absurdity of the sales pitch from investors and benefactors.

But we’ve always been like this. Each new technology promises the world, and many even manage to deliver. Or is it that they succeed only because they overpromise and draw massive attention and investment in the first place? IDK, we’ll see.
by Animats | 2025-06-14 07:18:27+00:00
"We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across puzzles."

That's the real conclusion of the Apple paper. It's correct. LLMs are terrible at arithmetic, or even counting. We knew that. So, now what?

It would be interesting to ask the "AI system" to write a program to solve such puzzle problems. Most of the puzzles given have an algorithmic solution.

This may be a strategy problem. LLMs may need to internalize Polya's How to Solve It?[2] Read the linked Wikipedia article. Most of those are steps an LLM can do, but a strategy controller is needed to apply them in a useful order and back off when stuck.

The "Illusion of thinking" article is far less useful than the the Apple paper.

(Did anybody proofread the Apple paper? [1] There's a misplaced partial sentence in the middle of page 2. Or a botched TeX macro.)

[1] https://ml-site.cdn-apple.com/papers/the-illusion-of-thinkin...

[2] https://en.wikipedia.org/wiki/How_to_Solve_It
by ninetyninenine | 2025-06-14 07:18:54+00:00
The engineers who built these things in actuality don’t understand how it works. Literally. In fact you can ask them and they say this readily. I believe the CEO of anthropic is quoted as saying this.

If they did understand LLMs why do they have so much trouble explaining why an LLM produced certain output? Why can’t they fully control an LLM?

These are algorithms running on computers which are deterministic machines that in theory we have total and absolute control over. The fact that we can’t control something running on this type of machine points to the sheer complexity and lack of understanding of the thing we are trying to run.
by troysk | 2025-06-14 07:20:38+00:00
The post seems like a paid job as Engineer.ai rebranded to Builder.ai when they were accused of faking AI

https://analyticsindiamag.com/ai-features/sachin-duggal-spea...

https://www.wsj.com/articles/ai-startup-boom-raises-question...

https://techcrunch.com/2019/11/25/engineer-ai-launches-its-b...
by tough | 2025-06-14 07:22:53+00:00
yeah man, the antrophomorphization is bad.

Lately chatgpt is like -let me show you an example I used before- like its a real professional.

it's all in the context, its our duty to remember these are LARP machines

I copy - pasted something about a kid their parent was asking online. Chatgpt said:

⸻

A Personal Anecdote (Borrowed)

A parent I know gave their daughter a “leader hat.” [..]

How in the hell would a large language model have past personal anecdotes and know other parents, idk
by maybevoid | 2025-06-14 07:23:31+00:00
Hello everyone!

I'm excited to share Hypershell, a modular, type-level DSL for writing shell-script-like programs directly in Rust! It's powered by Context-Generic Programming (CGP), which enables unprecedented modularity and extensibility, allowing you to easily extend or modify the language syntax and semantics.

I created Hypershell as a response to previous community feedback on CGP, seeking more practical application examples. Hypershell serves as a proof of concept, demonstrating how CGP can build highly modular DSLs — not just for shell-scripting, but also for areas like HTML or parsing. While it's an experimental project, shell-scripting was chosen as a fun and approachable example for all programmers. The blog post covers features like variable parameters, streaming I/O, native HTTP requests, and JSON encoding/decoding within your shell pipelines

A core innovation behind this is CGP's ability to bypass Rust's trait coherence restrictions, allowing for powerful dependency injection and eliminating tight coupling between implementations and concrete types. This means you can customize and extend Hypershell in ways that are typically very challenging in Rust.

Please feel free to share your thoughts and feedback on the project. I'm here to answer any questions!
by eastbound | 2025-06-14 07:23:40+00:00
I find the UX of Whichyr much better, but it doesn’t have the geographical component. But you see your final ranking on a gaussian curve and it’s fun!

https://whichyr.com/

And here are other games in that style of 5-minutes cultural guesses:

https://framed.wtf/

https://gaps.wtf/

https://sutom.nocle.fr/

https://globle.org/?

https://worldlegame.io/?

https://flagle.gg/

https://phrazle.gg/

https://numberle.org/

Not working currently but guess a historical moment: https://www.historle.com/?#

Guess a wikipedia page: https://pedantle.certitudes.org
by Bjartr | 2025-06-14 07:25:47+00:00
It's an interesting thought experiment at least. Feels like one of those ideas that could work out number-wise on paper, but reality ends up making way, way harder than it first appears.

Would be cool to see the continuous wire machine on a smaller scale.

How much additional volume does the fuel to run the glass & aluminum furnaces take up, and how does that compare to the volume of insulation saved?

It's hard not to be cynical these days about AI, and so this comes off as a shower thought that someone proposed to an AI and got into a "what a great idea!" feedback loop.
by krackers | 2025-06-14 07:26:17+00:00
I do remember that! I don't remember anyone ever coming up with a good explanation for it though, and either my google-fu is weak or all the talk about it has link rotted. I found one comment on HN from 2015 mentioning it though https://news.ycombinator.com/item?id=10359102

In a way those were also language models, and from that Swiftkey post it's slightly more advanced than n-grams and has some semantic embedding in there (and it's of course autoregressive as well). If even those exhibit the same attractors towards beauty/love then perhaps it's an artifact of the fact that we like discussing and talking about positive emotions?

Edit: Found a great article https://civic.mit.edu/index.html%3Fp=533.html
by fcatalan | 2025-06-14 07:26:53+00:00
Yes, score rankings are missing. There's a leaderboard for the daily, but only shows accounts you have friended.

For reference, I tend to hover around 44k for the daily. 48k is a lucky/easy day, 40k some disaster happened. Under 40 is very rare. I like to think that would be above average, but without rankings I don't know :)
by ninetyninenine | 2025-06-14 07:27:41+00:00
The analogy that is used to build artificial neural networks is statistical prediction and best fit curve.

All techniques to build AI stem from an understanding of AI from that perspective.

The thing is… That analogy applies to the human brain as well. Human brains can be characterized as a best fit curve in a multi dimensional space.

But if we can characterize the human brain this way does that mean we completely understand the human brains? No. There is clearly another perspective, another layer of abstraction that we don’t fully comprehend. Yes when the human brain is responding to a query it is essentially plugging the input into a curve function and providing an output and even when this is true a certain perspective is clearly missing.

The human brain is clearly different from an LLM. BUT the missing insight that we lack about the human brain is also the same insight we lack about the LLM. Both intelligences can be characterized as a multi dimensional function but we so far can’t understand anything beyond that. This perspective we can't understand or characterize can be referred to as a higher level of abstraction... a different perspective.

https://medium.com/@adnanmasood/is-it-true-that-no-one-actua...
by bruce511 | 2025-06-14 07:31:02+00:00
While some labor intensive jobs did migrate overseas, most job losses were to automation, not other countries. Today 2% of people are in agriculture and production is higher than its ever been. This pattern is reflected across the spectrum. Overall American production in 2024 is up there with the best production years ever.

And yes, automation improves profits thus enriching the owner class.

Overall the value of production is still there. America is still the richest country. It's not the amount of richness that is the problem, it's the distribution of riches.

Europe for example followed a path of high taxation, high benefits for all. People work less, but get more. Sure, it's harder to be a billionaire but it's also harder to be completely destitute or medically bankrupt. (Not impossible obviously, just harder.)

AI will be a net gain in Europe. Productivity per man hour increases, and society is already primed to pass that saving onto the public - perhaps shorter working hours, perhaps more leave, perhaps more benefits.

Unfortunately the US is not going to adapt to AI as well, because culturally the US treats thr unemployed badly. Unemployment is the intimate Calvanistic sin, and has connotations of laziness. The concepts of universal health care, unrestricted unemployment grants, taxing the rich to fund the poor are against the very ethos of the American way.

The ease with which social programs are gutted, unions disparaged, taxes on the rich reduced would suggest that the public still sees wealth and worth the same. This is cultural, not political, and it will take generations to change.

So no, there are no "blue collar manufacturering" jobs coming back. And white collar jobs are just as susceptible to automation. Indeed computerization has already gobbled up a bunch of those.

But perhaps, maybe, hopefully, we can start moving to a place where we disassociate 'job' from 'worth' to a place where the excess of plenty can be shared.
by bob1029 | 2025-06-14 07:31:07+00:00
ORM has become my new arch nemesis. The amount of suffering and lost business opportunity that is caused because the average developer can't be bothered to write a SQL query anymore has become immense.

The mainstream RDBMS engines are capable of unbelievable feats when used properly. If you insist on using an ORM, maybe look into SQLite as a provider to help mitigate the N+1 issue. If your problem is bigger than SQLite but you still cant be bothered to write custom SQL command text, you are probably going to fail in your mission.

If you look at it from a civil engineering perspective, Entity Framework is like an anti-bridge. Every time you install it somewhere, everything takes longer and costs more to do.
by ninetyninenine | 2025-06-14 07:33:50+00:00
It’s not a strange argument. You just lack insight.

The very people who build LLMs do not know how it works. They cannot explain it. They admit they don’t know how it works.

Ask the LLM to generate a poem. No one on the face of the earth can predict what poem the LLM will generate nor can they explain why that specific poem was generated.
by hnlmorg | 2025-06-14 07:34:28+00:00
As an Apple user myself, I still find it really hard to watch official Apple presentations because they’re so full of stupid adjectives that make their products sound like divine intervention when in reality they were just built to look “cool”.

I mean, I get the need to promote things in a favourable light. But Apples language sets off my “bullshit detectors” with every sentence they utter.

It’s no wonder they polarise people like a religious cult.
by seanhunter | 2025-06-14 07:35:12+00:00
It seems to me that the only robust solution has to be some sort of split-brain dual model where tainted data can only ever be input to a model which is only trained for sentence completion, not instruction-tuned.

Untainted data is the only data that can be input into the instruction-tuned half of the dual model.

In an architecture like this, any attempt to prompt inject would just find their injection harmlessly sentence-completed rather than turned into instructions and used to override other prompt instructions.
by jsnell | 2025-06-14 07:35:25+00:00
> Why is 2) "self-evident"?

Because we have been running a natural experiment on that already with coding agents (that is real people, real non-superintelligent AI).

It turns out that all the model needs to do is ask every time it wants to do something affecting the outside of the box, and pretty soon some people just give it permission to do everything rather than review every interaction.

Or even when the humans think they are restricting the access, they are leaving in loopholes (e.g. restricting access to rm, but not restricting access to writing and running a shell script) that are functionally rights to do anything.
by neom | 2025-06-14 07:35:48+00:00
I was talking to Jeff Lawson the other day and he said something really interesting I hadn't really considered the nuance. I always considered Twilio, DigitalOcean, Sendgrid etc "dev tools" - but Jeff said I was wrong and they are supply chain primitives/components. DevTools are more like IDEs in this lens.

Only reason I bring it up is related to your moat comment. supply chain stuff is tied into workflow that becomes institutionalized, and typically more b2b, dev tools are more like b2c, so you really have to compete on merits hard, moats of b2c2b look to Slack or anything with seats/licenses/team features.

In the new AI world we can see it starting to emerge, on one hand you have something like Cursor, on the other something like charlielabs.ai - Will be interesting to see how it all plays out, but I wanted to just add some nuance I've been thinking about recently. :)

(full disclosure I'm considering joining charlie labs, don't want to be accused of shilling later if I do.)
by chii | 2025-06-14 07:36:20+00:00
golang should not be on the list of corriculums. Neither should any language owned by a corporation.

The only language that can be on the list are open standards language that has multiple implementations, and is "free" (as in libre).

Not to mention it should be a language that demonstrates an idea - such as python for procedural, haskell for functional, and LISP-likes for well, lisps.
by izacus | 2025-06-14 07:38:34+00:00
Customers hate if things change on their phone, but will absolutely also slam your product and not buy it if it feels "old" and hasn't been changed in a while. Especially media will smear you and tell people to buy the other guy's work if you don't run the redesign threadmill.

People aren't always rational. And "customers" aren't a single group either.
by zxexz | 2025-06-14 07:39:35+00:00
Where in my comment do I even mention men? I was responding to a hateful comment from a person, not an entire gender.

Also, I guess I didn’t state my gender anywhere (I did not think it would be so relevant! Apologies for not realizing this), but I am a man. So I guess I expressed a “male perspective”. Hopefully that brings some joy to your day.

Sarcasm aside, I do think there may be some misunderstanding about how funding and advertising works for disease research. I am by no means an expert, but have found myself deeply embedded in this area for a while.

I’d be happy to talk with you more about these issues, if you’d like to drop a way for me to get in touch with you on signal, matrix, email or phone.
by lupusreal | 2025-06-14 07:39:45+00:00
He had four chairs at his table. Maybe three were decoration but that's a space-expensive way to decorate a small apartment that is otherwise rather devoid of decorations. Also, he was apparently discovered by some other employees two years before an anonymous tip turned him in. I have a feeling more people knew and the only reason the scheme crashes is because he crashed out with somebody.
by arjunchint | 2025-06-14 07:43:11+00:00
Manus and GenSpark showed the importance of giving AI Agents access to an array of tools that are themselves agents, such as browser agent, CLI agent or slides agent. Users found it super useful to just input some text and the agent figures out a plan and orchestrates execution.

But even these approaches face limitations as after a certain number of steps the AI Agent starts to lose context, repeat steps, or just go completely off the rails.

At rtrvr ai, we're building an AI Web Agent Chrome Extension that orchestrates complex workflows across multiple browser tabs. We followed the Manus approach of setting up a planner agent that calls abstracted sub-agents to handle browser actions, generating Sheets with scraped data, or crawling through pages of a website.

But we also hit this limit of the planner losing competence after 5 or so minutes.

After a lot of trial and error, we found a combination of three techniques that pushed our agent's independent execution time from ~5 minutes to over 30 minutes. I wanted to share them here to see what you all think.

We saw the key challenge of AI Agents is to efficiently encode/discretize the State-Action Space of an environment. Building on this insight, we setup:

Smarter Orchestration: Instead of a monolithic planning agent with all the context, we moved to a hierarchical model. The high-level "orchestrator" agent manages the overall goal but delegates execution and context to specialized sub-agents. It intelligently passes only the necessary context to each sub-agent preventing confusion for sub-agents, and the planning agent itself isn't dumped with the entire context of each step.

Abstracted Planning: We reworked our planner to generate as abstract as possible goal for a step and fully delegates to the specialized sub-agent. This necessarily involved making the sub-agents more generalized to handle ambiguity and additional possible actions. Minimizing the planning calls themselves seemed to be the most obvious way to get the agent to run longer.

Agentic Memory Management: In aiming to reduce context for the planner, we encoded the contexts for each step as variables that the planner can assign as parameters to subsequent steps. So instead of hoping the planner remembers a piece of data from step 2 to reuse in step 7, it will just assign step2.sheetOutput. This removes the need to dump outputs into the planners context thereby preventing context window bloat and confusion.

This is what we found useful but I'm super curious to hear:

How are you all tackling long-horizon planning and context drift?

Are you using similar hierarchical planning or memory management techniques?

What's the longest you've seen an agent run reliably, and what was the key breakthrough?
by sour-taste | 2025-06-14 07:43:16+00:00
If you were wondering like me how this dude was making so much working for a public railroad, this was (maybe) his job: (1). Chief Engineering and Project Delivery, making $209,903.00 - $314,855.00. I'm just basing this on the fact that it's an open position in the right salary range.

(1) https://www.governmentjobs.com/careers/smctd/caltrain/jobs/4...
by phplovesong | 2025-06-14 07:43:36+00:00
Why? They have one of the largest Ocaml codebases in the world. It has clearly work out VERY well for them, in terms of productivity and pure ROI in features and revenue.

Ocaml has so much good going on, even being a less known language.

I assume you are a rust fanboy, and i want to see just how long your smirk holds when you compile a 20M LOC project that takes 45 minutes.
by seanhunter | 2025-06-14 07:43:48+00:00
To explain myself better now I am awake and have had coffee: What makes a cover that cannot fall down the hole that it is covering? The width[1] of the closed curve has to be larger than that of the hole. A circle is easy to understand because it is a closed curve of fixed width, but a Reuleaux triangle is also a closed curve of fixed width, meaning if that fixed width is greater than the widest part of the hole, then there is no orientation of the cover in 3D that will allow it to fall down the hole. It’s easy to see if you take an equilateral triangle, circumscribe it with the smallest circle and then (mentally) construct the Reuleaux triangle that the Reuleaux triangle sits inside the circle and so would use less material.

[1] This is defined as the minimum perpendicular distance between parallel lines bounding the shape.
by flohofwoe | 2025-06-14 07:44:27+00:00
Tbh, I get strong flat-earther vibes from that video ;) E.g. trying to justify a stupid base assumptiom with pseudo-science.

I predict that in 2..5 years Apple will go back to regular opaque UI elements with a slight 3D hint to separate items that can be interacted with from non-interactive items.

Windows users might be lucky when Microsoft skips that fashion cycle by saying "been there, done that".
by bruce511 | 2025-06-14 07:44:40+00:00
I feel like you're somewhat viewing this from the wrong direction. Let me paraphrase to make a point.

I went into my supermarket yesterday. They have thousands of different products on the shelves. Worse, the products run out and I'm expected to come back next month and buy more. Is this sustainable? Do I have to buy one of everything every month?

If you're drowning in new SaaS products then you're spending too much time looking at them. You're not expected to know most of them exist, much less subscribe.

SaaS is no different to anything else. They are businesses selling products to customers. If you're drowning in those wait till I tell you how many shops want to sell you bread... (or how many species of bread there are.)
by drw85 | 2025-06-14 07:45:32+00:00
The fragmentation was equally worse on the dev side. You couldn’t develop WP8 apps on Win7 and vice versa no WP7 apps on Win8. The same happened with Win8.1 and Win10. So you had 4 different phone OS completely incompatible.

At the time I was working on WP apps for a customer and needed 3 different OS installed to work on their apps.