MurphyA/hacker-news huggingface dataset:
by ramesh31 | 2025-05-31 05:42:38+00:00
>The prevailing counter-narrative is that AI is going to flop. HARD. You can't get more diametrically opposed than that.

The answer (as always) lies somewhere in the middle. Expert software developers who embrace the tech whole heartedly while understanding its' limitations are now in an absolute golden era of being able to do things they never could have dreamed of before. I have no doubt we will see the first unicorns made of "single pizza" size teams here shortly.
by geerlingguy | 2025-05-31 05:42:42+00:00
Didn't antirez announce Redis was going (actual) open source again?

https://antirez.com/news/151

If so, is it too little, too late, to turn back the tide? I remember Node.js almost fell apart after the short-lived Io.js fork era, but the community patched up.

Is the same possible with Redis? I used to use it a lot, but haven't for the past few years, so I'm not able to get the same perspective on the community around these two projects.
by bayarearefugee | 2025-05-31 05:43:30+00:00
As a developer that uses LLMs, I haven't seen any evidence that LLMs or "AI" more broadly are improving exponentially, but I see a lot of people applying a near-religious belief that this is happening or will happen because... actually, I don't know? because Moore's Law was a thing, maybe?

In my experience, for practical usage LLMs aren't even improving linearly at this point as I personally see Claude 3.7 and 4.0 as regressions from 3.5. They might score better on artificial benchmarks but I find them less likely to produce useful work.
by Maxatar | 2025-05-31 05:43:56+00:00
Your point that reference counting can result in memory leaks is absolutely correct and a worthwhile point. However, it's also worth pointing out that tracing garbage collectors like those used by Java are also allowed to leak memory and never free it. In the most extreme scenario you have the epsilon garbage collector, which leaks everything:

https://openjdk.org/jeps/318

Implementing a garbage collector that is guaranteed to free memory when it's actually no longer needed is equivalent to solving the halting problem (via Rice's theorem), and so any garbage collection algorithm is going to have to leak some memory, it's simply unavoidable.
by kttjoppl | 2025-05-31 05:44:37+00:00
How are you going to ship a tool you don't understand? What are you going to do when it breaks? How are you going to debug issues in a language you don't understand? How do you know the code the LLM generated is correct?

LLMs absolutely help me pick up new skills faster, but if you can't have a discussion about Rust and Svelte, no, you didn't learn them. I'm making a lot of progress learning deep learning and ChatGPT has been critical for me to do so. But I still have to read books, research papers, and my framework's documentation. And it's still taking a long time. If I hadn't read the books, I wouldn't know what question to ask or how to evaluate if ChatGPT is completely off base (which happens all the time).
by xp84 | 2025-05-31 05:44:52+00:00
In the video example, the 'bad guy' tried to get the MCP server to read ~/.ssh/id_rsa and post it to the attacker site. The MCP Defender popup balked just by it trying to read a suspicious file so it didn't get to the point of making the network connection. It was unclear whether just getting it to ping a remote server with something less shocking than your private keys, such as for instance, source code or environment variables in the current project, would also be treated as malicious.
by infinitebit | 2025-05-31 05:46:02+00:00
I am SO thankful to see a news outlet take what tech CEOs say with a grain of salt re: AI. I feel like so many have just been breathlessly repeating anything they say without even an acknowledgement that there might be, you know, some incentive for them to stretch the truth.

(ftr i’m not even taking a side re: will AI take all the jobs. even if they do, the reporting on this subject by MSM has been abysmal)
by andrekandre | 2025-05-31 05:46:49+00:00
> It's much more important that people live a dignified life and be able to feed their families than "increasing shareholder value" or whatever.

its just my intuition, but talking to many people around me, i get the feeling like this is why people on both "left" and "right" are in a lot of ways (for lack of a better word) irate at the system as a whole... if thats true, i doubt ai will improve the situation for either...
by throwaway2037 | 2025-05-31 05:49:14+00:00
Regarding the impact of LLMs on non-programming tasks, check out this one:

https://www.ft.com/content/4f20fbb9-a10f-4a08-9a13-efa1b55dd...

> The bank [Goldman Sachs] now has 11,000 engineers among its 46,000 employees, according to [CEO David] Solomon, and is using AI to help draft public filing documents. > The work of drafting an S1 — the initial registration prospectus for an IPO — might have taken a six-person team two weeks to complete, but it can now be 95 per cent done by AI in minutes, said Solomon. > “The last 5 per cent now matters because the rest is now a commodity,” he said.

In my eyes, that is major. Junior ibankers are not cheap -- they make about 150K USD per year minimum (total comp).
by okayestjoel | 2025-05-31 05:49:47+00:00
In my mind, a well-formed nonogram is one that requires no backtracking. It's an interesting question though. I'll write some code in the next few days to check to see if my set of "unsolvable" puzzles include those with unique solutions given the clues.

Yeah, a "jump to unsolved" seems like its going to be essential. I'll work on that. I haven't heard of the scrolling issue. What device/browser are you using?
by adventured | 2025-05-31 05:50:02+00:00
There are exceptionally few less democratic countries that are functional in a manner such that they can take great advantage of the AI potential, much less execute in some sort of super fast manner compared to the democratic nations. You can count those less democratic nations on one hand.

It's overwhelmingly the case that affluence and national wealth goes hand in hand with greater democracy, there is a tight correlation (and of course there are exceptions). All you need to do is look at the top ~50 nations in terms of GDP per capita or median wealth per adult, then look at the bottom 50.

Less democratic nations will be left even further behind, as the richer democratic nations race ahead as they have been doing for most of the post WW2 era. The richer democratic nations will have the resources to make the required enormous investments. The more malevolent less democratic nations will of course make use of good-enough AI to do malicious things, not much about that will change. Their power position won't fundamentally change however.
by timschmidt | 2025-05-31 05:50:46+00:00
> I'd be prepared to argue that most humans aren't guessing most of the time.

Research suggests otherwise[1]. Action seems largely based on intuition or other non-verbal processes in the brain with rationalization happening post-hoc.

I've figured for an age that this is because consciously reasoning through anything using language as a tool takes time. Whereas survival requires me to react to the attacking tiger immediately.

https://skepticink.com/tippling/2013/11/14/post-hoc-rational...
by spyckie2 | 2025-05-31 05:50:48+00:00
but that’s precisely the point and it does give insight. Google scaled off of existing infrastructure like computers. Computers scale off of existing infrastructure like electricity.

The point is to compare current era of scaling to the previous era and see how much faster it is.

It’s not comparing Google to Open Ai. It’s comparing the environment that produced Google to the environment that produced Open Ai.
by practal | 2025-05-31 05:50:52+00:00
De Bruijn indices are great, I use them to construct what I call the "De Bruijn Abstraction Algebra"; it comes in two forms, one is used to give a characterisation of alpha equivalence for abstraction algebra, the other one is used to prove completeness of abstraction logic. It is described in [1], and I have described and proven correct everything in excruciating detail. That makes it quite a tough read, although in the end, it is elementary and simple.

[1] http://abstractionlogic.com
by concinds | 2025-05-31 05:51:45+00:00
We all know why. And we all know why they're still doing the weird Covid-era pre-recorded keynotes, unlike almost all tech companies. They're afraid of (mocking) laughter, booing, and jeering, from devs in the crowd that they keep treating like dirt.

The childish retribution for Gruber's mild criticism is just the cherry on top, but it might have happened regardless.
Show HN: Dagu 1.17-beta – Run complex workflows without the Airflow complexity
by yohamta | 2025-05-31 05:51:48+00:00
Hi HN! I'm the author of Dagu, a workflow engine that's been in development for a few years. Just released v1.17.0-beta.1 with some significant improvements.

The core idea: define workflows in YAML, run them as scheduled jobs or on-demand. Single Go binary, no external dependencies, stores everything locally.

New in this release: - Nested DAGs - workflows can call other workflows - Refactored execution history storage (10x faster for large histories) - Better debugging UI showing precondition results and variable outputs - Proper job queue management

Example workflow: ```yaml name: data-pipeline schedule: "0 10 * * *" steps: - name: fetch-data command: curl https://api.example.com/data > raw.json - name: process command: jq '.items[] | select(.active)' raw.json > processed.json depends: fetch-data - name: load command: python load_to_db.py processed.json depends: process ```

Try it: ``` docker run --rm -p 8080:8080 ghcr.io/dagu-org/dagu:1.17.0-beta.1 dagu start-all ```

Then visit http://localhost:8080

We use it in production for ETL pipelines, report generation, and system maintenance tasks. It's not trying to be Airflow - no distributed execution, no Python dependency hell, just a reliable way to run workflows.

GitHub: https://github.com/dagu-org/dagu

Would love feedback on the beta, especially around the nested DAG implementation and any performance issues you encounter.
by curtisf | 2025-05-31 05:52:12+00:00
What do you mean by "purely with logic, no guessing"?

"Guess and backtrack" is a totally valid form of deduction for pen-and-paper puzzles, it's just not very satisfying. But often (always?) there is a satisfying deduction technique that could have replaced the guess-and-check, it may just be fairly obtuse.

Or do you just mean where the clues for the raster don't result in a unique solution?
by lambdasquirrel | 2025-05-31 05:52:58+00:00
Anecdotal, but AI was what enabled me to learn French, when I was doing that. Before LLMs, I would've had to pay a lot more money to get the class time I'd need, but the availability of Google Translate and DeepL meant that some meaningful, casual learning was within reach. I could reasonably study, try to figure things out, and have questions for the teachers the two or three times a week I had lessons.

Nowadays I'm learning my parents' tongue (Cantonese) and Mandarin. It's just comical how badly the LLMs do sometimes. I swear they roll a natural 1 on a d20 and then just randomly drop a phrase. Or at least that's my head canon. They're just playing DnD on the side.
by w10-1 | 2025-05-31 05:53:24+00:00
Super helpful assembly of lots of data on point - a bit too much to digest quickly. Mary Meeker is great.

Some limitations:

- Unhelpful modernity-scale trend/hype-lines. Everyone knows prospects are big and real.

- No significant coverage of robotics, factory automation? (TAM for physical products is 15X search+streaming+saas)

- No insight? No new categories, surprising predictions, critical technologies identified?

Surprises:

- AI productivity improvements are marginal, esp. relative to the concern over jobs

- US ratio of top public companies by market cap increased from ~50% in 1995 to ~85% in 2025. Seems big; or is it an artifact of demographics of retirement investments? Or is it less significant due to growing private capital markets?

What I would like addressed: The AI means of production seem very capital-intensive, even as marginal cost of consumption is Saas-scalable (i.e., big producers, small consumers). I have some concern that AI development directions are decided in relatively few companies (which are biased to Saas over manufacturing, where consumers are closer to producers in size). This increases the likelihood of a generational whiff (a mistake I suspect China won't make).

As an aside, I wish Elon Musk would pivot xAI out of Saas AI (and science AI), focusing exclusively on manufacturing robotics -- dogfooding at Tesla, SpaceX and even Boring -- with the simpler autonomy of controlled environments but the hard problem of not custom building everything every time. They're well positioned, he could learn some discipline from working with downstream and upstream partners on par (instead of slavish employees, fan investors, and dull consumers or slow governments as customers). He'd redeem himself as a builder of stuff that builds, so we can make infrastructure for generations to come.
Show HN: C implementation of Karpathy's micrograd and with a Python Interface
by batuhandumani | 2025-05-31 05:53:44+00:00
Hi HN, I'm presenting the C implementation of Karpathy's micrograd. Although there are still issues in the memory management part, it is stable. If you have suggestions, you can write in the issues section on github or if you want to contribute to the project, you can send a pull request. Thank you!
by throwaway2037 | 2025-05-31 05:54:16+00:00
> One place where AI is being disruptive is research: where researchers are applying models in novel ways and making legitimate advances in math, medicine and other fields.

Great point. The perfect example: (From Wiki):

> In 2024, Hassabis and John M. Jumper were jointly awarded the Nobel Prize in Chemistry for their AI research contributions for protein structure prediction.

AFAIK: They are talking about DeepMind AlphaFold.

Related: (Also from Wiki):

> Isomorphic Labs Limited is a London-based company which uses artificial intelligence for drug discovery. Isomorphic Labs was founded by Demis Hassabis, who is the CEO.
by adgjlsfhk1 | 2025-05-31 05:54:16+00:00
Also Zen4C has AVX512 support while being only ~35% bigger than Gracemont (although TSMC node advantage means you should possibly add another 10% or so). This isn't really a fair comparison because Zen4c is a very differently optimized core than Intel's E cores, but I do think it shows that AVX-512 can be implemented with a reasonable footprint.

Or if Intel really didn't want to do that, they needed to get AVX-10 ready for 2020 rather than going back and forth on it fore ~8 years.
by butz | 2025-05-31 05:54:57+00:00
Somehow we ended up doing the opposite thing of compression (and I do not mean decompression): user writes three word email, AI expands it to several paragraphs of useless text, sends it to recipient. There another AI digests all that text to some other three words, loosing original meaning and not only wasting CPU on AI tasks, but wasting network bandwidth by sending a lot of useless data. I hate 21st century.
by mullingitover | 2025-05-31 05:56:38+00:00
A big reason for my strategy in attending office hours was I wanted to get to know the professors personally. Going from high school to college, I was shocked at how grades went from a hard 90+=A, 80-90=B, etc scale, to just...vibes? And if you were riding the line between an A and a B, and the professor knew you and knew you were diligent, you could catch a break sometimes. Plus they were usually very interesting and smart people, so interacting with them was time well spent.
by MrJohz | 2025-05-31 05:57:32+00:00
A good sample needs to be random and representative of the population as a whole, otherwise you introduce sampling bias. Imagine trying to do a survey of what people's favourite fast food restaurants are, but doing it inside a McDonald's — it doesn't matter how large your sample is, it's going to be heavily biased. This is why survey companies spend a lot of effort trying to find random, representative samples of the population, and often weighting their samples so that they match the target population even more.

If we treat elections like a survey, then they have a massive inherent bias to the sampling method: the people who will get "surveyed" are the ones who are engaged enough to get registered, and then willing to go to a physical polling station and vote. This will naturally bias towards certain types of people.

In practice, we don't treat elections like a survey. If we did, we'd spend a lot of time afterwards weighting the results to figure out what the entire country really thought. But that has its own flaws, and ultimately voting is a civic exercise. You can do it, you can avoid it: that choice is yours, and ultimately part of your vote. In a way, you could argue that the sample size for an election is 100% of the population, where "for whatever reason, I didn't cast a vote" is a valid box to check on this survey.

That said, the whole "samples can be biased" thing is very much relevant for elections because many political groups have an incentive to add additional bias to the samples. That could be as simple as organising pick-ups to allow their voters to get to the polls, or teaching people how to register to vote if they're eligible, but it could also involve making it significantly harder or slower for certain groups (or certain regions) to register or vote.
by anonymoushn | 2025-05-31 05:58:03+00:00
If you try to reproduce various open datasets like fineweb by scraping the pages again, you can't, because a lot of the pages no longer exist. That's why you would prefer to store them instead of losing the content forever.

It's not "all of the text", it's like less than 100 trillion tokens, which means less than 400TB assuming you don't bother to run the token streams through a general purpose compression algorithm before storing them.
by lambdaphagy | 2025-05-31 05:58:05+00:00
No culture treats men and women equally-- they differ in how they treat men and women differently. Just today in my progressive coastal startup, for example, there was a proposal to set up a dedicated ERG for the women employees. In a company where people are routinely pulling 60-80 hour weeks, it was considered a plausible priority to take time aside to especially ensure that the women were feeling comfortable.

Whether or not this proposal is a good idea is not even the point: the point is that it was considered plausible, and hence that not even coastal progressives actually think it desirable to treat men and women equally.

I'm not making any claims about what anyone can or can't handle. I'm simply observing that just about every mixed group ends up adopting female norms of communication. I'm not even saying that's necessarily a bad thing for a mixed group, I think it's to some extent natural and healthy in social settings. In fact taboos that proscribe the ways men may speak in the presence of women are also quite common cross-culturally. But the fact that there is a difference remains.
by PeterHolzwarth | 2025-05-31 05:58:08+00:00
I get what you mean, but the last year has been a story of sudden limits and ceilings of capability. The (damned impressive) video you post is a bunch of extremely brief snippets strung together. I'm not yet sure we can move substantially beyond that to something transformative or pervasively destructive.

A couple years ago, we thought the trend was without limits - a five second video would turn into a five minute video, and keep going from there. But now I wonder if perhaps there are built in limits to how far things can go without having a data center with a billion Nvidia cards and a dozen nuclear reactors serving them power.

Again, I don't know the limits, but we've seen in the last year some sudden walls pop up that change our sense of the trajectory down to something less "the future is just ten months away."
by financltravsty | 2025-05-31 05:58:56+00:00
I speak only from a very high-level POV. From a lower-level/in the "trees" -- yes, I don't disagree whatsoever with your characterization that a single company can achieve that. I know of many many many products I use (tech even!) that I could create exponentially better alternatives for, as well.

Now, there come a few considerations I don't believe you have factored in:

- Just because your company has struck gold: does that mean that pathway is available or realistic enough for everyone else; and to a more important point, is it /significant/ enough that it can scoop the enormous amount of tech talent on the market currently and in the future? I don't believe so.

- Segueing, "software products that I wish I had in my life." Yes, I too have many ideas, BUT: is the market (the TAM if you will) significant enough to warrant it? Ok, maybe it is -- how will you solve for distribution? Fulfillment is easy, but how are you going to not only identify prospective customers (your ICP), find them and communicate to them, and then convince them to buy your product, AND do this at scale, AND do this with low enough churn/CAC and high enough retention/CLTV, AND is this the most productive and profitable use of your time and resources?

Again, ideas are easy -- we all have them. But the execution is difficult. In the SaaS/tech space, people are burned out from software. Everyone is shilling their vibe-coded SaaS or latest app. That market is saturated, people don't care. Consumer economy is suffering right now due to the overall economy and so on. Next avenue is enterprise/B2B -- cool, still issues: buyer fatigue; economic uncertainty leading to anemic budgets and paralysis while the "fog" clears. No one is buying -- unless you can guarantee they can make money or you can "weather the storm" (see: AI, and all the top-down AI mandates every single PE co and board is shoving down exec teams throats).

I'm talking in very broad strokes on the most impactful things. Yes, there is much to create -- but who is going to create it and who is going to buy it (with what money?). This is a people problem, not a tech problem. I'm specifically talking about: "what more tech is there to sell -- that PEOPLE WILL BUY -- besides LLM integrations?" Again, I see nothing -- so I have pivoted towards finance and selling money. Money will not go out of fashion for a while (because people need it for the foreseeable future).

Ask yourself, if you were fired right now at this moment: how easy would it be for you to get another job? Quite difficult unless you find yourself lucky enough to have a network of people that work in businesses that are selling things that people are buying. Otherwise, good luck. You would have more luck consulting -- there are many many many "niche" products and projects that need to be done on small scales, that require good tech talent, but have no hope of being productized or scaled (hint!).
by directevolve | 2025-05-31 05:59:17+00:00
We’re already heading toward the sigmoid plateau. The GPT 3 to 4 shift was massive. Nothing since had touched that. I could easily go back to the models I was using 1-2 years ago with little impact on my work.

I don’t use RAG, and have no doubt the infrastructure for integrating AI into a large codebase has improved. But the base model powering the whole operation seems stuck.
by infinitebit | 2025-05-31 06:00:46+00:00
So glad to see a MSM outlet take the words of an AI ceo with even a single grain of salt. I’ve been really disappointed with the way so many publications have just been breathlessly repeating what is essentially a sales pitch.

(ftr i’m not even taking a side re: is AI going to take all the jobs. regardless of what happens the fact remains that the reporting has been absolute sh*t on this. i guess “the singularity is here” gets more clicks than “sales person makes sales pitch”)