MurphyA/hacker-news huggingface dataset:
by JSR_FDED | 2025-12-09 15:32:57+00:00
Apple doesn’t own a search engine either, and gets $20B per year from Google to direct search queries to them.

I hope they adopt the same model with AI - leverage whatever frontier model is best and provide their own privacy infrastructure in front.

At some point Apple will figure out a way to provide the right info from your calendar, messages, email etc as context and couple this with a bunch of secure tools for creating calendar entries, etc. Agentic AI will then be something I personally benefit from.
by firsttracks | 2025-12-09 15:33:00+00:00
Some feedback: The drill style approach seems helpful, but needing the variable names to exactly match threw me off. It would be great if we could _relax_ this constraint via a toggle for drill mode. "Precision Mode" feels like it's misnamed; when it's toggled on it feels more like a "guided mode" since chunks of boilerplate are written for you. It would be great if exiting Drill mode remembered choices, such as what portions were selected.

Ended up deciding to buy a subscription, but looks like the site still says "82% claimed" and "17 spots left". I appreciate the one-time purchase model, but feel that it's a bit shady of a tactic.
by dkarl | 2025-12-09 15:33:20+00:00
You should see if your language has a property-based testing library; it'll have a ton of useful functionality to help with what you're already doing!

A clarification on terminology, the "property" in "property-based testing" refers to properties that code under test is supposed to obey. For example, in the author's Example 2, the collection being sorted is the property that the test is checking.
by hmry | 2025-12-09 15:33:49+00:00
Hmm yeah it's quite badly written. Either way, let's just say it's an example of a place where one can look stuff like this up.

"Ending whiteness" does not mean "killing all white people" or "eradicating all European cultures" or whatever the parent comment was implying. (Or am I reading too much into it?) Though perhaps you can find some moron calling for that on Twitter.
by tomrod | 2025-12-09 15:34:15+00:00
Disclaimer: working and occasionally researching in the space.

The first paragraph is clear linear algebra terminology, the second looked like deeper subfield specific jargon and I was about to ask for a citation as the words definitely are real but the claim sounded hyperspecific and unfamiliar.

I figure a person needs 12 to 18 months of linear algebra, enough to work through Horn and Johnson's "Matrix Analysis" or the more bespoke volumes from Jeffrey Humpheries to get the math behind ML. Not necessarily to use AI/ML as a tech, which really can benefit from the grind towards commodification, but to be able to parse the technical side of about 90 to 95 percent of conference papers.
by alemanek | 2025-12-09 15:34:24+00:00
I don’t vibe code yet but it has sped me up a lot when working with large frameworks that have a lot of magic behind the scenes (Spring Boot). I am doing a very large refactor, major version spring boot upgrade, at the moment.

When given focused questions for parts of the code it it will give me 2-4 different approaches extending/implementing different bean overrides. I go through a cycle of back and forth having it give me sample implementations. I often ask what is considered the more modern or desirable approach. Things like give me a pros and cons list of the different approaches. The one I like the best I then go look up the specific docs to fact check a bit.

For this type of work it easily is a 2-3x. Spring specifically is really tough to search for due to its long history and large changes between major versions. More times than not it lands me on the most modern approach for my Spring Boot version and while the code it produces is not bad it isn’t great either. So, I rewrite it.

Also it does a pretty good job of writing integration tests. I have it give me the boilerplate for the test and then I can modify it for all my different scenarios. Then I run those against the unmodified and refactored code as validation suite that the refactor didn’t introduce issues.

When I am working in GoLang I don’t get this level of speed up but I also don’t need to look up as much. The number of ways to do things is far lower and there is no real magic behind the scenes. This might be one reason experiences may differ so radically.
by HighGoldstein | 2025-12-09 15:34:36+00:00
Why do you have such an issue with the donation to the IDF? I understand disputing that he's the largest donor, but I doubt he has ever written a big cheque directly to Trump (or in fact anyone except his family) either, is it also unclear whether he's a Trump donor?

Even if there were no mechanism for donating to the IDF available to the general public, do you believe someone like Ellison couldn't easily give money to whomever he wanted?
by surajrmal | 2025-12-09 15:34:48+00:00
Fuchsia isn't dead. People just like to spread random misinformation on the internet. Source: I work on fuchsia.

The intention is to have a stable driver abi which should allow you to build an arbitrary OS on top (fuchsia itself is exceptionally modular and doesn't have a lot of opinions it imposes on products built above it). Of course similar to a Linux BSP not helping Fuchsia run, such a layer wouldn't enable you to run other OS on top that are not built on top of fuchsia. There is also a limit to what you can generalize in the OS layers as some products may implement private apis between themselves and specific hardware drivers. A stable ABI also implies that the drivers won't necessarily need to be open source, but if the goal is to keep the rest of the OS updatable even if drivers themselves are not updated, that is a necessary concession. There are also many other practical benefits to keeping drivers open source regardless of license obligations to do so. That all said I'm very optimistic about this direction regardless of these caveats.
by hnfong | 2025-12-09 15:34:54+00:00
Doesn't matter. The countries with most chaos and internal strife gets a lot of practice fighting wars (civil war). Then the winner of the civil war, who's used to grabbing resources by force, and the one that has perfected war skills due to survival of the fittest, goes round looking for other countries to invade.

Historically, advanced civilizations with better production capabilities don't necessarily do better in war if they lack "practice". Sad but true. Maybe not in 21st century, but who knows.
by omnimus | 2025-12-09 15:36:05+00:00
Sure. There are also icons that are plain flat and don't use metaphor and work great. Play, share, hamburger, bluetooth, power... i am sure there are more. Icons are more about familiarity than anything.

I assume you were very familiar with Office 97. I can tell you people born in 97 are probably not. High chance they might not like and understand the icons because they aren't familiar with them.

It's like when everybody wants to design logo as unforgettable as Nike. But in reality anything people see 20 times a day people will remember.
by devsda | 2025-12-09 15:36:08+00:00
Can someone explain the VC economics behind this during a bubble. Is it

1. Funding for other projects gets diverted to AI

2. Traditional VCs see potential for greater risks, rewards and increases portfolio

3. Funds available to traditional VCs expands ?

4. New VCs jumping on to the hype train.

Is it any of the above or did I miss the mark completely.
by nerdsniper | 2025-12-09 15:36:49+00:00
The article says the battery lasts for years. Is that a misleading claim?

Edit FTA:

> How long does the battery last?

> Roughly 12 to 15 hours of recording. On average, I use it 10-20 times per day to record 3-6 second thoughts. That’s up to 2 years of usage.
by JeremyNT | 2025-12-09 15:36:51+00:00
I really like this framing.

A lot of people mysticise them or want to quibble about intelligence, and then we end up out in the "no true Scottsman" weeds.

We know what they are, how they work, what they generate. Let them be their own thing, do not anthropomorphize them.

The thing we don't understand so well is humans, really.
by HarHarVeryFunny | 2025-12-09 15:37:17+00:00
An LLM is a transformer of a specific size (number of layers, context width, etc), and ultimately number of parameters. A trillion parameter LLM is going to use all trillion parameters regardless of whether you train it on 100 samples or billions of them.

Neural nets, including transformers, learn by gradient descent, according to the error feedback (loss function) they are given. There is no magic happening. The only thing the neural net is optimizing for is minimizing errors on the loss function you give it. If the loss function is next-token error (as it is), then that is ALL it is optimizing for - you can philosophize about what they are doing under the hood, and write papers about that ("we advocate for viewing the prediction problem through the lens of compression"), but at the end of the day it is only pursuant to minimizing the loss. If you want to encourage compression, then you would need to give an incentive for that (change the loss function).
by groundzeros2015 | 2025-12-09 15:37:20+00:00
GitHub is filled with these because it’s always easier to make an engine than a game. You play with the fun tech and make the graphics engine. You never make any tough tradeoffs because you don’t have a target to aim at.

When an engine becomes useful is when it has to make a game. All your abstractions tend to get rearranged and hard decisions are made.
by oliveskin | 2025-12-09 15:37:59+00:00
Hi HN, I’m sharing an open-source project I’ve been building called Agent Tinman.

It’s a forward-deployed research agent designed to live alongside real AI systems and continuously:

Generate hypotheses about where models may fail

Design and run experiments in: LAB (sandboxed) SHADOW (mirrored production traffic) PRODUCTION (real users, gated)

Classify failures across: - Reasoning - Long-context behavior - Tool use - Feedback loops - Deployment & latency

Propose interventions

Simulate those interventions on real traces before deployment

Gate risky changes with optional human approval

It’s meant for teams who already run AI in production and want continuous, structured failure discovery, not just offline evals.

It’s: Open source (Apache 2.0) Python-first Designed to integrate as a sidecar via a pipeline adapter

Built around explicit modes, risk tiers (SAFE / REVIEW / BLOCK), and severity levels (S0–S4)

This is early but functional. I’d really appreciate:

Skeptical feedback

Edge cases you think would break this

Whether this solves a real problem for you or not

Repo: https://github.com/oliveskin/Agent-Tinman

Happy to answer anything technical.
by hamadev | 2025-12-09 15:38:00+00:00
Hey HN! Built Gitmore to solve a specific pain point: managing remote freelancers without constant check-ins. The problem: When you're paying contractors by the hour or project, you need visibility into progress. But scheduling syncs across timezones is a nightmare, and asking for daily updates feels micromanage-y (and they hate it too). The solution: Gitmore connects to GitHub/GitLab/Bitbucket and turns code activity into readable progress summaries. See what your freelancers shipped yesterday, what they're working on today, and where they might be blocked – all without a single Slack message. Why this matters for freelancers:

No timezone coordination needed They code, you stay informed automatically Less "are you done yet?" messages Clear evidence of work for invoicing discussions

Not trying to build a surveillance tool – just want to eliminate the awkward "so... what did you work on?" conversations while ensuring freelancers get paid fairly for visible progress. Would love feedback, especially from folks who manage distributed teams or work as freelancers themselves.
by simmonmt | 2025-12-09 15:38:29+00:00
I'm not normally a fan of tips, but this seems like a reasonable use of one to me. The picker isn't paid on the shininess of the apple they bring you -- they're paid to pick as quickly as they can from what's on offer. The potential for a tip incentivises them to go beyond that requirement -- to pick the nicest/freshest rather than the most convenient.
by layer8 | 2025-12-09 15:38:53+00:00
> How long does the battery last?

> Roughly 12 to 15 hours of recording. On average, I use it 10-20 times per day to record 3-6 second thoughts. That’s up to 2 years of usage.

Two years isn’t too bad, but at $99 the price is still a bit high for that.
Automating lead discovery across niche forums what's effective?
by betty_garet | 2025-12-09 15:39:09+00:00
I’ve been exploring ways to find potential clients without spending hours manually scanning Reddit, LinkedIn, X, and smaller niche forums. The goal isn’t just scraping it’s identifying posts where people are actively asking for solutions or services.

Some tools I tried either overwhelm you with irrelevant data or require constant manual tuning. I recently came across OptaReach, which promises to detect intent posts across multiple platforms and organize them for outreach. From a first glance, it seems to reduce noise and surface actionable leads.

Curious how others tackle this problem:

Do you rely on automated monitoring or manual searching for lead discovery?

Any strategies for filtering signal from noise without heavy coding?

How do you ensure outreach stays personalized and not spammy?

Has anyone tried tools like OptaReach or similar and did it actually save time?

I’m mostly interested in practical approaches, but happy to discuss technical aspects like scraping, NLP, and platform changes as well.
by georgeecollins | 2025-12-09 15:39:24+00:00
In theory yes, but a lot of the organizational reasons Siri is a flop are also similar to the reasons Apple Music loses to Spotify, Apple can't really get it together for ads.. I think Apple is a great company (disclosure : shareholder) but they have gotten so big and so stretched thin can't always take advantage of the opportunities in front of them.
by nebulous1 | 2025-12-09 15:40:10+00:00
That lifespan is based on the user recording for 12 to 15 hours over those two years. It's a $100 device that can record 12 hours of audio and then you throw it away. You could expend the battery on your first day by holding down the button.

Honestly I can see a niche use but this device strikes me as quite weird and I'm not sure why it isn't a button on their new watch.
by some-guy | 2025-12-09 15:40:18+00:00
I do think Siri is particularly behind, but they were behind long before the AI craze. I also understand you cannot simply make Siri “be smart” with an LLM without all kinds of consequences and edge cases to deal with.

It’s not the same, but PMs and VPs at my company think we can vibe code our way out of migrating a 1.6 million line codebase to a newer language / technology. Or that our problems can be solved by acquiring an AI startup, whose front end looks exactly the same as every other AI startup’s front page, and slapping a new CSS file that looks like that startup on top of our existing SPA because their product doesn’t actually do anything. It’s an absurd world out there.
by staticman2 | 2025-12-09 15:40:26+00:00
Episode 8 was subversive because it had self aware moments "trolling" the audience throughout like Luke mocking the idea Rey (and the audience) thought he would pick up a lightsaber again.

It also has weird "subversive" dialogue about sacrifice being bad that doesn't really fit what's happening in the movie itself where sacrifice of two characters saves the day. Which is "subversive" in the sense that a movie with dialogue saying "this is a shitty movie plot" is subversive.

It also rips off the ending of Return of the Jedi by killing the main bad guy so is "subversive" in that it trolls whoever was stuck making episode 9 without a functional villain.
by jagoff | 2025-12-09 15:41:06+00:00
I, and clearly many others based directly on your comment assertion, clearly disagree.

Whether or not you like MISTRA, “they left out the non-identical twins” is a side issue, and the broader evidence that IQ is substantially heritable is extremely strong.

Even if DZA were excluded, MZA alone provides a solid heritability estimate. The DZA sample was small and noisy, and MISTRA uses other twin and family data. Plus, meta-analyses confirm high IQ heritability, typically 0.5-0.8; Later structural-equation models applied to the full MISTRA cognitive dataset (MZA + DZA) estimated the heritability of general intelligence around 0.77, essentially the same ballpark as the original simple estimate. laplab.ucsd.edu. Intelligence is highly heritable, potentially reaching 80% in adulthood, supported by further studies like the Haworth et al. meta-analysis, showing age-related increases in heritability.

Strong evidence for polygenic and SNP heritability is shown from Plomin & von Stumm's 2018 research, showing how polygenic scores predict general intelligence.

Adoption studies further support the genetic influence, as adoptive siblings show weaker correlations compared to biological ones.

Environment is certainly NOT outweighed entirely or beyond merit, but evidence clearly shows the "uncomfortable" result having strong support despite desperate attempts to debunk.

In short; the op's story only works if including the DZA data actually drags the heritability estimate down into trivial territory. It does not.

Later analyses of the MISTRA sample that explicitly include both MZA and DZA twins and use full structural-equation models estimate heritability of general intelligence (g) at about 0.77 in adults. https://laplab.ucsd.edu/articles2/Lee2010.pdf

That is higher, if anything, than the original approximate 0.70. In other words:

The alleged “suppressed control group” does not turn the result into “no heritability”.

The more sophisticated models using that very same DZA data still say “IQ differences in this adult sample are heavily genetic.”

You just don't like this data, and don't want to accept it, because of the implications.
by masot | 2025-12-09 15:41:39+00:00
Playing with 'minimal' ways to add templates/generics/reflection to C has become a bit of a dumb hobby of mine (in this case shared/enabled by Akshay!).

Gamma was an experiment in templates without having to parse C. This led to some big annoyances that I guess aren't mentioned on that site but are in the PagedOut page[1]: when you instantiate a template "bar::[struct foo]" Gamma does a pretty bad job of knowing to copy the definition of "struct foo" (and all type definitions that "struct foo" depends on) from the caller into the template before compiling the instantiated template. (It gets even worse with circular dependencies, e.g., a "struct tree_node" that contains a "list::[struct tree_node]".)

More recently I've been writing MaC[2], which solves those problems by fully parsing the "header file" for each template. So it knows about all of the types in the program and can copy them between template instantiations as needed, but in the "main body" of the template you can use arbitrary GNU-C features. This has been a lot more reliable. As a test program for it I'm currently in the middle of writing an LR[k] parser generator[3] in MaC.

The big thing that gets annoying about all of these "don't-parse-the-code" approaches is there's no good way to do type inference. So you can't do multiple dispatch, e.g., have "print(x->foo(bar));" forwarded to the 'right' print function based on the type of its argument. (Actually, I've experimented with doing dynamic dispatch based on DWARF information, but that's a huge can of worms itself!)

[1] https://pagedout.institute/download/PagedOut_007.pdf#page=44 [2] https://lair.masot.net/mac/ [3] https://lair.masot.net/git/mac.git/tree/examples/lrk (sorry about the unreadable color scheme, still tuning it ...)
by jayd16 | 2025-12-09 15:41:56+00:00
It seems contradictory to move to new and rare technologies with clear customer outcomes.

Would it be wrong to say the advice is to hype chase, lean into new stuff and bail quickly when it's not working out? They hand wave away hype chasing by saying it's for the customer but I'm not sure that really changes things.

At least the advice about how the goal is to serve the customer not the tech is good.
by lapcat | 2025-12-09 15:42:02+00:00
There are some alternative policies, for example, banning smartphones in schools. This doesn't completely solve the problem, of course, but at least it limits social media use while the children are under direct supervision of the government.

A more extreme policy would be to treat smartphones themselves the same way we treat alcohol and cigarettes, enforcing an age minimum at the point of purchase. Of course the giant tech corporations would fly into rage over this suggestion and lobby heavily against it.
by qubex | 2025-12-09 15:42:10+00:00
I’m the insufferable Apple fanboy that chimes in to mention Pages, Numbers, and Keynote. I have a personal subscription to Office 365 for interoperability but I’ve been annoyed by their inescapable AI price hike and now this. I might as well just cancel my subscription. Excel for Apple doesn’t even have PowerQuery and PowerPivot, so it’s already a diminished experience.
by convenwis | 2025-12-09 15:42:25+00:00
This is the thing I've found amazing about people's complaints about Apple and AI.

Historically the strength of Apple was that they didn't ship things until they actually worked. Meaning that the technology was there and ready to make an experience that was truly excellent.

People have been complaining for years that Apple isn't shipping fast enough in this area. But if anything I think that they have been shipping (or trying to ship) too fast. There are a lot of scenarios that AI is actually great at but the ones that move the needle for Apple just aren't there yet in terms of quality.

The stuff that is at a scale that it matters to them are integrations that just magically do what you want with iMessage/calendars/photos/etc. There are potentially interesting scenarios there but the fact is that any time you touch my intimate personal (and work) data and do something meaningful I want it to work pretty much all the time. And current models aren't really there yet in my view. There are lots of scenarios that do work incredibly well right now (coding most obviously). But I don't think the Apple mainline ones do yet.
by epoch1677 | 2025-12-09 15:43:01+00:00
That's not all, my macbook (48 GM VRAM) can run better local LLMs at a workable speed than my RTX 5090 rig can, plus Apple has MLX and neural engines.

The reason there was such a narrative is because Wall Street and Silicon Valley are both narrative machines with little regard for veracity, and they are also not that smart (at least according to people who successfully beat their system, such as Buffett).

"Warren, if people weren't so often wrong, we wouldn't be so rich." – the late great Charlie Munger.
by embedding-shape | 2025-12-09 15:43:20+00:00
Firefox (with minor changes + addons) is what I use today, works well for what I care about. Thanks for the recommendation though!

While you're here, last time I came across your website (and it seems like it looks the same currently), I noticed that your browser comparison is not including Firefox, which is what you've forked from (as far as I can tell at least, it isn't made clear by the landing page actually, but the UI and name makes it obvious), which feels like it's a bit misleading almost intentionally.