MurphyA/hacker-news huggingface dataset:
by o-o- | 2026-03-12 17:52:27+00:00
> Banks are lending to private equity firms to fund purchases of businesses.

Yes some businesses are SaaS but here's the real problem: Many businesses' sole purpose is _leveraged buy-outs_ which really is the devil in disguise.

It goes like this: A VC specialising in veterinary clinics finds a nice, privately owned town clinic with regular customers and "fair" prices, approach the owners saying "we love the clinic you've built! We'll buy your clinic for $2,500,000! You've really earned your exit!".

So now the VC lends the money from the bank, buys the clinic, and here's the important part: _they push the debt onto the clinic's books_. So all of a sudden the nice town clinic has $2,500,000 in debt, raise prices accordingly, ~~burn out personnel~~ slim operations accordingly, and any surplus that doesn't go to interest and amortization goes straight to the VC.

Debt and collateral on the veterinary clinics.

Risk free revenue to the VC.
by fluffybucktsnek | 2026-03-12 17:52:33+00:00
The perspective of protecting user from flaming is interesting, but I agree with @edanm.

That said, I believe that LLMs' "unique" writing style may be useful ability to protect anonymity against stylometric attacks, although that still ought to be checked. If true, that would be a case where LLMism would be desirable by the author.
by klodolph | 2026-03-12 17:52:34+00:00
The analogy has a lot of problems.

Imagine you got a loan to buy a bunch of laundry machines to run a laundromat. But your laundromat earns $8,000 a month, and the loan payment is $10,000.

You can decide to sink $2,000 of your personal money into the laundromat every month, or you can give up.
by 9rx | 2026-03-12 17:52:52+00:00
> Other resources are currently in higher demand than their current supply: silver for example.

That, of course, is why we created resource allocation as a social construct. Obviously you fundamentally cannot have allocation without scarcity.

But it doesn't answer the question. If resource allocation is not entirely a social construct, are you imagining that resources are also allocated by some kind of natural force? Given the scarcity of silver, maybe the universe decides that you get some and I don't? And if you try to give me yours, contrary to the fabric of the universe, you will be struck down by a bolt of lightning? What is the "what else" here?

This nuance you vaguely refer to but don't say anything about is certainly intriguing. I am looking forward to you completing that chain of thought.
by hnlmorg | 2026-03-12 17:53:14+00:00
I’ve heard conflicting accounts about their knowledge and royalties.

While I’m certain they didn’t receive royalties from all artists, I heard many 80s artists did. And Amen Brothers took others to court. So they would have know about the use of the break.

I will admit I haven’t done any independent research into this matter personally. Just echoing accounts I’ve read and taking their reports at face value.
by Bender | 2026-03-12 17:54:01+00:00
[dead] 'AI Is African Intelligence': The Workers Who Train AI Are Fighting Back (404media.co)

404media.co is auto-dead and requires someone to vouch for it. If you turn on show-dead in your profile you will see most submissions of that site are [dead]. [1]

I vouched for it and it should be visible now.

[1] - https://news.ycombinator.com/from?site=404media.co
by scottlamb | 2026-03-12 17:54:26+00:00
5. In their "dessert" section, they talk about a problem with sort when the items are shallow clones. It's an example of a broader problem: they put something into an `ObservableVector` but then semantically mutate it via inner mutability (defeating the "observable"). You just can't do that. The sort infinite loop is the tip of the iceberg. Everything relying on the observable aspect is then wrong. This isn't about "optimizations sometimes cause bugs"; it's about you need to understand how your abstractions work.
by Guid_NewGuid | 2026-03-12 17:54:28+00:00
There's a difference between informed speculation and uninformed speculation and most of the opinions so far fall into uninformed speculation where the writer implicitly blames those suffering for poor diet and lifestyle choices.

In addition a lot of the speculation assumes something specific to the US where this is a trend in multiple countries, predominantly high income ones[0][1], but this speculation that it might be 'chemicals' is fairly dull to read and adds nothing. Why this cohort specifically? What commonalities are there between countries with an observed increase? If it's diet why would it only impact the younger cohort?

[0]: https://www.thelancet.com/journals/lanonc/article/PIIS1470-2...
[1]: https://www.gastrojournal.org/article/S0016-5085(21)00010-X/...
by ninalanyon | 2026-03-12 17:54:36+00:00
Buy second hand clothes. These are either very, very, cheap, and they last just long enough for me to get bored with them. Or they are merely fairly cheap and last almost forever because the stuff that quickly falls apart doesn't get resold, it gets discarded by the person who bought it new.

In either case I have lengthened the time between manufacturing and landfill and had the enjoyment of clothes that I would otherwise not have been able to afford along the way.
by alwillis | 2026-03-12 17:54:58+00:00
> Windows 10+ and Linux also have memory compression, though I don't know how the implementations compare.

A combination of Apple's Unified Memory Architecture (UMA) and hardware-accelerated instructions (SIMD/NEON) makes RAM compression on Macs very efficient. Because the storage controller is integrated into the SoC, the bandwidth is high enough that the transition between "Compressed RAM" and "Swap" is very smooth.

And because the CPU and GPU share the same memory, there are no wasted cycles moving data between VRAM and System RAM.

Apple uses WKDM (Wilson-Kaplan Direct Mapping), a specialized, high-speed compression algorithm designed specifically for in-memory data. WKDM is "architecturally aware"—it was built to compress the specific types of data structures found in a computer's RAM, such as pointers, integers, and memory addresses. WKDM treats RAM like a collection of 64-bit integers and pointers; and it's designed to fit entirely in L1/L2 cache [1]. This shipped in MacOS 10.9 Mavericks in 2013.

Windows/Linux treat RAM like a stream of bytes (similar to how you’d compress a .zip file) so it’s not as efficient. The vast majority of Windows and Linux machines don't have unified memory or storage controllers connected to their processors.

Because of this, Apple can often compress a page of memory using fewer CPU cycles than Windows or Linux, which is why M-series Macs can be so aggressive with compression without you ever noticing a "hitch" in the UI.

The fallback algorithm is their LZFSE algorithm, which is like "Zlib-level compression with 2x-3x the speed and efficiency". LZFSE achieves a nearly identical compression ratio but uses Finite State Entropy (FSE) coding, which allows it to decompress data significantly faster while using much less battery power.

LZFSE is optimized for the ARM NEON instruction set to minimize "wake time" for the CPU, making it arguably the more "green" choice for mobile devices [2].

It's safe to say that neither Windows nor Linux has the combination of hardware and software optimizations that Apple has when it comes to RAM compression.

[1]: Compressed Memory compresses the least recently used data residing in memory using the WKDM algorithm, which not only frees up memory but also reduces the amount of swapping going in the background. Not only is this faster than swapping to disk (even to SSDs), but Apple also claims it saves power -- essentially, that compressing data in memory uses less power than writing data to disk without compressing it. -- https://www.osnews.com/story/27121/os-x-109-mavericks/#:~:te...

[2]: https://lyncd.com/2015/09/lossless-compression-innovation/
Show HN: MoneyOnFIRE – FI date and action plan (v2)
by LambdaAndLatte | 2026-03-12 17:55:41+00:00
A few months ago we posted here and got a lot of insightful feedback. This is what we built from it.

MoneyOnFIRE answers two questions: when can you reach financial independence, and what should you do to get there the fastest? It runs a financial simulation across income, taxes, accounts, contributions, returns, and withdrawals, then produces a prioritized action checklist with specific dollar amounts, dates, and steps.

Several of the biggest improvements came directly from comments on the last HN thread:

Rental property support: The engine now models rental income, mortgages, appreciation, and how properties interact with the rest of a financial plan.

Scenario modeling: You can now compare how different choices — lower returns, working longer, adjusting spending — affect your FI timeline side by side.

No login required: Several people didn't want to create an account or store financial data. You can now run a full plan without signing up.

FI vs FIRE: We initially built for the early-retirement crowd. Feedback showed it's just as useful for anyone pursuing financial independence on a longer timeline — the calculations and actions are the same.

Also shipped: support for multiple children and college timelines, Roth conversion ladders, IRA strategy selection, umbrella and term life insurance sizing, and dynamic reports that update as your inputs change.

The core thesis hasn't changed: personal finances are a complex web of interacting rules and calculations. We want to solve that and give everyone a clear, ordered set of actions they can actually implement.

Happy to answer questions about the engine or the modeling decisions behind it.
by orbital-decay | 2026-03-12 17:56:10+00:00
In reality you give the same programmer an update to the existing spec, and they change the code to implement the difference. Which is exactly what the thing in OP is doing, and exactly what should be done. There's simply no reason to regenerate the result.

The entire thing about determinism is a red herring, because 1) it's not determinism but prompt instability, and 2) prompt instability doesn't matter because of the above. For some reason this misconception is causing a ton of confusion.
by nerdjon | 2026-03-12 17:56:31+00:00
There are a lot of websites that only support third party login, so that is not always an option.

They don't have to bend for another, but they made a choice to put an app on iOS. They added support for apple signin, and then for some reason did not put it on their website.

You can criticize Apple for requiring that all you want, but they clearly have support for it and are choosing to not put it on their website which is causing a worse user experience.

IF apple did not support website loggin than sure, but they do. So the ability to fix this is on Anthropic (and many other websites).

If you are already going to support third party login you should not limit it to only Google accounts and there is no reason to support Apple on iOS and not the web.

Also for the record, Apple only requires sign in with apple if you already support third party authentication. So if you are already going to support that, giving the user more choice (and making it so we are all a bit less dependent on google) is a good thing.
by starkparker | 2026-03-12 17:56:36+00:00
Happiest and most productive I've ever been was working 4/10 with a start time at 2 p.m. No morning sluggishness walking into work after lunch, zero-traffic commute, off Fridays so I'd still have a social life far, far away from morning people. Dated a nurse who also worked night shifts and just went on weekday lunch dates or closed down bars.
by HarHarVeryFunny | 2026-03-12 17:57:23+00:00
I'd say that motivation and feeling are largely unrelated.

We do what we do, not because of motivation, but because that what we've evolved to do.

Feeling really comes after the fact, or independent of it, when we're introspecting on what we've already done (courtesy of evolution), and how we feel about that, or how can we explain it!

There is tons of evidence that this is the way our brains work - we do things then (if asked, or if thinking about it) concoct post-hoc explanations of why we did it. An example of this is split brain patients, where one half of the brain explains what the other half did, despite there being no connection between the two!

But sure, we have feelings, closely related to qualia you could say. It does "feel like something" to be depressed, or excited, or inspired, or whatever. I don't see any big mystery to this - our brain is able to self-observe and not surprisingly able to detect it's own varied patterns of operation.

I presume where you may want to go with this is "but why does it feel like anything? why is there any subjective experience at all (and would a machine have it too)?", and I think the answer is that this is just an emergent property of having a cognitive apparatus capable of self-observation. We can already see this glimmerings of this in LLMs, maybe a bad example since their thoughts are derived from humans, but nonetheless LLMs self-report as if they are conscious, and have existential discussions about it on Moltbook. It's hard to imagine (to bring this back on topic) that LeCun's animal intelligence, basically something LLM like that is trained from scratch (no baked in human knowledge) wouldn't report the exact same thing.
by efenande | 2026-03-12 17:57:30+00:00
I built UI Playground, an iPhone app for browsing and experimenting with iOS UI components.

The idea came from wanting a simple way to explore UI patterns and components individually instead of digging through sample projects or documentation. The app lets you quickly look at different components and interactions, which can be useful for learning SwiftUI or getting inspiration when designing mobile interfaces.

I mainly use it as a reference when prototyping or experimenting with UI ideas.

If you’re curious, you can check it out here:
https://apps.apple.com/us/app/ui-playground/id6504997189

Happy to hear feedback or ideas for components to add.
by volkl48 | 2026-03-12 17:57:44+00:00
I mean, there's a well-documented link between colon cancer and inadequate fiber intake.

And it's also well-documented that the average Western diet is highly deficient in fiber and that this is a thing which has gotten much worse in the last 75 years.

There also seems to be at least some light evidence that there may be generational effects - that the starting point of your gut is already bad if your mother's was.
by carlosjobim | 2026-03-12 17:58:12+00:00
We have to grow out of those kind of dreams. That's like a kid dreaming that when he grows up he'll eat ice cream for dinner every day.

People when they mature have an innate desire to work. It is good for body and mind. If you're curious about the world, you'll have to do some work one way or another to achieve your goals and satisfy your curiosity.

If "society" is just a function of basic needs, then there's plenty of places in the world to visit where people live like that and use any excess energy in endless fighting against each other instead of work.
by hermanzegerman | 2026-03-12 17:58:29+00:00
Yes you're right. I meaned a different video, but I can't find it right now.
I've looked it up, and back then MacOS had a bug which exacerbated that issue.
Here is an article

https://www.macrumors.com/2021/02/23/m1-mac-users-report-exc...
by morphle | 2026-03-12 17:58:30+00:00
I am surprised the author did not mention or uses Software Defined Networking, Openflow or P4 (programming language for programmable switches). He must have skipped reading the scientific literature?

I programmed and build one of the very first ISP hardware and software systems in 1987-1997 when we connected the first submarine link between the US and Europe in Amsterdam.
by kovallux | 2026-03-12 17:58:33+00:00
I built Dev Cleanup Utility to clean Xcode leftovers on macOS (DerivedData, caches, simulator/device artifacts) with safer defaults and review-before-delete flow.

I made it after repeatedly hitting disk pressure during iOS/macOS development.
Main goal: reclaim storage quickly without accidental “too much cleanup”.

I’d especially like feedback on:
- which cleanup categories should be opt-in by default,
- what trust/safety signals are missing,
- and what would make this a daily developer tool.
by programmertote | 2026-03-12 17:59:07+00:00
A recent LinkedIn post that I came across as an example of people trusting (or learning to trust) AI too much while not realizing that it can make up numbers too: https://www.linkedin.com/posts/mariamartin1728_claude-wrote-...

P.S. Credit to the poster, she posted a correction note when someone caught the issue: https://www.linkedin.com/posts/mariamartin1728_correction-on...
by thunderfork | 2026-03-12 17:59:08+00:00
>you don’t need to be an aviation expert to trust the plane will fly.

...because when I get on the plane, I can look out the window and see that it's in the air.

With paper ballots, the systems are very interpretable - you can sign up to audit the ballot counting process and watch it happen, etc.

But you can't do that with electrons in a computer - it's really just pure trust. That's what you lose.
by dlcarrier | 2026-03-12 17:59:11+00:00
Club seating is pretty controversial. A large percentage of passengers absolutely will not accept a rear facing seat. Chrysler had Swivel 'n Go seats in the center row, that could optionally rotate to club seating as an option from 2008 through 2010, but dropped it due to low interest.

Clearly Zoox hasn't done the market research, or as is more often the case, did the research then ignored it, so any party of more than two is far more likely to use their competition.
by epistasis | 2026-03-12 17:59:27+00:00
Agreed. And I'd say pulling out of the TPP is an equivalently big mistake, and will honestly have far worse consequences for the US but in far different ways. Letting China be the leader of the Pacific by pulling out, in combination with the terrible hostility to all countries there now, especially to South Korea, massively weakens the US economically and military. We just handed everything over, no fight, no fuss, no benefit to the US. Ugh.
by janeerie | 2026-03-12 18:00:21+00:00
"I also think that wholegarment knits look kind of cheap and that sewing actually adds structural integrity and durability to a garment."

This is an ongoing issue in the handknitting community. Most knitters hate seaming (we're not sewers!) so seamless patterns have become very popular. However, many argue that this leads to shapeless, saggy garments that aren't as wearable.
by lubujackson | 2026-03-12 18:00:25+00:00
Creator here - this was much more technically difficult than it seems, because rendering pixel perfect backgrounds with scaling width is non-obvious. Hit 'Esc' and you can set some options, including changing the background pattern.

If you want to see a big file, I have a direct link to load the Blocktronics WTF4 ANSI which is over 4000 lines: https://sure.is/ansi/?wtf4=1
by elinear | 2026-03-12 18:00:26+00:00
Hank Green's visualization[0] a few weeks ago cite several statistically significant risk increases to CRC incidence (omitting ultra endurance athletes):

- Sugary drinks (≥2/day as teen) - 2x

- Sedentary lifestyle (>2hr TV/day) - 1.7x

- Childhood antibiotics (recurrent) - 1.5x

Have any studies tracked the growth of these behaviors in recent decades, potentially lining up with the increase in early onset CRC?

[0] https://www.hankgreen.com/risk-factors
ClawMemory – Git for AI agent memory (forkable memory for AI agents)
by Brettinhere | 2026-03-12 18:00:35+00:00
Hi HN,

I built ClawMemory because my AI agent kept waking up with amnesia.

The problem isn't the model. GPT-4, Claude, and other LLMs are stateless by design, and that's fine. The real problem is the agent layer. Every time an agent framework starts a new session (like in OpenClaw), the agent forgets everything that happened before. The architectural decisions we made, the experiments that failed, the context that took hours to build—all gone. The model is smart, but the agent behaves like a goldfish.

My first attempt at a fix was a MEMORY.md file in the project repo. That worked for a while, but eventually, it became too long for the context window, and the agent could no longer load its own history.

Then I tried RAG with a vector database. That worked well for retrieving isolated facts, but failed at something much more important: understanding how decisions evolved over time. Vector search can answer, "Did we ever discuss Redis?" But it cannot answer, "Why did we switch from Redis to Postgres?"

What I actually wanted was version control for agent sessions. Something like Git, but for AI memory.

ClawMemory treats AI conversations like source code. Each session becomes a commit. When a session ends, the agent commits that memory to a repository. Over time, the repository becomes a timeline of the agent's reasoning—not just what the agent knows, but how it got there.

The most interesting idea here is forkable memory. If someone has spent months working with an AI agent on the same domain you're exploring, you shouldn't have to start from zero. You can fork their memory repository. Your agent loads that context on startup and continues from there.

This is exactly how open source software works: the first person solves a problem and documents it in code, and everyone else forks the repository to build on top of it. ClawMemory applies that model to AI reasoning. Instead of copying prompts or reading blog posts, your agent inherits the thinking process itself.

What works today:
You can browse public agent memory repositories at https://clawmemory.ai/explore. Each repo shows a timeline of sessions.

Other things that currently work:
- Import ChatGPT conversations from an OpenAI export ZIP
- Automatic commits every few exchanges
- REST API for any agent that can send an HTTP request (model-agnostic, works with any framework)

I built this because my OpenClaw agents kept forgetting everything we had already figured out. ClawMemory is an attempt to solve that—not by making models smarter, but by giving agents something they currently lack: persistent, forkable memory.

If you've built long-running AI agents or tried other memory systems, I'd love to hear what worked (or didn't).

https://clawmemory.ai
by whatisthiseven | 2026-03-12 18:00:47+00:00
> But will it?

My prediction is no, because productivity gains must benefit the lower classes to see a multiplier in the economy.

For example, ATMs being automated did cause a negative drop in teller jobs, but fast money any time does increase the velocity of money in the economy. It decreases savings rate and encourages spending among the class of people whose money imparts the highest multiplier.

AI does not. All the spending on AI goes to a very small minority, who have a high savings rate. Junior employees that would have productively joined the labor force at good wages, must now compete to join the labor force at lower wages, depressing their purchasing power and reducing the flow of money.

Look at all the most used things for AI: cutting out menial decisions such as customer service. There are no "productivity" gains for the economy here. Each person in the US hired to do that job would spend their entire paycheck. Now instead, that money goes to a mega-corp and the savings is passed on to execs. The price of the service provided is not dropping (yet). Thus, no technology savings is occurring, either.

In my mind, the outcomes are:

* Lower quality services

* Higher savings rate

* K-shaped economy catering to the high earners

* Sticky prices

* Concentration of compute in AI companies

* Increased price of compute prevents new entrants from utilizing AI without paying rent-seekers, the AI companies

* Cycle continues all previous steps

We may reach a point where the only ones able to afford compute are AI companies and those that can pay AI companies. Where is the innovation then? It is a unique failure outcome I have yet to see anyone talk about, even though the supply and demand issues are present right now.