skysight-inc/HackerNews huggingface dataset:
> DCT compression, also known as block compression, compresses data in sets of discrete DCT blocks.[3] DCT blocks sizes including 8x8 pixels for the standard DCT, and varied integer DCT sizes between 4x4 and 32x32 pixels.[1][4] The DCT has a strong energy compaction property,[5][6] capable of achieving high quality at high data compression ratios.[7][8] However, blocky compression artifacts can appear when heavy DCT compression is applied.

https://en.wikipedia.org/wiki/Discrete_cosine_transform
Only if their portcos don't build products people buy.

Making a pure-research, foundation model company is silly. Make a product company that sells products.
In the past, we've seen that machines will always need someone to make sure they are running. People who upgrade them, replace broken parts, and make sure the power cord is connected. So that will continue to be for the foreseeable future.

What can't be automated is the ability to look around and see what humans need and solve the need. Look at every entrepreneur and you'll see that's what they are doing with their companies. There will always be a need for that.
Sure, if you stop after "nobody's vocal coords make noises above 4khz in normal conversation", but the rumbling of the vocal coords isn't the entire audio data which is present in-person. Clicks of the tongue and smacking of the lips make much higher frequencies, and higher sample rates capture the timbre/shape of the soundwave instead of rounding it down to a smooth sine wave. Discord defaults to 64kbps, but you can push it up to 96kbps or 128kbps with nitro membership, and it's not hard to hear an improvement with the higher bitrates. And if you've ever used bluetooth audio, you know the difference in quality between the bidirectional call profile, and the unidirectional music profile, and wished to have the bandwidth of the music profile with the low latency of the call profile.
The C language spec isn't what important here it's the ABI doesn't know the difference.
It has been amazing to read in this thread how many people seem to think that “nothing new in the UI” equals “nothing interesting about this distro”.

It really highlights how the relationship with computers is changing.

The goals of Chimera Linux have little at all to do with the UI. In my view, Chimera Linux is highly innovative and disruptive. Yet, some people can look at it and genuinely wonder what it does differently or what the point of it is at all. Fascinating.
Some years ago, I decided that if I could read the headline and the byline and then at least summarize the column, there was no point in reading an opinion piece. This has saved a good deal of time and stress.
Aren't floating point comparisons frowned upon?
https://github.com/google/pebble/blob/main/src/libc/math/flo...

I guess you can freeze some compiler options to give you consistent results.
Similarly, "new record for highest-grossing film", or "new record for number of popular votes received."
I was really hoping the Flipper team would champion something like this after they came out with that weird desktop busy panel. I've never understood the value proposition of a high pixel density, power hungry display on a watch. Can't wait to see what these guys can achieve with modern tech!
You need to also look at valuation and how much growth justifies that valuation.
A week or so ago I was commenting on how Rust doesn't really solve much, if any, of my problems with C++. In that comment I wondered if part of that might be because assembly language was one of the first languages I ever learned and I spent the first few years of my career writing and maintaining low-level assembly language code? Assembly really does change how you program in C/C++, especially when I came of age in an era where compilers had bugs and you really did have to check the assembled output to make sure it made sense. Also, symbolic debugging didn't exist yet and so you had to print out the linker maps so when you looked at the compiled code, which for a long time was the only code you could actually debug, and know which variables corresponded to which memory references. You also got really good at learning how most C/C++ compilers built a function stack frame: what order the parameters were pushed onto the stack, how local variables were stored on the stack and stuff like that. I was a few years into my career before we had symbolic debugging, debugging the actual source code and not the assembly code, and could inspect variables by name rather than knowing where they were stored on the stack or on the heap.

I'm not recommending we go back to those days, but as the author notes, it does change your programming. Maybe it's one of those things one should do a few times not from a perspective of being thankful how much better our tools our today, but from the perspective of this is actually what's going on under the hood. This is how your program is actually running.
Is there still evidence that more compute = better model?
They do support if you spend enough money with them. At least with GCP. Access to in-house cloud architects level of support, even.
Untrue, it's purist startup people and some ISVs who believe that Alma or Rocky are the somehow "better".

Meta runs 10M+ CentOS 9 Stream boxes migrating to 10 eventually.

Cent has shorter security update availability latency and they're shipped more consistently. The benefit with Rocky and Alma is double the lifecycle time and arguably better governance, unfortunately though they're both tiny operations that suffer from a narrow bus factor, are always playing catch-up, drifting away from RHEL compatibility, and are the definition of fragmentation.

If you need RHEL-ish for servers, use CentOS Stream. It's not great for desktop. Use Fedora or something more LTS for that.
> Apart from that, OAuth2 is such a vast area that we have been able to only answer the fundamental why questions and review the most popular delegation grants in this article.

So, what we've learned is that OAuth2 is not a single thing, and that it is too complex.
Based on this comment https://www.facebook.com/story.php?story_fbid=90140663553077... (can i even directly link to fb. Idk)

Some people were complaining about meta, but species seems the main one. And only the main site, the mobile site is fine.
Fair that I do seem to be asking for MBA speak. Not my intent.

For me, the big mental block is that I can't think of much I want to do from my watch. A readable screen is obviously nice. So is advanced battery life. But, if I'm going to be dipping into health tracking, it seems Garmin is the baseline there.

The UX flow is one that has me somewhat intrigued. How often are you interacting with the device? And for what reasons?
Weird to see we've put out non stop anti Chinese propaganda for the last 60 years instead of addressing our issues here.
I loved the idea of Pebble watches, but I found them too bulky for what they offered. I hope the relaunch focuses on the same core functionality in a much much sleeker design.
I'm still using it, in fact to the point that it's probably the biggest factor why I have been procrastinating on still staying on Android rather than trying alternatives like PinePhone.

That the OS has been open sourced is great news (though it's sad it was on GitHub... and hopefully other communities around Pebble will spring up outside of platforms (article only mentions Discord and Reddit)).
This is writing of someone steeped in "alpha male" youtube & podcast ideology.
Watches are cool, I wish I was a watch person but I am not. Pinetime for example seemed neat.

edit: there is one use case I'm considering it's for sleep tracking since my ideal life I sleep whenever and it seems my ideal operating mode is to be slightly sleep deprived (about 5 hours each night) so it knows when I fell asleep and sets a 5hr timer from there.
I interpret the interaction like this:

Person 1: I have built this thing, maybe somebody finds it useful. Then you can have it for free.
Person 2: I'm not going to use it unless you do as I say with your thing, and I'm not going to offer anything for you.

Well okay, then don't use it? Or if you want a feature, offer something in return. Or make the request towards a paid service – their ears are very eager to hear what features potential customers would like to have.
And the people who voted for him are going to end up suffering the most.
At this point, this reaction of the market means nothing. All these stocks were at an all time high, so the drop was inevitable. Tomorrow they can come up with a different spin and move the stock up again.
Right, so it sounds like it's working then given how much people are starting to care about them in this sphere.

We can laugh at that (like I like to do with everything from Facebook's React to Zuck's MMA training), or you can see how others (like Deepseek and to a lesser extent, Mistral, and to an even lesser extent, Claude) are doing the same thing to help themselves (and each other) catch up. What they're doing now, by opening these models, will be felt for years to come. It's draining OpenAI's moat.
Different users; different needs!
Copies are made in the formation of the training corpus and in the memory of the computers during training so there's definitely a copyright issue. Could be fair use though.
I recently discovered NodeBB while looking for Discourse alternatives, and it turns out they just released support for federation (ActivityPub)!

What does federation mean in this context? If you set up a NodeBB forum, you can interact with other thread based and federated websites, like Lemmy ... or just another NodeBB forum.

Really cool stuff!
This looks really useful. It is tuned to catch specific kinds of bad code that LLMs sometimes generate.
Memes don't need to be passed on hereditarily. It is entirely possible for a meme which absolutely prevents the host from reproducing to persist indefinitely so long as it spreads to new hosts at around the same rate hosts die out. See clerical celibacy. It is also possible for memes to lie dormant and then resurge after an extended period of time by which all the original hosts are dead.
What are the advantages of this over advocating for more and stronger state rights and lower federal taxes and removal of federal agencies?

California already has a state constitution which can enshrine positive rights. It is larger than most European countries and could implement socialized healthcare if it wished.
The Apple Watch is just okay but sells like 25x per year Pebble's entire lifetime sales. I guess people love their Pebbles but this seems like a lot of effort for something most people probably don't want.
Still overvalued IMO. Their market cap remains ludicrous.
Show HN: Makefunnybooks.com – AI-generated personalized books for unique gifts
Is all you care about in a job the money? And are you looking at your total comp, or your hourly rate?

In my experiences, the places that pay the most _have_ to pay that much because the job sucks. By the time you divide their salary by hours actually worked, people at FAANG end up making significantly less than I do. I value all my time, not just my bank account.

What does my reputation buy me? In the worst job market in the last 20 years, I had two offers in hands within three weeks. I can bring top performers willing to work for regular salaries into wherever I land. All of that is because a lot of people who worked with me in the past would like to work with me again, and the companies we build software for benefit.

I've built my career on jobs with _actual_ advancement, not just a bigger number. And it has been plenty lucrative.

Startups don't succeed because the code is good, but they sure can fail because it is bad. When a company needs to save itself after the underqualified mercantile engineers have left a spaghetti mess of lambdas scattered all over the org or a spaghetti mess of a monolith with every model in one folder, they are very happy to pay for actual expertise.
Deepseek R1 is the closest thing we have to fully open-source currently. Open enough that Huggingface is recreating R1 completely out in the open. https://github.com/huggingface/open-r1
> 3. Lack of Vision from Leadership

I think layoffs at my employer have been imminent for 5 years...
LTT water cooled a whole room of workstations in a house some years ago. Those were some fun videos, in a horrifying sort of way. (And decided he wanted to do it again with his new house.)
I think it's just a static redirect, it sent me to the Apple Watch page in Firefox on Linux. But I also wondered if it would shuffle between a few different brands or something (I guess not).
Soooo.. I loved my pebble. And I still have my Kickstarter one.

I don't think I'd still want one now though. My Samsung smartwatch does a lot more. Heartrate, ECG, payments, GPS. And with an acceptable battery life too.

Ps so Eric Migikovsky works for Google now? I thought he was working on beeper.
Every Pebble model used an LCD display. It being epaper has to be one of the most pervasive myths in tech history. It was a low powered, reflective LCD, so it did improve on other LCDs in those areas somewhat, but it wasn't eink technology.
I looked at this page on my Quest 3. For some reason, that banner image looks 3D!
Sorry what's a Cuba moment?
That could be a movie plot, some drive lost in the mail that has to be recovered. Where is it?! I don't know! Don't give me excuses give me answers!
I like my Fossil watch. Similar to Withings, less health features, marginally smarter. Analog watchface in front of an eink display, 3-4 weeks battery life. Of course they got discontinued as well.
Allerta inPulse too? ;-B
That's not a fair comparison. The user in the word example already had access to the infringing content to copy it, and then paste it into word.

But it has to have that copy, verbatim, to produce it, as you acknowledge.

If dropbox was hosting and serving IP from paramount, paramount would be able to submit a DCMA request to get that data removed.

Not only can you not submit a DMCA request to chatGPT, they can't actually obey one.
I am talking about the times where you were only limited by your imagination and skills. All you needed was a laptop and few hundred bucks for servers. Now, to compete, you would need magnitudes more cash. You can still do some things, but you are at a mercy of AI providers that they can cut you off on a whim.
So are the western ones, just in different ways.
Microsoft don't have to use OpenAI though, they could swap that out underneath for the business applications.
"MIP" ?

I thought it was called transflective : for transmittive + reflective ?
That would be a cinch with a Mac one decade ago, or 20 years or 30 years ago, or probably even back in 1985.

But these days, when it comes to external bootable drives, Apple vacillates between neglect and hostility.

I've lost track of the current status of bootable backups on the Mac. Based on the following blog, it seems there's some new insult to the process once or twice a year: https://eclecticlight.co
Which is really sad
This app does not use AI at all (nor is it AI slop—I used AI very sparingly to build the product). The app uses NextJS and Postgres, and it’s mostly CRUD and a bit of scraping+caching behind the scenes. It does solve a business problem that we’ve validated with a pre-MVP (low-tech Google Forms + Google Sheets + Twilio) via waitlist members.

The free version of our MVP is ready to launch to general users, though, and the only thing holding us back is that we haven’t signed an agreement about compensation.

We’ve already identified/communicated with some potential customers, and deals with these customers are typically 40K+/year based on our research (FOIA requests to public institutions using competitor products). Based on what’s built (and what I could finish building by August), I think I’d we’d have a fairly competitive offering in the market we’re targeting even if we changed nothing after August.

This risk of “dead equity” to future investors is why we have discussed doing profit sharing. Does this info change anything? Got any alternative proposals/advice? Thanks for your help—it is very much appreciated.
We also get paid a lot more than the dime-a-dozen coders.

As is so often the case, optimizing for the short term comes at the cost of the long-term.
DeepSeek just proved Lina Khan right
>Can you point us, or perhaps just me, to something that explains that and which countries this applies to?

Gladly
https://www.efginternational.com/us/insights/2021/gold-and-b...

>I think that I'd have a hard time paying the Norwegian tax authorities with gold or silver.

This might be even true since Norway is not in EU but I would be surprised if Norwegian banking had been that different from Basel system.
Why did I just discuss them, then?
The local models don't appear to do this. It happily answered "Tienanmen massacre" questions.
You can download the SDK and side load applications. No annual developer fee. Not sure how it’s more locked down.
I’m almost shocked this spooked the market as much as it did, as if the market was so blind to past technological innovation to not see this coming.

Innovation ALWAYS follows this path. Something is invented in a research capacity. Someone implements it for the ultra rich. The price comes down and it becomes commoditized. It was inevitable that “good enough” models became ultra cheap to run as they were refined and made efficient. Anybody looking at LLMs could see they were a brute forced result wasting untold power because they “worked” despite how much overkill they were to get to the end result. Them becoming lean was the obvious next step, now that they had gotten pretty good to the point of some diminishing returns.
> Is this in academia?

Yes and no. Industry AI research is currently tightly coupled with academic research. Most of the big papers you see are either directly from the big labs or in partnership. Not even labs like Stanford have sufficient compute to train GPT from scratch (maybe enough for DeepSeek). Here's Fei-Fei Li discussing the issue. Stanford has something like 300 GPUs[1]? And those have to be split across labs.

The thing is that there's always a pipeline. Academic does most of the low level research, say TRL[2] 1-4, partnerships happen between 4-6, and industry takes over the rest. (with some wiggleroom on these numbers). Much of ML academic research right now is tuning large models, made by big labs. This isn't low TRL. Additionally, a lot of research is rejected for not out-performing technologies that are already at TRL 5-7. See Mamba for a recent example. You could also point to KANs, which are probably around TRL 3.

> Arguably, the emergence of quant hedge funds and private AI research companies is at least as much a symptom of the dysfunctions of academia

Which is where I, again, both agree and disagree. It is not _just_ a symptom of the dysfunction of academia, but _also_ industry. The reason I pointed out the grumpy researchers is because a lot of these people have been discussing techniques that DeepSeek used, long before they were used. DeepSeek looks like what happens when you set these people free. Which is my argument, that we should do that. Scale Maximalists (also alled "Bitter Lesson Maximalists", but I dislike the term) have been dominating ML research, and DeepSeek shows that scale isn't enough. So will hopefully give the mathy people more weight. But then again, is not the common way monopolies fall is because they become too arrogant and incestuous?

So mostly, I agree, I'm just pointing out that there is a bit more subtly and I think we need to recognize that to make progress. There are a lot of physicists and mathy people who like ML and have been doing research in the area but are often pushed out because of the thinking I listed. Though part of the success of the quant industry is recognizing that the strong math and modeling skills of physicists generalize pretty well and you go after people who recognize that an equation that describes a spring isn't only useful for springs, but is useful for anything that oscillates. That understanding of math at that level is very powerful and boy are there a lot of people that want the opportunity to demonstrate this in ML, they just never get similar GPU access.

[0] https://www.ft.com/content/d5f91c27-3be8-454a-bea5-bb8ff2a85...

[1] https://archive.is/20241125132313/https://www.thewrap.com/un...

[2] https://en.wikipedia.org/wiki/Technology_readiness_level
It's the opposite? You don't need someone to vouch for you if you have a highly specialized skill set. I certainly haven't.

You might rely more on your network when you don't have any notable skill sets that set you apart from other developers.

Your claim isn't rational or practical?

This is what I mean, your attributing certain outcomes to an action that's effectively just a placebo effect. It doesn't actually matter.
The bulk of the computer work for the “knowledge class” is data mangling and transit. Like managing a SaaS app for your sales pipeline inputting results/outcomes of leads, aggregating stuff happening in various another places, uploading lists and connecting other SaaS apps together, which all then generates other data that gets translated to excel (because SaaS BI tools are rarely good enough) and humans analyze it and communicate the data.

Even though we have a million web services there’s still tons of work getting the data in and across them all as they are all silos with niche usecases and different formats.

There’s a reason most Zapier implementations are as crazy as connected Excel sheets

AI bots will remove a ton of this work for sure
Distillations are showing (and have been for a while) though that you don't necessarily need big hardware for decent inference. If that's the case, and you can run it on your Macbook trivially or even your smartphone.
I forgot one tech that’s actually commercialized by lamini.ai:

https://arxiv.org/abs/2406.17642

https://medium.com/pythons-gurus/mixture-of-memory-experts-l...

Re your reply

I’ll add two things to “drive us to models that have emergent, reasoning abilities:”

1. It didn’t have that until they spent around $30 million pre-training a specific architecture (GPT3-176B) on TB’s of data with portions having built-in, reasoning steps it could extrapolate. Doing the same with the other architectures might have led to similar, better, or worse results. GPT3’s training investment, not Transformer architecture itself, led to seeing them as reasoning engines.

2. The memory-less architectures also had lots of hallucinations. Another submission said they’re like professional liars who also don’t know when they’re lying. It used to hurt me a lot with even GPT4-generated code. What reduces that, especially simply using huge models, drives pre-training cost back up to exorbitant levels.

So, if you have tens of millions, you can pretrain models for reasoning that also confidently lie to us. The open question is whether that’s built into the prevailing architectures. I think it is given brains’ performance. Also, how specific components of brains being damaged causes hallucinations. Brain architecture has layers of mitigations for it.

If integrating memory, we might see architectures with few to no hallucinations. That can be combined with other techniques already in use. They might also be cheaper to train, too, if they build on what they remember. That’s a bigger maybe.
> it made me realize that the popularity contest in high school was a farce.

Observing outside life has led me to conclude that the high school popularity contest, is, in fact, the most authentic, freest form of human social behavior.