skysight-inc/HackerNews huggingface dataset:
I’ve been able to do more far complex things with ESP32s and RPis in an evening without knowing the first thing about python or c++.

I can also tell when it’s stuck in some kind of context swamp and won’t be any more help, because it will just keep making the same stupid mistakes over and over and generally forgetting past instructions.

At that point I take the last working code and paste it into a new chat.
Maybe...the cynic in me says that someone feels this needs to be preserved because it will become another Google relic in favor of something newer, or more shiny. Or likely something written in Go...just a guess on my part...
It is fascinating to think that before digital circuits phone calls were accomplished by an end-to-end electrical connection between the handsets. What luxury that must have been! If only those ancestors of ours had modems and computers to use those excellent connections for low-latency gaming... :-)
If you are baking cookies using standard reference ingredients, you had best be using a standard reference recipe.

https://www.dla.mil/Portals/104/Documents/TroopSupport/Subsi...

If you please, I request Type III, Style D, shape (d). Low-fat sugar cookies in patriotic shapes, including but not limited to Uncle Sam, Bald Eagle, Torch of Liberty, Letters "USA", American Flag, Statue of Liberty, etc.
NVIDIA sells shovels to the gold rush. One miner (Liang Wenfeng), who has previously purchased at least 10,000 A100 shovels... has a "side project" where they figured out how to dig really well with a shovel and shared their secrets.

The gold rush, wether real or a bubble is still there! NVIDA will still sell every shovel they can manufacture, as soon as it is available in inventory.

Fortune 100 companies will still want the biggest toolshed to invent the next paradigm or to be the first to get to AGI.
to be fair, there's no way these rates will be sustained for a decade.
Please, for the love of all things privacy, make this new Pebble work on Linux Phone
The weights are public. We can't verify their claims about the amount of compute used for training, but we can trivially verify the claims about inference cost and benchmark performance. On both those counts, DeepSeek have been entirely honest.
Yes two sets of heads that are accessing different platters I believe. Actually this approach has been used in the past: https://en.wikipedia.org/wiki/Conner_Peripherals#Performance...
Uhhh, isn’t it about coal?
> Leveraging productive-but-often-misguided junior devs is a skill every dev should actively cultivate!

Feels like this is only worthwhile because the junior dev learns from the experience; an investment that yields benefits all around, in the broad sense. Nobody wants a junior around that refuses to learn in perpetuity, serving only as a drag on productivity and eventually your sanity.
Time to buy some Nvidia
You don't burn out because you weren't working. That's not a thing.

I am concerned about how you describe coding as an addiction. That sounds like something worth bringing up with a therapist & investigating the root cause of. It can be literally dangerous to identify that much with only our work, especially in this economy.

But if you don't want to do that, if you have some rare code-or-die health condition, just contribute to some Apache project instead. The entire internet is build on projects people wrote that their companies didn't pay them to write. We don't have to give our whole creative selves to our employers.
P/E ratio is better indicator. Price/Earnings. NVidia: 46, Microsoft: 35, Apple: 34, Amazon: 50.

As you see NVidia doesn't stand out much, it's even lower than Amazon.
How much work is being done in the RTOS space? I would think a lot, but then I hear programmers complain it’s not worth it.

Are RTOSs still the future?
Why do you consider these something that can't be automatically detected?
That still means that that AI firms don't have to buy as many of Nvidia's chips, which is the whole thing that Nvidia's price was predicated on. FB, Google and Microsoft just had their their billions of dollars in Nvidia GPU capex blown out by $5M side-project. Tech firms are probably not going to be as generous shelling out whatever overinflated price Nvidia was asking for as they were a week ago.
What solution would you propose?
The amount of us who clicked no is amazing. I loved my Pebble Time but I'm going to give money to yet another Kickstarter and have it be killed shortly after.
> Stations will only appear on odd-numbered frequencies, like 88.1 MHz, 94.5 MHz, 107.3 MHz, etc. There's a technical reason for this - every FM broadcast takes up about 150 KHz of bandwidth, and spacing the broadcasts like this allows for an extra 50 KHz of wiggle room.

I believe the odd-numbered frequencies were chosen because if only even were allowed then there would be legal knock-down-drag-out fights over the round numbers.
gates, governments, genera(tor/l(itie))s, things that start with g
Fake CBC ads have flooded X with sketchy headlines. We looked for the source
I'm also a bit unclear on why LeCun is so well regarded. I've nothing against him, and his opinions shared on Twitter seem eminently sensible, but at the end of the day it seems his main accomplishment (and/or credit assignment) was inventing CNNs back in the 80's and using them for reading handwriting on checks.

Looking back at the PDP handbook, it's not even clear that LeCun deserves the credit for CNNs, and he himself gives credit for the core "weight sharing" idea to Rumelhart.

Chollet's claim to fame seems to be more as creator of Keras than researcher, which has certainly been of great use to a lot of people. He has recently left Google and is striking out to pursue his own neuro-symbolic vision for AGI. Good luck to him - seems like a nice and very smart guy, and it's good to see people pursuing their own approaches outside of the LLM echo chamber.
https://mathshistory.st-andrews.ac.uk/Biographies/Heaviside/

>His neighbours related stories of Heaviside as a strange and embittered hermit who replaced his furniture with 'granite blocks which stood about in the bare rooms like the furnishings of some Neolithic giant. Through those fantastic rooms he wandered, growing dirtier and dirtier, and more and more unkempt - with one exception. His nails were always exquisitely manicured, and painted a glistening cherry pink'

Genius confirmed.

EDIT: one of the traits of creative genius according to Hans Eysenck is psychoticism which includes an indifference to social norms:

https://philipperushton.net/wp-content/uploads/2015/02/Impur...
A single PR doesn't really "prove" anything. Optimization passes on well-tested narrowly scoped code are something that LLMs are already pretty good at.
I'm sure it helped, but yeah, not only e2e bandwidth but also the total network throughput increased by vast orders of magnitude.
As opposed to not trusting an LLM, and ending up on day 4 of an afternoon project? :P

I've been doing that since way before LLMs were a thing.
My Garmin has a dedicated hardware button that says "LAP"
What’s wrong with copying from other projects if they’re indeed offering good ideas worth copying? You say it as if the Golang community MUST only ever have unique ideas no one else has ever thought of — something that’s increasingly rare and unlikely.
No, the DMCA amended the law to give search engines (and automated caches and user generated content sites) safe harbor from infringement if they follow the takedown protocol.
Eh, I take a much more "gloves off" approach to this mentality. If you bought an Airport thinking that it would be your forever router, you're a moron. Same goes for buying an eero, or Google Home, or even an iPad at this point. You don't own this hardware, you control nothing about it. The idea that products live forever is a bedtime story we tell ourselves, an utter fairytale of the software industry.

Everyone kills software products. The problem is our attitude of entitlement towards things we sign an EULA to use. You "own" TikTok on the App Store? Pssh, please. You don't even own the software runtime you use TikTok with.
Um This doesn't even sound like a use case where 100% uptime is totally necessary. Hn moment indeed.
One strategy I've been experimenting with is maintaining a 'spec' document, outlining all features and relevant technical notes about a project. I include the spec with all relevant source files in my prompt before asking the LLM to implement a new change or feature. This way it doesn't have to do as much guessing as to what my code is doing, and I can avoid relying on long-running conversations to maintain context. Instead, for each big change I include an up-to-date spec and all of the relevant source files. I update the spec to reflect the current state of the project as changes are made to give the LLM context about my project (this also doubles as documentation).

I use an NPM script to automate concatenating the spec + source files + prompt, which I then copy/paste to o1. So far this has been working somewhat reliably for the early stages of a project but has diminishing returns.
It already exists in Gemini and it existed in Bard....now it's called "Double-check response". Here is pic of it in action: https://i.imgur.com/aIISzt5.png
- one suggestion is to also include youtube videos a person appears in.
- another is to aggregate any web / social presence.
- TV appearances
- Movies
- Live events and performances
- Courts (like if someone appears in court :| )
I think SIMD is not so much better than SIMT for solved problems as a level in claiming a problem as solved.
AI-powered IoT devices for industrial application
Hi HN,

We're on a mission to modernize factories by combining AI, IoT, and real-time analytics. Our system features industrial cameras (optical, thermal, infrared) alongside vibration and sound sensors, seamlessly integrated into a centralized platform.

Factories face a tough challenge: slow adoption of digital tools and reliance on outdated hardware-bound solutions. We're addressing this by making our devices lightweight and scalable, offloading computing to central servers for unlimited processing power. This allows manufacturers to access advanced analytics without replacing their entire infrastructure.

We’re currently in the pre-prototype stage, but our team has already built IoT solutions in partnership with T-Mobile. Now, we're leveraging our experience to create tools that give factories "Eyes, Ears, and Senses" to work smarter and make data-driven decisions.

We’d love your feedback, ideas, or thoughts on Industry 4.0 and smart manufacturing!
that's not entirely relevant.

the simplest way to present the counter argument is:

- suppose you could train the best model with a single H100 for an hour. would that hurt or harm nvidia?

- suppose you could serve 1000x users with a 1/1000 the amount of gpus. would that hurt or harm nvidia?

the question is how big you think the market size is, and how fast you get to saturation. once things are saturated efficiency just results in less demand.
Regarding their earnings at the moment, I know it doesn't mean everything, but a ~50 P/E is still fairly high, although not insane. I think Ciscos was over 200 during the dotcom bubble. I think your question about the 5 major customers is really interesting, and we will continue to see those companies peck at custom silicon until they can maybe bridge the gap from just running inference to training as well.
Did anyone consider that our society IS artificial conditions? Not sure about you all, but western society feels much more akin to a zoo than the wilderness.
I am frankly tired of seeing this kind of post on HN. I feel like the population of programmers is bifurcating into those who are committed to mastering these tools, learning to work around their limitations and working to leverage their strengths… and those who are committed to complaining about how they aren’t already perfect Culture Ship Minds.

We get it. They’re not superintelligent at everything yet. They couldn’t infer what you must’ve really meant in your heart from your initial unskillful prompt. They couldn’t foresee every possible bug and edge case from the first moment of conceptualizing the design, a flaw which I’m sure you don’t have.

The thing that pushes me over the line into ranting territory is that computer programmers, of all people, should know that computers do what you tell them to.
How Did DeepSeek Train Its AI Model on a Lot Less – and Crippled – Hardware?
Er, how would that reduce the cost? You still need to train the model, which is the expensive bit.

Also, the base model for V3 and the only-RL-tuned R1-Zero are available, and they behave like base models, which seems unlikely if they used data from OpenAI as their primary data source.

It's much more likely that they've consumed the background radiation of the web, where OpenAI contamination is dominant.
Researchers develop a way to power wearables through human skin
This is missing the historical context completely. One cannot expect China to be 4x productive ehole its socio-economic development level is like 50s-60s of USA. Their population is still mostly peasants. This applies even more to India.
if using an int* over an int[] changes the memory layout of a struct, that necessarily implies a difference in the ABI.

As an example, C++'s std::array can be implemented as a struct containing a fixed-size C-style array. This can be passed-by-value. This means that returning a std::array can be more performant than returning a std::vector (which might be implemented as an int* that is reallocated when you add too many elements), because a std::array is returned on the stack while a std::vector is returned on the heap.

I was bit by this once when returning a std::unordered_map because I was doing a heap-allocation in a performance-critical section.
That's the thing. Nvidia's future growth has been potentially kneecapped by R1's leaps in efficiency.
A similar efficiency event has occurred in the recent past. Blackwell is 25x more energy-efficient for generative AI tasks and offer up to 2.5x faster AI training performance overall. When Blackwell was announced nobody said “great we will invest less in GPUs”. Deep Seek is just another efficiency event. Like Blackwell it enables you to do more with less.
If I’m understanding their paper correctly (I might not be but I’ve spent a little time trying to understand it), they showed you only need a small amount of supervised fine tuning “SFT” to “seed” the base model, followed by pure RL. Pure RL only was their R1-zero model which worked, but produces weird artifacts like switching languages or excessive repetition.

The SFT training data is hard to produce, while the RL they used was fairly uncomplicated heuristic evaluations and not a secondary critic model. So their RL is a simple approach.

If I’ve said anything wrong, feel free to correct me.
At least a comment if not its own section.
sure, but what nobody expected how QUICKLY the efficiency progress has been - aviation took about 30 years to progress from "the rich" to "everybody", personal computers about 20 years (from 1980s to 2000s), I think the market expected to have at least 10 years of "rich premium" - not 2 years and get taken to the cleaners by the economic archenemy, China
Imagine what you can do with all that Nvidia hardware using the deep mind techniques.
But this time the technology is open sourced, it's not like Uber operating at a loss to make other startup fail. It might however become like that when there is no more competition. However, at least for now it's not like that
They aren't "giving out for free", though. If you're not paying for something from a US tech company, unless it's explicitly a non-profit, it's fairly safe to assume that you, dear reader, are the product.

You pay with your data.

This could very well be the long-term plan with DeepSeek, or it could be the AI application of how China deals with other industries: massive state subsidies to companies participating in important markets.

The profit isn't the point, at least not at first. Driving everyone else out is. That's why it's hard to get any real name brands off of Amazon anymore. Cheap goods from China undercut brand-name competition from elsewhere and soon, that competition was finding it unprofitable to compete on Amazon, so they withdrew.

I used to get HEPA filters from Amazon that were from a trusted name brand. I can't find those anymore. What I can find is a bunch of identical offerings for "Colorfullfe", "Der Blue" and "Extolife", all priced similarly. I cannot find any information on those companies online. Given their origin it's safe to assume they all come from the same factory in China and that said factory is at least partially supported by the state.

Over time this has the net effect of draining the rest of the world of the ability to create useful technology and products without at least some Chinese component to the design or manufacture of the same. That of course becomes leverage.

Same here. If I'm an investor in an AI startup, I'm not looking at the American offerings, because long-term geopolitical stability isn't my concern. Getting the most value for my investment is, so I'm telling them to use the Chinese models and training techniques for now, and boom: it just became a little less profitable for Sam Altman to do what he does. And that's the point.
There is apparently huge amounts of goodwill for Pebble!

But. Isn’t most of the value of a codebase like this not the code itself, but all the knowledge of the people who worked on it day to day? Where are they?

It’s really intriguing. People who have a lot of goodwill towards Pebble BELIEVE the source code is valuable. That doesn’t mean that it is.
It is somewhat like asking, "python or javascript", without saying if you're talking about doing data science or a frontend-heavy website.
In addition to the sonic boom issue it was also cost. It cost a stupidly high amount in fuel to fly. The engines were horribly inefficient.

Not only that the low capacity meant that tickets would have to be orders of magnitude higher than even first class in any other commercial jet.

It's why I cant see Boom doing what people are assuming they'll do and make a commercial airliner. Unless you fancy spending $20k for a one way trip from JFK to LHR it's not really viable.
No.

In fact, not just "no". No, with... I don't even know enough expletives to add to that "no".

And, making it so that Trump can but Obama can't? Hidden in this amazingly fawning praise of Trump is fear - the fear that, if Trump ran against Obama in 2028, Obama would win.
Think that was the cause of their stock increase? I feel like investors use opportunities like this to pile money into safer bets rather than just bail on stocks altogether.
does this apply to genocide too?
I don't know why, but clipboard libraries tend to be really poorly implemented, especially in scripting languages.

I just checked out clipboardy and all they do is dispatch binaries from the path and hope it's the right one (or if it's even there at all). I think I had a similar experience with Python and Lua scripts. There's an unfunny amount of poorly-written one-off clipboard scripts out there just waiting to be exploited.

I'm only glad that the go-to clipboard library in Rust (arboard) seems solid.
I use CoPilot pretty much as a smarter autocomplete that can sometimes guess what I'm planning to type next. I find it's not so good at answering prompts, but if I type:

r = (rgba >> 24) & 0xff;

...and then pause, it's pretty good at guessing:

g = (rgba >> 16) & 0xff;
b = (rgba >> 8) & 0xff;
a = rgba & 0xff;

... for the next few lines. I don't really ask it to do more heavy lifting than that sort of thing. Certainly nothing like "Write this full app for me with these requirements [...]"
Looks like panic sell off, but main question is:

do models with DeepSeek architecture still scale up?

If yes, then bigger clusters will outperform in near future. NVidia wins as tide rises all boats, and them first.

If not, then it's still possible to run several models in parallel to do the same, potentially big, job. Just like humans team. All we need is to learn how to do it efficiently. This way bigger clusters win again.
Deepseek trained their models on GPT4o and Claude 3.5 and Llamma outputs. In other words, DeepSeek models needed billions in training cost done for them before they could train their models for $5.5m in compute cost.
I do wonder how they successfully justified this to the higher ups. If anything, I would like to learn from them so I can better justify the things I want to do..
Cosmologists Try a New Way to Measure the Shape of the Universe
The juniors are doing negative work. I need people who are neutral or positive shortly after they come on board. Besides that, once a junior gets experience, they are going to jump ship.

My expectations of a mid level developer is once given mostly clear business requirements, they should be able to turn those requests into code. They should be able to handle any “straightforward” task I throw at them.

From the definition I have seen from leveling guidelines:

Straightforward problems or efforts have minimal visible risks or obstacles. The goal is clear, but the approach is not, requiring the employee to rely on their knowledge and skills to determine the best course of action.

I expect a senior to handle “complex” tasks.

Complex problems or efforts involve visible risks, obstacles, and constraints. This often requires making trade-offs that demand expertise, sound judgment, and the ability to influence others to build consensus on the best approach.
We have both? Modern computing devices like smart phones use SoCs with integrated GPUs. GPUs aren't really specialized hardware, either, they are general purpose hardware useful in many scenarios (built for graphics originally but clearly useful in other domains including AI).
Big brother becomes little brother
One example: I would like to program a (dumb/ID-only) RFID card into it to unlock a door. I can't as the NFC off limits to apps.
Yes absolutely. I guess I meant medium range in terms of dev and running costs. R1 is a premium product at a corner store price. :)

People are also forgetting that High-Flyer's ultimate goal is not applications, it's AGI. Hence the open source. They want to accelerate that process out in the open as fast as they can.
Sure, I have no problem with people wanting to fix it.

My datapoint was just in reply to someone writing that tearing under X has not occurred for 5 years.
No problem, I'm sure if Fastly stopped doing it JiaTanCo would step up
Either this is something you believe in or not.

If you don't believe in it, maybe negotiate up to 10% and work your regular job.

If you do believe, go all in. Move back in with your parents, get a part time retail gig for spending money. This startup is your full time job now.

Sounds like you don't really think it's good to work out. What's the difference between 3% of 0 or 10% of 9.

Furthermore, once you leave the product is probably going to collapse.

Ideas are worthless FYI. What is your co founder going to do when you leave ? Hire someone ? Find another person to build it ?
I don't know the fine details of it but afaik FSR <3.0 is Lanczos with a few tricks to both speed it up and make the visuals more stable.
Definitely choose Django
> Hard to think they plan to

They already are. You can make a paid account and use their API from most countries around the world. This is what doing business looks like.