skysight-inc/HackerNews huggingface dataset:
Show HN: MVIII – Psuedo Search Engine
I'm 20 something and I built my first production(closer?) search engine, I've been programming for last 8 years, now, but always wanted to have to work on a search engine, so I applied several times to Google - the only big tech search engine that provides beautiful results as a college student, but never got in, so one year after graduating I'm now working on my own search engine, with some obvious perks, but yeah, obviously it feels amazing, it's like large hadron collider of knowledge but compact.

So, if you test out the pre-release-pre-alpha-pre-proper-search version, do comment the feedback, I'll try to improve upon it.

Also thanks to people at https://sevalla.com who provided me free credit to work on this project after I utilized all of the other free credit programs by Google Cloud, Azure, Alibaba and AWS, few months ago.

Also, note that the version you're viewing is a product of 90+ hours of weekend work. After my startup started struggling with funding.

Thanks, and cheers.

here's full blog post describing my journey https://unlace.app/?url=https%3A%2F%2Fx.com%2Fvednig%2Fstatu...

Also, huge thanks to everyone who supported me here
https://news.ycombinator.com/item?id=42592720

and everyone who posted their thoughts here https://news.ycombinator.com/item?id=23202850

edit: updated mviii to mviiii
Almost one in 10 people use the same four-digit PIN
Long term: Likely true.
Short-to-medium term: Nope.
Evergreen. The same observation applies to any subject you know anything about that is discussed on HN.
> LLMs fall over miserably at even very simple pure math questions

They are language models, not calculators or logic languages like Prolog or proof languages like Coq. If you go in with that understanding, it makes a lot more sense as to their capabilities. I would understand the parent poster to mean that they are able to ask and rapidly synthesize information from what the LLM tells them, as a first start rather than necessarily being 100% correct on everything.
Honestly, I’m not sure I’m completely sold on the value of LLMs long term but this is the most realistic and reasonable take I’ve read on this post so far.

If anything, it’s an downward adjustment in the cost implications but could actually unlock exponential improvements on a shorter time horizon than expected because of that. Investors getting scared probably is a good opportunity to buy in.
Welcome to 2020 Facebook, except they're coverage of valid topics to ban and censor has expanded more broadly now. This might've been avoided had more of its users sent a message 4-5 years ago that social media censorship isn't acceptable in a society that prides itself on free speech.
It doesn't. Inference is still expensive, and demand for it is high, as evidenced by Anthropic's frequent "we're out of quota" messages and Deepseek's crap-out under load last night. On the training side right now only the top flight labs can conduct serious, ambitious research, and even they don't do as much research as they'd like. Witness Meta effectively train the exact same architecture on similar data mixtures for the past couple of years. More or less the same situation is happening across the board - compute bandwidth (and therefore the ability to experiment) is scarce. What this means is inference will remain quite expensive in the foreseeable future, especially multimodal and long-context inference. Believe it or not, even Google is compute constrained. When I was there some days I couldn't even get a handful of TPUs to do my job - everything was allocated to training Gemini. Even if it gets a lot cheaper to train models, you could just train larger, more capable models and do more architectural / efficiency research, and iterate faster, with tremendous payback in the long run. NVIDIA is the only viable seller of shovels for this gold rush for everyone but Google and Anthropic. Bypassing the gatekeepers, and making capable AI models available to more people makes their product more valuable.
> Seems like most experts estimate 3-10 years.

That’s spitting distance from “we don’t know”.

I think most of these valuations are made by people without the expertise to predict what the next one year of AI looks like, never mind 3-10 years.
Possibly security, if it recognizes individual humans, controls door locks and cameras, with no option to install apps.
Did you actually test the math done? Usually LLMs are terrible at math as, as I mentioned in another comment, they are language models, not calculators. Hopefully that changes when LLMs leverage other apps like calculators to get their results, I am not sure if Claude does that already or it's still in development.
I just switched to https://www.trae.ai/ - basically the same thing. but it's free (for now haha)

On the philosophical part, well, I can say things to composer like "build out a fastapi server with xyz routes, with a nice folder structure" and it'll do a lot. I still want it to be better, though, re: system design.

Maybe there's hidden workflows, like write your thoughts in a doc and let the LLM see that. Or maybe you can have the agent write down its thoughts in a doc as a sort of working memory.

I'd love to hear more about people's non-conventional usages of Cursor
What hardware do you need to be able to run them?
I think the fact that they used synthetic/distilled high-quality data from GPT4-o output to train in the style of Phi models are of significance as well.
wise man once said: "the devil doesnt need advocates, hell is full of them".

you can both talk your book and also sincerely believe what you say. ad hominem (or whatever the latin equivalent is of ad bookinem) is not as substantive a criticism as you make it out to be. he can be both biased and correct.
IBM probably couldn't have done things differently given the antitrust scrutiny they were under. And that was likely the best outcome for the industry anyway.
You didn't understand my comment.

My point is that the calculus for Russia isn't "is there a precedent for breaking this type of international law?", it's rather "can we get away with it?". NS2 didn't figure in the Russia's calculus when planning the undersea cable interruption.
It's the LLM paradox, seniors get more senior with them while juniors get more junior, creating a bimodal distribution in the future simply because juniors will start depending on them too much to learn how to code properly while seniors (who some may also exhibit the previous trait) will by and large be able to rapidly synthesize information from LLMs with their own understanding.
As well established as dominance hierarchies are, they aren't the only social structure, and just as this article explains, it's rather ironic that in the case for wolves, that isn't the prevailing social structure in their natural setting. That wolves have become the prevailing reference point for the concept, is metaphorical on a lot of levels.
Little Boy certainly had "profound impacts on cost savings, productivity, and quality of life" in Hiroshima.

Impact isn't inherently positive.
Trump White House orders freeze on federal grants, loans
Everyone and their dog keep trying to force me to use their apps, like there's a mantra inside their marketing divisions that apps are the shit. Doesn't look like apps are over. The market is just saturated of "app for X". The hype of getting rich from zero through one app is over, but apps aren't.

VR movies was a fad. You can date a TV by it's "VR mode" feature on the remote to a few years. No one is trying to sell me VR TVs anymore. That's what a fad looks like.
K, just keep this conversation in mind in a decade or two when you realize that your input is the product, not what you got out of it.
Grandparent was talking about hardware. Despite hardware being deep tech, the compensation is so different that it's practically a non-sequitur to refer to "high salaries in the technology sector" in a discussion about hardware. I don't know how many of those H1Bs are coming as electrical engineers; some, I'm sure, but I don't know how it measures against counterflows back into China.
We lose the race when politicians platform to bring AI jobs back from China.
...or, it's just a solid case of confirmation bias.
Another example: people like to cite how the people who really made money in the CA gold rush were selling picks and shovels.

That only lasted so long. Then it was heavy machinery (hydraulics, excavators, etc)
"Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray's case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them.

In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know."

– Michael Crichton (1942-2008)
A pretty good read that succinctly picks apart the realities of current AI businesses. Easily something I’d reference as a “primer” to someone that is more business-minded than technically-minded.

One point I’ll agree on is his final one: that the true big players haven’t even been founded yet. Right now, the AI hype seems to still revolve around the dream of replacing humans with machines and still magically making Capitalism work in the process, which is something I (and other “contrarians”) have beaten to death in other threads. That said, what these companies have managed to demonstrate is that transformer-based predictive models are a part of the future - just not AGI.

If I were a VC, I’d be looking at startups that take the same training techniques but apply them in niche fields with higher success rates than general models. An example might be a firm that puts in the grunt work of training a foundational model in a specific realm of medicine, and then makes it easier for a hospital network to run said model locally against patient data while also continuously training and fine-tuning the underlying model. I wouldn’t want to get into the muck of SaaS in these cases, because data sovereignty is only going to become an ever-thornier issue in the coming decades, and these prediction models can leak user data like a sieve if not implemented correctly. Same goes for other narrow applications, like single-mode logistics networks or on-site hospitality interfaces. The real money will be in the ability to run foundational models against your own data in privacy and security, with inference at the edge or on-device rather than off in a hyperscaler datacenter somewhere.

Then again, I could be totally wrong. Guess we’ll all find out together.
Framing and Apple fanboying as it is. It just takes time to integrate, deploy and benchmark new models, that's the whole secret. The first big confirmation that DeepSeek-R1 is good was published only few days ago[1], it just takes some time to crowdsource votes.

[1] https://x.com/lmarena_ai/status/1882875989610594542
> at a certain point though, one wonders if you can trust people to accurately report how much is written by an LLM.

That's an interesting thought. I think there are ways to automate this, and some IDEs / tools track this already. I've seen posts by both Google and Amz providing percentages of "accepted" completions in their codebases, and that's probably something they track across codebases automatically.

Also on topic, here's aider's "self written code" statistics: https://aider.chat/HISTORY.html

But yeah I agree that "written by" doesn't necessarily imply "autonomously", and for the moment it's likely heavily curated by a human. And that's still ok, IMO.
Baader-Meinhof phenomenon, but also because everyone is writing about GPU demand and Jevon's paradox is an easy way to express the idea in a trite keyword.
Why not short term?
DeepSeek Janus-Pro Playground
That's my hope too- been at this company for 3+ years and there was a moment of time when it did truly feel like we would just spend time with only larger companies.

However, what's interesting is there is a direct correlation to the amount of conversations we can support and churn! We did put the walls up I'd say 18 months ago. Although it resulted in an okay business outcome as today, user churn numbers actually increased controlling for pricing changes.

Ever since we invested in our support tooling, we've been able to field more conversations and I think we (and the users) are better for it.

Talking to users- no matter how "small" they may be is a potential relationship for us to invest in. We will try to keep our heads to the ground always listening to users.

Talking to users and using our own product are the two hills we will die on. A company dies when they stop doing both, long before any financial reckoning I feel.
All the references to Jevon’s paradox fail to account for three things:
1. There’s no good forecasting model to account for how aggregate demand moves as a function of efficiency gains in this space
2. Aggregate demand isn’t the same as Nvidia’s share of market, which could drop if alternative paradigms for training or inferencing gain traction
3. Forecasting time horizons matter for discounted cash flow/valuation calculations, which nobody has a good basis for
IMO, there’s just a lot of uncertainty, and it’s fair for the market to discount the optimistic trajectory aggressively based on net new info.
My Pebble Time is still my daily driver!
I mainly use it as a watch and for notifications, and occasionally for timers, but it's still a great piece of tech IMO.
Said another way…Elizabeth Holmes product hurt people by failing. Sam’s product may well end up hurting society by working.
Always hilarious to see westerners concerned about privacy when it comes to China, yet not concerned at all about their own governments that know far more about you. Do they think some Chinese policeman is going to come to their door? Never heard of Snowden or the five eyes?
DeepSeek on Consciousness
When you get more marginal product from an input, it's expected you buy more of that input.

But at some point, if the marginal product gets high enough, the world needs not as many, because money spent on other inputs/factors pays off more.

This is a classic problem with extrapolation. Making people more efficient through the use of AI will tend to increase employment... until it doesn't and employment goes off a cliff. Getting more work done per unit of GPU will increase demand for GPUs ... until it doesn't, and GPU demand goes off the cliff.

It's always hard to tell where that cliff is, though.
Oh yeah—tell that to my AI pipelines on my local compute.
But restrict says "I promise that these things don't overlap, on penalty of my program going to hell". Is that what's going on in Fortran?
Trump to impose 25% to 100% tariffs on Taiwan-made chips, impacting TSMC
So it's a podcast client with cryptocurrency wallet functionality?
Fair. I didn't phrase my question correctly.

It wasn't really about MXRoute specifically, but the bus factor in general when trusting your email to a tiny company.
It doesn't feel like DeepSeek has a big enough breakthrough here. This is just one of many optimizations we're going to see over the next years. How close this brings us to "AGI" is a complete unknown.

The large investments were mainly for training larger foundation models, or at the very least hedging for that. It hasn't been that clear over the last 1+ years that simply increasing the number of parameters continues to lead to the same improvements we've seen before.

Markets do not necessarily have any prediction power here. People were spooked by DeepSeek getting ahead of the competition and by the costs they report. There is still a lot of work and some of it may still require brute force and more resources (this seems to be true for training the foundation models still).
check out usepylon.com - has customer support w/ native slack integrations
It uses them as tokens to direct the chain of thought, and it is pretty interesting that it uses just those works specifically. Remember that this behavior was not hard-coded to the system.
Only clicked the kink to find out why on Earth would someone opt for MongoDB and (besides other concerns) forego the option to use proper relational queries later on, and I (sort-of /s) wasn not disappointed:

Writing MongoDB queries felt like magic:

{ column: { $exists: true } }
This in SQL:
SELECT COUNT(column) WHERE column IS NOT NULL;
MongoDB: 29 chars. SQL: 45 chars.

That's it. MongoDB's queries are so much shorter than SQL queries... like magic!
AI being more available and locally runnable will induce demand for GPUs.
VCs with deep pocketbooks, their startups, and the hardware vendors they purchased from (not to mention politicians) are heavily incentivised to believe that their value-add can't be commoditized.

If your grand dream is to dominate the market through sheer massive scale and that's what you're selling to capital, you're not exactly looking for reasons to buy less hardware and your vendor is hardly going to talk you out of it.

"It's hard to get a man to understand something when his fat valuation depends on his not understanding it"
> NVIDIA is the only viable seller of shovels for this gold rush for everyone but Google and Anthropic.

Why do you except Google and Anthropic?
Use uBlock Origin to block posts with the keywords you don't want in the title, like AI or LLM.
https://news.ycombinator.com/item?id=42848939
A wild - but pretty accurate - perspective of societal priorities...
> Nvidia loses it's biggest moat and ability to monopolize the tech, CUDA isn't enough

CUDA is plenty for right now. AMD can't/won't get their act together with GPU software and drivers. Intel isn't in much better of a position than AMD and has a host of other problems. It's also unlikely the "let's just glue a thousand ARM cores together" hardware will work as planned and still needs the software layer.

CUDA won't be an Nvidia moat forever but it's a decent moat for the next five years. If a company wants to build GPU compute resources it will be hard to go wrong buying Nvidia kit. At least from a platform point of view.
Sorry about that! Looks like the server got OOMKilled at one point and failed to recover. It should be back up now and I'll work on a fix.
Pricing is the betting or wishing in the valuation. The buyer thinks it will increase; the seller thinks it's enough. No one knows what will happen in the future. Maybe the Fed will print too much money. Does anyone know what will happen in the future?
I don't think either are actually valuable, I don't see any application of blockchain anywhere that's better than traditional computing. Bitcoin itself seems to be the only good application so far.
> I was going to buy one of these until I realized it didn't have vector extensions

A statement like this pretty much disqualifies your opinions. Why would you expect it to have vector extensions? No one has ever claimed it does, the P550 materials clearly specify the ISA, and the RVA22 spec it implements does not require it.

However it is the fastest RISC-V CPU you can buy today, on a per core basis, by a factor of two, for all those many workloads that don't benefit from SIMD. For example general use as a desktop PC, compiling code etc.

It may well be eclipsed by another factor of two by this time next year (and by something with vector), but that's the rapid pace of progress in RISC-V.

If you need to develop non-vector code on RISC-V this year then this -- or the Mil-V Megrez for half the price and 30% high clock speed -- is the machine to have.

If you don't need RISC-V this year then ... go buy an x86. Or an Orion O6 (which I will also be getting)

> I think some sort of SIMD capability is table stakes for a lot of workloads these days, so I'm disappointed that there doesn't seem to be a CPU on the market that supports them

Some workloads, yes. Not all.

As for what is on the market, the CanMV-K230 with RVV 1.0 shipped in November 2023, and a wide variety of boards and laptops with the SpacemiT K1/M1 SoCs with octa core CPU with 256 bit RVV 1.0 shipped in the 2nd half of 2024, including the Banana Pi BPI-F3, Milk-V Jupiter, Sipeed Lichee Pi 3A, Deep Computing DC-Roma II laptop, MuseBook. Various of these have been reviewed by everyone from Jeff Geerling to Christopher Barnatt to Hackaday.

As for "some sort of SIMD" that's been available in RISC-V since the Allwinner D1 chip shipped in mid 2021, first on the AWOL Nezha EVB, later on things such as the Lichee RV and MangoPi MQ-Pro. These days the same C906 CPU (1 GHz, 128 bit vectors) is available in other SoCs for as little as $3-$5 in the 1 GHz Milk-V Duo with 64 MB RAM (runs a full Linux kernel), up to the $9.90 Duo S with 512 MB RAM.

There are also various boards with the 1.85 GHz quad core C910 TH1520 SoC, such as the Lichee Pi 4A and Milk-V Meles.

Not to mention the 64 core 2.0 GHz 128 GB RAM 64 MB L3 cache 32 PCIe lanes Milk-V Pioneer.
It's a bit like "the Cisco moment" (and lots of people have been observing this). The company was building hardware needed for building out networks. The web looked like it was going to be the next big thing, and people couldn't get enough of CSCO. The web didn't pan out the way people hoped (or as quickly), and CSCO fell quickly.

Cisco kept making and selling network hardware, and probably (citation needed) sold more from 2000-2006 than 1994-2000, but the stock trade was over. The web did become a serious thing, but only once people got broadband at home.

The Nvidia valuation was getting pretty weak. Lots of FAANGs with deep pockets started to invest in their own hardware, and it got good enough to start beating Nvidia. Intel and AMD are still out there and under pressure to capture at least some of the market. Then this came along and potentially upended the game, bringing costs down by orders of magnitude. It might not be true, and it might even drive up sales long-term, but for now, but the NVDA trade was always a short-term thing.
Though I would suggest avoiding his political blog posts. It's not even that I disagree with him (though of course I do in some areas), it's just that they are a real sad read.

To end on a positive note: https://scottaaronson.blog/?p=735 is a real gem!
Summarizing in Kevin Malone style - Nvidia good now. Later? No know.
It's okay, OpenAI is a non-profit dedicated to sharing the benefits of AI with all of humanity, I'm sure they're very happy about these developments.
There's also the (draft) release notes:
https://go.dev/doc/go1.24
And the docs:
https://go.dev/doc/modules/managing-dependencies#tools

I've done the blank import thing before, it was kinda awkward but not _that_ bad.
Why We're Bringing Pebble Back
The bubble we're living in is a financial one built on LLM snake oil. Stupid rich people get what they deserve. I hope deepfake takes their jobs too.
No. Even then. You may know assholes. User accounts may be compromised. Users may have different tolerances for gore that you don’t.

Not gotchas, I’m not arguing for the sake of it, but these are pretty common situations.

I always urge people to volunteer as mods for a bit.

At least you may see a different way to approach thing, or else you might be able to articulate the reasons the rule can’t be followed better.
New FPGA-powered retro console re-creates the Playstation, CD-ROM drive optional
You can train, or at least run, llms on intel and less powerful chips
No company with an open source solution shows their hand. I said the same thing about AI music stuff, bet you 1000% that record studios have been pounding away at AI generated music/lyricals for years now well before the likes of models we see today.
Taken over by Automattic after a publicity-stunt against Apple