Luna is about suppressing inexpensive Chinese model competition.
It's super simple.
Gigantic hyper margin ad network = artificial subsidization of cost for various tiers = put the boot on the neck of Chinese competitors. There's no scenario where they can compete with what advertising margins make possible in terms of artificially lowering prices charged.
> Luna is about suppressing inexpensive Chinese model competition.
I think so too. To me the so-called Chinese local models are a clear move to prevent US companies to establish a foothold and build a moat around their business. US companies are clearly invested in a strategy to make themselves relevant with claims of major impressive achievements with the so called frontier models, and how these and only these are unblocking whole ranges of applications. At the same time, they are heavily invested in pushing AI on all absurd types of mundane tasks, such as transcribing meetings and... talking to your own kids?
In the meantime it's rather obvious that, in spite of all the propaganda, frontier models are required only in ultra niche applications, whereas the ability to run any model at all already provides most of the value. In fact, US companies have been renownee by dumbing down older generation models in what seems to be a desperate attempt to make newer models look better and influence their uptake rate.
So there is no better way to take the wind out of the US AI companies' sail than pulling a two-punch attack consisting of not inly releasing capable models that refute the "only US frontier will do the job" thesis but also releasing them for free to commodities them and eliminate the business impact of dumbing down models.
> i don't doubt they're pushing for using AI for that, but i'm curious of examples of where they're doing this. commercials, ads, etc.
You just be living under a rock. Not do long ago Sam Altman was floating this fantastic usecases for AI was to have it explain to you your kids interests, and have it create a podcast for you to be able to keep in touch.
Everyone keeps saying this, but I don't believe it's true. I think Luna is just an MLA or sparse architecture like all the flash variants and it's just cheap to run.
In any case, over the past few years, the only thing that has gotten more expensive is the hardware to run local models while API and Subs have gotten more affordable or feature rich while remaining same price.
They can compete because they have the compute to run the volume and if it's good on agentic work, people will be less incentivised to use other models for "Tasky-y" work.
It's literally increasing their market opportunity
Chinese buy their tokens at home. West as a market is an afterthought for their companies them. Western AI is banned, so only used via resellers by small fish, not companies. US has zero presence at that huge market, and absolutely not a moat.
They are buying Huawei accelerators in bulk to serve their local customers. The whole system is currently optimized to deliver a lot of cheap LLMs and hardware for them to run on.
They're closing in a billion users. That's Google search territory.
OpenAI is sitting on a $100+ billion ad network, incoming.
They're not going to need a government bailout, they're going to be a spigot of cash production.
Every single thread on HN keeps saying the same ridiculous thing, going on a year now. It's like they've never heard of advertising, which SV specializes in. It's like they're oblivious to the fact that every mega platform with so many users becomes an ad goldmine, and GPT's context positioning is even richer than search.
> OpenAI is sitting on a $100+ billion ad network, incoming.
How can you make this sort of claim with a straight face, knowing that a chinese model downloaded for free from ollama works as well if not better than OpenAI's models, without costing you a cent.
GPT is still 20 a month for most people, 50-100 or 200 for pros.
Meanwhile, for local LLM's - everyone is chasing hardware that is crazy expensiv eand to get around it they're leasing it or putting it on credit card. DGX Sparks are insane, and you need 2 for most people talking in this thread. Mac Ultra 5 is amazing but 6k minimum 12k for the build most want so many people lease it for 240 a month which doesn't even include electricity or time in setup so instead of talking about facts, we get into weird arguments like console wars where people give APple a 5 trillion dollar company 250 a month for 36 months and don't even own their hardware money "because they can run local models" vs just paying for output from their choice of frontier.
I say this fully loving local llms and embracing them, but the reality is, local llms have gotten so expensive and continue to get expensive while we keep talking about this "Threat" of apis - where there are a lot more than openai and anthropic available much cheaper and competitive priced.
Oh, and they don't work better than OpenAI or else we wouldn't even have these discussions.
If you're able to use the OpenAI ecosystem, Luna's price/performance is really good. Almost like "they messed up and accidentally made it too good" good.
OpenAI didn't mess up. The model would have been 100 % pointless and obsolete without the large price cuts it got, because of the cheap Chinese models.
The open models are getting closer and closer, and because they're open, people are not forced to pay the silly markup that is often over 1000x the cost to serve the model.
I've really gone in the opposite direction: having a dumber model orchestrate. In my case, it's usually a Luna orchestrator spawning Sol/Astra subagents to do the "big brain" work of planning and reviewing.
Reason I went with "dumb orchestrator" was just to save tokens. Having Opus/Sol (let alone Fable/Astra) orchestrate was burning tokens like crazy for me even when much of the gruntwork was being done by Luna/Sonnet/Haiku subagents. (Luna is also really good, like way better than Sonnet...) Perhaps it was a skill issue on my end though, maybe I wasn't just managing context properly.
"benchmark margins have become a less
reliable guide to real-world differences"
sounds like a big problem.
My guesses:
1. Real-world use cases typically involve big, hairy, crufty, tech debt laden codebases and benchmarks do not.
2. AFAIK "success" in a benchmark essentially boils down to "do the tests pass and do we get the right result?" which is something the LLMs have been achieving with ease for a while, except maybe for uber-challenging coding tasks that would be outliers in just about any workplace. Whereas real-world software engineering is usually just a bunch of CRUD... and "success" involves harder to measure dimensions like "maintainability" and "did you overengineer this?" and "how did you cope with a bunch of vague and maybe contradictory business requirements?"
Having said all of that, I have never ever looked inside any of these benchmarks. I'm putting my guesses out here strictly in the tradition of "the quickest way to learn about something is to be wrong about it on the internet."
When visiting a Iowa-class WWII battleship, there were no shortage of things that impressed me. The huge guns, and the decades-ahead-of-their-time analog firing computers. Obviously.
But the galley was NOT far behind. From an impossibly tiny space they served 1,700 meals... three times a day... seven days a week.
The logistics of mass food preparation are so wild.
It's a brilliant idea, of course. But being considered "impossible" means it was considered previously and decided to be impossible. No?
I mean, crpytographically, it's ultra-trivial. You "just" need to solve the logistical issues of (1) shortwave radio existing (2) figuring out how to make sure your field agents possess and are not caught with the disposable one-time codes. I am surprised anybody would consider that impossible.
(I hope I am not downplaying the brilliance of the one-time pad idea itself)
It was partially explained by the article. It was not stated that ROSENOW was repeated in 173, and it was not obvious from the article text why ROSENOW would ever be repeated. Thus my curiosity.
A sibling commenter explained it - Rosenow is both a municipal name and a district name, so naturally it would be repeated. (Like "New York, New York")
It also makes sense that a longer crib
would generally be more effective than a
shorter one.
It would seem to me that the odds of looking for even a single ROSENOW in the decrypted message would be plenty. The odds of a single ROSENOW randomly occurring in incorrectly decrypted output are vanishingly small. So it seems to me that looking for ROSENOW is a safer bet vs. looking for ROSENOW ROSENOW -- a single ROSENOW is a great sign you've got the correct key, whereas looking for ROSENOW ROSENOW seems like it would deliver false negatives (think of all the times we say "New York" rather than "New York, New York")
I'm a novice at crypto though, so, maybe I've got that totally wrong.
The reason a crib is useful is because the enigma can't route a letter back to itself. So, you can slide the crib along the message until no letters line up, and that's possibly where it is. If your crib is "the", that's not terribly useful, because it could exist anywhere. The longer the better.
Maybe naive of me, but could it simply just be the overfitting of the same tokens being sent on the input twice because of repetition rather than some unknown implied intelligence.
1. Is natural language holding LLMs back by some %?
2. Is natural language serving as a hard gate that will prevent LLM intelligent progressing past some specific point?
The answer to 1 seems like an obvious yes to me.
Your thesis says the answer to 2 is "yes." That doesn't feel right to me. Think about all of the humans who have pushed various fields forward: Einstein, Newtown, Bach, whoever. If natural language doesn't prevent an entity from surpassing humans in one intellectual field, why would it prevent an entity from surpassing humans in all intellectual fields?
(To be clear, I'm not claiming superintelligence will or won't be achieved; I'm considering your specific thesis about whether or not natural language will be a hard gate)
Wow, this is wild. I'd never even heard of this game.
Based on the linked article, this seems exactly like the kind of game that would have had an AMAZING sequel.
You know the type of game I mean. The first game breaks all types of new ground with a bunch of innovative ideas and is fun, but also has a lot of jank and rough edges because they were trying so many new things at once. And then the sequel is where it all really comes together. The ideas that didn't work are fixed or discarded, everything else gets polish, and a few new ideas are introduced.
Large 4mil ziploc style bags are extremely durable and are about $0.26 each in bulk on Amazon.
I very much recommend them. As a bonus, you can label them if desired. (Print a label out, or slap some tape on there and write on that, or just stick a sheet of paper inside the bag and write on that)
Snark aside, Luna 5.6 was (is) an incredible game-changer.
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