Thought I was seeing a 10 years old cached show HN, but no:

Apparently there's an official reboot of the game:

https://www.reddit.com/r/airmash/comments/1whwxxg/the_offici...

So now every already written kernel can be re-written in Rust and have competitive performance to the cpp version?

If so, that's really big.

And to add the Next natural strp - custom codegen for simulating gpu compute and memory without Nvidia gpu.

I think the meaning of can't hallucinate in this model is that the type won't be hallucinated.

So if the generated schema is for a tool call for calculator, then the numbers will be valid numbers for sure (and not random words).

To me, it looks similar to BNF schema already introduced and implemented few years ago: generally speaking - it limits the next token that is allowed to be generated, probs are drawn from a subset tokens.

(tbh, I'm not sure why it didn't pick up as a more standard interface to LLMs, as it made a lot of sense back then, and now.)

Yeah, I thought about constrained generation as well. I've actually done something similar with local models before. And you can even get a "confidence" score by looking at the logits (something along the lines of logprob("YES") + logprob("Yes") + logprob("yes") - logprob("NO")...

There's also a cheeky "one of the models hallucinated a link" in the wiki jump example that most likely could have been avoided by properly using grammars. You can setup constrained gen so that only valid options (say from a list) can be outputted. Their own inference lib likely does that. So comparing to one that doesn't is a bit cheeky.

That being said, after a brief look at the site I could see this working. Especially if this can be ran locally, the speed and cost can enable some workflows where you have this as an "overseer" layer over say a cli agent. After each step you run through a list of "questions" ("is the task completed?" -> yes -> "does the edit touch files it shouldn't" / "does the edit follow our code writing policies") etc.

edit: extra points if the "question" rubric is also generated by a higher abstraction model. Say "/goal Build out auth" -> generate_rubrics(goal) -> "Is auth implemented on all endpoints" / "Has code touched anything else than auth" / "is this following the best practices" / ...

AFAICT it is the same interface as you describe, but the underlying inference algorithm is fundamentally different, hence the speed gains. There is an application I am currently working on right now where this typed output predictor is the performance bottleneck. I'd be very interested to see how this performs.

It did, no? If you send a JSON schema to an LLM endpoint, at least for OpenAI and Anthropic, they turn it into a grammar behind the scenes and use it for constrained decoding.

Is there a list of all LG tracking services and external DNS endpoints?

I'd just block them - and keep local streaming and remote control working

edit: just found out WebOS doesn't support DoH/DoT so nextdns won't work.

I wonder if one of the public dns providers got LG blocklisted natively with specific IP

Worse, if you watch the GN video this article is based on you'll find the TV can figure out how to leverage other non obvious networks (threads, etc) to reach LG servers. Worth a watch to see how bad this is: https://www.youtube.com/watch?v=6IFVTcM28KA

Its a 2+ hour video. Do you have a timestamp for this?

There’s a list of them in the Intercepting section of the source video.

You can spoof MACs all you want, but your MAC is tied to the address I give out. What happens if you spoof addresses? FAFO...

[citation needed]

If you have one, can't you just look at the router to the MAC being reported?

I'm not the one claiming TVs actively spoof their MAC address. Its on the person making claims to provide evidence.

It reminds me a site where you'd pick a point on map and see where you'd get to in 15/30/etc minutes, including walking iirc.

Then you could even pick *two* points in map and see their intersection in time period, which was great for finding places to meet friends.

Isochrones is the word you’re after.

Even cooler are distance cartograms, where the map distorts according to travel time.

https://riatelab.github.io/distanamo/articles/web_only/with-...

Sounds great, and documentation is very clear.

So - in theory - something like a massive chat client, discord like, can be implemented via this solution? And what would be the pricing of such a solution. Cheap-serverless-discord

Thank you! I spent a decent chunk of time designing PicoMQ, so the documentation had a natural progression.

Exactly, streams can essentially be rooms, and since the ordering is preserved, a Discord-like application is a strong use case. I’m even considering building one using PicoMQ as an example showcase.

The pricing is going to be dirt cheap, and the best part is how easy it is to scale up vertically or add nodes. For some raw numbers, assuming 1M messages/day, 200B per message, ~6GB/month, all-inclusive, it would be $30 to $150 a month, and storage would be the cheapest part.

Yes, discord as an example would be awesome, exactly what I thought.

Is this back of envelope pricing include the traffic/bandwidth of the readers?

That could easily be 10-100x of number of messages.

This solution together with a cheap/free caching layer (especially for non members/writers) could be amazing.

BTW, a classic example would be an hn mirror. ;)

Yeah, much of the base cost I mentioned was from the compute, networking and S3 write costs, which could sustain a lot more messages for sure. It also depends on the number of active streams/rooms, the throughput bursts, as opposed to a steady state.

But as we speak, I am in the process of running open-benchmarks on AWS with cost attribution. I'll be posting them in the docs with transparency soon enough.

Read and write through cache already exists today, which would work in favour of both cost, latency, and consumer reads fanout.

I like the HN Mirror over a Discord-like app for simplicity. No doubt that's where my weekend is going!

> They may only have bought themselves 6 more months of time given their purported burn rate

If they had only ~6 more months they (+auditor) had to issue a warning. The 6 is not a hard number, AFAIK, but surely a point where it must be reported.

So honestly, I doubt it's the case.

At least as written, GP says bought them six more months of time. Not implying they had 0 months to begin with.

That was indeed my intent. Pre-IPO, SpaceX already had _some_ cash in hand, and was burning >4b per quarter IIRC; so presumably _some_ runway. That said, they also got the anthropic/openai monthly payments coming in soon (already started maybe?). The next earnings release will be the interesting one IMO.

It is time to standardize EV batteries like wheels: 5-10 mainstream types & competition on quality vs. price vs. range.

Regulator help is needed here.

Historically, vehicles actually did have completely interchangable motive components. They were called horses.

It would have been difficult given the state of other technology at the time for the inventors of the internal combustion engine to have supplied it as a drop-in horse replacement for your carriage, but you could kind of imagine that working with current technology.

> Regulator help is needed here.

I don't like the impulse to regulate, especially as a formative measure. Regulating new technology before industry has time to experiment seems like a recipe for high prices, few choices, and low competition.

Regulations... in THIS economy and administration????

Well, the EU regulated USB-C and it affected the whole smartphones world.

Largest Chinese companies will define the standards.

Makes sense - This is very similar to fine tuning a down stream task in encoder-decoder architecture (~Bert style)

Was just reading the title and thinking it's a new and upgraded image gen model from Google.. Anyone else?