johndough · HackerTrans

If an uneducated acquaintance of yours was about to name their newborn child "Adolf", would you interject that this name might not be such a great idea?

johndough

그저께discuss

This once again shows that naming things is hard.

Not to be confused with 14 words: https://en.wikipedia.org/wiki/Fourteen_Words

I wonder whether there is some kind of search engine where you can check for near matches that you do not want to be associated with.

johndough

4일 전discuss

It would be great if this could be combined with quantization-aware finetuning. In my experience, Qwen3.6-27B has much fewer repetitions at Q6 quantization level as compared to e.g. Q4, but that leaves little space for context on my 24GB RTX 3090.

johndough

6일 전discuss

This does not seem to be true currently. In 2025, there were 45356 McDonald's restaurants worldwide and 13706 in the United States, which is about 3.3092.

https://corporate.mcdonalds.com/content/dam/sites/corp/nfl/p...

However, in 2023, the numbers were 41822 / 13457 = 3.1078, which is (almost) within 1 % difference.

https://corporate.mcdonalds.com/content/dam/sites/corp/nfl/p...

johndough

9일 전discuss

Any specific abliterated big models you can recommend?

johndough

10일 전discuss

Do you have any plans on mitigating the privacy consequences of microtransactions? I'm fine with paying for (some) content, but I'd prefer if there weren't some companies using that information to manipulate me or the more impressionable members of our society.

johndough

12일 전discuss

    > GLM-5.2 class models already need 1TB+ of RAM.

If you quantize GLM-5.2 to 4 bit, you can do it in less than 500GB: https://huggingface.co/unsloth/GLM-5.2-GGUF (table on the right)

If you find three finds that also have a 128GB MacBook, you can chain them together (the MacBooks, not your friends) and make it work.

You could also run GLM-5.2 on a single MacBook if you stream the active parameters from disk, but even with speculative decoding, you'd probably only get in the order of 1 token per second, so this is not really practical for most applications.

johndough

17일 전discuss

    > What provider do you use.

OpenRouter with pinned DeepSeek provider or OpenCode Go

    > Why do you trust it with serving full quality?

Quality seems good so far.

    > What harness do you use? Why do you trust it not to have malware (most harnessed are TS apps).

I wrote my own. A minimal harness without dependencies is only 65 lines of Python.

johndough

17일 전discuss

> Its not an issue to adhere with your private money if your product isn't causing damages.

Might happen really easily though. E.g. you install some package which has been compromised, infecting your software product and suddenly all your customer's systems are cryptolocked and you are on the hock for millions of €€€.

Or your db crashes in new and creative ways and your backups don't work for some reason and now your customer lost an expensive contract because critical data that was in your db is gone.

Of course, you can try to foresee every eventuality, but you will indubitably overlook something and probably never make it to market.

johndough

19일 전discuss

Another approach that often works for these kinds of problems and does not require much intelligence:

Work out the first few cases by hand (1,2,6,20 in our case) and then look up the sequence on "The On-Line Encyclopedia of Integer Sequences" (OEIS):

https://oeis.org/search?q=1%2C2%2C6%2C20&language=english&go...

johndough

20일 전discuss

> It’s unclear to me what the advantages of openrouter are but it seems to be a default I see many people talking about here.

The advantage of OpenRouter compared to using API providers directly is that you can switch between API providers without binding your money to a single provider.

The advantage of OpenRouter compared to OpenCode Go is that the price for DeepSeek-V4-Pro and MiMo-V2.5-Pro is better on OpenRouter.

For example, DeepSeek costs $0.435/0.87/0.003625 for 1M in/out/cached tokens (https://openrouter.ai/deepseek/deepseek-v4-pro), compared to an equivalent of $1.74/3.48/0.0145 under the OpenCode Go plan (https://opencode.ai/docs/go/#usage-limits), almost exactly 4x.

But since you get a monthly usage limit of $60 with the OpenCode Go plan for $10 (i.e. 6x), you might still come out ahead if you use it a lot (or use other models, where the pricing difference is smaller or non-existent).

johndough

20일 전discuss

I had a look at eurouter.ai and it seems like an extremely bad offer.

- The prices are ridiculous (15 % markup for free account).

- They have a rate limit of 1000 requests per month, unless you pay 40€ per month for ... what exactly is their value proposition?

- They have a single provider (TensorX) for DeepSeek-V4-Pro, with a cache read cost that is over 100 times higher than DeepSeek ($0.44 vs $0.003625). Notably, I had to look at the TensorX website for that information, since I could not find any information about cached token cost on eurorouter.ai.

johndough

20일 전discuss

> Mythos and other models are not brute-forcing passwords (and with this analogy passwords, ie. systems are the same).

I am not talking about literally bruteforcing passwords (although LLMs are being used for that, too), but bruteforcing passwords and solving verifiable domain tasks have quite a few similarities, especially when considering rule-based and probabilistic bruteforce methods.

> We're not talking about dogs, but LLM systems.

Well, clearly dogs are not LLM systems. It is an analogy. If there is an important point on your mind that makes the analogy break down, feel free to spell it out.

> Mythos is not exploring entire solution space either.

Yes, but weaker models do not find the solution right away, so they need to try more often. But if they only try the same thing every time, they will never succeed, so we need some kind of guarantee that they try something different every time.

> Usually looping is solved by repetition/frequency/presence/n-gram penalties/DRY/min-p sampling, not temperature but we're not talking about small models that have those classes of issues here.

Those might help to reduce looping (at the cost of biasing the generation), but to guarantee that a model can generate all possible generations, we need non-zero probabilities for all tokens, not lower probabilities for likely tokens.

johndough

20일 전discuss

I don't think that is necessarily true.

- With a weaker model, the time to break into the system might grow so larger that it becomes infeasible, similar to how password hashes can be bruteforced, but if the password is long enough, that is not going to happen in our lifetime.

- There might be problems which are inherently unsolvable with a lower level of intelligence. For example, your dog won't derive calculus from scratch, even if it lived forever.

- LLMs might be biased in such a way that they never explore the entire solution space, no matter how many attempts are made. Some models are notorious for getting stuck in a loop, trying small variations of the same approach every time, even though it is doomed to fail. This can be counteracted somewhat with higher sampling temperature, but that hurts reasoning capabilities.

johndough

20일 전discuss

Works for me though, even when using a proxy that is usually blocked everywhere.

johndough

25일 전discuss

Article 53 of the AI Act: https://ai-act-law.eu/article/53/

The definition of a "genral-purpose AI model" is described in more detail in the "Guidelines on the scope of obligations for providers of general-purpose AI models under the AI Act": https://ec.europa.eu/newsroom/dae/redirection/document/11834...

johndough

25일 전discuss

I see that OVH offers Qwen3.5-397B-A17B, which is a bit surprising to me. I thought that EU providers had to comply with the AI act where you have to provide opt-out and information about the training data once the model is sufficiently large (over 10^23 FLOPs, likely the case here), but providing information is not possible since people who train those models only give vague information at best.

Does anyone know if OVH is ignoring the law here, or whether it does not apply for some reason?

johndough

28일 전discuss

Probably not that important in practice. Firefox allows 2^20 - 4 and Chrome allows 2100000 characters. Also, 8000 characters already allows for an unreasonable amount of SQL and could be extended even further with compression. And if that should not be enough, the website already supports JSON exports. All in all, this seems like a worthwhile tradeoff for not having to store anything.

johndough

28일 전discuss

In academia, this is called "planar embedding" and can be computed in O(V) where V is the number of vertices of the graph.

However, there are graphs that do not allow planar embeddings (e.g. K_5 or K_3,3, see https://en.wikipedia.org/wiki/Planar_graph).

In this case, you'll probably want to look into heuristics that produce a low number of crossings and little distortion when new vertices are added.

johndough

28일 전discuss

Feel free to replace "racism" with "discrimination" if you prefer. English is not my first language and the minutiae elude me.

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