For clarity, the recent issue[0] likely wasn't intermittent. Cloudflare's malware blocking DNS server now blocks those archive.today sites. Doesn't affect the non-malware-blocking DNS server (1.1.1.1).

[0] https://news.ycombinator.com/item?id=47474255 "Cloudflare flags archive.today as \"C\&C\/Botnet\"; no longer resolves via 1.1.1.2"

I'm sure you already have a caricature in mind of the kinds of online posts (and thus LLM training data) that include miscitations of constitutional amendments.

That said, I still don't trust it and have it quarantined in a VPS. It's still surprisingly useful even though it doesn't have access to anything that I value. Tell it to do something and it'll find a way!

All this is running on a cheap VPS, where the worst it has access to is the LLM and Discord API keys and AnkiWeb login.

In recent LLMs, filtered internet text is at the low end of the quality spectrum. The higher end is curated scientific papers, synthetic and rephrased text, RLHF conversations, reasoning CoTs, etc. English/Chinese/Python/JavaScript dominate here.

The issue is that when there's a difference in training data quality between languages, LLMs likely associate that difference with the languages if not explicitly compensated for.

IMO it would be far more impactful to generate and publish high-quality data for minority languages for current model trainers, than to train new models that are simply enriched with a higher percentage of low-quality internet scrapings for the languages.

UBI isn't about giving everyone free money. It's about giving everyone a safety net, so that they can take bigger economic risks and aren't pushed into crime or bullshit work.

The upper half of society will only see the indirect benefits, like having greater employment/investment choices due to more entrepreneurialism.

The landlord of the marketplace should probably not dabble in the appraisal of products, whether for factuality or value.

As a content consumer, I'm also hoping to be part of the ecosystem. I already use Patreon a lot as "AdBlock absolution", but it doesn't fix the market dynamics. Major content platforms tend to stagnate or worsen over time, because they prefer to sell impressions to advertisers than a good product to consumers.

If you are thinking of adding a one-off-donation-style purchase method, consider giving annual reminders to renew it. At least in my case, I'm not unwilling to pay repeatedly if development continues, just unwilling to make an upfront ongoing commitment.

As long as all library code is compiled/run from source, a compiler/runtime can replace system calls with wrappers that check caller-specific permissions, and it can refuse to compile or insert runtime panics if the language's escape hatches would be used. It can be as safe as the language is safe, so long as you're ok with panics when the rules are broken.

It'd take some work to document and distribute capability profiles for libraries that don't care to support it, but a similar effort was proven possible with TypeScript.

Browsers used to try new ideas like RSS, widgets, shared and social browser sessions. Interfaces to facilitate low-friction integration with the rest of your life, and to multiplex data sources so that it's not a hassle to have many providers for [news, entertainment, social] experiences.

Likely no coincidence that this innovation languished once monopolies started pumping money into the ecosystem.

This may be merely a naming distinction, leaving the name open for a future release based on their recent research such as coconut[1]. They did RL post-training, and when fed logic problems it appears to do significant amounts of step-by-step thinking[2]. It seems it just doesn't wrap it in tags.

[1] https://arxiv.org/abs/2412.06769 "Training Large Language Models to Reason in a Continuous Latent Space" [2] https://www.youtube.com/watch?v=12lAM-xPvu8 (skip through this - it's recorded in real time)

This is the likely main explanation. RL fine-tuning repeatedly switches between inference to generate and score responses, and training on those responses. In inference mode they can parallelize across responses, but each response is still generated one token at a time. Likely 5+ minutes per iteration if they're aiming for 10k+ CoTs like other reasoning models.

There's also likely an element of strategy involved. We've already seen OpenAI hold back releases to time them to undermine competitors' releases (see o3-mini's release date & pricing vs R1's). Meta probably wants to keep that option open.

You can safely assume Qwen2.5-Max will score worse than all of the recent reasoning models (o1, DeepSeek-R1, Gemini 2.0 Flash Thinking).

It'll probably become a very strong model if/when they apply RL training for reasoning. However, all the successful recipes for this are closed source, so it may take some time. They could do SFT based on another model's reasoning chains in the meantime, though the DeepSeek-R1 technical report noted that it's not as good as RL training.

I also struggle with willpower and it took me ~10 big attempts over ~14 years before I managed to stay on it long enough to fix my metabolism. I just wanted to spread the message of hope that every attempt gets easier. Mindset plays a big role - I've seen a few people push themselves really hard then declare it impossible and never give it another shot. If you know you're playing a long game, take a break if you're really suffering, and don't beat yourself up over failures, it's easier to try again next time you have the energy.

[1] I've lots of intolerances - whitelisting was easier than blacklisting. Here's the list: flaxmeal porridge, keto bread + cream/cottage cheese, omelette w/ pizza toppings, egg & cheese salad, caesar salad (w/ chicken), mince+vege+cheese mealprep'd casserole

I think it was either changing my diet to focus on veges instead of meats (still 15-30g net carbs/day though), or adding artificial sweetener to maybe fool my body to making insulin? The science says that shouldn't happen, but idk what else it could be.

I've been in and out so often now I can happily switch between keto at home & unrestricted on vacation/occasions. At worst I get 1 day of dopiness starting carbs, and 1 day of mild cravings stopping them, but usually I don't even notice.

The paper[1] doesn't appear to have any other connections to the book/response/memes. A clear distinction is that the UB paper very directly and prominently states the question, rather than cloaking it in allusion or having a lengthy preface trying to contextualize it.

[1] https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2024/p34...