ashirviskas

How many euros or kilograms is it?

Better, take a look around. No random mobile focused redesigns to hit a random KPI.

Text only design also invites to focus on the discussion itself. It also has proper moderation.

I created this sheet to get proper model accuracy using the the lenz data, check it out.

Note: It may still not be perfectly accurate representation of truth as it uses user submitted data. I also used AI to build the sheet.

https://docs.google.com/spreadsheets/d/e/2PACX-1vSnZlURmyYX3...

I used AI to scrape the website and help build "Accuracy" comparison that everyone wants, thanks for this link!

https://docs.google.com/spreadsheets/d/e/2PACX-1vSnZlURmyYX3...

It's almost guaranteed with agents you could do the same job with less than half of 100k lines.

That's great, non-test code is only ~47k lines of code.

Gemini 2.0 Flash: $19

don't forget Gemini 2.0 flash at $0.10/$0.40

What does Malta manufacture?

What region is UK?

That's so interesting. Kind of explains company directions - quality vs quantity.

And what should we do about it? Only allow comments about top X world countries?

To me it is Relative Strenght Index

For things requiring Play Integrity, I picked up a $20 burner carrier-locked Motorola phone at Walmart for $30.`

So it's a $30 burner phone, not $20?

What? Training is not inference. Reading books is not the same as writing.

Does it matter what size the cup is? Usually you get the same amount of coffee water + additional water/milk/whatever.

I've been burned on openrouter getting routed through terrible quants with equally terrible quality. While paying maybe 15% less.

Nearly a year ago it was impossible to avoid it due to silly openrouter routing algorithm and the api. You had to set multiple things just right to make it work.

Similar to their other api quirks. You want valid json format response? sure, set response_format to "json" just like our documentation suggests. Oh, it only works some of the time? How silly, why would you expect it to work all of the time? If you want it to work more often, set require_params to true. We may still use other providers that don't offer it, but you want that, right? You don't? Well, then set our "very_require_params" to "very_true". And then switch a few toggles in the frontend. Oh and also add these 7 lines just so your other config options don't break. Oh wait they will break, how silly of us Is there any way to make it work as advertised? Of course no!

Sorry for the semi-offtopic rant. I still use them every day though, but not for open models anymore.

Can confirm, in 6 hours.

I'd argue there are `50 years / planck time` better times to plant a tree than now.

More fair comparison would be writing/talking about Russian language in English. That way you'd still focus on Russian. Same way with programming - it's not like you stop seeing any code. So why should you forget it?

If you work in marketing, you forgot to give us a link.

So, now that we're all here, what are you up to?

Nah, it's a decade away from *now*.

I'll admit, my brain was DDoSed by the article and I thought that maybe posting it here will get us someone with more DDoS proof brain to dissect it.

What if it is the quality of data? Internet is full of terrible python/js, but probably not Elixir.

I found it interesting that Elixir scores so high, but I'm not sure whether I can agree with the cause.

The biggest difference: Saturday: Build auth with Claude Sunday: Come back, describe next feature Claude reads REQUIREMENTS.md, sees existing auth schema Builds new feature without touching auth vs. the normal experience of Claude rewriting everything

What do you mean rewriting everything?

When I started properly structuring my projects, it just follows the pattern and doesn't just "rewrite everything". It finds things in places it expects to find.

Your project seems to solve a specific flaw in your flow. And as a npm package, which is super suspicious.

EDIT: Oh, it's just a useless product looking for problems to solve just for some $$$ a month.

Author does not know what they're talking about.

In other words, XML tags have not only a special place at inference level but also during training

Their cited source has 0 proof of that. It's just like python/C/html in training. Doesn't mean it's special. And no, you don't need to format your prompts as python code just because of that.

In truth, it does not matter that these tags are XML. Other models use ad hoc delimiters (as explained in a previous article; example: <|begin_of_text|> and <|end_of_text|>) and Claude could have done the same. What matters is what these tags represent.

Those strings are just representations of special tokens in models for EOS. What does it have to do with anything this article pretends to know about?

Please don't post such intellectual trash on here :')

Claude analysis of the article:

The author is making an interesting philosophical argument — that XML tags in Claude function as metalinguistic delimiters analogous to quotation marks in natural language, formulaic speech markers in Homer, or recognition sequences in DNA.

The core thesis is about first-order vs. second-order expression boundaries, which is a legitimate linguistic/information-theory concept. But to your actual question — do they understand what tokens are?

No, not in the technical sense you're pointing at. The article conflates two very different things:

1. Tokenizer-level special tokens — things like <|begin_of_text|>, <|end_of_text|>, <|start_header_id|> etc. These are literal entries in the vocabulary with dedicated token IDs. They're not "learned" through training in the same way — they're hardcoded into the tokenizer and have special roles in the attention mechanism during training. They exist at a fundamentally different layer than XML tags in prompt text.

2. XML tags as structured text within the input — these are just regular tokens (<, instructions, >) that Claude learned to attend to during RLHF/training because Anthropic's training data and system prompts heavily use them. They're effective because of training distribution, not because they occupy some special place in the tokenizer.

The author notices that other models use <|begin_of_text|> style delimiters and says Claude "could have done the same" but chose XML instead. That's a category error. Claude also has special tokens at the tokenizer level — XML tags in prompts are a completely separate mechanism operating at a different abstraction layer.

The philosophical observation about delimiter necessity in communication systems is fine on its own. But grafting it onto a misunderstanding of how tokenization and model architecture actually work weakens the argument. They're essentially pattern-matching on surface-level similarities (both use angle brackets!) without understanding the underlying mechanics.

Smaller quant or smaller model?

Afaik it can work with anything, but sharing vocab solves a lot of headaches and the better token probs match, the more efficient it gets.

Which is why it is usually done with same family models and most often NOT just different quantizations of the same model.

Someone ping me in 5 years, I want to see if this aged like milk or wine

Apple made lower than 16GB M3 models? Man, can't wait till the cheapest model is at least 128GB.