achrono

Ignoring the checkbox is an utterly offensive move, but repeatedly manipulating it contrary to stated consumer intent is a whole other level. I didn't know we were supposed to take 'frontier' literally in every sense of the word.

I have now witnessed this myself after not believing this at first. Of course, screenshots etc. will hardly prove anything. This needs a proper third-party audit!

To whoever downvoted this, it would be helpful if you actually reply with something substantive. The post I'm responding to makes sweeping characterizations, and I challenge it with a relatively good heuristic and indirect evidence, and only get downvoted?

Yes, evidence is needed but especially for the claim that distillation is what makes these open models good. Serious citation needed.

Think about it: even if they distill the shit out of frontier models, the model still gotta learn, right?

If anything, as you can see from the K2 Horizon release, aggressive (self-proclaimed) reliance on distillation does not result in a model that has remotely any frontier capability. Try asking K2 Horizon to write iambic pentameter for instance, or even give it the car wash prompt. I tried both these on the Q8 quant for the 7B model and the results were depressing.

Certainly, real-life is the ultimate benchmark. But for various reasons that isn't always immediately possible to go evaluate a model on.

Maybe my Greek idea sounded too high falutin' or simply seemingly clever (I give an example below -- try it out!).

So here's something way simpler that Qwen3.8 27B does not get; only GLM-5.2 and K3 do.

**

Analyze the 2 structural (not semantic) patterns in this text:

1. Morning revient.

2. Birds saluent Morgenlicht.

3. We suivons Waldwege toward maison.

4. Rain tombe plötzlich; we cherchons Schutz beneath sapins.

5. Night vient langsam; we trouvons Wärme near le Feuer, sharing quelques Geschichten together.

**

The answer should get not just the obvious cyclic E-F-G pattern but also the word counts being Fibonacci. Surprisingly few models get this. The only way I got Qwen to do this was on the 2.4T model, with extensive prompt scaffolding. Claude (Opus & Fable), Sol, Grok etc. got it on the first attempt. (All models, all attempts max reasoning level.)

Here's an example:

**

The stone remembers not what the cities gave, but what the goddess kept.

Begin with the first reckoning, under Ariston.

Find those who carried the Greeks in their name and the silver in their care. Take their name as we give it to them now, in capitals. Thirteen marks.

The goddess kept one from sixty. She asks the same of every mark: give each its ordinary alphabetic number, divide by sixty, and keep what she could not take.

Each remainder walks with the next. The last returns to the first.

The first of each pair turns upon itself; the second joins it; then comes the place where the pair began.

Eleven takes its fill. Keep what remains.

Raise eleven courses beneath the thirteen marks.

In course (r), beneath mark (i), add the course to what Eleven returned. Reduce again by Eleven. Cut the stone if the result is the remainder belonging to mark (i), or to the mark walking beside it.

Count courses from zero.

The mason turns where the stone turns: the first course goes with the writing, the next against it, and so on.

A cut is `#`. Stone is `.`.

Restore the fragment.

**

Answer is:

........#....

.#.##.......#

...#.........

........#....

#.........#..

#.....#.#....

.#.#.#......#

..#..#.#..#..

.........#...

.#...........

.#....##.....

Sounds obvious but just try using the models for anything outside the evals. Take something arcane from Greek history, use it to create a masked linguistic puzzle, which you then ask the model to solve mathematically, all wrapped as an ask to generate ASCII art. Yes, all these elements exist in some form in the evals but the key is in how utterly unconventional the elements are that you pick and in how you combine them.

I have consistently noticed Opus 4.8 and GPT-5.6 far outshine the Chinese models. Gemini is sort of middle of the road, Grok is better than Gemini but not really close to Opus/GPT. OAI & Anthropic still remain unbeaten by a wide margin in my eyes.

At the risk of sounding like a conspiracy theorist, this sounds like a great opportunity to make a statement. US or China, but likelier to be the former. Maybe Clem's on a call with the US government right now?

Well, sorry but look at the methodology on this. The study the post leans on did not study learning. 36 adults went around copying familiar words, and their "typing" is key presses using one index finger (!).

I'm afraid this one's close to going down the replication crisis whirlpool. According to Sol:

"Direct replications found no reliable learning advantage, while a 2024 meta-analysis found only a *small* handwriting benefit but a far larger typing advantage in information captured."

Typed Versus Handwritten Lecture Notes and College Student Achievement: A Meta-Analysis” by Abraham E. Flanigan and colleagues, published in Educational Psychology Review on July 12, 2024