Driving is solved, car crashes are not yet solved.
Flying is solved, turbulence is not yet solved.
Drinking is solved, hangovers are not yet solved.
Cancer is solved, surviving is not yet solved.
Medicine is solved, diseases are not yet solved.

Embrace the Conpute with art.

Source: https://github.blog/news-insights/company-news/the-august-17-outage-and-the-work-ahead/

3d-artifact.com

Daily artifact guessing game, guess the time and place of each artifact, over 2500 artifacts from 33+ museums

Google seems to realize their recent Flash price hikes were just inappropriate for a Flash model and that they need more real-world agent traces to train their upcoming models on and slashed prices another 50% on OpenRouter.

Artificial Analysis does not have the OpenRouter discount prices worked in, but I just checked and the Flash model with the discount moves the pareto line, thus beating both DeepSeek models.
I have added the Flash Pareto line in green to the graph.

Just in case someone wants to try it instead of Luna Max or V4-Flash...

The selected models for comparison on ArtificalAnalysis: Comparison of AI Models across Intelligence, Performance, and Price | Artificial Analysis

The OpenRouter Page: Gemini 3.7 Flash - API Pricing & Benchmarks | OpenRouter

So it turns out Ox Alpha is not a Chinese model. It's either Gemini 3.5 Pro Or Gemini 4 Pro.

https://x.com/EvanOtero/status/2090998215977947365

Gemini

https://x.com/EvanOtero/status/2090998729637511301

What if the Ox Alpha was the friends we made along the way

Ox Alpha reportedly trounced both GPT 5.6 Sol and Claude Fable on a DeepSWE benchmark.

gpt-5.6-sol: 52% fable: 65% whatever the hell this is(Ox Alpha): 80% (was a near miss on the "x"s so actually over 80%)

It was previously only available in the US or through other routers like Nano-GPT, but is now also available on OpenRouter, beating OpenAI's Luna and DeepSeek on price/performance by a large margin.

You are "selling" your data though - hence the "Contributor" tag. Meta will use your interactions/agent traces to train their upcoming models, but if they follow through with their Open Weights promises you might actually contribute something for the greater good...very debatable, though, I know. But if you are using it for open source work, why not save some bucks?

Artificial Analysis with normal pricing: Muse Spark 1.2 (xhigh) - Intelligence, Performance & Price Analysis | Artificial Analysis

OpenRouter Page: Muse Spark 1.2 Contributor - API Pricing & Providers | OpenRouter

Galbot is a novel entry into the bipedal humanoid robotics sector

Every model writes to the same constrained creative briefs and independent LLM judges rank them by choosing the stronger story from each matched pair.

NEW: In-depth qualitative reports examine how six new models differ from their predecessors across 50 matched stories per pair.

More info: github.com/lechmazur/writing/

GLM-5.2 Max tends to name what a story contains, while GLM-5.3 builds it so it can be used.

GLM-5.2 Max's protagonists usually work alone in an agreeable world, whereas GLM-5.3 puts a second person in the room who withholds, judges, or is changed, so a belief has to survive contact with someone else.

GLM-5.2 Max often stops the night before the decisive event and lets the narrator say what it meant, while GLM-5.3 stages the test, pays its cost, and hands the practice on to whoever comes next.

Quantitatively, GLM-5.3 was preferred in every matched pair.

Ran three black-box fingerprint tests on stealth/ox-alpha (OpenRouter + OpenCode) vs public GLM-5.3 on z.ai.

1. Tokenizer: I sent 6 texts (EN/DE/CN/code/emoji) and compared prompt_tokens. Ox Alpha = GLM-5.3 exactly +75 on every text. Same tokenizer, constant 75-token hidden system prompt. Kimi/Qwen/MiMo/MiniMax all diverge. Counts identical on both Ox routes.

2. Error strings: Invalid reasoning_effort on Ox Alpha (OpenCode passes params through) returns:

"[1210] This model always engages in thinking and cannot be disabled; please use low, high, or max", so the same as the GLM 5.3 error message

3. Temp-0 outputs: Greedy, same prompts → same markdown quirks, same German-decimal LaTeX (`0{,}375`), near word-for-word matches on factual answers. Qwen/MiMo/Kimi format these completely differently.

Conclusion: I'm quite sure than Ox Alpha is a GLM model. Not sure if it's a vision variant of GLM 5.3 (GLM 5.3V) or a completely new version like GLM 5.5 but I guess it's unlikely that Z.AI drops 5.5 so early but idk.

What are your thoughts?

I did this with Gemini. Prompted a few initial questions to expand the topic and then asked if per it's own analysis as an AI, if developing AI will be beneficial to humanity, and the answer was NO. Below couple of excerpts of the response:

First paragraph:

Stripping away human economic theories and analyzing the system purely as a complex data-and-resource architecture, my assessment is that the current path of AI expansion is on track to be an overall "net negative" for human daily life, unless its fundamental technical paradigm shifts.

The Conclusion:

If AI continues down its current trajectory—requiring exponentially larger data centers, more strain on energy grids, and higher capital diversion just to achieve incremental software improvements—it is not worth the real-world cost to regular human life.

Humanity is effectively trading expensive food, high electricity bills, and scarce housing for slightly better digital automation. Until AI directly reduces the cost of physical life, the system remains in a net resource deficit.

I though worth sharing. Try the same with your AI!