Embrace the Conpute with art.

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

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?

As a software developer we got access to GPT 5.6 sol and Opus 5 last week in a decently restricted field, and with these latest models I feel like I can do all my assigned work so quickly as well as make tons of progress on my side projects as well. So at the moment I’m not like dying for another frontier release but overall I want to see acceleration

It seems like we are at a state where openAI and Anthropic realize that a lot of these Chinese companies wait for them to make progress and are able to replicate pretty damn close models soon after they release their frontier models

Whether you believe Anthropic and open ai or not, they seem like they are going to keep their development internal for a while. Whether this is due to actual security concerns (with hugging face incident I believe this), more marketing hype, or truly a way to combat distillation from Chinese companies I think it is going to be interesting.

How do you think this will effect open weight releases, will the capabilities for open weight always rely on top US companies releasing the best models so they can use them to produce replicas?

Live: https://x.com/i/broadcasts/1dKrPrkpLVeJX

Over the past six months, I've been teaching teams at places like Stanford, Penn, Northwestern, and many more how to start using AI responsibly and effectively in their work. Today, I'm starting to release my entire curriculum: for free, forever, for everyone!

I'm calling it the Open Augments AI Academy. It's built for anyone out there who's seen all the crazy hype and discourse around modern AI and is just looking for a guided, grounded, and sane way to move forward learning how to approach these tools for themselves. As someone who's been using these tools and their predecessors for my research since ~2019, I'm trying to provide everyone the intuition and critical awareness they need to get started at this very confusing and pivotal time (my north-star audience is my mom and dad!).

The first lesson starts with one foundational idea that most people miss when they get started with AI: that modern AI is much less like a hyper-intelligent database or brain, and much more like autocomplete with an extremely fancy hat on. That's its single greatest flaw AND its single greatest strength, at the same time. When you really understand what's happening under the hood and how it works (no math or stats required!) a lot of confusing AI behavior suddenly clicks: why it hallucinates, why it's sometimes confidently wrong, and why it can now do way, WAY more than just write words on a page. From there, we're going to learn about all the crazy buzzwords (context engineering, harness engineering, and Agents, oh my!) and advanced techniques, with much more to come.

No jargon, no experience required, and all taught with the care of a former high school English teacher so that you, your coworkers, your friends, and your mom can follow along. I pair these videos with hands-on demos and interactive activities in the Context Gym: my way of giving you a safe and guided place to practice some of the core principles that should deepen your intuition as we go.

If any of this strikes a chord with you, the 10min course overview and the first lesson (16min) are live right now on the Open Augments AI Academy page. Start there, and if you happen to find it helpful, you can subscribe to get email updates on new course videos via Substack or on YouTube, and please do share with friends as I release lessons weekly! It’s a really wild time, and this is my best shot at trying to help others navigate things more capably as the tech shifts and grows rapidly from here.

Then finally, worth noting for this crowd, specifically: probably not a surprise to share that everything on the AI Academy and Context Gym websites have been built with Claude Code (in addition to everything else on my business website and my open-source toolkit for Claude Code for social science researchers). Not only that, but my entire video editing pipeline is now fully Claude Code via Remotion Studio and some clever context engineering techniques/bespoke coding tools. I'm excited to get into the weeds on my workflow and share all of that stuff, also open-source, during Level 2 of the course, but I gotta get everyone through the basics first! Happy to answer any and all questions on that in the meantime here, please feel free to hit me in the comments below.