@ybceo you seemed to represent this org based on your previous comments, is the parent commenter missing something here?

In my mind, I’m comparing the model architecture they describe to what the leading open-weights models (Deepseek, Qwen, GLM, Kimi) have been doing. Honestly, it just seems “ok” at a technical level:

- both models use standard Grouped-Query Attention (64 query heads, 8 KV heads). The card talks about how they’ve used an older optimization from GPT3, which is alternating between banded window (sparse, 128 tokens) and fully dense attention patterns. It uses RoPE extended with YaRN (for a 131K context window). So they haven’t been taking advantage of the special-sauce Multi-head Latent Attention from Deepseek, or any of the other similar improvements over GQA.

- both models are standard MoE transformers. The 120B model (116.8B total, 5.1B active) uses 128 experts with Top-4 routing. They’re using some kind of Gated SwiGLU activation, which the card talks about as being "unconventional" because of to clamping and whatever residual connections that implies. Again, not using any of Deepseek’s “shared experts” (for general patterns) + “routed experts” (for specialization) architectural improvements, Qwen’s load-balancing strategies, etc.

- the most interesting thing IMO is probably their quantization solution. They did something to quantize >90% of the model parameters to the MXFP4 format (4.25 bits/parameter) to let the 120B model to fit on a single 80GB GPU, which is pretty cool. But we’ve also got Unsloth with their famous 1.58bit quants :)

All this to say, it seems like even though the training they did for their agentic behavior and reasoning is undoubtedly very good, they’re keeping their actual technical advancements “in their pocket”.

For context, here’s a handful of the ChatGPT cues I see.

- “wasn’t just my backup—it was my clean room for open‑source development” - “wasn’t standard AWS incompetence; this was something else entirely” - “you’re not being targeted; you’re being algorithmically categorized” - “isn’t a system failure; the architecture and promises are sound” - “This isn’t just about my account. It’s about what happens when […]” - “This wasn’t my production infrastructure […] it was my launch pad for updating other infrastructure” - “The cloud isn’t your friend. It’s a business”

I counted about THIRTY em-dashes, which any frequent generative AI user would understand to be a major tell. It’s got an average word count in each sentence of around ~11 (try to write with only 11 words in each sentence, and you’ll see why this is silly), and much of the article consists of brief, punchy sentences separated by periods or question marks, which is the classic ChatGPT prose style. For crying out loud, it even has a table with quippy one-word cell contents at the end of the article like what ChatGPT generates 9/10 times when asked for a comparison of two things.

It’s just disappointing. The author is undermining his own credibility for what would otherwise be a very real problem, and again, his real writing style when you read his actual written work is great.

I’m not much of a conspiracy theorist, but I could imagine a blog post almost identical to this one being generated in response to a prompt like “write a first-person narrative about: a cloud provider abruptly deleting a decade-old account and all associated data without warning. Include a plot twist”.

I literally cannot tell if this story is something that really happened or not. It scares me a little, because if this was a real problem and I was in the author’s shoes, I would want people to believe me.

It’s just bizarrely uncompetitive with o3-pro and Grok 4 Heavy. Anecdotally (from my experience) this was the one feature that enthusiasts in the AI community were interested in to justify the exorbitant price of Google’s Ultra subscription. I find it astonishing that the same company providing free usage of their top models to everybody via AI Studio is nickel-and-diming their actual customers like that.

Performance-wise. So far, I couldn’t even tell. I provided it with a challenging organizational problem that my business was facing, with the relevant context, and it proposed a lucid and well-thought-out solution that was consistent with our internal discussions on the matter. But o3 came to an equally effective conclusion for a fraction of the cost, even if it was less “cohesive” of a report. I guess I’ll have to wait until tomorrow to learn more.

It spent the first few minutes analyzing the image and cross-checking various slices of the image to make sure it understood the problem. Then it spent the next 6-7 minutes trying to work through various angles to the problem analytically. It decided this was likely a mate-in-two (part of the training data?), but went down the path that the key to solving the problem would be to convert the position to something more easily solvable first. At that point it started trying to pip install all sorts of chess-related packages, and when it couldn’t get that to work it started writing a simple chess solver in Python by hand (which didn’t work either). At one point it thought the script had found a mate-in-six that turned out to be due to a script bug, but I found it impressive that it didn’t just trust the script’s output - instead it analyzed the proposed solution and determined the nature of the bug in the script that caused it. Then it gave up and tried analyzing a bit more for five more minutes, at which point the thinking got cut off and displayed an internal error.

15 minutes total, didn’t solve the problem, but fascinating! There were several points where if the model were more “intelligent”, I absolutely could see it reasoning it out following the same steps.

OP built an arena allocator in Go using unsafe to speed allocator operations up, especially for cases when you're allocating a bunch of stuff that you know lives and dies together. The main issue they ran into is that Go's GC needs to know the layout of your data (specifically, where pointers are) to work correctly, and if you just allocate raw bytes with unsafe.Pointer, the GC might mistakenly free things pointed to from your arena because it can't see those pointers properly. But to make it work even with pointers (as long as they point to other stuff in the same arena), you keep the whole arena alive if any part of it is still referenced. That means (1) keeping a slice (chunks) pointing to all the big memory blocks the arena got from the system, and (2) using reflect.StructOf to create new types for these blocks that include an extra pointer field at the end (pointing back to the Arena). So if the GC finds any pointer into a chunk, it’ll also find the back-pointer, therefore mark the arena as alive, and therefore keep the chunks slice alive. Then they get into a bunch of really interesting optimizations to remove various internal checks and and write barriers using funky techniques you might not've seen before

Broadly speaking I’ve found that most of the reported compatibility and usability concerns in their GitHub issues have boiled down to user error, rather than any kind of a defect with the shell itself. That’s not to say there aren’t any issues, but they’re few and far between, and it’s more than solid enough for regular use. It isn’t bash, and you shouldn’t expect to execute a bash script with xonsh or use bash idioms (even though some compatibility layers exist for e.g. translating your ~/.bashrc and sourcing envvars from bash scripts).