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- Hacker News
- I'm really impressed. Gave QwenCloud $18, handed 3.8-flash a few big forks of a lot of code, it did some archeology and made a clean merge. Then it used the project's tools to bisect a regression and fix it.
Was not expecting it to just get that right without any fuss, and it barely used 10% of this weekly limit. Something like 90M cached in/400k out for $0.45 is wild
- Pelican: https://gist.github.com/SerJaimeLannister/8fdef9c00175da0ca6...
Aside from the pelican, I am sort of impressed by the fact that things are going the way in terms of really impressive small models.
Also I love how this uses N-gram embedding. I think that Longcat was the first one who used it (I submitted that submission on hackernews because I really just loved the idea of it that I understood), I am certainly more interested in local LLM models and its interesting how they are utilizing new architectures to do some really impressive optimizations!
(Do note that I created it using a free rate limited end-point that I found on the huggingface space section: https://victor-chat-with-qwen3-8-flash-next.hf.space)
- this is a new architecture (foreshadowing qwen 4)
> trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board
by tosh - Can someone explain the intuition behind the en-gram idea? I know DeepSeek published a paper about it a few months ago and the Gemma models have a lightweight version of it; but it hasn’t clicked for me yetby schopra909
- Waiting for llama.cpp support to land, but this might be a big deal for Strix Halo users.
6B active params helps around the memory bandwidth constraints, but a 128GB box can probably run the Q3/Q4 quants fairly easily with a decent context size. This might actually be better for strix users than 27B, which was already very good.
by a_humean - Didn't expect it to beat 3.8 27B so cleanly.
Opus 4.6 Max self-hosted at 30 tok/s on a 5k Macbook in Aug 2026. The LLM timelines are crazy.
by rohansood15 - > Qwen3.8-Flash-Next features a 125B-parameter main model, supplemented by an additional 51B N-gram embeddings, with 6B parameters activated per token.
Didn’t see this mentioned yet. I wonder what this means for the effective size. It’s evidently ~176B paramètres, but how does that get quantized. A 4-bit quant under 100GB seems unlikely, I’m suspecting this won’t run in 128GB unified memory
In principle I like the idea of trading more memory for compute though, even if there’s a memory shortage right now
by andy99 - I ran some pelicans at the four different reasoning levels (none, low, medium, xhigh - apparently high and xhigh are aliases of each other) on a DGX Spark using Unsloth's unsloth/Qwen3.8-Flash-Next-GGUF (UD-IQ1_S):
https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
Surprised I didn't get one I liked as much as the Qwen 3.8 27B one https://simonwillison.net/2026/Aug/16/qwen-38-27b/#the-defau... , maybe because of quantization.
by simonw