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Hah you’re probably dating yourself. Keras came out in 2015 and that was one of the early examples with Theano backend. You could train MNIST since 2015 pretty straight forward. But comparing Jev to image classification is an unfaithful argument. Comparing it to ELmo or BERT is analogously better.

I conduct interviews pretty often for various levels. Interviews seems so broken from both sides. Hard to find jobs that aren’t ghost jobs. Hard to find candidates that won’t cheat using AI and actually know their stuff. Just feels like AI has made hiring in tech even more inefficient which says a lot.

Interesting, I think it would be interesting to gauge how a 4B model would run compared to a frontier one

Surprised they didn’t try Qwen’s recently open sourced driving model https://huggingface.co/Qwen/Qwen-Drive-1.0-4B

I actually find the name System-1 as a nod to Daniel Kahneman’s Thinking fast and slow book kinda nice. It’s an interesting analogy

All these new models are such tease for us folks with 128GB of shared memory. Buying another unit now to expand to 256GB is a mortgage payment but it’s getting tempting…

Thanks! I really like how the author packaged everything into a container. Definitely going to give it a go over the weekend!

Halogen as in this right? https://github.com/peonist-ai/halogen-flash-server

Thanks! Have you seen issues with quantizing the kv cache?

Vulkan, I have never used ROCm on it but have been debating since the latest big update. How is your prefill? Do you hit over 1K? If it’s 1000K prefill, and 40 TG, I might have to try this over the weekend. Also, can you fit 128K without offload the ngram onto SSD?

Can you point me towards the model you use, both the main model and the flash model? Curious if I can get ~30 with a higher quant.

I’m on a strix halo @ GPU-5 with MTP and I get 600 prefill and 30 TG which pushes it into a very usable range. The odd thing is that Dflash2 is really slow for me, like sub 10 TG.

> audio-visual performance close to Gemini 3.8 Flash and overall audio performance that exceeds Gemini 3.8 Flash

Wow crazy if true. I think Gemini's audio capability and multi language was the "selling point" for a lot of people. Other capability also matches or exceeds 3.8 Flash.

They also made a new harness but github link seems to 404.

I said this before but I wonder if Dan Kan will reboot Atrium. Rally up some old partners and hope Anthropic buys them out for a couple billion.

It’s not obvious but you can use this with your own local (or any) models.

https://support.mozilla.org/en-US/kb/smart-window-byom

I would pay a good chunk of money if Apple released a local AI hub to coordinate all AI usage locally (including photo indexing).

Wow thanks for the link. I have zoom on my personal laptop which isnt ideal. I always wanted to run it sandboxed

Has anyone have good success using AI generated CAD parts? I’ve been trying but it’s always 95% there, but with all hardware, you need 100% right. It’s often quicker and cheaper for me to do it by hand (but I was a mechanical design engineer for about a decade prior)

Surprised no one is talking about it but the 0.1 version bumped the parameters from 284B to 552B but “more efficient”, particularly kv cache usage

I wonder if that’s why 3.8 got so much better? Mixing the reasoning traces from both sides seems to be effective.

I’m surprised they allow open lid drinks in the lab. One wrong bump and poof 300K easy.

> 23 agents total.

This hit a bit too close to home. Sol has the same issue, spawns a lot of agents for no good reasons (besides burning tokens).

I’m more curious how each 4 bit quant compares. It seems like NVFP4 outperforms Q4_K_M in terms of speed and top 1 but is only good for expensive Nvidia cards

Qwen 3.8 27B is the real deal BUT remember to use froggeric template and/or medium reasoning.

https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates

I swear, Qwen 3.8 27B @ Q8 is smarter than Sonnet 5 most of the time. Why wouldn’t corporate America self host at this point, especially with better options like Deepseek Flash and GLM 5.3 flash that’s a middle ground between Sonnet and Opus

I’m honestly surprised this is better benchmark wise than the text only model. I figured the addition of vision would take away from some of the text capabilities.

How do people bypass captcha or robot checks? All I wanted is a price aggregator but it always gets blocked by major retailers.

If you get a children’s card, they print your kids name right on it. It’s a nice little souvenir to keep.

Ironically, our administration pushing for ban of the AI chips to China is forcing them to make smaller and more efficient models which seems like a requirement for running on Chinese chips. I wouldn’t be surprised this model was tailored to run purely on Chinese chips. Same thing with Deepseek MLA, the drastically lower KV cache memory requirement was born out of necessity so it runs on the Huawei chips.

I’m more curious on the size. If it’s smaller than or equal size to GLM 5.3, this would be a crazy good model. If it’s closer to deepseek pro, it would be a good model. If it’s near Kimi K3, I think it’s competitive but nothing particularly differentiating.