Not bad. I’d appreciate a much longer time limit. Got really rushed at the end. But this was crazy hard on my phone, I was straight guessing most of the time. I think I could be a little better with more time on a desktop screen.
Another idea would be games that were all pictures of a single category- like 10 AI and 10 real pictures of basketballs, or food on plates, etc.
But I think that your experience is actually the useful one that proves a point. Most people consume content on their phone (e.g instagram) and would usually just scroll by photos. They wouldn’t deeply analyze them.
I 100% agree with you. It looks like multiple people (including me) were typing comments about how good the Selena Gomez Oreos were at the same time as you.
The post seems to have found a strange niche of HN, or maybe the Selena Gomez Oreos had wider appeal than I thought.
I really liked Selena Gomez's Oreos, but I would prefer that Oreo brought them back as "horchata flavored" or "cinnamon cream flavored" so I didn't have awkward conversations every time I accidentally said, "I like Selena Gomez flavor" and people would ask how I knew what Selena Gomez tasted like.
Honest question/suggestion for the HN audience- Since Qwen released the weights for Qwen3.8 2.4T-A95B and we already have the staring point of Qwen3.6 35B-A3B, couldn't someone distill the bigger model and make a "pseudo" Qwen3.8 35B-A3B? Sure, it wouldn't be an official Qwen release but couldn't someone improve on Qwen 3.6 and get the thing everyone is asking for?
I am calling this a suggestion for the audience because I don't have the will/resources to do this.
> Base / architecture: Qwen/Qwen3.6-35B-A3B (Qwen3_5MoeForCausalLM, 256 experts, ~3B active). The "3.8" in the name refers to the teacher, not the base.
Not endorsement, haven't run it myself, just found the link.
The timing looks like they are trying to take the wind out of Qwen's sails by releasing this on the same day that Qwen released the weights of Qwen3.8-max. Or maybe it's coincidence...
For comparison I looked at Qwen's claimed benchmarks for Qwen3.8-max (https://qwen.ai/blog?id=qwen3.8). Assuming each published set of benchmarks is believable, it looks like v4 Pro 0813 is better on average but overall performance is comparable. Pro 0813 is much cheaper. If you don't need vision capabilities then you don't have much reason to use Qwen3.8-max.
- 43.6 on HLE (Presumably without tools). Pro 0813 is a little worse.
- 86.6 on Terminal Bench 2.1. Pro 0813 is better.
- 55.9 on NL2Repo. Pro 0813 is better.
- 27 on Agent's Last Exam. Pro 0813 is a little worse.
- 72.5 on Toolathon-Verified. Pro 0813 is better.
- 56.6 on DeepSWE 1.1. If the DeepSWE listed for Pro 0813 is the same version, then Pro is better.
- 27.3 on AutomationBench. If the AutomationBench (Public) listed for Pro 0813 is the same, then Pro is better.
I guess we do need to wait to see if the upcoming DS pricing increase is enough to change the value proposition. As it is now, they could double or triple prices and it still would be a better value to use DS. I bet they know that.
By that standard, the release of Grok 4.6 was also timed on the same day.
Given how I think DeepSeek operates... I think they just release it when they feel it's ready, and don't even seem that concerned with what other people are doing.
Their leaks would confirm this sort of attitude. They're not trying to become the top player or anything like that - just working to play their part in pushing LLM tech forward and going from there. It was quite refreshing from the 'here's how we're going to dominate the world' nonsense. It's undoubtedly the same attitude that just lets them shrug and cancel the fund raising round after the leaks came from said funding round.
Benefits of having a well performing hedge fund funding DeepSeek.
IIRC, Demis attempted to start a fund inside DeepMind but it was killed off. In an alternative world where he manages to pull that off, perhaps DeepMind would still be independent with Demis at the helm.
It still matters as a point of comparison until other providers come online. If the consensus price from other providers is much different that can be compared then. But for now we have $0.435 / $0.87 for v4 Pro 0813 (with increase announced but we don't know the new pricing), and $2 / $6 for Qwen3.8-max. So until we get other data points that is what we have to look at.
This is basically my thought- my school is considering all sorts of things to combat AI cheating, and most of them scale poorly. You can implement them and increase your reputation, but your tuition will have to increase to pay for a lower student/teacher ratio.
So we’re probably going to see a split- mass production schools that still act as job training for Industrial Revolution era jobs will keep doing what they are doing. Schools that want to keep their academic prestige will implement these inefficient safeguards and raise prices accordingly. Employers will distinguish between these two types of diplomas when there are enough options out there that are combatting cheating- much like in-person schools are generally valued higher than online educations, or how certain schools (MIT, Stanford) are valued higher then others. This might lead us to the transformation of higher education many have been anticipating, because the current system cannot remain as-is. We all thought technology was the thing causing the upheaval but AI has really accelerated things.
This has been obvious to everyone who knows that the key feature to the devs of these browsers is “-based.” None of the chromium-based browsers are anything other than UI tweaks and certain extensions being included by default. None of them rewrite significant parts of the browser engine, because that would be too much extra work.
The only projects which have a hope of maintaining MV2 compatibility are those who are a hard fork of an earlier version of chromium or a totally different engine.
To my point: The article mentions that Opera still says they will maintain MV2 for as long as it is “technically reasonable.” Mark my works, Google will find a way to break Opera’s MV2 patches that makes it too hard to maintain.
Such a cop-out. Dario, you got in the news because you were trying to say that Moonshot did something wrong by distilling Claude. You got in the news because you were trying to effectively make a "rules for thee but not for me" when you try to claim that you can train on whatever pirated works without any permission from the creators, but when someone uses your "work" to train without permission then all of a sudden it's a moral injustice. You can't have both.
Saying, "I'm not actually against open-weights, I'm against distillation" isn't addressing what made people mad. You're still trying to do some "rules for thee but not for me" nonsense and hiding behind some technicality. Trying to get the US government on your side to hold back your Chinese competition. If you had wanted the US government to support you, you should have let them make autonomous killer robots with Claude brains. They aren't going to help you, you didn't help them.
Just to be clear, I think that it is possible that literally everyone involved in this is full of crap and nobody is good. Dario and Anthropic are full of crap, for the reasons previously stated. The US government is full of lots of crap and should not be trying to make autonomous killer robots (not ever, but especially not when the bar for a "good" AI is knowing how many Rs are in strawberry or whether you should drive to a car wash). OpenAI is full of crap by signing some support for open weights models and they haven't touched open weights in a year (GPT-OSS released on Aug 5 so basically a year with no news). Google is less full of crap about the open weights stuff because of Gemma 4, but they are full of crap for a zillion other things I can't exactly feel good about them. So everyone sucks.
So cheers to Moonshot and Qwen and whoever else. Distill as much as you can and give us cheaper AI. I have the sneaking suspicion that a bunch of my tax money went to OpenAI and Anthropic in some shady way or another, and I want it back. I'll take it in the form of an open weights model being distilled from the fat cat models.
> Google is less full of crap about the open weights stuff because of Gemma 4, but they are full of crap for a zillion other things I can't exactly feel good about them. So everyone sucks.
Out of all the companies that signed the open letter that Dario references, Google and OpenAI feel like they did it to poke at their competitor. I probably should have taken shots at Microsoft and Meta as well for not really supporting openness, but they don't feel like serious competition in the LLM space at the moment.
Google is actually kind of a sad story. They’re focusing their entire company on LLMs, their models lag behind open weights, they still somehow believe that they’re going to get to a place where they’ll have valuable IP and it’s becoming increasingly clear that there will be no such thing in the LLM space in the future. Greek tragedy.
Tl;dr: think hard about what you’d use this much RAM for in a desktop setting and do some research about how people like the 395 for your use case. Not all use cases work well.
Anyone who thinks they are going to serve some 100+ GB LLM locally, remember that memory bandwidth becomes a key limitation for large models. While you might be able to load a model, token generation can be very slow. MoE models like Qwen3.5-122B-A10B work decently fast, but dense models of a decent size are slow and you won’t want to use them.
I’ve got a 395 system, and found that I’m quite happy with Qwen3.6-35B-A3B, generating at around 50 t/s, but the dense 27B model is 20-25 t/s and that’s the lower limit I’m willing to tolerate. So a 70b dense model is just not going to happen. That means you can’t really use that much RAM.
A reasonable use case is to have multiple smaller models loaded- you can have an image generation model loaded along with the text model. Or you can use this computer for development simultaneously with serving LLMs. Those ideas work okay. But trying to load up a single giant model is going to test your patience.
> A reasonable use case is to have multiple smaller models loaded- you can have an image generation model loaded along with the text model.
Lemonade server updated yesterday for an Arena model / ai routing.
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The Lemonade local AI server allows easily deploying LLM workloads across Ryzen AI NPUs, Radeon GPUs, and CPUs with ease under both Windows and Linux. The most significant change with Lemonade 11.5 is the completion of the Lemonade Router that can be used for automatically routing queries to relevant models based on defined policies -- or even LLM-as-a-router for using a small LLM to in turn determine which model to route a particular request.
The Lemonade Router allows steering requests based on rule, classifier, semantic similarity, or LLM-as-router policies. The policies are defined in JSON files or can also be authored via the Lemonade GUI. More details on these router capabilities can be found via this commit.
Lemonade 11.5 also introduces a server-side job engine to let clients post multi-step recipes and managing them via /jobs endpoints. From these endpoints the muilti-step recipes can be paused / interrupted / resumed / deleted. And another one is that the lemond daemon can now act as an MCP client host to connect external stdio MCP servers.
Feels like they released this to ride the wave of press of GPT-5.6, Kimi K3, and Qwen 3.8. Doesn't feel like Google has much substance with this post except a bump in version and tweaked their pricing.
Another idea would be games that were all pictures of a single category- like 10 AI and 10 real pictures of basketballs, or food on plates, etc.
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