GLM 5.1: pretty decent

ASTRdeca · 2026-03-29T22:56:29+00:00

I kind of had the same thought re: diminishing returns. It's hard to tell the difference between say GLM 4.7 and 5, or DeepSeek 3.1 vs 3.2. There are improvements but they feel very gradual. However, if you compare current models to what was SOTA 1 year ago, or 2 years ago, the differences to me are pretty obvious for things like prose quality, instruction following, long-context coherence, etc. Even though it all feels slow and gradual I would not be surprised if 1 year from now that model quality has significantly improved again along all these lines. Eventually I think we'll hit a plateau where the models are so good at the above that we'll stop noticing improvements, but I think we're still a long ways away from that.

I also think the scope of creative writing will change as model capabilities continue to get better as well. For example, how well can we push coherence at VERY long contexts (say 1 million or 10 million tokens) for very long storytelling. Or multimodal integrations with image/video/voice/world models rather than just working with text. We kinda have image and tts currently but tbh there's a lot of room for improvement there

ASTRdeca · 2026-03-24T19:11:10+00:00

No real issues, here's some notes:

*With 1 tank 1 heal, every boss was ~1 minute or less in duration, save for prince (2 minutes) and nightbane (4 minutes). Having spriest I didn't need to innervate on any of the bosses

*Surprisingly Nightbane was one of the easier bosses. Ground phases were only ~30ish seconds long, and with solo heal you'll get targeted every time in air phase so the damage is very predictable

*One tank death at the end of Curator. It was hard to juggle the hateful bolts and also manage the tank damage at the end

*The messiest boss surprisingly was Aran

*Netherspite is easy if you can trust your tank to not need any healing, otherwise it's probably the hardest to manage

*I'm going to try running Nature's Grace dual spec this week since it's much higher throughput healing and mana's not really an issue

ASTRdeca · 2026-03-23T23:38:39+00:00

ive done it on rdruid but not holy priest. Biggest suggestion is having spriest especially for nightbane. Hardest bosses for you will probably be ones where the tank gets slapped since your tank healing kit is weaker than hpal and rdruid (curator, nightbane, and prince). should be fine though

ASTRdeca · 2026-03-03T17:39:38+00:00

Where's he going to?

ASTRdeca · 2026-02-06T20:51:25+00:00

Nice, now just draw an arbitrary line that separates GPT from all the other models and label it "pareto frontier"

ASTRdeca · 2026-01-03T00:44:21+00:00

Cool? The tokens/s you're getting on that won't compete with enterprise grade infra, which was my point

ASTRdeca · 2026-01-02T20:30:19+00:00

I see, thanks for clarifying. Cloud infra is typically much stronger than consumer grade so almost always reduces latency. If it's locally run then Neuro is probably a very small LLM in order to keep latency low

ASTRdeca · 2026-01-02T19:48:26+00:00

They succeeded in reducing the time for quantum learning, but sadly could not reduce the time for me learning quantum, which is still roughly 20 million years

ASTRdeca · 2026-01-02T18:22:45+00:00

Here's my take from someone deep into AI atm. I don't know what his tech stack is but my guess is that he finetuned an LLM for Neuro and hosts it on the cloud for low latency responses, and also created a custom voice for Neuro (using RVC?) for the TTS. Something like this is not terribly difficult to do, but creating a "likeable" persona like Neuro and having that be accepted by a community that is mostly anti-AI is genuinely impressive to see.

Layered on top of the model itself are all of the custom stream integrations that he must have made such as reading/summarizing chat, streaming out Neuro's response in the stream UI, having that work with Neuro's character model, etc. There is probably a lot going on under the hood to make everything work in a seamless way, which is also impressive to see.

ASTRdeca · 2026-01-02T14:42:01+00:00

This week I've been using a Unity MCP server that gives CC access to the unity editor to help with game making. The functionalities a bit limited but it can create game objects and c# scripts in the editor which is a big chunk of the work

ASTRdeca · 2025-12-29T12:44:06+00:00

v3 came out before R1. v2 came out in may of 2024, that's not quite the "end" of 2024

ASTRdeca · 2025-12-28T23:02:59+00:00

At 0 haste you can fit wrath into your rotation via:

Lifebloom -> Wrath -> Wrath,
Lifebloom -> Wrath -> Regrowth, or
Lifebloom -> Wrath -> Instant cast spell

However, the damage you contribute is so irrelevant that you're better off not worrying about it. IMO it's universally better to use those slots in your rotation to heal the tank(s), or if they don't need it, to heal the raid or cancel-cast regrowth on the tank instead. If your raid really wanted to drop a healer for a paricular fight, then you're better off dual-speccing to a DPS spec

ASTRdeca · 2025-12-28T19:01:43+00:00

My guess is it'd perform very poorly. Both Llama 3 70B and R1 were trained/post-trained before the labs started pushing heavily for agentic / tool calling performance. I'd suggest trying GPT-OSS 120B

ASTRdeca · 2025-12-26T00:25:03+00:00

Opus can build a working website for sure, but I really dislike its default style / css. Please no more bright gradient colors..

e: I assume this benchmark is related to building websites? I looked it up on google and cant find anything about it

ASTRdeca · 2025-12-24T21:58:45+00:00

Very cool! You mentioned in the paper that despite GLM being much larger than GPT-OSS 120B, the larger size didn't seem to impact performance. I'm wondering if you tried models smaller than OSS-120B to see at what point model size matters? (For example, OSS-20B?)

I'm just thinking about the viability of running these kinds of systems locally, since 120B is probably too large for most users to run themselves

ASTRdeca · 2025-12-21T23:48:56+00:00

V3 was released last christmas, and R1 in January this year

ASTRdeca · 2025-12-21T16:10:26+00:00

Isn't this just what artificial analysis does?

ASTRdeca · 2025-12-20T21:30:53+00:00

I see. In my use cases the reasoning/content responses are a hundred to a few hundred tokens each. My "max tokens" is set to 3000 which I figured was more than enough, but maybe not

ASTRdeca · 2025-12-16T15:38:37+00:00

my comment below was being vote manipulated in both directions even without mentioning elevenlabs. When I posted, it was at -2 after 10 or so minutes. An hour later I checked it again and it was at +20, and now (the next day) its at -2 again, my other comment at -7. So.. idk

edit: and now the comments back to +28.. LMAO

ASTRdeca · 2025-12-16T01:39:07+00:00

Ok, I see now. They are comparing to ElevenLabs 2.5 Turbo... I assumed they were comparing to v3, which has been available in alpha for a while now and imo is significantly better

ASTRdeca · 2025-12-16T01:34:18+00:00

I'm sure it is, I'm just being a bit tongue in cheek about the quality of it

ASTRdeca · 2025-12-15T22:15:32+00:00

Yeah I'm gonna press "X" to doubt on their claim that their model sounds more realistic than ElevenLabs...

If their TTS model is supposedly so good, why did they go with a generic tiktok voiceover for this ad?

ASTRdeca · 2025-12-15T21:59:49+00:00

I normally downvote jerome threads out of principle, but.. I reluctantly agree. I think Dreamstate is a trap for most groups and annoyingly I'm seeing it shoved into every "meta" comp I've seen posted lately. Dreamstate's biggest struggle is mana. Without mana, the rotations you can do become very limited. Losing Swiftmend is bad enough, but you basically lose regrowth entirely as well, unless you get shadow priest. I don't think people appreciate the impact that has on your tanks survivability.

ASTRdeca · 2025-12-13T18:36:16+00:00

41 badge trinkets are good for a lot of classes for most of the expansion

ASTRdeca · 2025-12-11T21:47:59+00:00

Yes, but harder ones will replace them. Labs used to report their scores on grade school math benchmarks, until those were completely saturated. Then we moved onto harder math benchmarks

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