Post
141
Wow, the new Gemini Pro climbed really fast, after just one month. The inference is quite fast too.
onekq-ai/WebApp1K-models-leaderboard
onekq-ai/WebApp1K-models-leaderboard
ONEKQ AI
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1 day ago
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330
I'm now testing the new 🐋DeepSeek🐋 R1 and like all reasoning models, it's awfully slow. 🐢🐢
I don't expect it to break SOTA. In fact, it will be a win if it beats the old R1, which already stands very high in the leaderboard.
onekq-ai/WebApp1K-models-leaderboard
IMO the world needs a better vanilla LLM, e.g. 🐋DeepSeek🐋 v4 or v3.5, which we will use in daily life. That's the direction Gemini Flash took which I praised.
I don't expect it to break SOTA. In fact, it will be a win if it beats the old R1, which already stands very high in the leaderboard.
onekq-ai/WebApp1K-models-leaderboard
IMO the world needs a better vanilla LLM, e.g. 🐋DeepSeek🐋 v4 or v3.5, which we will use in daily life. That's the direction Gemini Flash took which I praised.
Post
2198
🎉🥳 SOTA!!! 🚀👑
🥇 Claude 4 Opus !!🥇
7 months!! ⌛⌛
I thought the day would never come. But here it is.
onekq-ai/WebApp1K-models-leaderboard
Cost me quite a bit of 💵money 💵 but it is all worth it.
Enjoy and make out of this as much as you can!
🥇 Claude 4 Opus !!🥇
7 months!! ⌛⌛
I thought the day would never come. But here it is.
onekq-ai/WebApp1K-models-leaderboard
Cost me quite a bit of 💵money 💵 but it is all worth it.
Enjoy and make out of this as much as you can!
- 4 replies
Post
2193
Highly recommend the latest Gemini Flash. My favorite Google I/O gift. It ranks behind reasoning models but runs a lot faster than them. It beats DeepSeek v3.
onekq-ai/WebApp1K-models-leaderboard
Reasoning is good for coding, but not mandatory.
onekq-ai/WebApp1K-models-leaderboard
Reasoning is good for coding, but not mandatory.
- 1 reply
Post
477
Hmm,
codex-mini is a finetuned version of o4-mini, but on my leaderboard it performs worse than its base.
onekq-ai/WebApp1K-models-leaderboard
codex-mini is a finetuned version of o4-mini, but on my leaderboard it performs worse than its base.
onekq-ai/WebApp1K-models-leaderboard
onekq
authored
a
paper
23 days ago
Post
943
This paper introduced the notion of "Tests as Prompt". It compiled results and findings of WebApp1K published in previous three papers.
https://huggingface.co/papers?q=2505.09027
The central argument here is that test-driven development is a natural fit to LLMs, which scale better than humans. I bet the future will see thousands of such leaderboards (many more proprietary ones), each dominated by a specialized model.
https://huggingface.co/papers?q=2505.09027
The central argument here is that test-driven development is a natural fit to LLMs, which scale better than humans. I bet the future will see thousands of such leaderboards (many more proprietary ones), each dominated by a specialized model.
Post
460
If you also tuned into Altman's second congress hearing (first in 2023) along with other AI executives, my takeaway is two words: New Deal (by FDR almost a century ago).
The causal link is quite fascinating and worthy of a few blogposts or deep research queries, but I won't have more time for this (I really wish so), so here goes.
* AI workload loves GPUs because they allocate more transistors than CPUs for computing, and pack them by high-bandwidth memory
* More computing in the small physical space -> more power draw and more heat dissipation
* more heat dissipation -> liquid cooling
* new cooling and heavier power draw -> bigger racks (heavier and taller)
* bigger racks -> (re)building data centers
* new data centers with higher power demand (peak and stability) -> grid update and nuclear power
The causal link is quite fascinating and worthy of a few blogposts or deep research queries, but I won't have more time for this (I really wish so), so here goes.
* AI workload loves GPUs because they allocate more transistors than CPUs for computing, and pack them by high-bandwidth memory
* More computing in the small physical space -> more power draw and more heat dissipation
* more heat dissipation -> liquid cooling
* new cooling and heavier power draw -> bigger racks (heavier and taller)
* bigger racks -> (re)building data centers
* new data centers with higher power demand (peak and stability) -> grid update and nuclear power
Post
2279
The new Mistral medium model is very impressive for its size. Will it be open sourced given the history of Mistral? Does anyone have insights?
onekq-ai/WebApp1K-models-leaderboard
onekq-ai/WebApp1K-models-leaderboard
Post
3279
This time Gemini is very quick with API support on its 2.5 pro May release. The performance is impressive too, now it is among top contenders like o4, R1, and Claude.
onekq-ai/WebApp1K-models-leaderboard
onekq-ai/WebApp1K-models-leaderboard
Post
605
Okay, Grok 3 has API support too, and beats Gemini 2.5, but is behind QwQ 32b and DeepSeek v3
onekq-ai/WebApp1K-models-leaderboard
onekq-ai/WebApp1K-models-leaderboard
Post
1753
I didn't noticed that Gemini 2.5 (pro and flash) has been silently launched for API preview. Their performance is solid, but below QwQ 32B and the latest DeepSeek v3.
onekq-ai/WebApp1K-models-leaderboard
onekq-ai/WebApp1K-models-leaderboard
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- 2 replies
Post
1798
I tested Qwen3 235b and 32b and they are both worse than Qwen2.5 32b.
onekq-ai/WebApp1K-models-leaderboard
I used non-thinking mode because the thinking mode is too slow 🐢🐢🐢 to be usable in any way.
Sigh ...
onekq-ai/WebApp1K-models-leaderboard
I used non-thinking mode because the thinking mode is too slow 🐢🐢🐢 to be usable in any way.
Sigh ...
- 12 replies
Post
495
The Qwen3 235B (MoE) is awfully slow 🐢🐢🐢.
I heard it is able to switch between reasoning and non-reasoning, but for my question, it always goes straight to the reasoning mode without an override switch. I tried Fireworks, DeepInfra, and OpenRouter, and they are all the same.
What is your experience with Qwen3?
I heard it is able to switch between reasoning and non-reasoning, but for my question, it always goes straight to the reasoning mode without an override switch. I tried Fireworks, DeepInfra, and OpenRouter, and they are all the same.
What is your experience with Qwen3?
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- 2 replies
Post
2003
AxB stand for Approximately xB or Activating xB (for a Mixture-of-Expert model), this is really interesting naming 😅
Qwen/Qwen3-235B-A22B
Qwen/Qwen3-30B-A3B
Qwen/Qwen3-235B-A22B
Qwen/Qwen3-30B-A3B
- 1 reply
Post
2014
I've recently attended a panel on AI applications. The panelists are managers/directors of Fortune 500 companies. These people make things happen and own results, so their stories and pain points are fresh.
(1) Models are used EVERYWHERE, customer facing and internal support, etc.
(2) A successful application must improve one of the following: revenue (💵💵), cost (💵💵), CSAT (still 💵💵)
(3) They proactively search on 🤗HF🤗 for models and use them. Open source models (especially small ones) can flexibly fit into their existing workflows/infras, which enable them to deliver, and fast.
(4) The main barrier for adoption is license. A director told me they picked a model and finetuned it, then learned they would have to share enhancements. As a result, they dropped this model and the million dollar impact went to another model.
So to fellow model builders:
(1) celebrate that our work is useful and generate lots of values
(2) make your license permissive if you want maximum impact
(1) Models are used EVERYWHERE, customer facing and internal support, etc.
(2) A successful application must improve one of the following: revenue (💵💵), cost (💵💵), CSAT (still 💵💵)
(3) They proactively search on 🤗HF🤗 for models and use them. Open source models (especially small ones) can flexibly fit into their existing workflows/infras, which enable them to deliver, and fast.
(4) The main barrier for adoption is license. A director told me they picked a model and finetuned it, then learned they would have to share enhancements. As a result, they dropped this model and the million dollar impact went to another model.
So to fellow model builders:
(1) celebrate that our work is useful and generate lots of values
(2) make your license permissive if you want maximum impact
- 1 reply
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820
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- 6 replies
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432
This post discussed the same trend as the Sutton post, but is more concrete and down-to-earth.
https://ysymyth.github.io/The-Second-Half/
Two takeaways for me. (1) deep neural network is the backbone to unify everything. RLHF will stand the test of time because it brings two distinct fields (NLP and RL) onto the same model weights. (2) language model will continue to play a central role in the era of agent. It probably won't be the end game to AGI, but definitely not offramp.
https://ysymyth.github.io/The-Second-Half/
Two takeaways for me. (1) deep neural network is the backbone to unify everything. RLHF will stand the test of time because it brings two distinct fields (NLP and RL) onto the same model weights. (2) language model will continue to play a central role in the era of agent. It probably won't be the end game to AGI, but definitely not offramp.