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# ![@hillbig Avatar](https://lunarcrush.com/gi/w:26/cr:twitter::12483052.png) @hillbig Daisuke Okanohara / 岡野原 大輔

Daisuke Okanohara / 岡野原 大輔 posts on X about oneyear, karpathy, future of, gpu the most. They currently have XXXXXX followers and XX posts still getting attention that total XXXXX engagements in the last XX hours.

### Engagements: XXXXX [#](/creator/twitter::12483052/interactions)
![Engagements Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::12483052/c:line/m:interactions.svg)

- X Week XXXXXX +26%
- X Month XXXXXX +146%
- X Months XXXXXXX +140%
- X Year XXXXXXX +83%

### Mentions: X [#](/creator/twitter::12483052/posts_active)
![Mentions Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::12483052/c:line/m:posts_active.svg)


### Followers: XXXXXX [#](/creator/twitter::12483052/followers)
![Followers Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::12483052/c:line/m:followers.svg)

- X Week XXXXXX +0.81%
- X Month XXXXXX +2.30%
- X Months XXXXXX +7.50%
- X Year XXXXXX +14%

### CreatorRank: XXXXXXX [#](/creator/twitter::12483052/influencer_rank)
![CreatorRank Line Chart](https://lunarcrush.com/gi/w:600/cr:twitter::12483052/c:line/m:influencer_rank.svg)

### Social Influence [#](/creator/twitter::12483052/influence)
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**Social topic influence**
[oneyear](/topic/oneyear) 7.69%, [karpathy](/topic/karpathy) #69, [future of](/topic/future-of) 7.69%, [gpu](/topic/gpu) 7.69%, [cb](/topic/cb) XXXX%
### Top Social Posts [#](/creator/twitter::12483052/posts)
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Top posts by engagements in the last XX hours

"Second-order optimization methodssuch as SOAP and Muonhave recently regained attention for training large language models (LLMs). While these approaches are inherently approximate due to computational constraints they conducted experiments using a GaussNewton (GN) preconditioned second-order optimizer on a 150M-parameter model trained on up to 3B tokens to examine their theoretical underpinnings and practical limitations. Traditionally second-order optimization methods have been criticized for their susceptibility to local minima and divergent behavior. However decomposing the Hessian matrix"  
[X Link](https://x.com/hillbig/status/1978246902622965780) [@hillbig](/creator/x/hillbig) 2025-10-14T23:49Z 36.1K followers, 3829 engagements


"The Potential of Second-Order Optimization for LLMs: A Study with Full Gauss-Newton:"  
[X Link](https://x.com/hillbig/status/1978243812926382475) [@hillbig](/creator/x/hillbig) 2025-10-14T23:37Z 36.2K followers, 3983 engagements


"In his 2.5-hour interview Andrej Karpathy offers numerous thought-provoking insights into the future of AI its current challenges and potential paths forward. While many summaries have already circulated Ive compiled and commented on some particularly noteworthy points. Karpathy argues that AI agents wont reach the competency level of human interns or full-time employees within a one-year horizon. Instead such progress will likely take at least a decade. Current models still lack many of the essential capabilities required for practical deployment. X. The lack of Continuous Learning Modern"  
[X Link](https://x.com/hillbig/status/1980058216466940272) [@hillbig](/creator/x/hillbig) 2025-10-19T23:47Z 36.2K followers, 4638 engagements


"Andrej Karpathy AGI is still a decade away:"  
[X Link](https://x.com/hillbig/status/1980058298218160356) [@hillbig](/creator/x/hillbig) 2025-10-19T23:47Z 36.2K followers, 3204 engagements


"Cautious Weight Decay:"  
[X Link](https://x.com/hillbig/status/1980390162090877358) [@hillbig](/creator/x/hillbig) 2025-10-20T21:46Z 36.2K followers, 2171 engagements


"Cautious Weight Decay selectively applies decay only when the update direction aligns with the parameters sign. This selective mechanism eliminates bias in the original objective function while steadily reducing parameter magnitudes within the set of gradient-zero points"  
[X Link](https://x.com/hillbig/status/1980390512835350842) [@hillbig](/creator/x/hillbig) 2025-10-20T21:47Z 36.2K followers, 2504 engagements


"Predictable scaling laws have not yet described the effectiveness of RL in LLM. In this study using over 400000 GPU hours of experiments the authors succeeded in modeling both the computational requirements and performance of RL with a sigmoidal scaling function: R_C = R_0 + (A - R_0) / (1 + (C_m / C)B) Here R_C represents model performance (reward) at a given computational cost C R_0 is the initial performance before RL begins A is the maximum achievable performance B represents learning efficiency and C_m is the midpoint cost where performance reaches half of its maximum. This framework"  
[X Link](https://x.com/hillbig/status/1978958908921368806) [@hillbig](/creator/x/hillbig) 2025-10-16T22:59Z 36.2K followers, 4476 engagements


"Andrej-Karpathy AGI is still a decade away:"  
[X Link](https://x.com/hillbig/status/1980054622376255512) [@hillbig](/creator/x/hillbig) 2025-10-19T23:33Z 36.2K followers, 6432 engagements


"Cautious Weight Decay:"  
[X Link](https://x.com/hillbig/status/1980390556237983776) [@hillbig](/creator/x/hillbig) 2025-10-20T21:47Z 36.2K followers, 2167 engagements