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Kendrick Lamar and Drake: The Rap Battle of the Decade In the world of hip-hop, few rivalries have captured the attention of fans and critics alike as much as the ongoing battle between Kendrick Lamar and Drake. This feud has become the stuff of legend, with both artists dropping diss tracks that are as clever as they are cutting, showcasing their lyrical prowess and deep understanding of the

(kendrick, drake, the, to, is, and, lamar, kendrick lamar)

Best tweets on this topic

omie

@123omie
May 06, 2024

drake really sat in the studio for 24 hours just to say "if im a liar... why arent my pants on fire?"



Kendrick and Drake really the Messi and Ronaldo of rap… Kendrick is short and Drake is a rapist



Kendrick Lamar reveals Drake has been hiding an 11 year old daughter in ‘Meet The Grahams’ “He should've been in your life // instead he in Turks paying for s*x and popping percs, I want to tell you that you're loved, you’re brave, you’re kind” “His father never claimed him neither, history repeats himself”


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Kids and Their School Life

(kids, to, the, their, is, and, school, in)

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I wanna propose a bill that keeps children off social media until the age of 18. Parents can’t post their kids either.



African parents will use Nicolas Jackson as a moral lesson for the reason they keep telling you to barb lowcut. 😂😂



So Norway banned smartphones in schools and 3 years later, girls’ GPAs are up, their visits to mental health professionals are down 60% and bullying in both boys and girls is down 43-46%. That’s wild


AI Integration and Evolution in Technology and Society The landscape of artificial intelligence (AI) is rapidly evolving, influencing various aspects of technology and daily life. From Apple's potential AI advancements in iPhone and iOS to the challenges with Siri, the journey of AI integration presents a mixed bag of progress and hurdles. The historical CUDA/C++ origins of Deep Learning, highlighted by the ImageNet/AlexNet breakthrough in

(ai, the, to, and, of, for, is, on)

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We trained a robot dog to balance and walk on top of a yoga ball purely in simulation, and then transfer zero-shot to the real world. No fine-tuning. Just works. I’m excited to announce DrEureka, an LLM agent that writes code to train robot skills in simulation, and writes more code to bridge the difficult simulation-reality gap. It fully automates the pipeline from new skill learning to real-world deployment. The Yoga ball task is particularly hard because it is not possible to accurately simulate the bouncy ball surface. Yet DrEureka has no trouble searching over a vast space of sim-to-real configurations, and enables the dog to steer the ball on various terrains, even walking sideways! Traditionally, the sim-to-real transfer is achieved by domain randomization, a tedious process that requires expert human roboticists to stare at every parameter and adjust by hand. Frontier LLMs like GPT-4 have tons of built-in physical intuition for friction, damping, stiffness, gravity, etc. We are (mildly) surprised to find that DrEureka can tune these parameters competently and explain its reasoning well. DrEureka builds on our prior work Eureka, the algorithm that teaches a 5-finger robot hand to do pen spinning. It takes one step further on our quest to automate the entire robot learning pipeline by an AI agent system. One model that outputs strings will supervise another model that outputs torque control. We open-source everything! Welcome you all to check out the paper, more videos, and try the codebase today: https://t.co/RwiBT3z78H Code: https://t.co/ERp4Gl0N36



we are aware there are some unofficial rabbit OS app/website emulators out there. we understand the passion that people have to get a taste of our AI and LAM instead of waiting for their r1 to arrive. that being said, to clear any misunderstanding and set the record straight, rabbit OS and LAM run on the cloud with very bespoke AOSP and lower level firmware modifications, therefore a local bootleg APK without the proper OS and Cloud endpoints won’t be able to access our service. rabbit OS is customized for r1 and we do not support third-party clients. using a bootlegged APK or webclient carries significant risks; malicious actors are known to publish bootlegged apps that steal your data. for this reason, we recommend that users avoid these bootlegged rabbit OS apps.


Andrej Karpathy

@karpathy

# CUDA/C++ origins of Deep Learning Fun fact many people might have heard about the ImageNet / AlexNet moment of 2012, and the deep learning revolution it started. https://t.co/2xjLWODMOf What's maybe a bit less known is that the code backing this winning submission to the contest was written from scratch, manually in CUDA/C++ by Alex Krizhevsky. The repo was called cuda-convnet and it was here on Google Code: https://t.co/ch137VSYZ4 I think Google Code was shut down (?), but I found some forks of it on GitHub now, e.g.: https://t.co/zYhzdUxoEN This was among the first high-profile applications of CUDA for Deep Learning, and it is the scale that doing so afforded that allowed this network to get such a strong performance in the ImageNet benchmark. Actually this was a fairly sophisticated multi-GPU application too, and e.g. included model-parallelism, where the two parallel convolution streams were split across two GPUs. You have to also appreciate that at this time in 2012 (~12 years ago), the majority of deep learning was done in Matlab, on CPU, in toy settings, iterating on all kinds of learning algorithms, architectures and optimization ideas. So it was quite novel and unexpected to see Alex, Ilya and Geoff say: forget all the algorithms work, just take a fairly standard ConvNet, make it very big, train it on a big dataset (ImageNet), and just implement the whole thing in CUDA/C++. And it's in this way that deep learning as a field got a big spark. I recall reading through cuda-convnet around that time like... what is this :S Now of course, there were already hints of a shift in direction towards scaling, e.g. Matlab had its initial support for GPUs, and much of the work in Andrew Ng's lab at Stanford around this time (where I rotated as a 1st year PhD student) was moving in the direction of GPUs for deep learning at scale, among a number of parallel efforts. But I just thought it was amusing, while writing all this C/C++ code and CUDA kernels, that it feels a bit like coming back around to that moment, to something that looks a bit like cuda-convnet.


Trading Insights and Wisdom for Aspiring Traders

(trading, you, your, the, and, to, trader, on)

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Once you finally find your model... Trading will feel like an illegal money printer. You know when to execute, and when to sit back. You know what to look for, and when to look for it. You know how much to risk, and on which accounts. Cheatcode.



I made $100k in April trading. You want to know what I did? - I took 1 setup ONLY for 30 days. - I traded the same 1-3 tickers all month. - I took 2 trades on average daily. - I followed my plan for every trade. That’s it. Stop over complicating it, simplicity is key.



If you want to get rich investing, follow this mind-blowingly simple rule: Buy low and sell high. Sounds obvious, yet so many chase highs and ignore the lows. It doesn't get any simpler than this.


How to Gain Followers and Make an Impact on Social Media

(followers, to, the, you, my, in, for, days)

Best tweets on this topic


3000 followers... I must be dreaming! I started building in late November. Made over $10K by March. And now I have 3K beautiful people. Believing in me. To anyone who thinks it can’t happen, it can. From no likes. To lots of likes. No followers. To amazing followers. No money. To good money. In 5 short months. Thanks to each of you. Feeling incredibly lucky today.



How to get 100,000 followers in 90 days: pic.twitter.com/t79CgAWfrm



impact ≠ going number 1 on spotify impact = touching the lives of millions of fans w their music enough to mobilize them for good causes, cementing their legacy as artists who inspire change in their fans, strenghtening community & solidarity between fans from across the world



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