whats

专业意淫你的女友，老婆，闺蜜或者本人，想听听别人的意淫和评价吗，私信我照片哦

[whats @jraap10]

正确的私信方式：
需要提供被意淫对象和你的关系：本人 女友 老婆 闺蜜等
至少需要一张生活照，私密照可有可无

#崇韩 #媚外 #绿帽奴 #羞辱 #辱骂 #调教 #反差 #盯射 #配字 #文 #阳痿 #废物 #意淫 #贱狗 #射精控制 #榨精 #媚黑 #出卖 #曝光

[trish @TrisH0x2A]

Doom ran on a 486 with 4MB of RAM in 1993

the entire world was rendered using a binary space partition tree

John Carmack built the BSP when the level loaded not every frame

the tree split the map into regions and stored the draw order ahead of time

so during rendering the engine just walked the tree instead of figuring visibility from scratch

a BSP node divides space into front and back

if the player is in front render the front subtree first

if behind render the back subtree first

the tree already knows what should appear first

this is why Doom did not need a z buffer

correct visibility came from the BSP traversal itself

[Vintage @xvxvintage]

【这么漂亮的东北御姐，一开口说话我也有点软了】偷偷摘套玩脱了，没有忍住直接射进去了，女主摸一下逼发现精液，居然还没有生气，这性格真的是太豪爽了吧！
#后入御姐 #精选 #顶级名媛

[祸池鱼🐟 @Huochiyu_]

再次找去年在潮玩店抓娃娃认识的07黄头发眼镜妹第二篇
这次喝了点酒后整个人变得没有那么害羞了。做的时候不断地在说 我的水真的好多，我是一个省钱小能手，因为我不需要用润滑油🥵
完整版30min已更新🎬

@Xiaoxiaofoer
@Xiaoxiaoyyii
@Xiaoxiaoyyii
@TaoHuaBang

[bbshare @shadouyoua]

Anthropic官方团队亲自演示了，到底该怎么正确给Claude写提示词。

全程只用24分钟，而且完全免费，还是由Claude的开发者亲自讲解。

一定要看完这场工作坊，记得收藏起来。

[豆豆 @18plsadult]

【卧槽我第一次见这么猛的女主，女上骑乘这么扭谁受得了啊】三位顶级骚货母狗，第一次见女上位这么牛逼的！还要m类型母狗用胸罩遮住眼睛，跪求主人下次买个好看的眼罩！还有一开始抗拒拍摄后面被干喷水了！
#精选 #女上位 #高潮

[女孩子的快乐 @nvhaizikuaile]

诱导相处七年的清纯女闺蜜给我舔脚当女奴、从拒绝到下跪尝试闻舔，再到被辱骂抽耳光，按着头用脚草嘴、好几次都快玩哭了！表面抗拒，内心应该都爽死了...
#女女 #女女调教 #舔脚 #女同 #女sub #女主女奴 #素人开发 #女虐女 #调教骚货 #女m #母狗 #les #调教 #高潮

[炼金叔叔 @AirdropAlchemis]

一个很有意思的实验：

Signs让150多个人凭记忆画苹果、宜家、阿迪达斯品牌Logo

结果证明，人类对品牌的记忆大概是：

[Manning Publications @ManningBooks]

SAVE 45%, only on April 28th!

Today, you can SAVE on Rearchitecting LLMs by @PereMartra and other related titles: https://t.co/cVTSmP0By8

As you work through this practical book, you’ll perform hands-on surgery on popular open-source models like Llama-3, Gemma, and Qwen to create cost-effective local small language models (SLMs). Along the way, you’ll learn how to combine behavioral analysis with structural modifications, identifying and removing parts that don’t contribute to your model’s goals, and even use “fair pruning” to reduce model bias at the neuron level.

[Manning Publications @ManningBooks]

Save 45%, only on April 19th!

Today, you can save on CUDA for Deep Learning by @elliotarledge and other related titles: https://t.co/GWvojERUZo

Written for the latest NVIDIA hardware, the book builds a deep understanding of CUDA (Compute Unified Device Architecture) fundamentals that will stay relevant as chips upgrade and evolve. CUDA delivers direct control, debugging power, and acceleration at the GPU level that can’t be matched by other types of optimizations.

[ani @anirudhbv_ce]

Notes from last week on @nvidia's Groq 3 LPX system architecture.

- Attention-FFN disaggregation
- Tensor, Pipeline and Data parallelism across the LPX and GPUs
- Kernel design patterns (2D grid-based persistent kernels applied on a SWA kernel)
- Practiced hand-written cuTile CUDA kernels
- Made a PR for TileGym collection

[Tech with Mak @techNmak]

This is the best way to learn how LLMs work.

Interactive. 3D. Step-by-step.

Covers:
→ Embedding
→ Layer Norm
→ Self-Attention
→ MLP
→ Transformer layers
→ Softmax
→ Output

Stop reading papers. Start seeing.

Link in comments.

Save this immediately.

[曹长卿 joker @alexglare2]

【91曹长卿原创】 女帝 身高178 唱歌跳舞全能

我最喜欢的类型 天生性感尤物
而且任何姿势都可以自己动 让男人欲罢不能啊

哈哈哈哈哈

[玖芊原创 @JQYC250213]

这一期的小姐姐身材特别好，看着就让人欲罢不能；反应和怕痒程度也特别到位，后面甚至直接挠哭了！特别优秀的一部

[Manning Publications @ManningBooks]

Python still underpins how we understand and build modern AI systems.

@rasbt's April 14th keynote at @PyConDE will cover the full journey — from model design and training loops to scaling across devices, and why Python still holds up even as backends evolve.

Details: https://t.co/Itysj2Lelt

Explore his work: https://t.co/GuyVqD9LMf

Virtual tickets available via the main conference page.

[Amit Shekhar @amitiitbhu]

Learn LLM internals step by step - from tokenization to attention to inference optimization.

https://t.co/ZtnNfr446w

[Amit Shekhar @amitiitbhu]

Paged Attention borrowed an old idea from operating systems and solved a new problem in LLMs.

Operating systems solved memory fragmentation decades ago with paging.

LLMs had the exact same problem with KV Cache. Same solution.

[查小理 @ByCha22112]

天花板了！到顶了！
勾引理发店老板 手法真不错啊👍
母狗太优秀了 NPC也很会玩

玩母狗 认准查小理By.Cha😈