Jensen Hung of $nvda having hot pot dinner with $TSM CEO in Taiwan following fried chicken dinner with CEOs of Samsung and Hyundai in Korea

I hate how Material UI made all websites look the same, I miss the good old bootstrap, you kids can't even imagine how good it was in the old days

using Promise.all feels so satisfying for some reason

don't leave babe, I'll find next critical soon

"Drop Flutter, try React Native"

#1 sign he’s a 10x Software Engineer:

what is the best Mexican food in the Bay Area?

Hey @X algorithm, #CONNECT with people who have interest in: - Web-Dev - FrontEnd - React/Next.js - Backend - San Francisco - Gen AI - AI - ML - Deepseek/Kimi - Software Development Let’s connect 🤝 and grow together!

database migration worked first try in production

this is low-key how you train a junior developer..

Never gave a fvck about hair loss.

POV: You reached endgame as a developer

China saved opensource LLMs and not only that, it made the FRONTIER opensource between July 16th and today, these are the major releases: > Kimi-K2-Thinking (1T-A32B) > MiniMax M2 > DeepSeek V3.2 > GLM-4.6 (335B-A32B) > Qwen3-VL-30B-A3B (Instruct & Thinking) > Qwen3-VL-235B-A22B (Instruct & Thinking) > Qwen3-Next 80B-A3B (Instruct & Thinking) > GLM-4.5V (VLM, 106B-A12B) > DeepSeek V3.1 > Doubao 1.6-Vision (multimodal, tool-calling) > Doubao Translation 1.5 (ByteDance, 28 Languages) > ERNIE X1.1 (Baidu, Reasoning) > Hunyuan-MT-7B & Chimera-7B (Tencent Translation Specialists) > MiniCPM-V 4.5 (8B), Tiny but GPT-4o-level VLM > InternVL 3.5 (MASSIVE Multimodal Family of Models, 1B to 241B Sizes) > Step-3 (VLM, 321B/38B) > SenseNova V6.5 (SenseTime, Multimodal) > GLM-4.5 Air (Base & Instruct, 106B-A12B) > GLM-4.5 (Base & Instruct, 335B-A32B) > Qwen 3-Coder-30B-A3B (Instruct & Thinking) > Qwen3-Coder-480B-A35B (Instruct & Thinking) > Qwen3-30B-A3B-2507 (Instruct & Thinking) > Qwen3-235B-A22B-2507 (Instruct & Thinking) > Kimi K2 (1T-A32B) US & EU need to do better

MoonshotAI has released Kimi K2 Thinking, a new reasoning variant of Kimi K2 that achieves #1 in the Tau2 Bench Telecom agentic benchmark and is potentially the new leading open weights model Kimi K2 Thinking is one of the largest open weights models ever, at 1T total parameters with 32B active. K2 Thinking is the first reasoning model release within @Kimi_Moonshot's Kimi K2 model family, following non-reasoning Kimi K2 Instruct models released previously in July and September 2025. Key takeaways: ➤ Strong performance on agentic tasks: Kimi K2 Thinking achieves 93% in 𝜏²-Bench Telecom, an agentic tool use benchmark where the model acts as a customer service agent. This is the highest score we have independently measured. Tool use in long horizon agentic contexts was a strength of Kimi K2 Instruct and it appears this new Thinking variant makes substantial gains ➤ Reasoning variant of Kimi K2 Instruct: The model, as per its naming, is a reasoning variant of Kimi K2 Instruct. The model has the same architecture and same number of parameters (though different precision) as Kimi K2 Instruct and like K2 Instruct only supports text as an input (and output) modality ➤ 1T parameters but INT4 instead of FP8: Unlike Moonshot’s prior Kimi K2 Instruct releases that used FP8 precision, this model has been released natively in INT4 precision. Moonshot used quantization aware training in the post-training phase to achieve this. The impact of this is that K2 Thinking is only ~594GB, compared to just over 1TB for K2 Instruct and K2 Instruct 0905 - which translates into efficiency gains for inference and training. A potential reason for INT4 is that pre-Blackwell NVIDIA GPUs do not have support for FP4, making INT4 more suitable for achieving efficiency gains on earlier hardware. Our full set of Artificial Analysis Intelligence Index benchmarks are in progress and we will provide an update as soon as they are complete.

🚨 Today is a turning point in AI. A Chinese open source model is #1. Kimi K2 Thinking scored 51% in Humanity's Last Exam, higher than GPT-5 and every other model. $0.6/M in, $2.5/M output. The best at writing, and does 15tps on two Mac M3 Ultras! Seminal moment in AI. Try it on OpenRouter:

China is gaining increasing confidence in the AI race. The Chinese government issued a directive requiring state-funded AI data-centres to use only domestically-manufactured AI chips, forcing foreign suppliers like Nvidia out of a critical market. This marks a stark escalation in Beijing’s drive for tech self-sufficiency and intensifies the U.S.–China high-tech rivalry.