Ryan Huang

490 posts

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Ryan Huang

Ryan Huang

@nvbkdw

AI Infrastructure Engineer / Part-time Indie hacker 📓 https://t.co/h1CVhPiYCx 🎨 https://t.co/v6Hq3RReGo

Seattle, WA Присоединился Ağustos 2011
2.2K Подписки110 Подписчики
Ryan Huang
Ryan Huang@nvbkdw·
@balajis China never wanted to be number one, they just want to live better life, competition is an illusion of US
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Ronak Malde
Ronak Malde@rronak_·
This paper is almost too good that I didn't want to share it Ignore the OpenClaw clickbait, OPD + RL on real agentic tasks with significant results is very exciting, and moves us away from needing verifiable rewards Authors: @YinjieW2024 Xuyang Chen, Xialong Jin, @MengdiWang10 @LingYang_PU
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Ryan Huang
Ryan Huang@nvbkdw·
AI labs are coming to grab all data from all industries, and related all other SaaS with better AI agents
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Charles 🎉 Frye @ GTC
Charles 🎉 Frye @ GTC@charles_irl·
We find that the adoption of Cursor leads to a statistically significant, large, but transient increase in project-level development velocity, along with a substantial and persistent increase in static analysis warnings and code complexity. arxiv.org/abs/2511.04427
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Kimi.ai
Kimi.ai@Kimi_Moonshot·
Introducing 𝑨𝒕𝒕𝒆𝒏𝒕𝒊𝒐𝒏 𝑹𝒆𝒔𝒊𝒅𝒖𝒂𝒍𝒔: Rethinking depth-wise aggregation. Residual connections have long relied on fixed, uniform accumulation. Inspired by the duality of time and depth, we introduce Attention Residuals, replacing standard depth-wise recurrence with learned, input-dependent attention over preceding layers. 🔹 Enables networks to selectively retrieve past representations, naturally mitigating dilution and hidden-state growth. 🔹 Introduces Block AttnRes, partitioning layers into compressed blocks to make cross-layer attention practical at scale. 🔹 Serves as an efficient drop-in replacement, demonstrating a 1.25x compute advantage with negligible (<2%) inference latency overhead. 🔹 Validated on the Kimi Linear architecture (48B total, 3B activated parameters), delivering consistent downstream performance gains. 🔗Full report: github.com/MoonshotAI/Att…
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Hao AI Lab
Hao AI Lab@haoailab·
At #NVIDIAGTC, Jensen showed the industry where AI infra is heading: disaggregate the stack. NVIDIA’s Groq LPX push applies this to inference with Attention–FFN Disaggregation. Our view: this idea matters even more for long-context LLM training. 🧵 github.com/hao-ai-lab/Dis…
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Carl Zha
Carl Zha@CarlZha·
Why are all the on the ground footages from Strait of Hormuz coming from Chinese sailors posting on Douyin? Is there a deliberate suppression on the US media platform???
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Paul Graham
Paul Graham@paulg·
Prediction: When fighting Iran gets too painful because of oil prices or polls or whatever, Trump will claim that the current state of things, whatever it happens to be, was his goal, declare victory, and retreat.
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Ryan Huang
Ryan Huang@nvbkdw·
History of software development process : waterfall —> agile-> interactive on-demand
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Ryan Huang
Ryan Huang@nvbkdw·
Openclaw is the new Ruby on Rails
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Ryan Huang
Ryan Huang@nvbkdw·
@dwarkesh_sp Is AI can do coding and math, does math still matters?
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Dwarkesh Patel
Dwarkesh Patel@dwarkesh_sp·
What should I ask Terence Tao?
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Zara Zhang
Zara Zhang@zarazhangrui·
Never have I seen a larger gap between how startups and how large companies: - use and work with AI - select talent - organize their teams
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Gergely Orosz
Gergely Orosz@GergelyOrosz·
One thing that endlessly frustrates with Anthropic, a $300B+ dollar company, where most code is written with AI: Their landing page for paying customers, Claude .ai has been broken for weeks UX-wise, and no one notices or cares or fixes: It "loses" stuff I type while it loads:
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Ryan Huang
Ryan Huang@nvbkdw·
Why do SOTA models stop at 1M context? seem like it is an engineering limit, after 1M tokens, quadratic scaling really "catches" you
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Ryan Huang
Ryan Huang@nvbkdw·
**Corrected & concise version:** Why do SOTA models stop at 1M context? Is it an engineering limit or diminishing returns from the model itself?
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China in English
China in English@En_chinaNews·
⚡🇮🇱🇨🇳 BREAKING: Netanyahu calls on China to intervene to calm Iran and mediate to stop the war and prevent regional escalation.
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Christos Tzamos
Christos Tzamos@ChristosTzamos·
1/4 LLMs solve research grade math problems but struggle with basic calculations. We bridge this gap by turning them to computers. We built a computer INSIDE a transformer that can run programs for millions of steps in seconds solving even the hardest Sudokus with 100% accuracy
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Ido Salomon
Ido Salomon@idosal1·
AgentCraft v1 is live ⚔️ Control your agents like it's an RTS game! It's early. It's rough. It's fun. npx @idosal/agentcraft
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