Kangying L.(Connie) @timcanby

kaonavi ←SB Intuitions←https://t.co/m94NkbccQb←RecursiveAI(joined pj: https://t.co/CyM5PgT2Pn) ←JSPS DC2(図書館情報学&DH)Ritsumei| Women in Tech🙌 Build in Public💪 linkedin.com/in/kangying0501 Linkedin Joined March 2014

Tweets

6K
Followers

295
Following

2K
Likes

9K

Kangying L.(Connie) @timcanby

18 minutes ago

🥹

ollama @ollama

3 hours ago

Gemma 4 is now nearly 90% faster on Apple Silicon with Ollama using MLX! The speedup comes from improved multi-token prediction (MTP), now on by default for Gemma 4, with more models to come. Ollama automatically tunes how many tokens to draft as it runs, so it never slows

14 40 503 27K 158

0 0 0 5 0

View Details

Jim Fan @DrJimFan

11 hours ago

Today, we give robots a /skills library that self-evolves and compounds indefinitely! Introducing ASPIRE: a robot solving its 100th task is no longer as clueless as solving its first. Coding agents observe multimodal sensory traces from simulation and real robots, launch an evolutionary search over control programs, and distill the best know-how into an ever-expanding library. ASPIRE is a new type of continual learning: "training" is skill refinement instead of gradient descent. "Trained model" is a repo of sensorimotor skills instead of floating weights. “Distributed training” is a panel of agents each practicing a different skill instead of sharded minibatches. Here's the beauty: ASPIRE gives the tired terms "sim2real transfer" and "cross-embodiment transfer" a whole new meaning. Bridging the sim-to-real gap is notoriously brutal. An end-to-end policy has to swallow both the visual shift (sim looks toyish next to a real camera) and the subtle contact physics it never quite gets right. ASPIRE sidesteps the mess, because it doesn't ship pixels or weights across the gap, but ships the know-how. The robot still has to practice in the real world, not zero-shot, but it gets there way faster because it isn't rediscovering the strategy from scratch. Same for going single-arm to bimanual hardware, which usually requires new data and retraining from zero. ASPIRE achieves up to ~10x cut in "transfer learning” tokens (yes, tokens are the new unit of *training* compute ;) Check out our gallery of 150+ tasks and 90+ skills the robots taught themselves, all on the website! Kind of wild that we can ship the "learned weights" as an HTML page rather than a GGUF. We'll open-source the full stack so your own robot library starts compounding from ours! Deep dive in thread:

37 113 782 61K 544

View Details

ClaudeDevs @ClaudeDevs

10 hours ago

Claude Sonnet 5 is here. Top-tier performance on coding and tool use at Sonnet pricing, with a 1M context window. It's the new default in Claude Code for Pro users, and available everywhere on the Claude Platform, including the API and Managed Agents.

Claude @claudeai

10 hours ago

Introducing Claude Sonnet 5, our most agentic Sonnet yet. It makes plans, uses tools like browsers and terminals, and runs autonomously at a level that just a few months ago required larger and more expensive models.

2K 4K 36K 5.3M 5K

348 665 8K 929K 954

View Details

Kangying L.(Connie) @timcanby

6 hours ago

壁打ち(｀・ω・´)

0 0 0 19 0

View Details

Kangying L.(Connie) @timcanby

8 hours ago

パラダイムシフト😇

0 0 0 23 0

View Details

Kangying L.(Connie) @timcanby

10 hours ago

abhik.ai/articles/how-t…

0 0 0 22 0

View Details

Kangying L.(Connie) @timcanby

10 hours ago

blog.roboflow.com/best-object-de…

0 0 0 21 0

View Details

Kangying L.(Connie) @timcanby

11 hours ago

日本に留学して以来、完璧な玉子焼きは一度も成功していません🫠

0 0 0 49 0

View Details

Lukas Ziegler @lukas_m_ziegler

a day ago

Build your first robot in simulation! 👾 📌 If you’re self-learning robotics, this is genuinely one of the better repos to save for later. @NVIDIARobotics released a "Getting Started with Isaac Sim" tutorial series covering everything from building your first robot to hardware-in-the-loop deployment. What's inside? → Building Your First Robot Explore the Isaac Sim interface, construct a simple robot model (chassis, wheels, joints), configure physics properties, implement control mechanisms using OmniGraph and ROS 2, integrate sensors (RGB cameras, 2D lidar), and stream sensor data to ROS 2 for real-time visualization in RViz. → Ingesting Robot Assets Import URDF files, prepare simulation environments, add sensors to existing robot models, and access pre-built robots to accelerate development. → Synthetic Data Generation Learn perception models for dynamic robotic tasks, understand synthetic data generation, apply domain randomization with Replicator, generate synthetic datasets, and fine-tune AI perception models with validation. → Software-in-the-Loop (SIL) Build intelligent robots, implement SIL workflows, use OmniGraph for robot control, master Isaac Sim Python scripting, deploy image segmentation with ROS 2 and Isaac ROS, and test with and without simulation. → Hardware-in-the-Loop (HIL) Understand HIL fundamentals, learn NVIDIA Jetson platform, set up the Jetson environment, and deploy Isaac ROS on Jetson hardware. The progression makes sense: start with basics (build a robot), add perception (sensors and data), generate training data (synthetic generation), develop software (SIL), then deploy to hardware (HIL). For robotics teams, this is the path to faster iteration. Simulate first, validate in software-in-the-loop, generate synthetic training data at scale, then deploy to hardware with confidence. 🎓 If this helps at least one engineer to become more fluent in the world of robotics, means a lot to me! 🫶🏼 Here's the course (it's free): docs.nvidia.com/learning/physi… ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com

4 67 351 15K 396

View Details

Kangying L.(Connie) @timcanby

a day ago

感谢来听公开课的朋友！希望有在Boot Camp正课和大家一起肝穿第一个自进化系统的缘分❤️coderbar.com/course/video?i…

Kangying L.(Connie) @timcanby

a week ago

欢迎大家来玩呀😆

0 0 0 659 1

0 0 0 200 0

View Details

Kangying L.(Connie) @timcanby

a day ago

今日の自己反省：　 Fact-based Analysis→Internal Alignment / Manage Up→Options & Recommendationにするはずだ！

0 0 0 54 0

View Details

Kangying L.(Connie) @timcanby

a day ago

Stakeholder Management linkedin.com/learning/stake…

0 0 0 44 0

View Details

Kangying L.(Connie) @timcanby

2 days ago

0 0 0 46 0

View Details

Kangying L.(Connie) @timcanby

2 days ago

ARD🤔🤔🤔🤔🤔🤔

0 0 0 47 0

View Details

Kangying L.(Connie) @timcanby

2 days ago

天才すぎる....

Kangying L.(Connie) @timcanby

2 days ago

GitHub - unconv-ai/Un-0: Un-0: an image generator powered by a simulated system of coupled oscillators, an example of an emerging physical computing substrate. · GitHub github.com/unconv-ai/Un-0

0 0 0 98 0

0 0 0 75 0

View Details

Kangying L.(Connie) @timcanby

2 days ago

GitHub - unconv-ai/Un-0: Un-0: an image generator powered by a simulated system of coupled oscillators, an example of an emerging physical computing substrate. · GitHub github.com/unconv-ai/Un-0

0 0 0 98 0

View Details

Kangying L.(Connie) @timcanby

2 days ago

GitHub - microsoft/mattersim: MatterSim: A deep learning atomistic model across elements, temperatures and pressures. · GitHub github.com/microsoft/matt…