dragy

420 posts

dragy

dragy

@dragonboll122

Katılım Ocak 2024
82 Takip Edilen2 Takipçiler
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LeviDing
LeviDing@levidingX·
谢谢 OpenAI 🥹 送了 6 个月的 200 美金/月 Pro 套餐
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Shubh
Shubh@TheSuperEng·
The best FREE YouTube courses for backend engineering
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Lukas Ziegler
Lukas Ziegler@lukas_m_ziegler·
Modern Robotics course for free! 📚 📍Save this for later if you are starting in robotics. The robotics textbook "Modern Robotics" by Kevin Lynch and Frank Park is now available for free as YoutTube course. The Specialization consists of six short courses, each taking approximately four weeks at five hours per week: Course 1: Foundations of Robot Motion (Chapters 2-3)  Course 2: Robot Kinematics (Chapters 4-7)  Course 3: Robot Dynamics (Chapters 8-9)  Course 4: Robot Motion Planning and Control (Chapters 10-11)  Course 5: Robot Manipulation and Wheeled Mobile Robots (Chapters 12-13)  Course 6: Capstone Project - Mobile Manipulation The textbook covers configuration space, rigid-body motions, forward kinematics, velocity kinematics, inverse kinematics, dynamics, trajectory generation, motion planning, robot control, grasping and manipulation, and wheeled mobile robots. Supporting materials include practice exercises with solutions, exam sets from Seoul National University (2017-2020 with solutions), video lectures filmed with Northwestern's Lightboard, software in Python/MATLAB available on GitHub. Here's the link: youtube.com/playlist?list=… ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com
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Seika Karamatsu
Seika Karamatsu@SeikaKaramatsu·
チューリッヒ工科大の2026年春のロボット学習の授業の資料が一部公開されてるみたいです! cvg.ethz.ch/lectures/Robot… 模倣学習・RLの基礎、VLA、ロボティクス基盤モデルなどの講義が計12週分あります。 全部無料だとバズっていますが、実際確認したところスライドはパスワード保護されており、外部からは開けません。 外部の人が見られるのは ・ゲスト講義のYouTube録画(無料・登録不要) ・GitHubのコーディング課題(PyTorch、模倣学習、RL) です。 ゲスト陣は ・Cheng Chi(Diffusion PolicyとUMIの作者、Sunday Robotics共同創業者) ・Quan Vuong(Physical Intelligence共同創業者、π0.6の回) ・Scott Reed(NVIDIA GEAR Lab) ・Dieter Fox(AI2ディレクター) などと分野の最前線の方々が並んでるみたいです! 12週分は多いので、VLAに興味があるならQuan Vuongさんの授業、模倣学習ならCheng Chiさんの授業、のようにつまみ食いして見るのが良いと思います。 自分も面白そうなの見てみます!
Ilir Aliu@IlirAliu_

ETH Zurich just open-sourced their entire 2026 robot learning course. Not a MOOC. The actual course. Slides, lecture recordings, coding assignments, GitHub repo. The curriculum goes from imitation learning and RL all the way to Vision-Language-Action models and foundation models for robotics. Guest lectures from the co-founder of Physical Intelligence. The creator of Diffusion Policy. Pieter Abbeel. Dieter Fox. 12 weeks. Free. No signup. If you want to understand where robot intelligence is actually heading… this is the reading list the field is using right now. 📍[cvg.ethz.ch/lectures/Robot…] —— Weekly robotics and AI insights. Subscribe free: 22astronauts.com

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Kent Fujiwara
Kent Fujiwara@kentfuji·
画像生成において複数枚に同じスタイルを持たせるのは一貫性の観点で難しいから画像の似たセットを使ってガイダンスし、複数枚同時に作ってしまおうとする研究
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Roan
Roan@RohOnChain·
this should not be public. Stanford quants published the exact ML stack a real HFT market maker runs on every millisecond. 15 pages. Random Forest, SVM, SGD applied to the order book. Bookmark & get this before someone takes it down.
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Roan@RohOnChain

x.com/i/article/2056…

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Kent Fujiwara
Kent Fujiwara@kentfuji·
単著で凄い!しかも学習なし手法!
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Andrei Bursuc @CVPR
Andrei Bursuc @CVPR@abursuc·
VGGT-Omega had a really nice presentation today. tl;dr: scaling up leads to sweets benefits. This is known as but the VGGT architecture needs to be prepared for actual scale-up. The authors propose 2 axis of improvement: architecture and data #cvpr2026
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F. Güney
F. Güney@ftm_guney·
I think this is my favorite paper this CVPR: Magician. before they explore in active view selection, they imagine how gaussians and occupancy map would look like and then compute a coverage metric based on that. during planning, they try 10 views like that in 10 steps in a tree search with pruning and get planning for free. they even have real-world experiments with a drone and a toy car. how are they not an award candidate, it blows my mind.
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Matthew Walmer
Matthew Walmer@MatthewWalmer·
We’re looking forward to presenting UPLiFT at #CVPR2026! Efficiently extract pixel-dense features from pretrained backbones like DINOv3. We’ll be at the final poster session on Sunday (6/7) from 3:30-5:30pm at Poster 474, so please come by! Website: cs.umd.edu/~mwalmer/uplif…
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Chuhan Zhang
Chuhan Zhang@ChuhanZhang5·
Congrats to the team for wining CVPR Best Paper Award!! 🏆 Come to our oral session (Mile High Ballroom 13:00-14:15) and poster (16:00-18:00) today for more details 🚀
Chuhan Zhang@ChuhanZhang5

A SINGLE encoder + decoder for all the 4D tasks! We release 🎯 D4RT (Dynamic 4D Reconstruction and Tracking). 📍 A simple, unified interface for 3D tracking, depth, and pose 🌟 SOTA results on 4D reconstruction & tracking 🚀 Up to 100x faster pose estimation than prior works

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Kat ⊷ the Poet Engineer
Kat ⊷ the Poet Engineer@poetengineer__·
fed the hopfield network chinese glyphs this time - radicals and components instead of latin letters. as memory decays it starts drafting characters that don’t exist - forgetting becomes a way of inventing.
Kat ⊷ the Poet Engineer@poetengineer__

experiment with a memory system that keeps rewriting itself: a hopfield network remembers an alphabet. as memories decay, it begins to hallucinate glyphs it was never taught - forgetting becomes a way of inventing.

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Arjun Virk
Arjun Virk@virkvarjun·
I just spent months handwriting a 200 page guide on the entirety of ML foundations and math from scratch. The guide features: - Neural Nets (Backprop, Adam, SGD, Batch Norm) - ML Algorithms (SVM, Grad Boosting, K-means, PCA) - Hardware (Tensor Cores, Systolic Arrays, CUDA) - Transformers (Multi-Head Attn, KV Cache, LoRA) - Vision (ViT, Convolutions, MAE, IoU, NMS, VLM) - Agents (OpenClaw, ReAct, Memory, Orchestration) Everything I wish I had years ago, for free.
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0xDipper
0xDipper@Dipper_pol·
Nassim Taleb: pick two people at random If their combined height is 4.1m, it's basically 2.05 + 2.05. If their combined wealth is $36M, it's almost never 18 + 18 - it's ~$1,000 and ~$36M. Height lives in "Mediocristan," where the average tells you everything. Wealth - and markets - live in "Extremistan," where one event dominates the whole picture. Ruin there never comes from a string of bad days. It comes from a single one. ~1hr lecture, free. The Black Swan author at Cambridge on why the statistics you were taught break exactly where it matters. Being right on average means nothing if one tail empties the account.
0xRicker@0xRicker

x.com/i/article/2062…

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