cloudameoba retweeted
Steve, e morto miliardario a 56 anni, senza poter sconfiggere il cancro.
Le sue ultime parole danno molto su cui riflettere:
"Alla fine, la ricchezza è solo una parte della vita a cui mi sono abituato.
Sdraiato in questo letto e guardando indietro, capisco che tutta la fama e i soldi che ho avuto non valgono nulla davanti alla morte.
Abbiate cura di voi stessi e rispettate gli altri.
Con gli anni uno diventa più saggio e realizza le cose semplici:
un orologio da 30 dollari e uno da 30.000 segna la stessa ora.
Un portafoglio economico o un portafoglio molto costoso tengono i soldi.
Un'auto da 10.000 o 100.000 dollari ti porta comunque dal punto A al punto B.
E anche se la tua casa è di 40-400 metri quadrati, la solitudine è uguale.
La vera felicità non sta nelle cose.
Se un aereo cade, non importa se eri in classe economica o in prima classe, il risultato sarà lo stesso.
Quindi la vera felicità è nelle persone, negli amici e nelle persone care con cui condividi la vita. ”
Cinque principi che vale la pena ricordare:
Non educare i tuoi figli ad essere ricchi.
Insegna loro ad essere felici.
Quando cresceranno, capiranno il valore — non solo il prezzo — delle cose.
Vedi il cibo come una medicina, o un giorno la medicina sarà il tuo cibo.
Chi ti ama davvero non se ne va, anche se ha cento motivi.
Ne troverà sempre uno con cui stare con te.
C'è una grande differenza tra essere "persona" ed essere "umano".
Se vuoi andare veloce, vai da solo.
Ma se vuoi andare lontano, vai accompagnato.
Steve Jobs
cloudameoba retweeted
Handbook on Understanding Harness Engineering for AI Agents [48-page PDF]: drive.google.com/file/d/1PT-…
cloudameoba retweeted
UC Berkeley's "CS 188: Introduction to Artificial Intelligence"
by John Canny & Oliver Grillmeyer
The legendary Pacman-based AI course: search, CSPs, games, MDPs & ML, taken by tens of thousands online.
Course Website: inst.eecs.berkeley.edu/~cs18…
Lecture Videos: youtube.com/playlist?list=PL…
cloudameoba retweeted
The AI Scientist v3 is out: The Agentic Scientist.
GitHub: github.com/SakanaAI/AI-Scien…
Blog: pub.sakana.ai/agentic-scient…
arXiv: arxiv.org/abs/2610.18933
The Agentic Scientist is the 3rd iteration of our automated AI Scientist research system, and the shift this time is from “one agent writes a paper” to “a scaffold of parallel agents orchestrates the full research loop.”
The system now orchestrates a pool of parallel workers that share memory and distilled conclusions. Crucially, it features Evidence Generation: isolated agents automatically run controlled baselines and ablations to prove why an idea actually works.
It works as a co-scientist. We used a development version as a co-scientist to explore biologically plausible learning rules. The agent handled the empirical heavy lifting, proposing mechanisms and running experiments. We stayed in the loop to steer direction, verify results, and moderate claims. That feels like the right division of labor for where the technology actually is.
This resulted in a new biologically plausible learning algorithm, and the resulting paper was accepted at ALIFE 2026 as an oral presentation.
We are also releasing AutoIllustrate. It generates editable draw.io vector figures instead of uneditable, hallucinated raster images. Real scientists need vectors, not pixels. I am an SVG junkie ;)
Incredible work by Robert Lange, Luca Grillotti, Yutaro Yamada, Rujikorn Charakorn, and the team!
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery
Blog: pub.sakana.ai/agentic-scient…
GitHub: github.com/SakanaAI/AI-Scien…
arXiv: arxiv.org/abs/2610.18933
Since 2024, we’ve used The AI Scientist as a way to test how far AI alone can take scientific research, from v1 to v2 to the paper published in Nature earlier this year. The Agentic Scientist is the next step in that line of work, built for the era of coding agents.
Coding agents such as Codex and Claude Code now carry out long software tasks on their own. Handing them a research problem, however, tends to produce a strong hyperparameter optimizer rather than a scientist: scores go up, but ideas stay narrow, and the reasons for the gain are rarely examined.
The Agentic Scientist makes the research loop explicit. It wraps off-the-shelf coding agents in a scaffold that controls what they see, what they write, how they are evaluated, and how results are handed to the next stage.
Parallel workers test hypotheses against rubrics defined before any experiment runs, share successes and failures through a common memory, and then ablate the winning program to generate evidence.
We used an early version of the system to study Error Diffusion, a biologically motivated alternative to backpropagation. Our researchers set the question and the rubric, chose directions at each stage, and verified the results, references, and claims before submission. The resulting paper was accepted as an oral presentation at ALIFE 2026. In autonomous runs, the system also raised Doc-to-LoRA retrieval accuracy from 52% to 98% on multi-needle tasks and designed a learned evolution strategy that improves over its CMA-ES reference on unseen tasks.
Alongside it we release AutoIllustrate, a pipeline for scientific figures that keeps an editable draw.io file as the source of truth, so every diagram can be corrected by hand.
Humans define the question and the standards of evidence; agents expand the experiments researchers can run. We call this Co-Discovery, and we are releasing both systems as open source for the community to build on.
cloudameoba retweeted
Este libro de algoritmos y estructuras de datos es espectacular y te lo recomiendo mucho.
Muy completo, ilustraciones detalladas, con pseudo código y temas desde lo más básico a avanzado.
Disponible en PDF totalmente gratis
→ jeffe.cs.illinois.edu/teachi…
ALT Portada de Algoritmos de Jeff Erickson
ALT Ejemplo de árboles binarios de profundidad 3
ALT Ejemplo de Towers de Pisa
cloudameoba retweeted
"Calculus" by Gilbert Strang is a free textbook from MIT that has become a true classic in mathematics education.
With nearly 700 pages, it provides an exceptionally clear and comprehensive introduction to calculus, covering limits, derivatives and their applications, integration techniques, the Fundamental Theorem of Calculus, differential equations, exponential and logarithmic functions, infinite series, polar coordinates, complex numbers, vectors and matrices, multivariable calculus, partial derivatives, multiple integrals, and vector calculus.
This book is an absolute must-have for anyone approaching calculus for the first time, but also an excellent reference for reviewing its fundamental concepts. I highly recommend adding it to your personal library as a valuable resource to consult whenever you need it.
ocw.mit.edu/courses/res-18-0…
cloudameoba retweeted
This GitHub repository (with 100+ case studies) contains everything you need to become proficient in System Design: github.com/Coder-World04/Com…
cloudameoba retweeted
Linear Algebra by V. V. Voyevodin
a comprehensive united course in linear #algebra and analytic #geometry; intended mainly for those in whose education computational #mathematics
#soviet
mirtitles.org/2018/05/09/lin…
cloudameoba retweeted
谁把教材上传Arxiv了？
《Agentic Large Language Models》（智能体大语言模型）：来自荷兰莱顿大学的AI教材，内容涵盖大模型工作原理、Agent原理等相关AI内容，共472页。
地址：arxiv.org/abs/2610.13455
cloudameoba retweeted
Princeton's Introduction to Robotics! 🎓
Princeton University released their full Introduction to Robotics course publicly with lecture videos, notes, slides, and assignments.
This course provides fundamental theoretical and algorithmic principles behind robotic systems with hands-on experience.
Topics covered:
→ Feedback Control (dynamics, PD control, Linear Quadratic Regulator)
→ Motion Planning (discrete planning with BFS/DFS, optimal planning with Dijkstra/A*)
→ State Estimation, Localization, and Mapping (Bayes filtering, Kalman filtering, particle filtering, SLAM)
→ Vision and Learning (optical flow, deep learning, convolutional networks, reinforcement learning), and broader topics including robotics and law, ethics, and economics.
Assignments include theory, programming, and hardware implementation components. The final project has students program drones for vision-based navigation with attached cameras transmitting real-time images.
All lecture videos, notes, slides, and assignments are freely available.
Prerequisites include multivariable calculus, linear algebra, basic probability, basic differential equations, and some programming experience in Python.
‼️ GO FOR IT: irom-lab.princeton.edu/intro…
~~
♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com
cloudameoba retweeted
how to study and learn really difficult subjects:
1. don’t panic. hard just means dense. it’s not impossible, it’s just packed. unwrap it slowly.
2. get a map before diving in. watch an overview video, read the table of contents, or skim the wikipedia page. you need context first.
3. use multiple sources. one book will confuse you, two will clarify, three will enlighten.
4. build intuition, not memory. visualize it. simulate it. code it. teach it to a friend or a wall.
5. tolerate confusion. you’ll feel dumb 90% of the time; that’s the price of learning something that rewires your brain.
6. connect it to reality. every abstract thing has a real-world analog. find it. relate it. ground it.
7. revisit the same topic after a week. mastery is not about reading once; it’s about returning after your brain has “chewed” on it.
8. don’t romanticize genius. smart people aren’t born knowing it. they just survived longer in the confusion phase.
the truth is; learning hard stuff isn’t about intelligence.
it’s about endurance, humility, and curiosity.
you stay long enough in the fog until it clears.
