surya
28 followers · 36 following · 2836 views
on the atlas — 106
- Lojban2 savers
- AIs can now often do massive easy-to-verify SWE tasks and I've updated towards shorter timelines — LessWrong5 savers
- Haus Otto1 savers
- Download Dimensional Drawings - Accessories - Apple Developer1 savers
- Larger Pacific striped octopus1 savers
- cognition in interaction design3 savers
- EE364a: Convex Optimization I1 savers
- ASCII characters are not pixels: a deep dive into ASCII rendering5 savers
- Design bookmarks2 savers
- worrydream.com/refs/Hofstadter_2001_-_Analogy_as_the_Core_of_Cognition.pdf3 savers
- What I've been up to – Parental Leave Edition1 savers
- Shneiderman1983Direct.pdf1 savers
- Tutorial | Voronoi Go1 savers
- Good websites7 savers
- Are.na Editorial1 savers
- Notes on “Taste” | Are.na Editorial24 savers
- Walk on Decomposed Subdomains2 savers
- DiGRA Conference Publication Format2 savers
- laurie, play chords from your laptop1 savers
- CME 295 - Transformers & Large Language Models1 savers
- Flâneur collective margin — glass1 savers
- Flâneur collective margin — final1 savers
- Flâneur collective margin — fluid1 savers
- Alex Widua5 savers
- Status Seeking Is Killing Your Business - YouTube1 savers
- Ti Morse on X: "My first interview with @sama, Co-Founder of @OpenAI. 0:04 How to start a startup 3:30 Trusting exponentials 4:57 Operating in chaotic environments 6:12 Learning to enjoy painful experiences 8:03 Creating abundant intelligence 11:15 Keeping core suppliers on OpenAI’s timelines https://t.co/vPnmWfPwVe" / X3 savers
- [2607.20064] PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning1 savers
- Charlie Kerr on X: "My girlfriend has thousands of TikTok’s saved- day trips, vacation ideas, restaurants, gifts, etc. I couldn’t keep track of it. Now she texts the videos to Hermes, it watches them + stores in a vault, I text the agent to pull from her lists and plan. Magic. https://t.co/eTiDSLxUfi" / X1 savers
- Surya Dantuluri blog1 savers
- PRO-LONG vs RLM1 savers
- map · backscroll1 savers
- Syntax6 savers
- receipt demo1 savers
- qwqw - Google Search1 savers
- People followed by Jensen Huang (@JensenHuang) / X1 savers
- Building Soulmate with AI – Cory Etzkorn2 savers
- [2607.16097] Understanding Reasoning from Pretraining to Post-Training1 savers
- Example Domain1 savers
- Geospot1 savers
- Energetics_Chapter_9_proof_03182019.pdf2 savers
- flâneur — a map of Curius links18 savers
- Ergo — Philosophy Begins in Wonder3 savers
- [1912.13213] Online Learning: A Modern Introduction Using Convex Optimization1 savers
- cvxbook_additional_exercises/additional_exercises.pdf at main · cvxgrp/cvxbook_additional_exercises2 savers
- [2606.06614] Re-Centering Humans in LLM Personalization2 savers
- agi_emh.pdf2 savers
- Giovanni D'Antonio2 savers
- Highly Opinionated Advice on How to Write ML Papers — AI Alignment Forum8 savers
- https://cs.stanford.edu/~jsteinhardt/ResearchasaStochasticDecisionProcess.html31 savers
- Minqi Jiang4 savers
- fivethirtyeightindex3 savers
- Training SID-1 to beat GPT-5 at search with 1k+ QPS RL1 savers
- BrrrViz1 savers
- Collect UI - Daily inspiration collected from daily ui archive and beyond. Based on Dribbble shots, hand picked, updating daily.3 savers
- Post-Labor Economics | Building for the World Beyond Work3 savers
- Gavin Nelson, Designer1 savers
- apps - solderless.engineering2 savers
- My Mood Wrapped1 savers
- The means of some change | Zhengdong4 savers
- What will be scarce? - by Alex Imas - Ghosts of Electricity14 savers
- The Way of Code | Rick Rubin14 savers
- Zugunruhe | Nathan's Notes6 savers
- The Intelligence Consolidation2 savers
- Let's discuss sandbox isolation1 savers
- AI infrastructure in the "Era of experience"4 savers
- Fluid Functionalism1 savers
- Cybernetic Arbitrage · Soren Larson3 savers
- Microhabitat (film)1 savers
- How Complex is my Code? · Sofia Fischer; Philodev1 savers
- NASA Image and Video Library1 savers
- Omni Model Inference: How We Move Tensors Between Stages | LinkedIn1 savers
- A Primer on the Symmetry Theory of Valence – Opentheory.net3 savers
- Principles of Vasocomputation: A Unification of Buddhist Phenomenology, Active Inference, and Physical Reflex (Part I) – Opentheory.net9 savers
- John Palmer3 savers
- bobbie-releases/gpu-sandbox-white-paper.pdf at main · 4014-Labs/bobbie-releases1 savers
- The One Billion Row Challenge in Go: from 1m45s to 3.4s in nine solutions1 savers
- The Second Wave of the API-first Economy — brandur.org1 savers
- Caching — PlanetScale2 savers
- Hero's Journey3 savers
- The Bitter Lesson78 savers
- Thirty Observations at Thirty62 savers
- I should have loved biology36 savers
- 2025 letter | Zhengdong33 savers
- Why We Think | Lil'Log25 savers
- OpenAI Email Archives (from Musk v. Altman) — LessWrong20 savers
- The Second Half – Shunyu Yao – 姚顺雨17 savers
- Historical Tech Tree16 savers
- RLHF Book by Nathan Lambert14 savers
- Inside vLLM: Anatomy of a High-Throughput LLM Inference System - Aleksa Gordić11 savers
- Most AI value will come from broad automation, not from R&D | Epoch AI11 savers
- Failing to Understand the Exponential, Again10 savers
- Growing Neural Cellular Automata10 savers
- Seeing like a software company10 savers
- Why I don’t think AGI is right around the corner9 savers
- How Figma’s multiplayer technology works9 savers
- Ideas — Noah Zender9 savers
- The Kekulé Problem - Nautilus8 savers
- Diátaxis7 savers
- PyTorch internals : ezyang’s blog7 savers
- How we built our multi-agent research system \ Anthropic6 savers
highlights — 8
"No one can tell you what you can or can not do. With no rules to follow, this adventure is up to you" -- Official Minecraft Trailer 2011
Hero's JourneyTechnological improvements continued, but change became more evolutionary than revolutionary.
AI Will Not Make You Rich - ColossusIn the mid-1980s, many [venture capitalists] experimented with investments in such low-tech industries as specialty retailing. Results were generally disappointing, in part because the financiers often didn’t understand what they were getting into. (Wall Street Journal, “Venture Capitalists Find Low-Tech Firms Appealing”, 6/20/1991.)
Heat Death: Venture Capital in the 1980s | Reaction WheelUnfortunately, this theoretically nice thing about the internet has been defeated by a sadly definite thing about the internet: it is great at creating hiveminds, at funneling 99% of attention to 1% of things. Now not only can the people who bought the guidebooks learn about That One Restaurant Only Locals (And You) Go To but you can learn about it, too, and so can anyone else with access to the internet, and probably you will each hear about it from a different influencer saying something different, though equally exaggerated, about it.
Unconventional Adventures - Quarter MileI tell founders after we lead their seed round, that the most important decision they will make for the first year of the company, is their choice in office lease. If you don’t have a top-tier office, top-tier talent will subconsciously be averse to joining you.
Thirty Observations at ThirtyArriving in Silicon Valley in 2012 I was overwhelmed by how much there was to learn about who actually built large tech companies and how it was done. Now, a decade later, I realize that the set of players relevant today has a massive overlap with those a decade ago. Not a whole lot changes.
Thirty Observations at ThirtyIn the world of steel manufacturing, historically, steel was made in massive integrated mills. They made high quality steel with reasonable margins. Then came along electric mini mills. These mills were able to make the lowest quality steel at a cheaper cost. The large steel manufacturers saw this, shrugged, and focused on making high quality steel at a (relatively) high margin. Over time, the electric mini mill operators figured out how to make higher and higher quality steel, moved upmarket, and killed the massive integrated mills (US Steel— once the 16th largest corporation by market cap in…
The evolution of the LLM API market - by Finbarr TimbersIn speculative decoding, one has two models: a small, fast, one, and a large, slow one. As the inference speed for a modern decoder is directly proportional to the number of parameters, with a smaller model, one can run multiple inferences in the time it takes a large model to run a single inference.
Transformer inference tricks - by Finbarr Timbers