Open library
The essential reading of generative AI and agents, open in a clean reading view, with Meow and Standard summaries when available. No account needed to read.
560 papers · 558 with a Meow summary · 333 posts
The selected papers and blogs: the essential papers of generative AI and agents, and the key lab publications and essays, each with Meow and Standard summaries.
Meredith Ringel Morris, Jascha Sohl-Dickstein, Noah Fiedel et al.
We propose a framework for classifying the capabilities and behavior of Artificial General Intelligence (AGI) models and their precursors. This framework introduces levels of AGI performance, generality, and autonomy, providing a common language to compare models, assess risks, and measure progress along the path to A…
Ling Yue yuel2@rpi.edu, Kushal Raj Bhandari bhandk@rpi.edu, Ching-Yun Ko cyko@ibm.com et al.
Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval, tool use, code execution, memory updates, and verification. This survey reviews recent methods for designing and optimizing such workflows,…
Jingqi Tong, Mingzhe Li, Hangcheng Li, Yongzhuo Yang, Yurong Mou,
Scientific discovery depends on expert judgement and foresight, which we call scientific taste: the ability to judge and propose research ideas with the potential for long-term scientific impact. Scientific taste is largely concentrated among highly experienced researchers, whose expertise is usually limited to a few…
Wei Zhou, Jun Zhou, Haoyu Wang et al.
Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications. Driven by (i) rising demands for application-ready data (e.g., for analytics, visualization, decision-making), (ii) increasingly…
Anthropic
Anthropic argues that scaling could make AI transformative within roughly a decade, while robust alignment remains unsolved—so safety work should begin before capabilities outrun oversight.
Anthropic
Anthropic makes safety readiness a condition of scaling: higher-risk models face stronger requirements, and training may pause if safeguards lag.
Anthropic
Anthropic replaces standalone principles with a detailed rationale meant to help Claude apply broad values in unfamiliar situations.
Andrej Karpathy
Karpathy argues that AI automates work more readily when attempts are verifiable, not merely specifiable.
Andrej Karpathy
The essay argues that frontier LLMs are not yet clean, experience-only learners: human data and engineering shape them. Still, the author treats pretraining as a practical cold start, not a dead end.
Andrej Karpathy
The author identifies RLVR—training against automatically checkable rewards—as 2025’s major training shift: it enabled longer optimization and reasoning strategies, but helped produce intelligence that remains sharply uneven.
Simon Willison
By the end of 2024, GPT-4-level performance had become a crowded field, but capability gains had not solved reliability. Willison’s practical emphasis is on testing the system you build, not trusting fluent output.
Simon Willison
In 2025, LLMs moved from impressive answers toward useful multi-step work; that shift makes verification and permissions part of the product, not extras.
Chip Huyen
Ten open research directions are less a model checklist than a systems agenda: reliability, data, compute, interfaces, and whose preferences count.
Leopold Aschenbrenner
An OOM-based forecast, with the data wall as its biggest warning label.
Dario Amodei
Amodei’s upside case is conditional: powerful AI could compress major advances into years, but physical bottlenecks and human decisions—not intelligence alone—shape the pace and who benefits.
Dario Amodei
Amodei argues DeepSeek’s efficiency gains do not weaken chip controls: lower costs can fuel larger training runs, making access to millions of chips—not one model’s reported training bill—the key question for 2026–2027.
Dario Amodei
The essay’s central question is whether human institutions can mature quickly enough to govern powerful AI without surrendering its benefits or its safeguards.
Sam Altman
Altman argues AI progress is becoming self-reinforcing, but the promised abundance depends on aligning systems and governing access as the gains and risks compound.
Sam Altman
Altman argues that AI’s economics could sustain rising investment and spread agents across knowledge work. But he says broad benefits and individual control will depend on choices about safety, policy, and distribution.
Ethan Mollick
AI can boost professional work, but its invisible, uneven capability boundary makes human judgment part of the tool—not an optional extra.
Ethan Mollick
The author argues that AI is shifting from co-intelligence—people guide and correct it—to “wizards” that deliver impressive work while hiding how they made it.
Dwarkesh Patel
As of June 2025, Dwarkesh argues that the key AGI bottleneck is continual learning: systems must build context and improve through work, not merely perform a task once.
Anthropic
Amodei rejects banning open-weights models as a category; he argues policy should target specific national-security risks instead.
Anthropic
Anthropic is complying with an order to remove two models, but argues the reported narrow jailbreak does not justify a recall.
Anthropic
Amodei supports defense AI, but says Anthropic will keep two safeguards.
METR
METR’s researchers cannot rule out extended, rapid capability acceleration: they say the evidence for proposed bottlenecks is not overwhelming.
Simon Willison
Willison’s September keynote is a year-so-far threshold story: coding agents became useful for everyday work, spawning personal agents and serious reported failures during training.
Simon Willison
Willison argues that Claude Code/Cowork and Codex have found product-market fit: enterprises are paying API-level prices for agents that use far more tokens and are becoming daily work tools.
Simon Willison
The article uses one deliberately absurd SVG prompt to thread a six-month account of shifting LLM capabilities.
Andrej Karpathy
Karpathy argues that AI agents are becoming a programmable layer for digital work, shifting programming toward directing agents—and making verification and human judgment more important.
Dwarkesh Patel
Patel separates intelligence from power: authority, trust, and cooperation can explain power better than an exceptionally strategic individual brain.
Hamel Husain
Are data scientists obsolete in the LLM era? The article’s answer: shipping changed, but evaluation and reliability work did not.
Adina Yakefu · Hugging Face
On the Hub, the model getting the most attention is not necessarily the one most embedded in working pipelines.
Nathan Lambert
The author’s case against near-term “true RSI” is that AI may speed lab work and lower costs without overcoming the harder constraints on peak intelligence.
Nathan Lambert
The author presents a guide to open models as a set of trade-offs—economic, technical, geopolitical, and safety-related—not a simple open-versus-closed choice.
Nathan Lambert
Lambert argues Kimi K3 makes frontier open-weight AI a real competitive force—and shifts the question to how to widen access while managing economic and safety trade-offs.
Daniel Kokotajlo · AI Futures Project
The authors moved their median Automated Coder (AC) estimates earlier: Daniel’s from late 2029 to mid 2028 and Eli’s from early 2032 to mid 2030.
Dean W. Ball · Hyperdimensional
The author argues that AI research automation is beginning, but its significance is uncertain: it could speed familiar progress or change the field’s dynamics.
Sean Goedecke
LLMs make it easier to produce passable work outside your specialty, but the author argues that domain expertise—not prompt tricks—is what lets people steer and judge the model’s output.
Thinking Machines Lab · Thinking Machines
The authors argue that open-weight release should expand only as evidence about the model and readiness of its surrounding ecosystem support it.