# papers Published articles for papers. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Review of the Self-Defining Systems proposal for AI-driven system development DevFeed: [Review of the Self-Defining Systems proposal for AI-driven system development]() Original publisher: [Read original article]() Author: Aleksey Charapko Published: 2026-01-30T01:24:00Z Content type: opinion Language: en Sources: [Aleksey Charapko]() Topics: [systems](), [Artificial Intelligence](), [Specifications](), [Processes]() Tags: [agentic](), [agents](), [ai](), [changes](), [improvements](), [llm](), [one-page-summary](), [papers](), [performance](), [research](), [specifications](), [systems]() ### AI overview The article reviews Self-Defining Systems, a proposal in which agentic engineers design and build systems from system and operating-environment specifications. It argues that the proposal extends the familiar try, evaluate, and repeat loop by allowing agents to change the specification as well as implement it, while noting that the paper lacks a precise definition of the process. ### Source excerpt Self-Defining Systems (SDS) by Thomas Anderson, Ratul Mahajan, Simon Peter, and Luke Zettlemoyer is a bold proposal for AI-driven systems research. In SDS, agentic "engineers" get the system specification and operating environment specification, then design and build the systems to spec. Crucially, as the specification or environment changes, an army of agents should notice and [...] ## Murat and Aleksey Read Papers: "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass" DevFeed: [Murat and Aleksey Read Papers: "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass"]() Original publisher: [Read original article]() Author: Aleksey Charapko Published: 2026-01-14T15:41:13Z Content type: opinion Language: en Sources: [Aleksey Charapko]() Topics: [Cloud](), [Amazon Web Services](), [Hardware](), [Benchmark](), [dataset]() Tags: [analysis](), [architectures](), [aws](), [benchmark](), [cloud](), [compute](), [cost](), [cpu](), [dataset](), [efficiency](), [graviton](), [hardware](), [network](), [other-thoughts](), [paper](), [papers](), [reading](), [summary]() ### AI overview This article reviews the CIDR paper "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass," which examines AWS virtual hardware capabilities over ten years from a cost-efficiency perspective. The paper finds that cloud CPU cost efficiency improved about twofold, while core counts improved tenfold for non-Graviton offerings; network bandwidth cost efficiency improved substantially more. The article also notes limitations in the paper's analysis of memory bandwidth and specialized hardware features. ### Source excerpt The "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass" CIDR paper by Till Steinert, Maximilian Kuschewski, and Viktor Leis was the first paper I and Murat read this year. It was a short, but interesting read. Below is our reading video and my one-paragraph summary. The paper discusses the evolution of AWS cloud (virtual) hardware [...] ## NeurIPS 2023: Our Favorite Papers on LLMs, Statistical Learning, and More DevFeed: [NeurIPS 2023: Our Favorite Papers on LLMs, Statistical Learning, and More]() Original publisher: [Read original article]() Author: Emily Majewski Published: 2024-03-21T19:52:06Z Content type: article Language: en Sources: [Two Sigma Engineering]() Topics: [NeurIPS](), [machine learning research](), [LLMs](), [LLM evaluation / benchmarking](), [Machine learning]() Tags: [evaluation](), [llms](), [machine-learning-research](), [neurips](), [papers]() ### AI overview Two Sigma reviews selected papers and presentations from NeurIPS 2023, with particular attention to large language models and statistical learning. It discusses research arguing that some apparent emergent abilities in LLMs may result from nonlinear metrics, limited evaluation resolution, and insufficient sampling. ### Source excerpt The post NeurIPS 2023: Our Favorite Papers on LLMs, Statistical Learning, and More appeared first on Two Sigma. ## NIPS 2017 Summary DevFeed: [NIPS 2017 Summary]() Original publisher: [Read original article]() Published: 2017-12-11T00:00:00Z Content type: article Language: en Sources: [Alex Korbonits]() Topics: [NeurIPS](), [AI Research](), [Deep learning](), [Deep neural networks](), [Reinforcement learning]() Tags: [ai-research](), [deep-learning](), [fairness](), [machine-learning](), [model](), [model-interpretability](), [neurips](), [open-source](), [papers](), [reinforcement-learning](), [research]() ### AI overview A summary of key themes from NIPS 2017, including rapid progress in Bayesian deep learning, growing attention to model interpretability, bias, and fairness, the need for more theory in deep learning, advances in deep reinforcement learning, and ongoing questions about GANs. ### Source excerpt Key takeaways from NeurIPS 2017: Bayesian deep learning, model interpretability, fairness, and the state of AI research.