🧠 LLMs - maggieyerkes · Scour

A Guide to Context Engineering for LLMs ✍️Prompt Engineering

blog.bytebytego.com·3d

An application for training deep learning models in your browser ✍️Prompt Engineering

statmodeling.stat.columbia.edu·21h

Stateful Continuation for AI Agents: Why Transport Layers Now Matter ✍️Prompt Engineering

bestblogs.dev·2d

Patterns without desires 🧩Mental Models

aeon.co·1h

AI时代到来，家电家居新品进化成了什么样？ 🤖AI

weixin.sogou.com·2d

Google quietly releases an offline-first AI dictation app on iOS 🤖AI

techcrunch.com·3d

经济学家蔡昉万字长谈：AI如何重塑劳动力市场 ✍️Prompt Engineering

mp.weixin.qq.com·1d

几家编码套餐（Coding Plan）与运行环境（Harness）的使用体验对比——环境篇 🐹Go Lang

blog.inoki.cc·6d

从 Rule、Spec 到 Harness：AI Coding 的分阶段演进路径 - Phodal ✍️Prompt Engineering

phodal.com·3d

Meta Announces Muse Spark: Achieving 10x Pretraining Efficiency ✍️Prompt Engineering

bestblogs.dev·1d

AI短剧得有「规矩」了 🤖AI

weixin.sogou.com·3d

How AI Data Engineers Are 'Returning to Basics' in Applications ✍️Prompt Engineering

bestblogs.dev·2d

EP209: 12 Claude Code Features Every Engineer Should Know ✍️Prompt Engineering

blog.bytebytego.com·5d

Sam Altman on Building the Future of AI: Policy Blueprints and Superintelligence 🤖AI

bestblogs.dev·2d

The History and Future of AI at Google: A Conversation with Sundar Pichai ✍️Prompt Engineering

bestblogs.dev·2d

A Guide to Context Engineering for LLMs ✍️Prompt Engineering

bestblogs.dev·3d

69. Interview with Yuandong Tian: The Real Problems of LLMs, Shifts, the AI Flood, and the Path Not Taken ✍️Prompt Engineering

bestblogs.dev·3d

Continual learning for AI agents ✍️Prompt Engineering

bestblogs.dev·4d

How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines ✍️Prompt Engineering

bestblogs.dev·4d

Where Does Agentic Capability Come From? Deconstructing the Training Process of Foundation Models GLM-5 / MiniMax M2 ✍️Prompt Engineering

bestblogs.dev·3d

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