ai-career

GitHub blog 分享在 AI 改变开发工作方式的背景下，开发者应重点培养的三项技能：学会指挥 AI 代理而非仅使用它、不盲目信任 AI 的首个答案、以及利用省下的时间解决更大的问题。

An Anthropic researcher told PhD students that their career plans no longer exist, citing 10-million-fold compute growth, rapidly doubling AI task autonomy, and models solving open Erdős problems, with advice to use AI daily, pick compute-scalable projects, and stop planning stable careers.

Simon Willison quotes Carson Gross arguing that programming remains fundamentally about problem-solving and controlling complexity, skills that will stay valuable even as AI tools advance.

Nader Dabit shares career advice on thriving in the AI era: specializations are collapsing, token costs make experimentation nearly free, and cloud agents will scale massively, urging builders to embrace 10x workflows and avoid producing 'slop'.

Weijia Shi announced on social media that they have started working at Anthropic on pretraining, expressing excitement for the future.

A non-profit AI developer expresses guilt and fear due to societal backlash against AI, despite using it responsibly to assist program staff and adhering to strict guidelines against job replacement.

Andrew Ng has outlined crucial skills for becoming AI-native or transitioning into AI roles in 2026, highlighting key competencies in building and deploying AI applications.

Reflects on Andrej Karpathy's nuanced take on AI's impact on engineering careers, using the Jevons paradox to argue that cheaper code increases overall demand, and shares a personal anecdote about rule-based work versus human judgment.

A tweet recommending 12 YouTube videos for aspiring AI engineers to become world-class by 2026.

The blogger shares insights from a coffee chat with AI practitioner Charles, discussing the choice between big companies and AI startups, entrepreneurial paths, differences between B2B and B2C markets, and the importance of networking and resources.

This article maps the optimal AI-augmented path to becoming a GPU/CUDA engineer, highlighting compensation ranges and the growing demand for inference optimization specialists. It provides a realistic timeline and emphasizes the use of AI tools to accelerate learning.

A shared link to a Language Models Interview Handbook, likely containing study materials for AI/ML technical interviews.

Andrew Ng and Laurence Moroney delivered a Stanford lecture described as the most honest AI career playbook, covering why now is the best time to build in AI and what actually gets candidates hired in 2026.