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A staff engineer shares his approach to finding meaningful problems by listening to day-to-day noise, absorbing issues, and connecting patterns rather than trying to think strategically in isolation.
The article explores whether pursuing a career focused on AI agents is a viable and promising path in 2026, offering advice for those considering the field.
This article provides an in-depth analysis of the popular Forward Deployed Engineer (FDE) role, emphasizing that the AI model itself is not a moat—the real competitive edge lies in how models are deployed into real business workflows—and offers a 30-day path to building FDE capabilities.
An analysis of the Forward Deployed Engineer (FDE) role in AI, tracing its origins from Solutions Architect/Professional Services and explaining how AI has transformed the job's compensation, skills needed, and industry demand.
This tweet discusses the importance of asking clarifying questions in system design interviews, explaining how questions drive better architecture than rote diagrams.
A tweet distinguishes two AI career paths—API Caller vs. Architect—and recommends Stanford's free CS336 course for those wanting to become architects.
An interview with Turing Award winner David Patterson covering RISC vs CISC history, GPU/TPU comparisons, Moore's Law, and career advice.
A tweet shares how training an LLM from scratch helped the author stand out in AI interviews and answer questions on Transformers, fine-tuning, and RAG confidently.
High school students face new challenges in choosing a university major as AI advances. The article discusses factors like replaceability, trust, and interdisciplinary skills.
A tweet highlighting the importance of becoming an applied AI engineer to capitalize on the next big tech wave.
A guide on how to identify important topics to learn in AI by analyzing job listings and finding resources via RSS feeds and following experts. Emphasizes tracking in-demand skills and avoiding fads.
A thread advising that to get a high offer from Anthropic, one must ship real projects like a RAG system, an agent from scratch, an eval harness, a fully deployed product, and a business automation, rather than relying on certificates.
A practitioner who has worked at Scale, OpenAI, and Google shares career advice for the AI era: AI is good at solving problems with standard answers (e.g., exams, Leetcode), while the truly scarce abilities are those that cannot be defined by a loss function.
A career advice thread for the age of AI, arguing that valuable work involves problems that can't be graded within model training, and emphasizing the importance of time, relationships, reputation, and problem-finding skills over rote problem-solving.
A blog post outlining the skills needed to become a real-time graphics programmer, covering modern rendering APIs, path tracing, physically based rendering, and the role of machine learning in game development.
A tweet shares a strategy for using Claude (an AI assistant) to replace one's salary after job loss, starting with a specific prompt.
Anthropic's career article outlines a portfolio roadmap for engineers, covering Python, APIs, RAG, agents, and deployment, with a focus on agentic system patterns like prompt chaining, routing, and parallelization.
A Reddit discussion questioning whether older generations' life and career advice remains relevant in the age of AI like ChatGPT.
The author expresses concern that AI will render many industries obsolete and asks which higher education qualifications will still be valuable, mentioning personal interests in fitness, interior design, and dietetics.
A detailed guide on becoming an AI engineer in 2026 without a computer science degree, focusing on practical skills like integrating existing models and building pipelines, with a specific learning path.