Guide contents
What You Need to Know to Work in the Field of AI
Key skills, in-demand professions, and the foundational role of data in building an AI career
This guide is available to Plus subscribers
Get full access to this practical guide, ready-made configurations and terminal scripts.
Related Ecosystem Resources
Explore practical workflows, core terminology, and engineering tools
The Future of Junior Specialists in the Age of AI
An analysis of the transformation in the job market for junior specialists. Automation of basic tasks is dismantling traditional internships while empowering newcomers to independently create full-fledged products.
Gemini Prompting Fundamentals: The RTF Framework and Best Practices
A foundational guide to prompting Google Gemini: mastering the RTF framework (Role-Task-Format), prompt typology, battle-tested templates, and error handling.
Hermes Profile: Educational Methodologist & Course Architecture
Hermes instructional design profile: curriculum engineering via Bloom's taxonomy, interactive microlearning, hands-on workshops, and course completion rate optimization.
Developer Deskilling Anxiety
A psychological state of fear and professional uncertainty among developers regarding the potential loss of coding skills due to total delegation of coding tasks to artificial intelligence, leading to concerns about their ability to write syntax, algorithms, and architecture independently.
Four Resume Tips from OpenAI’s Head of Applied AI Development
Wulfie Bain, OpenAI’s head of applied AI development, shared four resume rules: keep it short, remove irrelevant details, highlight outcomes, and lead with the strongest achievements.
MIT researchers show LLMs can read and edit neural network weights without training
MIT CSAIL researchers introduced Weight-Space Programming, where an LLM directly reads and modifies model weights; Astra improved Qwen3.5-0.8B math scores without gradient descent or fine-tuning.